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1 FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

2 OPEN MEETING

3

4 REEVALUATION OF

5 THE HUMAN HEALTH EFFECTS OF ATRAZINE:

6 REVIEW OF NON-CANCER EFFECTS AND

7 DRINKING WATER MONITORING FREQUENCY

8 AND CANCER EPIDEMIOLOGY

9

10 DOCKET NUMBER: EPA-HQ-OPP-2011-0399

11 FIFRA SAP WEB SITE http://www.epa.gov/scipoly/sap/

12 OPP Docket Telephone: (703) 305-5805

13

14 UNITED STATES ENVIRONMENTAL

15 PROTECTION AGENCY

16 CONFERENCE CENTER LOBBY LEVEL

17 ONE POTOMAC YARD (SOUTH BUILDING)

18 2777 SOUTH CRYSTAL DRIVE

19 ARLINGTON, VIRGINIA 22202

20 JULY 26 -28, 2011

21
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1

2 July 26, 2011 - 8:30 a.m. Day 1

3

4 JOSEPH BAILEY: Hello, I'm Joseph Bailey, and I want to welcome 

5 everyone this morning to the FIFRA Scientific Advisory 

6 Panel Meeting. This meeting is a re-evaluation of the 

7 human health effects of Atrazine, review of non-cancer 

8 effects, drinking water monitoring frequency and cancer 

9 epidemiology. 

10

11 I just want to take a few minutes and go over our standard 

12 comments before the meeting. This is a Federal Advisory 

13 Committee Act meeting, meaning that it is a public 

14 meeting, and part of my responsibility is to make sure 

15 that all the requirements of the Federal Advisory 

16 Committee Act is met.

17

18 The Committee provides advice to the panel. All final 

19 decisions and regulatory decisions are left up to the 

20 agency, but they seek advice from the panel in a peer 

21 review capacity and consider the advice the panel gives in 

22 reaching their regulatory decisions.

23

24 Part of our responsibility is to make sure that all of the 

25 ethics requirements are met for panel members. And to do 

26 that, we have asked the panel members to fill out 

27 disclosure information for us to review to make sure that 

28 there are no financial conflicts of interest or bias on 

29 behalf of any of the panel members.

30

31 We have agendas out on the table so you can take a look at 

32 it. It is a pretty full agenda. It does provide an 
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1 opportunity for public comment. The public comment 

2 opportunity is scheduled to begin this afternoon. 

3

4 Hopefully we'll be on schedule but the agenda is floating. 

5 Things can shift a little bit, but we will begin the

6 public comment opportunity this morning and wrap it up 

7 tomorrow morning, at which time when that ends we will go 

8 into the charge questions with the panel discussions.

9

10 If anyone has not let me know ahead of time they wish to 

11 present public comments, we have a little bit of time left 

12 in the agenda slot for that opportunity. Either let me 

13 know or any of the other people in the Scientific Advisory 

14 Panel staff know. And if you have not made prior 

15 arrangements, we ask that you limit your comment period to 

16 five minutes time.

17

18 As usual, we have a public docket established. All the 

19 material that has been provided to the panel so far is in 

20 that docket. It is available electronically unless it is 

21 sensitive in any manner. And those documents that are 

22 sensitive can be accessed at the docket by visiting on the 

23 4th floor in this building.

24

25 EPA's presentations are in the docket. They should be 

26 available at some time today. And any public comments 

27 that are made, we will also put those in the docket if 

28 they are not already there. The docket number should be 

29 listed on the agenda. And as I mentioned, all of that 

30 information should be publically available unless it is 

31 sensitive information.

32
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1 At this point, I want to introduce Dr. Daniel Schlenk, who 

2 is the Chair for this session of the SAP. And again, I 

3 want to welcome the public here and EPA, as well as the 

4 panel. We have some new panel members and some returning 

5 members here, so I want to thank you all. Thank you.

6

7 DR. DANIEL SCHLENK: Thanks, Joe. Good morning, everyone. My 

8 name is Daniel Schlenk. I am a professor of environmental 

9 toxicology from the University of California, Riverside. 

10 I will be serving as the session chair today, but in place 

11 of my esteemed colleague, Dr. Portier, who has been able 

12 to turn his attentions to some of the questions that have 

13 been asked, particularly with regard to statistics.

14

15 What I would like to do right now is -- we've got a fairly 

16 large panel -- I would like to go around to each panel 

17 member and have them introduce themselves, where they are 

18 from and state briefly what their area of expertise is so 

19 that you guys can get a feel for what we have throughout 

20 this week. So Ken, you want to go ahead?

21

22 DR. KENNETH PORTIER: Good morning. I am Dr. Kenneth Portier. 

23 I am Managing Director of the Statistics and Evaluation 

24 Center at the American Cancer Society, national office in 

25 Atlanta. I am a biostatistician and the expertise I bring 

26 today is statistics in some of the environmental modeling

27 hydrology.

28

29 I should mention Dr. Chambers is going to be here a little 

30 later this morning. She is the third permanent panel 

31 member to make up our core for our meeting. She was 

32 delayed in Atlanta due to weather last night.
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1

2 DR. STEPHEN KLAINE: I am Steve Klaine. I'm a permanent member 

3 of the panel and I am a Professor of Ecotoxicology at 

4 Clemson University.

5

6 DR. ELLEN GOLD: I am Ellen Gold and I am professor and chair 

7 of the Department of Public Health Sciences at the 

8 University of California, Davis and an Epidemiologist.

9

10 DR. FRANK BOVE: I am Frank Bove. I am with the Agency for 

11 Toxic Substances and Disease Registry in Atlanta. I am a 

12 Senior Epidemiologist in the Division of Health Studies.

13

14 DR. HEATHER YOUNG: Hi. I am Heather Young from George 

15 Washington University, Department of Epidemiology. I am a 

16 cancer epidemiologist and also focus on reproductive 

17 outcomes.

18

19 DR. NELSON HORSEMAN: Nelson Horseman. I am at the University 

20 of Cincinnati. I am a physiologist and endocrinologist, 

21 and my research areas of interest currently are in mammary 

22 gland development, lactation and breast cancer.

23

24 DR. JAMES MCMANAMAN: I am Jim McManaman. I am at the 

25 University of Colorado. I am Professor and Chief of the 

26 Division of Reproductive Sciences and my interests are 

27 mammary gland biology and reproductive mechanisms.

28

29 DR. DANIEL GRIFFITH: I am Daniel Griffith from University of 

30 Texas at Dallas. I am an Ashbel Smith Professor of 

31 Geospatial Information Sciences. My area of expertise is 
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1 spatial statistics and I am working on the monitoring part 

2 of the project.

3

4 DR. HERBERT LEE: My name is Herbie Lee. I am a professor of 

5 statistics at University of California, Santa Cruz where I 

6 am also the Vice Provost for Academic Affairs. And my 

7 research areas include spatial statistics and computer 

8 simulation modeling.

9

10 DR. ROBERT GILLIOM: Bob Gilliom, U.S. Geological Survey, and I 

11 direct our national studies of pesticides as part of the 

12 National Water Quality Assessment program, and I am here 

13 for the monitoring aspects, primarily. 

14

15 DR. SUSAN AKANA: I am Susan Akana. I am in my second career 

16 as an instructor at City College of San Francisco. I am 

17 late of UCSF where I had a career in stress and its 

18 interaction with energy balance in rodent models.

19

20 DR. KEVIN O'BYRNE: My name is Kevin O'Byrne and I am from

21 King's College London. I am a professor of reproductive 

22 neuroendocrinology and my research areas span the neurone 

23 control of reproduction; that is what I do.

24

25 DR. KATHERINE ROBY: Kathy Roby from the University of Kansas 

26 Medical Center and my expertise is reproductive 

27 endocrinology. 

28

29 DR. BARRY TIMMS: Barry Timms, professor in the Division of 

30 Basic Biomedical Sciences, Sanford School of Medicine, 

31 University of South Dakota. My area of specialty is in 
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1 developmental biology of the prostate glands with a 

2 secondary interest in endocrine disruption.

3

4 DR. TRAVIS JERDE: My name is Travis Jerde, Indiana University 

5 School of Medicine, Department of Pharmacology and 

6 Toxicology, and my area of expertise is prostate biology 

7 with an emphasis on prostatic inflammation and resulting 

8 adult disease.

9

10 DR. PENELOPE FENNER-CRISP: My name is Penny Fenner-Crisp. I 

11 am a private consultant from Charlottesville, Virginia and 

12 a member of the Commonwealth of Virginia's Pesticide 

13 Control Board. My area of expertise is toxicology and 

14 human health risk-assessment.

15

16 DR. BETTE MEEK: And my name is Bette Meek. I am Associate 

17 Director of Chemical Risk-Assessment at the McLaughlin 

18 Center, University of Ottawa. I have a background in 

19 toxicology and spent most of my career in regulatory risk20 assessment at Health Canada.

21

22 DR. RICHARD GREENWOOD: I am Richard Greenwood. I am an 

23 Emeritus Professor at the University of Portsmouth, and I 

24 am here for my expertise in the area of pharmacokinetics 

25 and toxicology. 

26

27 DR. WILLIAM HAYTON: I am William Hayton, a Professor Emeritus 

28 College of Pharmacy, Ohio State University, and my area of 

29 expertise is pharmacokinetics.

30

31 DR. DANIEL SCHLENK: Thanks, everyone. Just a point of 

32 administrative comment, Dr. Barry Delclos will not be 
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1 joining us. He is listed as a panel member. He could not 

2 make it today due to a family emergency, so he will not be 

3 providing comments today, for those of you that are 

4 interested in that.

5

6 Welcome, everybody. At this point in time, as we begin 

7 our agenda, it is our standard practice to introduce Dr. 

8 Steven Bradbury, who is the Director of the Office of 

9 Pesticide Programs, and he is going to give us our opening 

10 remarks. Dr. Bradbury?

11

12 DR. STEVEN BRADBURY: Thanks. I would like to welcome the 

13 panel to this week's meeting. I appreciate you all 

14 volunteering to help us as we explore the scientific 

15 issues before us this week.

16

17 The role of the Scientific Advisory Panel for the 

18 Pesticide Program is very important in the work that we 

19 do. We use this peer review body to help us evaluate both 

20 new methodologies that we want to bring to bear in our 

21 pesticide risk-assessment process, as well as bringing to 

22 the panel specific questions sometimes around specific 

23 chemicals to help inform our regulatory decisions.

24

25 The scientific foundation to the decision-making process 

26 we make in the pesticide program is really fundamental to 

27 everything we do in our work at EPA, and not just for the 

28 pesticide program but for the agency as a whole, and the 

29 administrators.

30

31 We emphasized that numerous times, since she has been 

32 running EPA, the importance of the best peer reviewed 
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1 available science to inform our decision-making process. 

2 What's also very important in our decision-making process 

3 is that it's open and transparent and that the public can

4 participate in the process that we go through. So, not 

5 only having you all here and providing your scientific 

6 expertise, but also ensuring that the public has an 

7 opportunity to comment and the public has an opportunity 

8 to see all the documents that are coming before you and 

9 ultimately see your report, and see your report in a 

10 context of the deliberations we will be having during the 

11 course of the week.

12

13 So we are very much indebted to your time and service not 

14 only to us in the pesticide program, but more broadly to 

15 all the U.S. citizens, in terms of the kinds of decisions 

16 that we have to make, in terms of insuring pesticides that 

17 are affective for food and fiber production are also safe 

18 for human health and the environment. So your role is 

19 very instrumental in what we do.

20

21 I also want to thank the public as we embark on this 

22 week's activity for the comments they have already 

23 provided and the comments you will hear during the public 

24 comment period, and that input is very important for the 

25 deliberations that you will be making.

26

27 I also want to thank the Science Advisory Panel staff for 

28 helping us organize this meeting and the work it takes to 

29 reach out to all of you, and get you all lined up with all 

30 your paperwork so that you can be part of the panel and 

31 all the work it takes to put a meeting like this on and 

32 work towards the final report that will come out in the 
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1 coming months. So thanks, again, for all the effort that 

2 you have already done in getting ready for the meeting and 

3 the intense time you will have here, and then the intense 

4 time you will have in writing the report. We greatly 

5 appreciate it.

6

7 I thought it might be helpful if I could just spend a few 

8 minutes maybe reviewing where we have been, with regard to 

9 atrazine and what we are going to try to accomplish this 

10 week. And in that quick summary also try to weave in some 

11 of the broader issues and opportunities that we are 

12 looking at in terms of advancing our risk-assessment 

13 methodology in the pesticide program as we take a look at 

14 some of the new technologies and different approaches, 

15 evolving approaches as we go into the 21st Century. 

16

17 So, I will talk a little bit about atrazine and try to 

18 weave in some of the other techniques and approaches that 

19 are coming to bear in the atrazine risk-assessment. So, 

20 as you probably recall from some of the background 

21 documents you got, that in 2003 we re-evaluated atrazine, 

22 and that was part of an effort that was ongoing for a 

23 number of years in which every pesticide that was 

24 registered before 1984 had to be re-registered, in other 

25 words, re-evaluated to ensure that it met human health 

26 protection standard as well as environmental standards. 

27

28 And atrazine was re-evaluated in 2003, and in 2006 it was 

29 looked at again with other triazine herbicides to ensure 

30 that that group of herbicides together still met the 

31 safety standard associated with the Food Quality 

32 Protection Act, so looking at cumulative effects and 
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1 ensuring protection for the population in general, as well 

2 as looking as sensitive sub-populations, in particular, 

3 looking at children in terms of our safety finding.

4

5 The current re-evaluation program that we have that is 

6 dictated by statute is called registration review. And 

7 registration review requires that every pesticide be re8 evaluated every 15 years. And so atrazine, like every 

9 other chemical, is on its schedule and it is set to be re10 evaluated in 2013. But, just because a chemical is 

11 scheduled at a certain time in its re-evaluation schedule 

12 does not mean we have to wait before we take a look at a 

13 chemical if new information comes to bear that suggests we 

14 need to take a look, and just ensure that we are still 

15 meeting our safety findings.

16

17 In the case of atrazine, probably one of the most 

18 intensively studied chemicals in the scientific literature 

19 -- probably not the most, but among pesticides, probably 

20 one of the most heavily studied pesticides -- since 2003, 

21 there has probably been 150 or so papers that have been 

22 published with regard to atrazine. In addition, the 

23 registrant has submitted additional information over the 

24 course of the years as a part of the conditions of re25 registration, and there has been a lot of water monitoring 

26 going on, both in drinking water sources as well as in 

27 headwater streams in terms of taking a look at potential

28 ecological exposure and effects.

29

30 In 2009 the agency felt that, given the significant amount 

31 of information that has been published over the course of 

32 the last six to seven years, and the information coming in 
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1 through the monitoring program as a condition of re2 registration -- which you can get detailed information on 

3 atrazine concentration and drinking water sources -- it 

4 made sense to sit back and take a look at the new 

5 information, take a look at that information in light of 

6 the monitoring data that we had received.

7

8 It was not suggesting any areas of concern in terms of our 

9 risk-assessment in 2003, but take a look at new 

10 information since 2003 and reaffirm or make adjustments if 

11 necessary, based on the new science that was coming in. 

12 So, the end of 2009 we had a consultation with the Science 

13 Advisory Panel just laying out the peer review plan that 

14 we were going to ultimately execute in 2010 and here in 

15 2011.

16

17 And during 2010, we had three scientific advisory panels; 

18 one in February, one in April and then one in September. 

19 The February 2010 SAP was actually one that we had 

20 scheduled for some time and used that as an opportunity to 

21 look at atrazine as a case study, along with some other 

22 compounds.

23

24 And the major emphasis of the February SAP, Science 

25 Advisory Panel Review, was actually to take a look at a 

26 framework, a framework that was embracing the concepts 

27 that were coming out of the National Research Council's 

28 2007 Report on toxicology testing in the 21st Century, and 

29 beginning to think about ways to integrate experimental 

30 toxicology data as well as epidemiology data in a risk31 assessment process.

32
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1

2 And also, looking at that NRC report in the context of 

3 what the NRC report called toxicity pathways, what we in 

4 the agency have been starting to call adverse outcome 

5 pathways, in which of more focused effort of looking at 

6 initiating events and thinking about the chain of events 

7 that happened in biological systems, as a way of 

8 organizing your thoughts and integrating information 

9 around the linkage between adverse outcomes and the 

10 various processes that could lead to adverse outcomes. 

11

12 And how do you look at experimental toxicology data along 

13 with epidemiology data to try to integrate that 

14 information, and essentially, how you pull together a 

15 coherent story about what the potential of a chemical may 

16 be in terms of its effects, its exposure and ultimately 

17 how that can help inform a risk-assessment.

18

19 And so in that SAP of February 2010, trying to get some 

20 feedback on how to be thinking about these issues, which 

21 is not just toxicodynamics, but also toxicokinetics; how 

22 do we better understand what happens at one level of 

23 biological organization in terms of the magnitude of the 

24 effect, the duration of effect and how much of that has to 

25 happen in order to have another event happen further down 

26 or along the biological chain of events.

27

28 If you look at the NRC report, it talks about the fact 

29 that as we use these approaches, we will see perturbations 

30 in biological systems. Part of the challenge will be how 

31 much of a perturbation is necessary to elicit an adverse 

32 outcome, say a frank effect in the intact organism. And 
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1 so that February SAP was getting at some of the 

2 approaches, some of the things to think about as we go 

3 forward, and not just for atrazine, but more generally as 

4 we go forward with new risk-assessment methods.

5

6 But we also included in that SAP some case studies on 

7 epidemiology studies, ecological epidemiology study 

8 designs, primarily but not totally, and used that SAP to 

9 begin getting some feedback from you all on how to be 

10 looking at different kinds of epidemiology studies in 

11 terms of their design and their attributes, and how 

12 different types of epidemiology studies should be 

13 contemplated as you start to think about integrating 

14 epidemiology data with experimental toxicology data.

15

16 The studies that may be helpful in terms of formulating 

17 hypothesis versus the kinds of studies that may be very 

18 powerful in terms of establishing causation from an 

19 epidemiological perspective, and how to integrate that

20 with toxicological information. So that really helped us 

21 to sort of get our framework together and get some of our 

22 thoughts together about how to approach this risk23 assessment, as well as some initial thoughts on how to be 

24 taking a look at epidemiology studies. As you know, there 

25 are a lot of epidemiology studies associated with 

26 atrazine.

27

28 The April 2010 SAP then primarily focused on experimental 

29 toxicology data, looking at both in vitro information as 

30 well as in vivo information, and again, getting some 

31 feedback on the studies themselves and how to be thinking 

32 about those studies, and what are those studies telling us 
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1 in terms of what we knew in 2003, to the extent that there 

2 were some new insights coming since 2003. 

3

4 That gave us some good feedback in terms of the 

5 reproductive and developmental outcomes that have been the 

6 focus of 2003 in the context of LH surge suppression being 

7 sort of a key event that could lead to changes in 

8 reproductive or developmental outcomes, and how that 

9 related to an other kinds of experimental toxicology data 

10 that had come out in the previous years in terms of 

11 immunological effects or neurological effects.

12

13 We also used that SAP to get some initial feedback on 

14 sensitivity across different life stages and things that 

15 we should be thinking about in that regard. We also had 

16 some discussions around dosimetry and how to take a look 

17 at what could be happening at a sub-organismal level and 

18 be thinking about oral uptake of atrazine in drinking 

19 water, and steady-state exposures versus pulse exposures 

20 and versus the kinds of patterns we would see in drinking 

21 water systems. 

22

23 So that SAP also gave us some initial feedback on how to 

24 take a look at the monitoring data that already existed 

25 and how do you extrapolate across time and space, in terms 

26 of interpreting potential human exposure to atrazine.

27

28 The September SAP then had a heavy focus on noncancer 

29 effects, looking at both the experimental toxicology data 

30 as well as the epidemiological data that was available for 

31 noncancer effects, and again, also looking at drinking 
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1 water issues, in terms of monitoring designs, sampling 

2 frequency. 

3

4 Because, one of the challenges with this risk-assessment -

5 - it is a challenge for this risk-assessment, but through 

6 this risk-assessment, will give us insights for the future 

7 -- is that, as we understand what is happening in terms of 

8 the toxicodynamics and toxicokinetics clearly duration of 

9 exposure, timing of exposure is critical and it is highly 

10 variable in the real world.

11

12 We have nice steady-state exposures in the laboratories, 

13 typically, but that is not how real world exposures 

14 happen. And so, what is going on inside the organism in 

15 terms of differential exposure in time, and in the 

16 duration of those exposures, is really critical 

17 interpreting whether or not there is a perturbation in a 

18 biological system, and to extent those perturbations could 

19 be significant, in terms of eliciting adverse outcomes.

20

21 What is also challenging in the world of watersheds and 

22 the world out there in terms of the variability in timing 

23 of chemicals reaching drinking water sources. So the time 

24 and space in terms of chemicals being used in the 

25 environment, run off, getting into a drinking water system 

26 and realizing that in the real world, pesticide typically 

27 are not associated with nice quasi steady-state 

28 concentrations that you have seen from the reports; 

29 atrazine used in the spring, when there is a runoff event, 

30 it is usually happening after a rainfall event, not too 

31 far after it was applied. And so, we see very spiky, 

32 typically spiky exposures of atrazine, which may be in a 
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1 drinking water system for two, three, four, five, six days 

2 maybe, from beginning to end of a "spike".

3

4 And then you may not see any atrazine for days, weeks, 

5 months, maybe not for the rest of the growing season and 

6 into the next spring, or you may seen some periodic lower 

7 level spikes.

8

9 So how do we interpret those kinds of exposures in 

10 drinking water systems, relate that to human consumption 

11 and then get back to the experimental toxicology data, 

12 which probably had a different dosing regime than what we 

13 are seeing in the real world? And so, the toxicokinetics 

14 and the linkage of this in time and space is a critical 

15 part of the risk-assessment that we are doing. And you 

16 have all been experiencing it, some of you over the last 

17 year.

18

19 And as we come to this week's Science Advisory Panel, we 

20 hope to bring together many of these different threads 

21 that have been getting woven together over the course of 

22 the last year. So getting feedback again on the non23 cancer effects in terms of the role of LH surge 

24 suppression, how that fits into an adverse outcome pathway 

25 interpretation; what's a dosimetry? Both a dose and 

26 duration of exposure that is important to consider in 

27 terms of perturbations and perturbations that may be 

28 significant enough to cause adverse outcomes, which leads 

29 us to the toxicokinetics and how to be thinking about how 

30 we deal with the factor's variable exposure to interpret 

31 those effects. And then how does that link into the 

32 drinking water monitoring designs, and how do we interpret 
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1 that information so it's toxicologically relevant to the 

2 risk-assessment that we need to do.

3

4 And the final, really important, part of this peer review 

5 is taking a look at the potential cancer affects 

6 associated with atrazine. As you know, as the scheduling 

7 was playing out, we were working with our colleagues in 

8 the National Cancer Institute and the worker health study 

9 cancer, a very intensive prospective epidemiology study. 

10

11 The work associated with atrazine was finished a few 

12 months ago, so we will be able to bring the results of 

13 those studies to the table and take a look at the cancer 

14 issue then with that final epidemiological study 

15 completed; along with the other epidemiological studies as 

16 well as any experimental toxicology data and get your 

17 feedback on atrazine's potential with regard to cancer. 

18 Which today, has been concluded that it is not likely to 

19 be a human carcinogen, but we want to revisit that and see 

20 if that conclusion still holds, based on the most current 

21 information.

22

23 So my apologies for running a little long, but given the 

24 year and a half we have been working on this, I felt it 

25 probably made sense to spend a little bit of time just 

26 kind of reviewing where we have been, where we are, where 

27 we are heading, both in terms of atrazine and some of the 

28 underlying methods that we are hoping to bring to bear, 

29 not only for atrazine but for other risk-assessments in 

30 the future.

31
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1 So with that, I will pause and just turn it over to Jack 

2 Fowle here in a second, if it is okay, and thank all the 

3 scientists and OPP. They have been working on this as 

4 well as our colleagues in the EPA's office, research and 

5 development, and the NCI for their assistance as well, as 

6 we have been putting this information together. Let us 

7 turn back to the Chair and thank you for your indulgence.

8

9 DR. DANIEL SCHLENK: Thank you, Dr. Bradbury, for that 

10 oversight and background. It is very useful for us, I 

11 think. Our next speaker is Jack Fowle. He is the Deputy 

12 Director of the Health Effects Division from OPP. Jack?

13

14 DR. JACK FOWLE: Thank you, Dr. Schlenk, and thank you 

15 distinguished members of the Science Advisory Panel. I 

16 would like to echo Dr. Bradbury's comments in terms of 

17 thanking the panel for all your hard work. We have a few 

18 new members here today; welcome, too. And for the folks 

19 that have been here over the last year and a half, we 

20 really do appreciate all that you have done to help us 

21 focus and winnow down our efforts. 

22

23 Over the past year and a half, as you have heard Steve 

24 say, we have come to the panel on several occasions; three 

25 full-blown atrazine review panels and two meetings, one to 

26 sort of set the stage and one in the context of a case 

27 study for our epidemiology framework.

28

29 Because we are health protective with the Environmental 

30 Protection Agency, our mission is to ensure that, here in 

31 the pesticides program, pesticides use according to label, 

32 they are safe for human health and the environment. We 
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1 feel it is very important to take due diligence, and in 

2 essence, look at everything. So over the past year and a 

3 half, the topics we have come to have run the gamut from 

4 neuroendocrine mode of action to immunotoxicity, 

5 epidemiology, pharmacokinetics and various approaches for 

6 how we might analyze and integrate this information into 

7 drinking water monitoring data.

8

9 This meeting, in some ways, from a public health 

10 perspective -- not ecological effects, but from a human 

11 health effect -- is in some ways sort of an epilogue. We 

12 are trying to winnow things down and we are trying to 

13 bring things to a conclusion at this particular point in 

14 time.

15

16 So, basically what we will be presenting too, is sort of 

17 how we have built on your recommendations and how we have 

18 built on the guidance from the National Academy of 

19 Sciences and their report -- in particular, a 2007 report, 

20 Toxicity Testing at 21st Century -- to focus on what we 

21 understand about the adverse outcome pathway of atrazine 

22 and which of the biological changes that we are observing 

23 are leading to an adverse outcome, potentially, and which 

24 maybe are just bumps in the road due to normal homeostatic 

25 processes, would get us back to normal function.

26

27 So based on that, we are now focusing on suppression of LH 

28 surge and potential impacts on reproduction, and that‟s 

29 consistent with the report from your April Science 

30 Advisory Panel. You will hear that theme being woven in 

31 throughout the presentations today.

32
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1

2 Having said that, as Steve pointed out, that atrazine is a 

3 major chemical from the pesticides program and we do 

4 continuously monitor new data, new information that come 

5 in on our pesticide products, and we will continue to do 

6 that as we go into the future.

7

8 At this particular point in time, I would also like to 

9 note that we are not really giving a risk-assessment at 

10 this particular point in time, but sharing with you our 

11 understanding of the potential impacts of atrazine on 

12 human health up to this state of the science, as we 

13 understand at this particular point in time, and also the 

14 impacts and implications for drinking water monitoring.

15

16 We will be, as Dr. Bradbury noted, coming back to you with 

17 a review of our understanding of the potential ecological 

18 impacts of atrazine in 2012, and the actual risk19 assessment of atrazine will be conducted in 2013 when we 

20 conduct our registration review of the compound at that 

21 point in time.

22

23 There have been a few changes on the staff, at least for a 

24 temporary basis. You may recall in past meetings that Dr. 

25 Anna Lowitt has been leading the effort in terms of 

26 atrazine review. She, as you know, had a child that was 

27 born April 12th. She is on maternity leave and she may be 

28 coming today and perhaps -- oh, she is here. Oh, hi, 

29 Anna. I did not even know she was here. Welcome. She 

30 has a son. We miss her very much. 

31
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1 But in her absence, Dr. Elizabeth Mendez has stepped up to 

2 the plate and taken the lead for the effort, and she has 

3 just done an absolutely magnificent job in terms of 

4 leading the scientific team and all the various scientific 

5 challenges down to the nitty-gritty. You may note that 

6 you got the report a few days later. It is a 673, or 

7 whatever, page report, and it far exceeded the capacities 

8 of wonderful Bill Gates and his Microsoft products. 

9

10 So, Liz led the team through two days, 17 hours going line 

11 by line on the report -- quality controlling it -- to get 

12 it into shape so we could mail it to you. So she has left 

13 no stone unturned. Liz, I just cannot thank you enough 

14 for what you have done in this.

15

16 I would like to thank the team that has conducted the 

17 analysis and pulled together the material that is 

18 presented on this topic for the past several years and 

19 also today. I will not mention by name because there are 

20 roughly 17 from the pesticide programs, about 13 from ORD 

21 and roughly a handful from the National Cancer Institute; 

22 but we really appreciate their help. And I will turn it 

23 back to you, Dr. Schlenk.

24

25 DR. DANIEL SCHLENK: Thank you, Dr. Fowle. Before we move on 

26 to Dr. Mendez, I would like to introduce Jan Chambers who 

27 is one of our permanent panel members who just arrived. 

28 Jan, if you could just introduce yourself?

29

30 DR. JANICE CHAMBERS: Thank you. Let me assure you I did not 

31 oversleep. I have actually been up since 3:30 after 

32 several hours of indecision. Last evening, Delta 
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1 cancelled my flight and I spent the night in Atlanta and 

2 just got in on a flight this morning.

3

4 I am Jan Chambers with the College of Veterinary Medicine 

5 at Mississippi State University. I am a pesticide 

6 toxicologist, specializing mostly in metabolism and 

7 neurotoxicity. 

8

9 DR. DANIEL SCHLENK: Thanks, Jan. So with that, we will turn 

10 the microphone over to Dr. Mendez, if you could get us 

11 going and give us some intro and some status report here. 

12 Thank you.

13

14 DR. ELIZABETH MENDEZ: Good morning. Before I start with my

15 presentation I want to reiterate what Dr. Fowle and Dr. 

16 Bradbury said before. As a member of the team, your input 

17 over the past year and a half has been absolutely 

18 invaluable to us as we move forward, and we really do 

19 appreciate your guidance throughout this process.

20

21 So, today's talks are going to be reflecting how we have 

22 gone about incorporating some of the recommendations into 

23 our evaluation that were feasible under the current 

24 timeline. But they are also built upon the previous 

25 atrazine evaluations, going back to the 1980s. 

26

27 So, let's take a little trip down memory lane. In 1988 

28 the agency sought the panel's input on the mammary gland 

29 tumors seen in the rat. At that point, the panel noted 

30 that a hormonal influence might be an important 

31 consideration in the development of these mammary gland 
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1 tumors in the adult rats, and so they guided us to look 

2 into that aspect of the tumor genesis that we were seeing. 

3

4 So we returned to the SAP in 2000 for advice on atrazine's 

5 mode of action leading to the mammary gland tumors, the 

6 reproductive and developmental effects in the rats, as 

7 well as the human relevance of these findings.

8

9 The SAP agreed with the agency's proposal for atrazine's 

10 neuroendocrine mode of action and they concluded that it 

11 was highly unlikely that the mechanism by which atrazine 

12 induces mammary tumor in the adult female Sprague-Dawley 

13 rats could be operational in humans.

14

15 Based on the 2000 SAP guidance, the EPA reconsidered its 

16 position in atrazine and reclassified it from a possible 

17 carcinogen to not likely to be carcinogenic to humans. 

18 But another thing that the SAP did tell us at that point 

19 was that it was not unreasonable to expect that atrazine 

20 might cause adverse effects on the HPG axis if it was 

21 perturbed enough and that the effects that that could lead 

22 to in development and reproduction could indeed be 

23 relevant to human.

24

25 So, although it was not relevant for the mammary tumors, 

26 it was likely that it was relevant for other adverse 

27 outcomes. And that has sort of been the impetus of our 

28 research since, at that point. We sort of narrowed it 

29 down to that.

30

31 In 2003 we came back to the SAP, and that time was for the 

32 evaluation of the prostate cancer. There were a number of 
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1 studies, particularly one in an atrazine manufacturing 

2 plant, the St. Gabriel Atrazine Manufacturing Plant, and 

3 we wanted to look at what was happening there because 

4 there appeared to be an increase in the prostate cancer 

5 cases that we were seeing.

6

7 When we came back to the SAP, the conclusions of the 

8 panel, to a certain extent, was that those increases 

9 could, in part, be explained by PSA screening being more 

10 readily available to the staff at the plant, but that it 

11 could not entirely dismiss other contributing factors to 

12 the increase.

13

14 And their feedback to us was to maintain vigilant, keep 

15 looking at the data and look for the agricultural health 

16 study that was looking at the prostate cancer, as well. 

17 And that sort of brings us where we are today and why we 

18 have come back.

19

20 The goal of this re-evaluation is to determine if the 

21 risk-assessment for atrazine should be revised. And the 

22 basis and the genesis for that, as Dr. Bradbury and Fowle 

23 mentioned is, over the past seven years, since the 2003 

24 IRED, the atrazine researchers have been a rather prolific 

25 group and have produced over 100 articles that we wanted 

26 to take a look at and make sure that we brought the state 

27 of the science to bear on this risk-assessment.

28

29 We wanted to evaluate that experimental toxicology data, 

30 both non-cancer and cancer effects. We have evaluated 

31 epidemiology data. There have been a few dozen 
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1 epidemiology studies that have been put forth since the 

2 2003 IRED. 

3

4 And the other thing that we wanted to do, which we had not 

5 done very clearly before was try to integrate the 

6 experimental toxicology and the Epi data, and that is what 

7 the February SAP was all about, how we were going to go 

8 about doing this in a systematic way. Another thing that 

9 we wanted to do was ascertain if the critical life-stages 

10 are adequately assessed. 

11

12 Now, talking about a neuroendocrine mode of action, of 

13 course, makes us think about what is happening in the 

14 neuroendocrine system across different life-stages. So, 

15 we wanted to be very cognizant of the differences in the 

16 hormonal malaria at different life-stages and how it may 

17 be impacted by the atrazine exposure, also ascertain if 

18 the durations of exposure assessed are the most 

19 appropriate.

20

21 So in the case of atrazine, it is not only the life-stage 

22 that is important, but also how long during that life23 stage the exposure occurs and trying to understand if what 

24 we were doing in terms of monitoring would help us get a 

25 good grip on that information. So, that brings us to 

26 identifying methods for analyzing the uncertainty in 

27 drinking water monitoring data. 

28

29 So, I mean, ideally, we want to have the ideal dataset --

30 sometimes we do, sometimes we do not -- and we have to 

31 then understand and get a good handle on what those 

32 uncertainties may be and how we can address them in our 
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1 efforts to continue to produce a risk-assessment that is 

2 health protective.

3

4 So, I am just going to go over these very, very quickly 

5 because these are the atrazine 2010 SAPs. Some of you 

6 have been with us throughout this entire process and I can 

7 only imagine how very glad you are to hear us say that 

8 this may be the last one for human health.

9

10 In February 2010, the draft framework was brought to you 

11 incorporating the epidemiology studies and human health 

12 incident data in risk-assessment. That was actually 

13 intended to be a framework in general, not an atrazine14 specific meeting, but we had two atrazine case studies as 

15 part of that and that helped to sort of set the stage, 

16 give us some ideas on what the panel was thinking so that 

17 we could start doing that as we came back for atrazine 

18 itself.

19

20 In April 2010 we had our preliminary evaluation of in 

21 vitro and in vivo lab studies, and at that point we 

22 concentrated on the non-cancer data. As I mentioned 

23 earlier, we have well over 100 studies that have been 

24 published in 2003, so we found ourselves in a situation of 

25 trying to apportion this in manageable sizes so that we 

26 could wrap our arms around it and not overwhelm you with 

27 600-page documents, although it did happened at the end. 

28 And we also came back and talked to you about the 

29 frequency of atrazine monitoring in drinking water sources 

30 and there was a proposal for some approaches as to how we 

31 were thinking about going about doing that.

32
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1 The September SAP in 2010 was the one that based on 

2 epidemiology studies; non-cancer, and was our first 

3 attempt to really start integrating the Epi and 

4 experimental Tox data into the hazard characterization for 

5 non-cancer. Based on some of the feedback that we got 

6 from the panel in April, we also had an update and 

7 analyses of the frequency of atrazine monitoring in 

8 drinking water sources.

9

10 So, those were the things that we brought to the panel and 

11 this is some of the feedback that we got from the panel. 

12 Obviously, the reports are rather extensive and lengthy so 

13 I have just sort of captured the highlights at this point.

14

15 But in February, SAP in general, the panel concurred with 

16 the agency's proposed approach for evaluating the Epi 

17 data. They told us to consider the likely contribution of 

18 human data and the scoping and problem formulation 

19 process, consider the tox and epidemiological databases to 

20 identify uncertainties and critical data gaps and consider 

21 overall quality of the Epi data; quality of exposure 

22 assessment, sample size, statistical power, careful 

23 definition of outcomes, source bias, et cetera.

24

25 Then in April when we came with a non-cancer data, from 

26 the experimental side we looked at the cancer data that 

27 had been generated since 2003. And the panel reaffirmed 

28 the conclusions of the previous SAP regarding the 

29 classification for atrazine; it is not likely to be 

30 carcinogenic to humans.

31
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1 It concurred with the agency's conclusions that atrazine2 induced effects on the neuroendocrine function remain as 

3 the most sensitive vaccine to date. And an important 

4 recommendation that they made to us was they started 

5 shifting our thinking from external dosimetry to internal 

6 dosimetry. 

7

8 We had some PK data that would allow us to do that, which 

9 we do not often have in a lot of our chemicals. So, in 

10 this instance, let's make use of all the information that 

11 we have to the best of our abilities. And they also noted 

12 something that I just mentioned, that the toxicological 

13 duration of concern is key to determining the sampling 

14 frequency. 

15

16 So for an effect that -- maybe a critical window of 

17 exposure may be two days, you may need more frequent 

18 sampling than if the critical window of exposure is, say, 

19 90 days or 30 days; and sort of got us thinking about how 

20 we would integrate those two aspects.

21

22 The September SAP, we brought in for the first time the 

23 mammary gland development data. There were some 

24 discrepancies between some studies and we wanted to get 

25 the panel's input on that. They agreed that, at this 

26 point in time the mammary gland development is not 

27 sufficiently robust, the dataset, for us to move in that 

28 direction. But again, we brought in our use of internal 

29 dosimetry and asked for your input on, were we on the 

30 right track; did we need to be redirected somehow? The 

31 panel concurred with us that the LH suppression is 

32 protective of the other effects and the use of Dr. 
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1 Cooper's data from 2010 to establish the point of 

2 departure.

3

4 One of the other critical things that we heard from the 

5 panel at that time was that, based on the available data 

6 that we have to date, there was no evidence of a higher 

7 sensitivity of the young relative to the adults. That is 

8 to say that when we are looking at LH suppression in the 

9 adult females, the point of departure for that is lower 

10 than any of the developmental reproductive effects that we 

11 see across different life-stages.

12

13 The non-cancer Epi findings were also brought at that time 

14 and we saw that they were helping us in certain terms of 

15 qualitatively ground-truthing our experimental findings 

16 because there was some consistency. And so, although we 

17 could not use it in terms of a quantitative assessment, we 

18 could use it as part of our weight of the evidence 

19 analyses.

20

21 And finally, there was a recommendation of a combination 

22 of statistical and modeling methods to quantify the 

23 uncertainty in exposure estimates from the monitoring 

24 data, which has been one of the things that we have been 

25 struggling mightily with over the past few years. 

26

27 So what are we going to do over the next four days? We 

28 are going to review new experimental toxicology studies. 

29

30 Since July of last year, which was when we closed the 

31 submissions to us for being included in the September SAP 

32 paper, up until April 29th of this year, there have been 
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1 approximately a dozen new experimental tox studies. Those 

2 studies have come in from industry from our own labs down

3 in the park, as well as the open literature.

4

5 We are going to be looking at the species extrapolation 

6 from the rat to human and the duration of exposure and 

7 life-stage sensitivity analysis, approaches for analyzing 

8 monitoring data in the drinking water.

9

10 And one of the things that we wanted to bring to you --

11 and we are not going to be asking you questions or having 

12 a presentation on it -- is an update on the exposure 

13 assessment collaborative project with the AHS. It is part 

14 of your packet. We just wanted to give you an update on 

15 that but we have not, at this point in time, reached any 

16 conclusions in that project. So we felt that, given the 

17 voluminous amounts of data that we have to go through in 

18 other areas, we would just give you an update on paper.

19

20 The epidemiology studies; we are going to concentrate on 

21 cancer this time. And finally, we are going to try to 

22 integrate the weight of our evidence with Epi and 

23 experimental toxicology.

24

25 So in his opening remarks, Dr. Bradbury mentioned that we 

26 are trying to look at all of this in the context of the 

27 NRC's toxicity testing in the 21st century and how we go 

28 about this. So we have our compound; we have our 

29 metabolites. Dr. Cooper is going to be talking about the 

30 mode of action. And we are going to be assessing the 

31 biological perturbations, which is the GnRH pulsatile, 

32 pulse generator, the effected pathway which is the HPG 
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1 axis, and the dose response analyses for the perturbations 

2 of toxicity pathways.

3

4 This part is actually going to be a talk by Dr. Chester 

5 Rodriguez, so there is a little bit of an overlap there. 

6 Dr. Christensen is going to be talking about the 

7 epidemiology. The human exposure is going to be addressed 

8 by Nelson Thurman. And the human dosimetry and internal 

9 dosimetry is also going to be addressed by Dr. Chester.

10

11 Somewhere in here, although it is not explicitly here, we 

12 are going to start talking about the life-stage 

13 sensitivities, as well as the integration of the 

14 epidemiology and the toxicology data. So with that, I am 

15 just going to give you a brief run-down of the 

16 presentations. Dr. Cooper will be coming right after me 

17 talking about the adverse outcomes and mode of action.

18

19 Dr. Christensen will be reviewing the atrazine cancer 

20 epidemiology data. Then I am going to come back and talk 

21 about the integration of epidemiology and tox data into 

22 the health risk-assessment. Atrazine updates to the dose 

23 response assessment will be addressed by Dr. Rodriguez. 

24

25 Mr. Nelson will be talking about the drinking water 

26 monitoring data. I will then come back, yet again, to 

27 talk about the potential sensitivity of infants and 

28 children, so that‟s the life-stage sensitivity. And 

29 finally, we are going to wrap up with a case study that 

30 attempts to overlay the water monitoring data and the 

31 water exposure with our dosimetry approach to try and 

32 bring it all together.
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1

2 So, as you can see, we have a full agenda. The hope here 

3 is that, with this four days, we are going to wrap 

4 everything that we have gone through from February, April 

5 and September into a coherent story of what is atrazine 

6 doing. And with that, if there are any questions?

7

8 DR. DANIEL SCHLENK: Thanks, Dr. Mendez. What I would like to 

9 do is hold all our questions to perhaps after Dr. Cooper's 

10 presentation, if that is okay, just to keep this on time 

11 here. So with that, I would like to introduce Dr. Ralph 

12 Cooper who is with the National Health and Environmental 

13 Effects Research Laboratory, Office of Research and 

14 Development EPA. Dr. Cooper?

15

16 DR. RALPH COOPER: Thank you. And I would like to echo the 

17 remarks of my colleagues here at the front table about how 

18 much we appreciate the input that we have received from 

19 the panel on this arduous task of pulling all this stuff 

20 with atrazine together.

21

22 My presentation today really focuses on three things. A 

23 lot of it will reiterate what Liz just mentioned about the 

24 mode of action and why LH was selected as the key event or 

25 one of the key events in the evaluation of atrazine 

26 toxicity. 

27

28 And then, there is two sort of related but separate 

29 components of the presentation where we are trying to 

30 respond to some of the earlier comments that this panel 

31 has made concerning some analysis of the mammary gland 

32 work that has been done, the development work that has 
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1 been done, and some presentation and data that was 

2 recently published by ORD, and then also talk a bit about 

3 one of the requests from the September panel about getting 

4 a better handle on exactly what happens with short-term 

5 dosing and this whole question about 1-day, 2-day, 3-day 

6 kind of thing.

7

8 So with that said, historically, as Liz Mentioned, the 

9 mode of action and hazard assessment of atrazine was 

10 focused around the fact that atrazine was found to cause 

11 premature development of mammary gland tumors in the 

12 female rat.

13

14 And to make a long story short and to avoid using a lot of 

15 slides, what was shown essentially is that these tumors 

16 develop earlier. There does not appear to be necessarily 

17 more of them later in life, but they come on earlier in 

18 life. And because of the nature of reproductive aging in 

19 the rat, the fact that when the rats go through an estro20 pause or stops the normal reproductive cycle, the pattern 

21 of hormone secretion that develops in the aging female is 

22 just one of high estrogen and unopposed estrogen and 

23 prolactin secretion, and therefore, that is conducive to 

24 the growth of the tumors.

25

26 At the same time, the literature is pretty solid on the 

27 argument that one of the key moving factors in 

28 reproductive aging in the rodent is the disruption of the 

29 regulation of the gonadotropin, in particular, LH. 

30

31 With advancing age, there is essentially a little bit of 

32 slippage in timing of the occurrence of the surge and the 
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1 amplitude gets progressively lower to the point where it 

2 can no longer sustain ovulation. The ovaries then develop 

3 persistent follicles, which secrete the estrogen and 

4 feedback onto the pituitary to increase prolactin 

5 secretion, as I just mentioned.

6

7 So with that background, it led to the studies where 

8 investigators looked more directly at the regulation of 

9 luteinizing hormone and what the dose response 

10 characteristics would be, and duration of exposure for the 

11 effects of atrazine on LH, and that was where they were 

12 almost at the 2000 SAP where, when that literature and 

13 that data was reviewed, they agreed that atrazine induced 

14 alterations of the secretion of LH. 

15

16 Specifically, the ovulary surge of LH was the key event in 

17 the development of mammary gland tumors. It occurred 

18 earlier in the atrazine-exposed animals. 

19

20 They presented essentially a toxicity pathway of sorts 

21 then. Some of it has been upheld. Some of the more 

22 molecular events have not been upheld. But essentially, 

23 it says that the brain, particularly the hypothalamus, 

24 seems to be a target site for this herbicide, and that 

25 through the effects on the brain, the regulation of the 

26 pulsatile release of gonadotropin releasing hormone out of 

27 the brain is disturbed. Therefore, the amplitude or the 

28 secretion of LH is altered.

29

30 Once that happens, you get the persistent estrous, as I 

31 said, the persistent secretion of estradiol and prolactin, 
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1 and these induce the proliferative effects on the mammary 

2 glands themselves.

3

4 Liz mentioned, and again I will reiterate -- and since I 

5 just mentioned the reproductive aging in the rat is driven 

6 by changes in the brain, or that is what is believed --

7 there are still some descending view points. But the 

8 primary changes take place in LH, and that is quite clear, 

9 but that is not the same thing that happens in humans.

10

11 When humans age it is the depletion of the follicles from 

12 the ovary that seems to be the driving factor. The 

13 hormonal environment present in the post-reproductive 

14 woman is quite different than that in the post15 reproductive rat, and therefore, there was the basis for 

16 the conclusion that it would be highly unlikely that 

17 atrazine would have the same outcomes in humans.

18

19 At the same time, there were data available at that SAP 

20 that said, "Well, if you are going to look at LH and you 

21 are going to look at the regulation of this hormone and 

22 what the other physiological roles of this hormone plays 

23 in the male and female, what is happening there and is 

24 that relevant to the risk-assessment of atrazine?" And 

25 there were a number of studies that were done looking at 

26 the impact of changing LH secretion at different life27 stages and in different sexes. What I have up there --

28 the first three bullets in black are outcomes that appear 

29 to be dependent upon alteration of the LH. 

30

31 It is a regulation of luteinizing hormone, either during 

32 development where there were two studies from ORD -- and 
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1 they were replicated by other investigators -- that showed 

2 that this chemical will delay puberty in both the male and 

3 the female rat.

4

5 The data that I put there -- this is to add some numbers 

6 and to help get where I'm going in a slide or two -- is, 

7 the data I put there are the LOAELs for the adverse 

8 affect, that is the delays wherein the male 12 and a half 

9 milligrams per kilogram and the female 30, that the 

10 disruption of ovarian cyclicity which, again, appeared to 

11 be dependent on changes in LH, came in at about 75 

12 milligrams per kilogram for a 21-day study.

13

14 There was a longer-term study where somewhere around 22 

15 milligrams was the LOAEL. The lowest LOAEL up here is one 

16 that showed that the disruption of regular cycling in the 

17 female, the early reproductive senescence came in at about 

18 3.65 milligrams per kilograms after a six-month exposure.

19

20 And then there was a study looking at comparing different 

21 strains of animals during pregnancy. Rat pregnancy has an 

22 LH-dependent phase. And in this study, the investigator 

23 dosed only during that stage of pregnancy and showed that 

24 there was a differential sensitivity cross-strains; the 

25 most sensitive strain being the Fischer 344, that when you 

26 dose with atrazine during that period it lowered the LH 

27 and you got full-litter resorption; that‟s what FLR stands 

28 for.

29

30 There have been two other adverse outcome types of studies 

31 that have been conducted that do not appear to be relevant 

32 to or dependent upon some change in luteinizing hormone, 
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1 per se. That was the work done by Dr. Tammy Stoker 

2 looking at the development of prostatitis after the dam 

3 was treated early in life, presumably was shown to knock 

4 down prolactin in the dam. And when you knock down 

5 prolactin in the dam, that impacts, based on the studies 

6 in the basic literature, the development of certain 

7 dopaminergic neurons in the brain which then alter, when 

8 that animal grows up, its ability to regulate prolactin 

9 and bad things happened in this case to the prostate where 

10 you saw prostate inflammation around the 120 days of age.

11

12 And there has been some similar work done following up 

13 with looking at the prostate where the animals were dosed 

14 during gestation. And then the last bullet up there, the 

15 altered memory gland development was one of the focuses of 

16 the September SAP. And mechanistically, or how that 

17 relates to LH, I am not clear if it does at all. 

18

19 Let me just back up one minute. With the prostatitis, the 

20 LOAEL for that was 25 milligrams per kilograms. As I 

21 mentioned, full-litter resorptions came in at 50. And the 

22 mammary gland data, I don't have a number there. The 

23 majority of studies used 100 milligram per kilogram, but 

24 the work of Dr. Fenton, she has publications that shows 

25 that that dose could be run down considerably and you 

26 would see changes there. And again, we feel that might 

27 need to bear repeating before we can actually put 

28 something on those numbers.

29

30 What I show in this next slide though is -- and this is 

31 getting at the rationale why the agency feels that if we 

32 look at luteinizing hormone itself that we can actually --
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1 since we are seeing changes in the physiology of animals 

2 that are dosed with atrazine at higher levels than the 

3 changes that we see in luteinizing hormone, that using 

4 this measure could be protective, or could be considered a 

5 centennial measure for the adverse outcomes.

6

7 What I have there is the top two lines show the studies 

8 looking at the pulsatile release of GnRH out of the brain 

9 using a surrogate measure that is luteinizing hormone 

10 pulses themselves in experimental models. Those have come 

11 in at 25 and 50 milligrams per kilogram. The duration of 

12 dosing there is four days. 

13

14 And down, the next bigger block there, pituitary 

15 attenuation of LH surge. If you look on the left-hand 

16 side you will see the different durations and the doses 

17 that were considered LOAELs and NOAELs, and I highlighted 

18 the ones that seemed to come in at the lowest dose 

19 necessary to produce a change.

20

21 We had some one-day studies that demonstrated that you 

22 could identify a LOAEL, at least in the Long Evans 

23 animals, but it was a very high dose; 300 milligrams per 

24 kilograms. I will discuss in a moment some new data that 

25 we have taken a somewhat different approach, experimental 

26 design to look at the potential adverse effects or 

27 potential changes in LH secretion after one day of 

28 exposure; work by Jerome Goldman. 

29

30 You have the data. You will see the study that he is 

31 conducting. There have been studies where the animals 

32 were exposed for three days. Prior to the SAP in 2010 was 
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1 a paper by Cooper et al. in 2007 where there was a LOAEL 

2 of 6.25 milligrams per kilogram after exposing the animals 

3 for four days.

4

5 And these are cycling animals, so they were dosed 

6 throughout one estrous cycle. That was discussed 

7 extensively in the September report or the panel meeting. 

8 And we came back upon request from OPP and finished out 

9 that dose response, and they performed a benchmark dose 

10 and found that the significant change occurred at 2.56 

11 milligrams per kilogram per day. That is a 4-day 

12 exposure.

13

14 That is not too far different than the LOAEL that was 

15 reported or derived from the Morseth where she dosed for 

16 28 weeks. And that study was also the one where the -- I 

17 mentioned the estrous cyclicity was effected. So in that 

18 one there was an effect that the same level as there was 

19 for LH. 

20

21 And then the other ones there -- we have already mentioned 

22 the higher levels or higher concentrations that were 

23 required to perturb estrous cyclicity in the shorter 

24 durations; the one, the four and the 21-day exposures.

25

26 So again, the point being that if you want your most 

27 sensitive measure for perturbations of LH-dependent 

28 outcomes, look at LH, and look at it under the right 

29 circumstances and you will find those doses. 

30

31 And again, this just going back to the rationale for that 

32 -- and I highlighted it in green -- that we see the 
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1 adverse outcomes are consistent with the LH or alter GnRH 

2 mode of action, and therefore, the proposed mode of action 

3 for development effects shares considerable overlap of the 

4 proposed mode of action for carcinogenicity. 

5

6 Now that was to say that, okay, you saw your mammary 

7 glands that were dependent on LH. You see these other 

8 adverse outcomes. And that these neuroendocrin actions of 

9 atrazine are probably the dominant mechanism by which 

10 atrazine exerts its reproductive and developmental 

11 affects. 

12

13 So again, if you protect for effects on the hormone itself 

14 you should be protecting for the effects of the chemical 

15 on the reproductive and development physiology. 

16

17 I cannot get away without showing the slide of some LH 

18 data. These are the typical kinds of results that you 

19 see. This is actually a study from Fredis et al. where 

20 they had measured not only the parent compound, or looked 

21 at the effects of the parent compound and the dose 

22 response, but looked at also the different metabolites. 

23 And I put in there DIA as one of the ones, and we naively 

24 thought we could dose equimolar doses of atrazine and DIA 

25 based on what we applied. But the point is that, when you 

26 do that you do see dose responses that are not that 

27 different, where you see a suppression of the peak.

28

29 And again, this was coming in at about -- I think in that 

30 study our lowest dose was 6.25 or something -- but these 

31 were coming in about 12.5 for atrazine as being 

32 significant at the peak and then the equimolar dose of the 
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1 intermediate metabolite DIA came in at 10 milligrams per 

2 kilogram.

3

4 And then, to get back at the toxicity pathway and the mode 

5 of action -- but again, where we are with this, I think, 

6 after 15 years of looking at the effects of atrazine and 

7 how it influences the neuroendocrine function, it is sort 

8 of disappointing from the standpoint that, as you go 

9 across that adverse outcome pathway from where the 

10 toxicant interacts with the cells and disturbs or binds to 

11 a receptor or alters DNA or some protein and you see 

12 changes in activation of the genes or the production of 

13 different protein, we are really limited in our knowledge. 

14 There have been a number of papers, and that April SAP 

15 showed that there have been a number of papers that have 

16 been published, but there are no clear linkages and story 

17 that I think can be told.

18

19 There is somewhat of a consistency in that atrazine 

20 somehow disrupts the cyclic AMP-dependent cascade, but 

21 that‟s pretty diffuse effects that you can see. And to be 

22 able to link that to the particular alterations in the 

23 pulsatile release of GnRH; I think that is the key there, 

24 is that if you got back to the primary molecular or 

25 cellular change that is consistently seen in these studies 

26 by different labs, is that those GnRH neurons do not seem 

27 to be as active under atrazine as they are compared to the 

28 controls.

29

30 There is work done by Foradori where they looked at the 

31 cFOS staining and activity of those neurones, and you see 

32 it is down and it can be correlated roughly with the 
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1 decrease in pulse events as well as the decrease in LH. 

2 Where we have the stronger evidence though is highlighted 

3 in yellow. In my mind, the altered signalling is the GnRH 

4 pulses.

5

6 We have a lot of evidence that supports the altered 

7 physiology, the disruption of homeostasis tissue, changes 

8 in tissue development, as I mentioned -- not necessarily 

9 always with LH that prostatitis was that one study that 

10 was done and the other outcome.

11

12 So, still LH seems to be the key event for the majority of 

13 the adverse outcomes. It is quite strongly linked to some 

14 change in neural signaling coming out of the brain, but 

15 what is really taking place prior to that, we have limited 

16 knowledge. 

17

18 Now I am going to switch a little bit and address one of 

19 the requests from the September 2010 SAP. And if I seem a 

20 little uncomfortable in this it is because this is really 

21 a little bit outside my life space mammary gland -- tumor 

22 evaluations -- but my colleague Jerome Goldman is here if 

23 questions come up for it.

24

25 There is a difference. The Fenton study shows

26 consistently that there appears to be some type of 

27 developmental delay in the mammary glands of rats when the 

28 mother is treated gestationally, and you will look at the 

29 offspring. 

30

31 In attempt to replicate the Fenton work -- that is the 

32 Rayner/Enoch papers up there -- Hovey did a rather 
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1 extensive study looking at the same strain of animals and 

2 used more objective techniques or quantitative techniques 

3 to evaluate development. He presented that data here in 

4 September and could find no difference across the 

5 different life-stages measured or doses that were used. 

6 Okay? You guys are very familiar with that.

7

8 One of the questions that came up was is it a technique 

9 issue. Is it the measurement issue as opposed to what you 

10 are seeing issue? So OPP requested that -- we were at the 

11 time running a gestational study where we were dosing dams 

12 with atrazine at different doses from 1 to 100 milligrams 

13 per kilogram, gestation day 14 to 21. And those animals -

14 - we did it for a totally different purpose, but some of 

15 the offspring were available for looking at mammary glands 

16 and Jerome Goldman -- I want to say he agreed to, in his 

17 lab, evaluate those and try to use both the quantitative 

18 and qualitative measures that have been published.

19

20 So the animals that were available to him -- and this was 

21 an important note in even Sprague-Dawley rats -- the 

22 animals that were available to him were 45 days of age. 

23 The animals were available to him just before 45 days of 

24 age, and that happened to be the age at which the workshop 

25 on mammary gland tumors suggest it would be the best time 

26 where they were getting the most consistent data feedback 

27 when they did the round-robin evaluation of different 

28 ages.

29

30 So Jerome Goldman just said, "Yeah. I'll go ahead and 

31 look at those animals at postnatal day 45 using both the 

32 quantitative and the qualitative measures." And he worked 
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1 with different individuals to make sure that the Fenton 

2 data subjective scale that she used was the same scale 

3 that she used, with working with some of her former 

4 technical staff.

5

6 And also, with getting information on different sources on 

7 how the quantitative measures were to be made. And so he 

8 did that study and that study was published by Lori Davis. 

9 I think it jut came out about a month ago. 

10

11 I just want to show the summary of the two different 

12 techniques so that you can see what is going on here, at 

13 least on postnatal day 45 in the Sprague-Dawley females. 

14 These are the quantitative evaluations. What Jerry 

15 looked at was the branching in a particular segment of the 

16 mammary gland 3 by a 3 millimeter square of the slide.

17

18 He looked at the number of terminal ends buds present in 

19 the animals and he looked at the distance from the lymph 

20 node to the most distal portion of the gland. These 

21 measures are familiar to some of you guys. I have seen 

22 them and they seem relatively straight-forward. But the 

23 important point here is he has his number of animals 

24 evaluated noted there in white in those bars -- and there 

25 does not seem to be a difference, or he could not find a 

26 difference in any of those measures across that wide range 

27 of dosing.

28

29 Now, if you notice up there, these are animals that were 

30 dosed twice a day -- but we also did animals that were 

31 dosed once a day; a higher dose hitting them up to a 

32 hundred -- and neither dataset showed a difference.
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1

2 The subjective or the qualitative scale measures that were 

3 made are shown here, comparing just 100 doses in this case 

4 against the controls. And again, this is both for the one 

5 dose a day on your left and the two doses a day on your 

6 right. And again, they could not identify any difference.

7

8 And Jerry can go into the details of this study, but the 

9 way this study was run is there were three individuals who 

10 went through the scoring techniques, then they went 

11 through the same way to develop the range of scores that 

12 they could see. Once they agreed on that, they came back 

13 and blindly and independently looked at all the slides and 

14 then came back and broke the code and also looked at 

15 integrator reliability and those kinds of things, and they 

16 are all in the paper.

17

18 So, the bottom line here is that, again, this is a limited 

19 answer to your question. It is postnatal day 45. They 

20 are Sprague-Dawley rats, so these are some issues that may 

21 or may not be important. But in this kind of attempt to 

22 get at the question of whether or not there is a 

23 difference, no difference could be found with either or 

24 any of those type measures. 

25

26 So to summarize, the reproductive and developmental 

27 effects in the rat, like we were looking at delayed 

28 puberty, ovarian cycling, full-litter resorption; they are 

29 all consistent with a primary mode of action of atrazine 

30 on the HPG and on LH secretion. And the alterations in 

31 serum LH provide the lowest LOAELs and NOAELs available. 

32 And these serum hormone measures serve as a sentinel for 
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1 two of the three adverse outcomes identified; that is 

2 puberty and ovarian function.

3

4 We are not a hundred percent certain about the FLR because 

5 of the limited number of doses that we chose and things, 

6 but I am pretty confident that we could say that also 

7 about the full-litter resorptions.

8

9 And although the proposed mode of action for prostatitis 

10 is quite different than the LOAELs and NOAELs for that 

11 effect, are below those that we see when we look at 

12 adverse outcomes or the actual measurements of luteinizing 

13 hormones. So, by default, the argument is, is that we are 

14 protecting against prostatitis, as well, if we use the LH 

15 measures as our point of departure. 

16

17 Now, this is the final section of my presentation, and 

18 again, it is to go over some work that has been done to 

19 address one of the questions that was posed in the 

20 September SAP where the panel was struggling with the 

21 dosing durations and what is going on and how long you 

22 have to dose.

23

24 We had, if you will, limited data. At the time, four days 

25 was the shortest day that we had to complete dataset. And 

26 there was questions about what one single dose would do 

27 and what two doses or -- is do you need four doses and you 

28 were not seeing anything with one dose and those kinds of 

29 things. 

30

31 So this is drawn right out of their comments. It says 

32 that it is clear that identifying that greater than one 
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1 pulse of exposure to atrazine is necessary for attenuation 

2 of the LH surge. For example, single doses of over 100 

3 administered on the morning of proestrus did not alter the 

4 characteristics of the LH surge occurring the same day. 

5 And I will show you that dataset in a moment because it is 

6 kind of curious the way it happened, but it is true; there 

7 was no effect with single high dose in the study that we 

8 did in 2000.

9

10 Additionally, data clearly demonstrate a once daily dose 

11 for four days and beginning of the day of estrus can 

12 induce a significant inhibition of the LH surge peak, and 

13 that was the data that was scrutinized and scrutinized in 

14 September, and so, there is a big difference between no 

15 effect and four days later of LOAEL of about -- whatever 

16 that benchmark does was -- 2 point something.

17

18 In this instance, a dose response is observed. However, 

19 what is not clear -- and this is the key -- however, what 

20 is not clear is, if less than four days, but greater than 

21 one days' exposure is sufficient to alter the LH surge. 

22 Further complicating the matter, it is not clear if a four 

23 day exposure, beginning on a different day of the estrus 

24 cycle, and you start to note all these permutations could 

25 lead to differences.

26

27 So understanding the relationship between the duration of 

28 exposure in the phase of the cycle will be key in 

29 translating rodent data for humans for the risk-assessment 

30 purpose. So actually, there is a tremendous amount of 

31 work behind that paragraph, if you tried to answer all 

32 those questions in that paragraph. 
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1

2 And, again, we tried to address some of those issues, and 

3 in doing so, tried to incorporate some of the information 

4 that we had reviewed in the April SAP about the potential 

5 effects of atrazine on the adrenal access where we saw, 

6 and others have reported a significant increase in 

7 adrenal, progesterone and corticosterone after a single or 

8 three or four doses.

9

10 So, what we did is design a study where -- we did not do 

11 it in intact animals, again, because we had enough 

12 headaches just dealing with some of the changes in the 

13 ovarian hormone. So knowing that atrazine also 

14 increases progesterone from the adrenals, we decide what 

15 we would do is evaluate one, two and four days of exposure 

16 to atrazine in animals that were treated with estradiol or 

17 not. And the rationale behind this study is to understand 

18 the very, very basic observation about the role of 

19 estradiol and progesterone on regulation of LH. 

20

21 An animals that is primed with estradiol first, you will 

22 see an LH surge three or four days later, but it is a very 

23 modest one. That is what is shown in blue in this figure 

24 here, and that is usually about six to 10 nanograms per 

25 mil at the peak. What I am showing here is a plot the way 

26 that -- you are going to see the data where you are 

27 looking at the peak on the fourth day of exposure, and the 

28 peaks are aligned based on the highest value that w see 

29 for LH. So zero is the peak and the surge curves on 

30 either side. And you can look at the area that it curve 

31 and do some other analyses with this.

32
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1 If you give that animal three days of estradiol and on the 

2 fourth day you dose it with progesterone -- in this case, 

3 subcutaneously, you see it facilitates the LH surge or 

4 dramatically increases the amount of luteinizing hormone 

5 that is released.

6

7 Estradiol prepares the hypothalamic pituitary tissues. 

8 Estradiol produces progesterone receptors. Progesterone 

9 receptors are there and, boom; you get one of the only 

10 examples that I know of synergy, if you will, where you 

11 see this tremendous increase over estrogen alone if you 

12 add progesterone.

13

14 If you flip that order -- and this is where timing gets to 

15 be important -- where you put progesterone in the animal 

16 first, or concomitantly with estradiol then you are not 

17 going to have those preparatory changes take place in the 

18 hypothalamic and pituitary. And you will get nothing on 

19 the fourth day and that is what the green line is supposed 

20 to represent, where you hit P+E or progesterone followed 

21 by E. 

22

23 So the hypothesis was this; that if we are going to 

24 evaluate the role of atrazine -- these one-day, two-day, 

25 three-day kinds of exposures -- we should take into 

26 account what it is doing not only to LH, per se, but also 

27 the other hormones that are involved in this. Even if we 

28 have ovariectomized animal, you had the adrenal hormones. 

29

30 And our hypothesis was that atrazine should work like 

31 progesterone because one dose, we know, increases 

32 progesterone from the adrenals. And if we dosed once in 
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1 an estrogen-primed animal we should facilitate the surge; 

2 not knock it down; increase it. And if we dose 

3 consistently for three days with atrazine, and if it does 

4 induce that progesterone each day, then it should start to 

5 attenuate the surge. So that was sort of not really what 

6 you would glean from the literature, but we went back and 

7 we looked at our 2000 study -- actually OPP went back and 

8 looked at our 2000 study and said, "You didn't get any 

9 effects in one day but --" 

10

11 When we did, this is actually what we had. And if you 

12 look at it -- this study was done where we dosed the 

13 animals with estradiol for the preceding three days and 

14 then gave atrazine once. And we killed the animals by 

15 decapitation. So we could not get area under the curve, 

16 we could not line up the peak heights and all that kind of 

17 thing.

18

19 And animals do not always have the same exact, precisely, 

20 the same peaks. But I was surprised, what is circled 

21 there is a tendency for there to be an increase as we 

22 increase the dose of atrazine, so that was curious. So 

23 maybe one day, if you did the study right you might see 

24 that a single dose does do something. 

25

26 And this is the data that Jerry Goldman and his colleagues 

27 collected where they dosed animals with estradiol. They 

28 ovariectomized them and put the silicon estrogen29 containing capsule subcutaneously for three days. On the 

30 fourth day, instead of dosing with progesterone, they 

31 dosed with atrazine at 12:00, 1:00, 1300, and low and 

32 behold, you see there was a significant increase in the 
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1 peak height and the area under the curve for LH, which 

2 tells us now that this is a single dose. We are having an 

3 effect on regulation of LH and that it is consistent with 

4 our prediction that this might have something to do with 

5 adrenal progesterone.

6

7 If you dose for two days, however -- and this is -- again, 

8 all these data are in the docket. We use zero and 100. 

9 These data were using zero and 100 milligrams per 

10 kilogram. The point is that there is no difference when 

11 you dose for two days. We saw no effect. There was a 

12 slight decrease in the area under the curve, but if you 

13 look on the right hand panel there, it is down 77 percent 

14 after 24 hours. After six hours it was up 181 percent, 

15 the amount of LH secretes. 

16

17 So there is a big difference between day one and day two, 

18 but just the way we analyzed it did not really demonstrate 

19 that. And then again, if you dose for the four days, as 

20 we have in the past, you see that there is the suppression 

21 of the surge or the peak as well as a decrease in the area 

22 under the curve.

23

24 I do not know whether we are going to help answer that 

25 question about how long to dose and what the different 

26 durations would be, but you can see that there is nothing 

27 magical about that 4-day. The closer we get to 

28 understanding the basic physiology or interaction of the 

29 steroid hormones with this system, that maybe you can get 

30 at what is going on a little more clearly. So, we do have 

31 data now on one, two and four doses that there is a 

32 significant increase after one; two does of atrazine 
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1 revealed no real change in the LH from controlled animals, 

2 and four doses had the previously described decrease in 

3 LH.

4

5 Again, all changes in this case where we are substituting 

6 atrazine with changes in what might be imagined, if you 

7 will, from the changes that we see in adrenal 

8 progesterone. And it is important that we note at this 

9 point that these changes just speak to the duration of 

10 dosing. I do not think they really tell us anything or 

11 imply anything about the adverse effects of increased 

12 luteinizing hormone in these animals.

13

14 So back to our mode of action and toxicity pathway data. 

15 I think there is a wealth of data that talks about 

16 alterations and -- I think there has got to be, now, at 

17 least 20 studies looking at changes in LH after exposure 

18 to atrazine under a variety of different conditions and 

19 they all come from the ORD lab. Several different labs 

20 around the country have worked on this.

21

22 It is clear that you can see changes in reproduction and 

23 development. The linkage is back to the toxicity pathway, 

24 however, are still challenging and we do not really know 

25 what is taking place back there. But, again, the data 

26 that we have to date really, I think, supports the 

27 argument that if you use the LH measures that exist, and 

28 that they exist under, and the rationale that they are 

29 precursory changes to the changes in the physiology that 

30 using LH as the sentinel or the marker for the point of 

31 departure in the risk-assessment, it should be on safe 

32 grounds. Thank you.
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1

2 DR. DANIEL SCHLENK: Thank you, Dr. Cooper. At this point, I 

3 would like to sort of group or questions or clarifications 

4 together for doctors Bradbury, Fowle, Mendez and Cooper, 

5 if you would. So anybody have any questions or 

6 clarification?

7

8 DR. DANIEL GRIFFITH: Numbers that you showed in the charts 

9 with the percentage changes, are they a factor of the 

10 smallness of the numbers or are they actually meaningful 

11 percentage changes?

12

13 DR. RALPH COOPER: Which slide?

14

15 DR. DANIEL GRIFFITH: The ones at the very end; these ones. So 

16 those percentage changes that you are showing, are they 

17 more a function of relatively small numbers or are they 

18 the meaningful percentage changes?

19

20 DR. RALPH COOPER: Well, it is just the percent. The number of 

21 animals is quite large. I think there were 10 animals in 

22 each one of those groups. So you have got a good dataset 

23 there. That percentage is just the percent -- you have 

24 the controls, and then what was the percentage of LH that 

25 was produced in the treated animals. 

26

27 DR. NELSON HORSEMAN: I just have one question for 

28 clarification. The peak levels in the three LH 

29 experiments you showed us, in the controls, which are the 

30 blue bars, are a little different from experiment to 

31 experiment. Could you comment on whether those are 

32 meaningful?
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1

2 DR. RALPH COOPER: One thing that is important, and we looked 

3 at this so the answer is no. But it is important. And 

4 this speaks to the way that you measure the hormone and 

5 what your control values look like. I just ran through 

6 them real quickly, but we are looking at about four and a 

7 half -- the question is whether or not our controlled 

8 animals varied in the different groups. We had the 

9 different days, okay? And they varied in this case, after 

10 four days of dosing they were approximately four and a 

11 half nanograms per mil at the peak. And then in this one 

12 they were up to six, which is -- these are typical 

13 numbers. I have seen as low as three in the published 

14 literature. They are usually less than 10. And in this 

15 case you, again, were at four and a half, so I do not put 

16 much stock in that. And as a matter of fact, if you 

17 compare those statistically, there was not a difference.

18

19 DR. NELSON HORSEMAN: These are not dose; these are control 

20 animals right?

21

22 DR. RALPH COOPER: Well they get the vehicle.

23

24 DR. NELSON HORSEMAN: The vehicle. The reason I ask is, 

25 because in the two-day exposure the treated animals are 

26 significantly lower, but they are not different from the 

27 controls in the other two experiments, so it is a question 

28 of whether --

29

30 DR. RALPH COOPER: No. The two days was not statistically 

31 significant.

32
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1 DR. NELSON HORSEMAN: Oh, it is not statistically significant, 

2 it is just the difference between the 77 and 180?

3

4 DR. RALPH COOPER: Right. No, that is not significant either, 

5 neither one of those. I am sorry. The point I tried to 

6 make of that was not -- no. Two days was not different. 

7 If you did that study and killed the next day, you would 

8 say, "Oh, there is no effect at all of atrazine on the 

9 surge." What I pointed out there was -- I was struck, and 

10 it might just be my own weird way of thinking was that I 

11 was struck by a six-hour exposure having that much LH 

12 released, 181 percent of control and a peak height up to 

13 10 -- that is pretty high for an estrogen-only treated 

14 animal -- and then dropping down within 24 hours to just 

15 the 77 percent. That is the only thing I pointed out. 

16 It's only statistically significant -- we did not do a 

17 three-day. The 4-day was down.

18

19 DR. KEVIN O'BYRNE: A very simple point of clarification. On 

20 the four days, you were giving atrazine on four days, 

21 starting from the time of the estradiol implant; is that 

22 correct?

23

24 DR. RALPH COOPER: Correct; one o'clock. It was rat time, 1:00 

25 p.m. from the day of -- and that is what we typically did. 

26

27 DR. KEVIN O'BYRNE: So on the two-day treatment, was that two 

28 days into that treatment? So is it two days relative to 

29 the peak of the surge? Okay. Thank you.

30

31 DR. RALPH COOPER: The day before, 20 whatever hours earlier.

32
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1 DR. KEVIN O'BYRNE: 24 hours?

2

3 DR. RALPH COOPER: No, not 24 --

4

5 DR. KEVIN O'BYRNE: 48.

6

7 DR. RALPH COOPER: -- 28 hours, 28 or 30 or something like 

8 that.

9

10 DR. KEVIN O'BYRNE: Starting at 1300 hours.

11

12 DR. RALPH COOPER: Yeah.

13

14 DR. JAMES MCMANAMAN: Yes; just a point of clarification. 

15 There was some discussion in the September meeting about 

16 stress -- this is for the mammary gland, slide 11 --

17 having an effect on the outcome. And I am just wondering 

18 did you consult with Dr. Fenton's group? Because she made 

19 a big point about the way these animals are handled can 

20 affect the outcome. And since stress could affect the 

21 hypothalamus, I am just wondering is there any kind of 

22 connection and was that controlled for at all?

23

24 DR. RALPH COOPER: This study was run in four blocks, and one 

25 of the underlying reasons for running this study was to 

26 evaluate the role that gestational exposure to the dam may 

27 have on her pituitary adrenal access, and then what 

28 outcome that may have on the offspring. So in terms of 

29 stress, we were really quite fastidious, if you will, 

30 about the way we handled the animals. As a matter of 

31 fact, there was a lot of discussion about weighing them, 

32 even if we should weigh them, because we were concerned 
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1 whether or not that might compromise the potential effects 

2 that we were looking at from the gestational exposure. 

3

4 So we took a lot of care in making sure that we did not 

5 have any stress, and we did not have the cannibal -- and 

6 these were Spragues. The cannibalism that was discussed 

7 there seems to be unique, I think -- this is my opinion --

8 to the Long-Evans rats. A lot of times the Long-Evans are 

9 just a little more fussier than the Spragues in terms of 

10 handling them during the perinatal period.

11

12 DR. DANIEL SCHLENK: All right. Any other questions on adverse 

13 outcomes or mode of action? Okay. At this point, we will 

14 go ahead and take a 15-minute break. We have lost our 

15 standard clock up here on the door here, so I am going to 

16 relegate to my watch, which I have 11 after. So let us 

17 come back at -- let's say 25 after 10. Look on your cell 

18 phones; I guess that is the best way.

19

20 DR. DANIEL SCHLENK: All right. Let's go ahead and get 

21 started, please. Our next presentation will be the 

22 review of atrazine cancer epidemiology, and it will be 

23 presented by Dr. Carol Christensen who is from HED/OPP. 

24 Dr. Christensen?

25

26 DR. CAROL CHRISTENSEN: Yes. Thank you and good morning. My 

27 name is Carol Christensen. I'm an epidemiologist with the 

28 pesticide program office. And over the next several 

29 slides I will be happy to review with you our assessment 

30 of the Atrazine Cancer Epidemiology literature.

31
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1 So, as Dr. Mendez mentioned, EPA has presented its review 

2 of this database at two previous SAP panel sessions. In 

3 June of 2000, although other issues were discussed, EPA 

4 presented its assessment of epidemiological studies 

5 relating to breast cancer, ovarian cancer, prostate tumors 

6 as well as Hodgkin Lymphoma.

7

8 As other EPA presenters have mentioned, in July of 2003, 

9 the agency convened an SAP panel to discuss prostate 

10 cancer specifically, primarily studies among an 

11 occupational exposed cohort of Triazine manufacturing 

12 plant workers.

13

14 So, as a result of those external evaluations as well as 

15 other external evaluations and internal assessments, in 

16 October of 2003, EPA preliminarily concluded that the 

17 agency does not find any results among the available 

18 studies that would lead us to conclude that potential 

19 cancer risk is likely from exposure to atrazine.

20

21 However, the agency committed to revisit this issue when 

22 additional epidemiology studies became available, 

23 particularly those from the agricultural health study. As 

24 Dr. Bradbury noted earlier, within the last several 

25 months, researched with the Ag-Health Study have published 

26 an updated evaluation of the carcinogenic potential of 

27 atrazine in the human population. Those data are 

28 integrated within this presentation.

29

30 And I will just note for your information that Dr. Laura 

31 Beane-Freeman who is a co-principal investigator with that 

32 study with the National Cancer Institute, is here today 
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1 and available to address any clarifying questions you 

2 might have on the content of that research. 

3

4 So, given that new information is available, the current 

5 evaluation updates in extents our assessment of the 

6 atrazine cancer epidemiology database. 

7

8 So, what contrasts this review, perhaps, from previous EPA 

9 evaluations is our use of the draft framework for 

10 incorporating epidemiology into risk-assessment. As 

11 stated earlier, this framework was discussed with the SAP 

12 panel in February of last year. So using this draft 

13 framework, EPA described its methodology for identifying 

14 studies evaluating potential atrazine cancer risk in the 

15 human population. When looking at individual 

16 investigations, we evaluated the strengths and 

17 limitations, considering factors such as the consistent 

18 application of inclusion and exclusion criteria, use of 

19 valid and reliable exposure assessment methodologies, 

20 outcome ascertainment and so on, as well as an evaluation 

21 of the potential for systematic error or bias in the 

22 study. These factors are, again, reflected within our 

23 draft framework for incorporated epi into risk-assessment.

24

25 Looking across the cancer epidemiology database and moving 

26 to integrating with the experimental toxicology database, 

27 EPA utilized the framework considering the postulated mode 

28 of action, key events, as well as the, for example, 

29 observation of exposure response relations across the 

30 suite of epidemiology studies available on a particular 

31 cancer site, the strength of the measured association and 

32 the consistency of that association across the studies, in 
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1 addition to scientific judgments. So bringing together 

2 the experimental toxicology data and the observational 

3 epidemiology data, using the general principles described 

4 in this framework, today we are presenting our preliminary 

5 conclusions regarding atrazine cancer epidemiology 

6 literature.

7

8 So, EPA performed a comprehensive literature review 

9 purposely broad in nature. We search major biomedical 

10 databases, Pubmed, Web of Science, utilizing a search 

11 string developed with the aid of an EPA referenced 

12 librarian, for example. Our inclusion criteria, again, 

13 were broad. We considered studies that measured either 

14 atrazine or triazines in relation to any cancer outcome. 

15

16 We did however exclude investigations for which no full 

17 text manuscript was available. Some of the initially 

18 identified investigations were exposure only. We excluded 

19 those that were editorial in nature. 

20

21 So, having identified a suite of studies potentially 

22 meeting our inclusion and exclusion criteria, several EPA 

23 scientists from across the agency met to qualitatively 

24 adjudicate the relevance of those studies to the question 

25 of atrazine carcinogenicity among humans.

26

27 So, overall, we've identified 40 studies in the current 

28 epidemiology lit review. That span in publication date 

29 between the mid-1980s through the very recently published 

30 Atrazine cohort analysis by researchers with the Ag-Health 

31 Study. So these 40 studies are generally grouped in the 

32 following broad categories, several investigations of 
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1 endocrine and reproductive system. Tumors were evaluated, 

2 breast cancer, ovarian, prostate cancer and thyroid 

3 tumors.

4

5 In addition, several investigations of the potential 

6 relation between atrazine or triazines with multiple 

7 myeloma, non-Hodgkin lymphoma and other sub-types of 

8 lymphohematopoietic cancers, which is particularly 

9 relevant because it's understood that different sub-types 

10 may indeed have different etiologies.

11

12 In addition, we identified a few studies looking at other 

13 anatomical cancer sites, two studies on glioma published 

14 by researchers with NIOSH, and two on cancers in the 

15 paediatric population. And again, because research from 

16 the Ag-Health Study was deemed to be particularly 

17 important and relevant to the question of atrazine 

18 carcinogenicity in the human population, given the 

19 strength of the design and methods of that government20 sponsored study, for the sake of completeness, we've 

21 delineated each of the atrazine cancer risk estimates made 

22 available over time since 2003, in either the two cohort 

23 evaluations, which we will discuss, and the sixth Nested 

24 case control evaluations on specific cancer outcomes. So 

25 these are also briefly summarized.

26

27 Regarding prostate cancer, as Dr. Mendez briefly alluded 

28 to, EPA had previously reviewed studies of an occupational 

29 cohort of Triazine manufacturing plant workers whereas 

30 there was an initial observation of an increased incidence 

31 of prostate cancer among men who were actively employed in 

32 this triazine manufacturing plant.
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1

2 Additional analyses, including presentation to the SAP in 

3 July of 2003 concluded that the provision of a prostate 

4 cancer screening program, or the availability of the 

5 prostate specific antigen or PSA test among two men 

6 employed at this plant, was likely, at least, a partial 

7 explanation for this initial observation of an increased 

8 risk for prostate cancer. In fact, follow-up studies 

9 published subsequent to the last SAP review -- and that‟s 

10 the case control analysis within this occupational cohort 

11 in which authors were able to measure individual exposure 

12 levels -- seemed to support that conclusion when 

13 stratifying among men who received at least one PSA test 

14 over the course of their employment. No evidence of an 

15 association was observed between triazine and prostate 

16 cancer. There are other factors that support this 

17 conclusion delineated in the written material.

18

19 So moving on using data from the California State Cancer 

20 Registry, pesticide use information also reported to that 

21 state -- researchers reported evidence of a correlation 

22 between atrazine and prostate cancer among black men. 

23 This is based in an ecologic study in which atrazine 

24 exposure was measured at the group level, so it lacked 

25 individual exposure measures.

26

27 Using similar data sources, and that‟s the case control 

28 study among men who were part of a farm worker labor 

29 union, authors reported moderately elevated risk of 

30 prostate cancer in association with cyanazine. And I will 

31 just briefly note that this is the only study included in 

32 our review that did not actually meet our inclusion 
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1 criteria. It is the only one. We reflected it here 

2 because this study was brought to the SAP in 2003, and so 

3 its presented here for the sake of completeness.

4

5 So, within the Ag-Health Study, there are three point 

6 estimates to consider, or three evaluations, I suppose, to 

7 consider. In 2003, a preliminary case control study did 

8 not observe an association between ever use of atrazine 

9 over the lifetime in prostate cancer.

10

11 Similarly, in a preliminary, and in a very recent follow12 up cohort analysis within that study, researchers did not 

13 observe evidence of an association with prostate cancer. 

14 Looking at quartiles and quintiles of atrazine exposure, 

15 the risk estimates do not significantly differ from the 

16 null, nor, obviously, was there a suggestion of a trend.

17

18 So, considering these available studies, those were the 

19 stronger design in methods, particularly the Ag-Health 

20 Study, I do not seem to suggest a positive association in 

21 those population studied. So this statement is supported 

22 by the fact that there is relatively consistent 

23 observation of essentially no association across study 

24 design and across target population, when you consider the 

25 role of detection bias in that early observation of an 

26 association. And I will note as well that both a 

27 preliminary cohort analysis published in 2004, as well as 

28 the resent follow-up study from the Ag-Health Cohort were 

29 performed with sufficient statistical power to detect a 

30 relationship with atrazine if one exists.

31
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1 However, looking across this database, there are 

2 relatively few studies, 10 included, many of which the 

3 earlier ones reflect an aggregate exposure assessment 

4 methodology. The latter ones with stronger exposure 

5 assessment methods are primarily conducted among Caucasian 

6 male population. And we know that the initial 

7 correlation, noted in the ecologic study published in 

8 1998, has not yet been replicated in other populations or 

9 in that population.

10

11 So, considering the breast cancer epidemiology database in 

12 relation to atrazine exposure, three ecologic studies 

13 provides some weak and somewhat inconsistent evidence of a 

14 possible relation, on initial study observed a significant 

15 10 to 20 percent increased risk, however, this was not 

16 replicated in follow-up. Researchers attempted to use the 

17 same study design and the same population with very 

18 similar methods and did not repeat that initial finding. 

19 More recently, in an ecologic study in England, 

20 researchers observed potential association in only one of 

21 two of the geographic studies that‟s included in that 

22 study, so therefore, internally somewhat inconsistent.

23

24 And again, utilizing data reported to the California 

25 Cancer Registry, authors did not observe an association 

26 between atrazine and breast cancer among California 

27 Latinos, which is a target population. The study also 

28 utilized an ecologic-type exposure assessment method where 

29 atrazine was determined at a group level and assigned to 

30 individuals. 

31
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1 In a population-based case control study in the state of 

2 Wisconsin, authors measured atrazine in drinking water, 

3 actually in well water among residents in rural areas. 

4 These authors utilized a relatively robust exposure 

5 assessment method. They used an arcGIS method to 

6 interpolate atrazine exposure over time and across 

7 sampling sites. However, the range of exposure was 

8 somewhat narrow. So in this study, again, no observation 

9 evidence association was reported.

10

11 And within the Ag-Health Study, there are two 

12 investigations to consider. Among female spouses of 

13 enrolled pesticides applicators, authors did not observe 

14 evidence of a relation between atrazine and prostate 

15 cancer, and this is only using the ever/never metrics. So 

16 it has a woman ever been indirectly exposed to atrazine 

17 within this cohort.

18

19 And then, in the recent updated evaluation, authors were 

20 able to look at 36 cases of breast cancer among women who 

21 were directly exposed or who actually were licensed 

22 pesticide applicators. In this study authors, again, did 

23 you observe evidence of an association with breast cancer.

24

25 So across these data, although somewhat limit, they 

26 indicate that a strong positive association is unlikely. 

27 Consistent results; we observe consistent results across 

28 the available analytic studies which were performed in 

29 high use areas, which is the strength of the database. 

30 Each of these studies was able to measure and utilize 

31 breast cancer risk factor variables and its assessment of 

32 the association. And the range of exposure assessment 
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1 methods were significant in that, that one study utilizing 

2 the ArcGIS method for interpolation, and the measurement 

3 of both direct and indirect exposure in the Ag-Health 

4 Study is a strength of this database.

5

6 However, the limitations of exposure include the 

7 relatively narrow range reflected in the population-based 

8 study in Wisconsin in addition to the use of the 

9 ever/never metric and the ecologic exposure assessments in 

10 several of the available studies, in addition to a small 

11 number of exposed cases; so overall, strengths and 

12 limitations of this database.

13

14 Concerning ovarian cancer, two ecologic studies are not 

15 supportive of an association when observed an inverse 

16 risk; the latter observed no evidence of an association. 

17

18 In a population-based study performed a couple of decades 

19 ago, authors observed three-fold elevated odds of ovarian 

20 cancer in relation to triazine exposure. This is a study 

21 previously reviewed by EPA and brought to the panel in 

22 June of 2000. 

23

24 Critiques at that time included the lack of adjustment for 

25 other pesticides and the relatively small sample included 

26 in this study. Again, more recently utilizing information 

27 reported to the California State Cancer Registry and 

28 Pesticide Use Data supplemented by self-administered 

29 questionnaire, authors reported limited evidence of an 

30 association between triazine and ovarian cancer and little 

31 evidence of an association between atrazine and ovarian 

32 cancer. However, as noted, these assessments were based 
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1 upon a small number of atrazine or triazine-exposed 

2 ovarian cancer cases. Authors noted that fewer than 10 

3 percent of that sample reported use of triazines. 

4

5 And within the Ag-Health Study, again, the recent 

6 evaluation made available within the last several months 

7 observed three-fold elevated odds of ovarian cancer in 

8 relation to ever use of atrazine. So again, this is among 

9 women who are licensed pesticide applicators. However, 

10 due to the fact that there were only nine cases of ovarian 

11 cancer available for analysis, four of which were exposed 

12 to atrazine, authors were only able to evaluate the 

13 ever/never exposure metric in this study.

14

15 So, although available epidemiology database is limited, 

16 we note that positive associations were observed across 

17 these studies. However, at this time, alternative 

18 explanations may likely exist and cannot be excluded.

19

20 Strengths of this database include the observation of 

21 positive associations in high quality analytic studies, 

22 including the Ag-Health Study. A few of these 

23 investigations were hypothesis testing in nature and were 

24 conducted in a highly occupational exposed population of 

25 women. However, the small number of studies and the small 

26 number of exposed cases must be noted as a limitation.

27

28 So again, the recent Ag-Health evaluation was actually 

29 among the only point estimates that we identified in the 

30 database, looking at a potential association between 

31 atrazine and thyroid cancer. Within that study, authors 

32 observed evidence of a four-fold increased odds of thyroid 
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1 cancer in the middle and upper exposure range trend 

2 significant at the point (0.10) level. The Ag-Health 

3 Study, as I've alluded to over the course of this 

4 presentation, is a large scale prospective study with many 

5 strengths and design and methodology.

6

7 Authors, with respect to the thyroid cancer finding, note 

8 that there was only one case of thyroid cancer among women 

9 enrolled in the study, so therefore, this evaluation is 

10 among men only. Authors were able to adjust four body 

11 mass index, or BMI, which has quite recently been shown to 

12 be a risk factor for thyroid cancer. However, a 

13 limitation noted by EPA is the grouping of thyroid cancers 

14 together, and different sub-types of this tumor type may 

15 indeed have different etiologies. 

16

17 So, based upon only one epidemiologic study or results in 

18 which a positive association was observed, the data are 

19 somewhat inclusive at this time and replication in other 

20 populations, perhaps with a larger number of exposed 

21 cases, is required before we can determine this causal 

22 nature of this potential association.

23

24 Strengths of the study are -- again, it is based within an 

25 epidemiology study that has relatively strong design and 

26 methods in which an exposure response was evaluated. But 

27 it is only one point estimate available at this time in 

28 which thyroid cancer sub-types were grouped together.

29

30 So there were actually a number of studies to consider 

31 regarding the potential association between atrazine or 

32 triazine and lymphohematopoietic cancers; leukemias, 
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1 lymphomas, multiple myeloma -- and they are listed briefly 

2 in the slides.

3

4 So, in the mid-1980s, the National Cancer Institute 

5 conducted a series of population-based case controlled 

6 studies in the Midwest in which they evaluated several 

7 pesticides, I think 38 specific herbicides, including 

8 atrazine, several dozen insecticides and other types of 

9 pesticides. So -- evaluated many different pesticides in 

10 association with these cancer outcomes.

11

12 So, looking across these three studies, authors did not 

13 observe an association between atrazine, specifically, in 

14 either multiple myeloma or leukemia. EPA notes that these 

15 studies are relatively strong in design; the low potential 

16 for systematic error. However, the studies were 

17 reflective of a relatively small number of exposed cases, 

18 in some instances.

19

20 In our literature search, we also identified two 

21 evaluations which are hospital-based case-controlled 

22 studies conducted in France. They are connected by the 

23 same group of researchers using similar methods, but at 

24 different points in time; so, again, looking at several 

25 different pesticides, including atrazine. So across these 

26 two studies, authors did report some evidence of non27 significant elevated risk in association with triazine 

28 use. These authors evaluated triazines only.

29

30 A significant association with hairy cell leukemia --

31 that‟s the HCL acronym that I did not define for you --

32 were also noted by EPA. These studies, particularly the 
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1 latter study by Ochi in 2009 reflected good quality 

2 exposure assessment. I think it was self-reported 

3 exposure information administered by a trained interviewer 

4 with a small validation study included. However, the 

5 studies were, again, reflective of a few numbers of cases 

6 and did not adjust for the exposure to other pesticides 

7 over the relevant exposure period.

8

9 So, with respect to non-Hodgkin lymphoma, the same set of 

10 population-based studies conducted by the NCI in the mid11 80s looked at atrazine in association with NHL. So, 

12 initially, some non-significant positive associations were 

13 reported. However, upon adjustment for the exposure to 

14 other pesticides, those associations attenuated 

15 significantly and became statistically non-significant.

16

17 In a pooled analysis using hierarchical regression 

18 techniques, so authors were able to co-adjust for exposure 

19 to the 47 other pesticides evaluated across these studies. 

20 Authors brought together the individual observations 

21 across the three case-controlled studies into kind of one 

22 big pool of the study, and again, looked at atrazine 

23 exposure, as well as exposure to other pesticides in 

24 relation to NHL and reported an odds ratio of 1.5, which 

25 was statically significant.

26

27 Within, again, the same group of studies, other 

28 investigators were able to measure the presence of the 

29 t(14:18) chromosomal translocation. This chromosomal 

30 anomaly is thought to be a risk factor for NHL in the 

31 human population. 

32
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1 So, among participants who were positive for the t(14:18) 

2 translocation, authors reported an elevated odds of non3 Hodgkin lymphoma. However, this is the only study of the 

4 potential effect modifying role of this chromosomal 

5 translocation available.

6

7 Within the Agricultural Health Study cohort, while the 

8 initial cohort study on atrazine exposure, looking at 

9 various cancer outcomes, reported some suggestive, not 

10 significant, associations with non-Hodgkin lymphoma and 

11 multiple myeloma, this finding was not replicated in the 

12 recent follow-up study with over twice the number of non13 Hodgkin Lymphoma cases. 

14

15 So across NHL, multiple myeloma, leukemia and several 

16 different sub-types of lymphohematopoietic cancers for 

17 which these authors had the sufficient number of cases to 

18 evaluate -- including evaluation of an exposure-response 

19 trend -- across each of these risk estimates the odds 

20 ratios did not significantly differ from one or from the 

21 null; so a lack of evidence of an association.

22

23 So, among the available studies, those with stronger 

24 design and methods, again, do not suggest a positive 

25 association in the populations studied. The recent 

26 studies are prospective in nature. Again, the Ag-Health 

27 Study reflects a large sample with the ability to evaluate 

28 exposure response and control for the use of other 

29 pesticides.

30

31 Limitations of the database are the small number of cases 

32 for some of these sub-types reflected in a few of the 
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1 studies presented. The possible effect modifying role of 

2 the t(14:18) chromosomal translocation was observed in 

3 one, and only one, study with a relatively small number of 

4 cases and controls and has not yet been reproduced. And 

5 again, the target population across several of these 

6 studies is Caucasian male population. 

7

8 So very briefly, we also identified epidemiological 

9 evaluations of other cancer sites. Research with NIOSH 

10 looked at the association between glioma, a major type of 

11 brain tumor, and pesticides including atrazine. These 

12 authors did not observe an association among either men or 

13 women.

14

15 In addition to an ecologic study on instance of cancers in 

16 the pediatric population, there is one hypothesis 

17 generating a case controlled study produced by the 

18 researchers with the Northern California Childhood 

19 Leukemia Cohort. In this analysis, authors observed four20 fold elevated odds of acute lymphocytic leukemia in 

21 relation to triazine exposure.

22

23 Authors modeled pesticide exposure using maternal 

24 residence as a proxy for pesticide exposure. And we note 

25 that the elevated association was observed in the midrange 

26 group, but not in the upper ranged groups. So the data 

27 lacked an exposure-response trend -- and this is the only 

28 study available on this cancer endpoint in the pediatric 

29 population.

30

31 So, again, given the relative strengths of the design and 

32 conduct of the Ag-Health Study for the sake of 
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1 completeness, we have delineated each of the atrazine 

2 cancer-specific point estimates available across the two 

3 cohort studies, and the six Nested case-controlled studies 

4 made available since 2003. And overall, very briefly, 

5 authors did not report evidence of an association between 

6 atrazine in any of these cancer sites.

7

8 So, available preliminary studies concerning these other 

9 anatomical sites are not strongly suggestive of an 

10 association. Several of these studies were hypotheses11 generating in nature among studies with relatively strong 

12 design, however they lacked a priori hypotheses.

13

14 The one observation of elevated odds of pediatric acute 

15 lymphocytic leukemia is just that, only one observation. 

16 It has not yet been replicated in this database at this 

17 time. It's inclusive as to whether or not this 

18 association is a true one.

19

20 So as we move toward integrating with the experimental 

21 toxicology database, I thought I would just take a moment 

22 to briefly summarize our view of the epidemiology datasets 

23 discussed. Concerning the lymphomas and leukemia, as well 

24 as prostate cancer, our preliminary conclusion is that the

25 stronger studies are not suggestive of a positive 

26 association in those target populations evaluated; mainly 

27 white male pesticide applicators.

28

29 With reference to breast cancer, thyroid cancer and 

30 ovarian cancer, the available epidemiological database is 

31 limited. However, some positive associations are 

32 observed. But at this time, alternatives cannot be 
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1 excluded; namely, the relatively small sample size, a few 

2 number of exposed cases across several of these studies. 

3 With respect to the relatively heterogeneous grouping of 

4 other anatomical cancer sites, at this time these data are 

5 not strongly suggestive of a positive association. 

6

7 So this slide summarizes information previously presented 

8 by other EPA presenters regarding the experimental 

9 database on the potential carcinogenic effect of atrazine. 

10 So the current cancer mode of action is mammary gland 

11 tumors in the rat due to the disruption of the 

12 hypothalamic, pituitary and gonadal axis. However, 

13 additional mechanistic data and the results of internal

14 and external review determine that this mechanism is not 

15 operational in the human population. Therefore, the 

16 current cancer classification, as has been stated, is not 

17 likely to be a carcinogen.

18

19 EPA notes that there are no other tumors identified in the 

20 experimental toxicity data, and that the weight of the 

21 evidence does not support genotoxicity or mutagenicity of 

22 atrazine. So over all, considering these factors, the 

23 experimental evidence does not indicate a role for 

24 atrazine in the carcinogenic process in humans.

25

26 So integrating the experimental toxicology and 

27 observational epidemiology datasets, the two streams of 

28 evidence are consistent in our view, and are supportive of 

29 the conclusion of no association. There is no evidence of 

30 prostate hyperplasia or tumorigenesis in the rat although 

31 EPA acknowledges that the rodent bioassay is a poor 

32 predictor of human prostate cancer.
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1

2 Across the observational studies, those available with 

3 stronger design and methods do not suggest a positive 

4 association. So, considering these streams of evidence, 

5 available data at this time do not support the association 

6 between atrazine exposure and prostate cancer. That is 

7 our preliminary conclusion upon which we are asking your 

8 feedback.

9

10 Regarding breast cancer, again, the experimental 

11 toxicology and available epi data are consistent. The 

12 mammary gland tumors are not relative to the human 

13 population, given the mode of action identified. And the 

14 available epidemiology database, however limited, is not 

15 indicative of strong positive associations. So, for these 

16 reasons, our preliminary conclusion is that available data 

17 from the toxicology and epidemiology literature do not 

18 support an association between atrazine exposure and 

19 breast cancer.

20

21 Concerning ovarian cancer, the tox and epi databases are 

22 somewhat inconsistent. This is mainly the observation of 

23 some significant risk in the human population or 

24 associations identified in studies in the human 

25 population.

26

27 Regarding experimental toxicology, there is no evidence of

28 ovarian pathology or tumorigenesis in animal studies. EPA 

29 notes that tumors may indeed result from an altered 

30 endocrine environment however, the current mode of action 

31 is actually suggestive of a reduced risk of ovarian 

32 cancer.
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1

2 And, again, the available epidemiological database is 

3 limited, but some positive associations were observed. 

4 However, at this time, given the relatively few number of 

5 studies and small number of exposed cases reflected in 

6 many of these studies, alternative explanations may exist.

7

8 So, for these unique reasons, considering the toxicology 

9 and epidemiology data, our preliminary conclusion is that 

10 available data do not support an association between 

11 atrazine exposure and ovarian cancer.

12

13 Concerning thyroid cancer, we only have that one point 

14 estimate made available through the recent updated 

15 atrazine cohort analysis within the Ag-Health Study 

16 cohort. Therefore, results are somewhat inconsistent. 

17 With respect to the toxicology database, there is no 

18 evidence of thyroid hyperplasia or tumor formation, nor is 

19 there evidence of altered thyroid hormone in rodents. We 

20 note, particularly, that the rat is sensitive to thyroid 

21 carcinogenesis, and no observation of tumor formation was 

22 identified in these studies. So again, within the 

23 observational database, based upon only one evaluation in 

24 which a positive association was observed, the data at 

25 this time are inconclusive and require replication. So 

26 given these specific reasons, our preliminary conclusions 

27 is that available data do not support an association 

28 between atrazine exposure and thyroid cancer.

29

30 Concerning the leukemias and lymphomas, these datasets are 

31 consistent. There is no evidence of lymphohematopoietic 

32 tumor formation in animal models. And in addition, there 
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1 is no evidence that disruption of the HPG axis or any 

2 other hormonal component is related to the etiology of 

3 lymphohematopoietic cancers. 

4

5 And within the observational epidemiology database, those 

6 with stronger design and methods, at this time, are not 

7 suggestive of a positive association in the population 

8 studied. So for these reasons, again, our preliminary 

9 conclusion is that available data do not support an 

10 association.

11

12 Concerning the other cancer sites, again, we observed 

13 little evidence of an association in either the 

14 experimental tox or the epi database, and we find these 

15 two databases to be consistent in this way. 

16

17 So as stated at the conclusion of chapter 3 of our draft 

18 issue paper, EPA believes that while epidemiology studies 

19 are weakly suggestive of an association across some of the 

20 anatomical cancer sites evaluated, considering the 

21 different lines of evidence from both experimental, 

22 toxicology and observational epidemiology studies, 

23 evidence does not strongly suggest a role for atrazine in 

24 human carcinogenesis.

25

26 So at this time, the weight of the evidence supports that 

27 atrazine is not likely to be a carcinogen in the human 

28 population. So that concludes my presentation and my 

29 review of these 40 observational studies and integration 

30 with the toxicology database and I would be happy to take 

31 any clarifying questions.

32
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1 DR. ELLEN GOLD: So, I have a question about the data 

2 collection in the AHS regarding -- you know, they updated 

3 their analyses several times, and I am wondering if they 

4 did repeat administrations of a questionnaire to update 

5 the exposure information as well as the covariate 

6 information?

7

8

9 DR. CAROL CHRISTENSEN: Yeah. I can briefly address that, and 

10 maybe I will ask my colleague, Dr. Laura Beane-Freeman to 

11 elaborate. Yes, to my knowledge, reviewing these papers, 

12 the Ag-Health Study researchers have updated exposure. 

13 However, in the recent analysis made available earlier 

14 this year, that information was not included. Do you want 

15 to add anything to that?

16

17 DR. LAURA BEANE-FREEMAN: No. This is Laure Beane-Freeman from 

18 the National Cancer Institute. What Dr. Christensen said 

19 was correct. In the most recent analyses, they have all 

20 relied on exposure data that was collected at phase 1 or 

21 enrollment, which was collected from 1993 through 1997. 

22 However, they were asked about their lifetime use of 

23 pesticides or chemicals at that time. 

24

25 The second questionnaire elicited information on their 

26 use. It took place about five years after the initial 

27 questionnaire was administered and elicited use about 

28 their use of chemicals within that intervening five years. 

29 So it would have only added an additional five years of 

30 exposure information. 

31
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1 Our cancer incidence data actually only goes through 2007, 

2 in that most recent publication, and so some of that 

3 exposure information, it would have only been pertaining 

4 to 1998 through about 2003.

5

6 DR. ELLEN GOLD: Covariate information?

7

8 DR. LAURA BEANE-FREEMAN: We used the same covariate 

9 information that was also included at phase 1.

10

11 DR. SUSAN AKANA: Just at point of clarification. On your 

12 weight of evidence, are there sites that includes -- I 

13 assume there is no evidence to support adrenal tumor 

14 formation in rodents?

15

16 DR. CAROL CHRISTENSON: That is correct. We did not identify 

17 any epidemiological evaluations of that cancer site; 

18 that‟s correct.

19

20 DR. KENNETH PORTIER: By the way, it is a very good summary. I 

21 really enjoyed reading it and it was very clear. The only 

22 thing that kind of caught my attention is the thyroid 

23 cancer study and the four-fold increase in two of the four 

24 exposure categories. Can you give me some insight as to 

25 why they choose those exposure categories? Because the 

26 ones that have very low odd ratios are the ones that have 

27 very few cases. So they have like three cases and there 

28 are 18 cases and then it is five cases and then 23 cases 

29 or something like that. 

30
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1 DR. CAROL CHRISTENSEN: Sure. I think I will defer to Dr. 

2 Beane-Freeman as that question relates to the conduct of 

3 the research.

4

5 DR. LAURA BEANE-FREEMAN: Sure. So, since we didn't a priori 

6 think that we did not have any evidence for thyroid 

7 cancer, we based our cut points on the distribution of all 

8 cancer cases. So what you are seeing is the reflection of 

9 a distribution of -- the cut points are based on the 

10 distribution of all cancer sites combined.

11

12 We did not change them specifically to be thyroid cancer 

13 cases. Certainly, we could have made those quartiles 

14 specific to thyroid cancer cases, but we had made the 

15 decision up front to base cut points on all cancer sites 

16 combined.

17

18 DR. FRANK BOVE: Yes. On the slides on ovarian cancer, it 

19 mentions that alternative explanations may exist. Have 

20 you thought of what those are?

21

22 DR. CAROL CHRISTENSEN: Yes. Again, there were only a few 

23 studies available to consider in our synthesis. Among 

24 those studies available, the relatively small number of 

25 exposed cases included just sample size, increasing sample 

26 size and possibly issues with method of exposure. I 

27 think, for each of the ovarian cancer studies, authors 

28 were only able to look at ever/never exposure, whether 

29 additional clarity would be provided through review of 

30 exposure response is a question.

31
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1 DR. BARRY TIMMS: I have some questions about the prostate 

2 cancer study. In that study, did they --

3

4 DR. CAROL CHRISTENSEN: Are you referring to the study of 

5 Triazine manufacturing plant workers or which --

6

7 DR. BARRY TIMMS: Yes, the agricultural study on atrazine 

8 exposure to the workers. Did they show a difference in 

9 the timeframe from the initial diagnosis to clarification 

10 of prostate cancer or a change in the Gleason score in 

11 those workers that were exposed compared to the controlled 

12 group? 

13

14 DR. CAROL CHRISTENSEN: I will ask one more time if you could 

15 clarify which set of studies you are referring to. Let me 

16 try to go back to that slide. The studies produced by the 

17 Agricultural Health Study or the Triazine manufacturing 

18 plant studies?

19

20 DR. BARRY TIMMS: The manufacturing plant studies.

21

22 DR. CAROL CHRISTENSEN: Oh, manufacturing plant studies. And 

23 again, your question is whether there is information 

24 regarding Gleason score?

25

26 DR. BARRY TIMMS: Gleason score and/or the timeframe from the 

27 initial diagnosis to the actual confirmation of prostate 

28 cancer through prostatectomy or biopsy.

29

30 DR. CAROL CHRISTENSEN: So, I believe across these studies, 

31 these were all PSA -- the cancers identified were 

32 indicative of PSA era prostate cancers in which they were 
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1 identified among men who were generally younger, and only 

2 among men who were currently employed in the plant during 

3 the time period of the PSA testing program.

4

5 Tumors were generally of low-grade and low-stage, although 

6 I do not recall Gleason score being reported within that 

7 study by Hessel et al. in 2004, and I do not recall 

8 information regarding the time period between employment 

9 and diagnosis.

10

11 DR. HEATHER YOUNG: Just a minor clarification on your slide 11 

12 on the ovarian cancer. For the Italian study, I think 

13 that is actually a 90 percent confidence interval. You 

14 have that correct in your appendix B, but on the slide you 

15 have it as a 95 percent. So, if people have not read the 

16 Appendix B, it is a 90 percent confidence interval.

17

18 DR. CAROL CHRISTENSEN: Thank you very much for that 

19 clarification. I apologize for my error. 

20

21 DR. TRAVIS JERDE: I have just a follow-up on Dr. Timms' 

22 question regarding the Gleason score. So, I looked around 

23 and I see about 50 men in the room. Twenty-five of us 

24 will get prostate cancer in our life, but only one or two 

25 of us will probably die from it. So, this is a disease 

26 about strength progression, what type of cancer. I see 

27 from your chart that you say relatively few studies 

28 available, and you have got database limitations. Do you 

29 know if those data are extractable from those studies that

30 one could go and look at Gleason grade biopsy and things 

31 like that from those studies? To get that answer, would a 

32 new study have to be done?
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1

2 DR. CAROL CHRISTENSEN: You are considering the totality of 

3 that database or considering the manufacturing plant 

4 workers? So with regard to Gleason score, that 

5 information, I believe -- and I will defer to Dr. Bean6 Freeman -- that information is available through the 

7 Agricultural Health Study, and I believe have been used in 

8 other analysis, although not in the recent cohort 

9 evaluation. That information is collected and reported to 

10 the respected state cancer registries. Presumably, the 

11 cancers reported in the states in which the triazine 

12 manufacturing plant employees resided is also 

13 theoretically available, although I would have to look 

14 more into that question.

15

16 DR. DANIEL SCHLENK: Any other questions or clarification? 

17 Okay. Thank you, Dr. Christensen. Let me just point out, 

18 too, just for the agency; when questions come to you, can 

19 you please state your name for the record when you make 

20 the answer? I know it sounds repetitive but we need that 

21 for the auditory record, so I just want to highlight that 

22 a bit. Okay?

23

24 Our next presentation will be by Dr. Mendez again, I think 

25 two or three; got another couple to go here. And she is 

26 from HED/OPP.

27

28 DR. ELIZABETH MENDEZ: All right. As Dr. Schlenk just 

29 mentioned, this is the second of my third presentation. I 

30 will be coming in periodically to sort of into the nodes 

31 to start tying things up together as we progress during 

32 today's talks.
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1

2 So, the second presentation is the integration of 

3 epidemiology and toxicity data into the health risk4 assessment. And Dr. Christensen just went through the 

5 integration of these two disciplines with respect to 

6 cancer in great detail, so I am just going to go very 

7 quickly over that. And I am going to concentrate 

8 primarily on the integration of the noncancer effects and 

9 the epi data.

10

11 Now, typically, the agency's human health risk-assessment 

12 relies heavily on experimental toxicity data. But 

13 realistically, we are not trying to protect the rodent, we 

14 are trying to protect the human population. So it is 

15 important for us to -- our ultimate goal is to evaluate 

16 the potential impact of the toxicant on the human 

17 population.

18

19 And this instills an understanding of the mode of action 

20 as well as what is happening within the epidemiology data. 

21 Because that way we can start to extrapolate what we are 

22 seeing in the rodent or what we are seeing in the test 

23 species to see if that is relevant to the human and how 

24 good our model might be.

25

26 So, we start out our evaluation with a draft framework for 

27 integrating these two streams of evidence, and the concept 

28 in the draft 2010 framework are based on peer-reviewed 

29 robust principles and tools. 

30

31 The Bradford Hill criteria has been used for many, many 

32 years in helping us organize data to look at data when we 
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1 have multiple lines of evidence. And to that effect we 

2 have used modified, Bradford Hill criteria to look at all 

3 of these datasets.

4

5 But in addition, it is integrating new approaches based on 

6 the recommendations from the National Academy of Sciences 

7 in their reports of 2007 and 2009; Toxicity testing in the 

8 21st Century: Science and Decisions and Advancing Risk9 assessment.

10

11 So what we are trying really to do is move beyond just 

12 looking at adverse outcomes and trying to understand the 

13 underlying biology that is leading us to these adverse 

14 outcomes. 

15

16 But using these processes, it allows us the flexibility to 

17 incorporate information from different sources, and it 

18 also provides as a transparent tool for organizing, 

19 reviewing and interpreting the complex information that is 

20 also not only useful to us, but to the general public who 

21 is looking at our work so they can see how we are looking 

22 at these data.

23

24 So, in February 2010, the SAP reviewed the agency's 

25 proposed draft framework, and in general, they were in 

26 agreement with our proposal. And so, you have seen this 

27 image before when Dr. Cooper spoke about the mode of 

28 action, and Dr. Christensen just spoke about the human 

29 epidemiology data. So my job, at this point, is to try to 

30 bring those two disciplines together.

31
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1 The NRC report had two very important statements for us. 

2 One is that we are trying to shift away from a focus on 

3 adverse health effects and experimental outcomes towards a 

4 deeper understanding of biologic perturbations in the key 

5 toxicity pathways.

6

7 But the other thing that the committee cautioned us is 

8 that, virtually, all environmental agents will perturb 

9 signalling pathways to some degree. And the challenge for 

10 us to determine when is that perturbation meaningful in 

11 terms of leading to a toxic effect, and when it is just -

12 - as Dr. Fowle usually refers to it -- a blip in the road.

13

14 So, let's just go into a little bit of detail in the 

15 adverse outcomes pathway. So the organism is going along 

16 and it has its biological inputs and its got its normal 

17 biological function. And in comes and exposure, some sort 

18 of insult to the system. And there is a delivery to the 

19 target tissue and there is a perturbation.

20

21 Now, when we have a toxicity pathway, what happens is that 

22 that perturbation is of either such a magnitude or such 

23 duration that it can cause an early cellular change. The 

24 question then is, can there be adaptive responses or 

25 mechanisms that can lead us back to the normal biological 

26 function, or is the insult of such degree that it actually 

27 causes a cell injury and an adverse outcome? And our goal 

28 here is to identify the difference between this and this, 

29 and trying to make sure that what we are regulating on is 

30 letting us prevent going from this path on. 

31
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1 So then, to the mode of action framework, the modified 

2 Bradford Hill criteria; we have a postulated mode of 

3 action which is the neuroendocrine disruption of the HPG 

4 axis. As Dr. Cooper mentioned in his presentation, we 

5 have identified sequence of key events on the path to the 

6 health outcome.

7

8 We have a rather robust dataset with experimental support 

9 that has concordance of dose response for key events as 

10 well as temporal relationships for the key events. The 

11 data shows that all of these events are biologically 

12 plausible.

13

14 As Dr. Christensen mentioned, we have strength and 

15 consistency not only within the experimental tox data, but 

16 also when we look across the epi data. We keep our eye 

17 out for other modes of action, but we have not seen one as 

18 of yet. And we have tried to the best of our abilities to 

19 identify the uncertainties and tried to reach some 

20 preliminary conclusions based on the data that are 

21 available to us today.

22

23 But the goal here is to promote the maximal use of the 

24 relevant information that is available to us. So the 

25 organization of the draft framework for integrating epi 

26 and tox data -- we are reviewing the epidemiology studies 

27 for using pesticide risk-assessment. 

28

29 And as Dr. Christensen elaborated in her presentation, we 

30 have looked at a variety of types of studies. We have 

31 looked at the scientific factors that need to be 
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1 considered in reviewing. What is the exposure? It is an 

2 ever/never? How are we doing the odds ratios? 

3

4 The benefits and uses of epidemiology and risk-assessment 

5 cannot be denied. Ultimately, that is the population we 

6 are trying to protect. 

7

8 The way that we have gone about this is looking at it from 

9 a proposed weight of evidence analysis. We consider the 

10 mode of action information that we have in the test

11 species. We consider the experimental toxicity data and 

12 we consider the epidemiology data all wrapped together; 

13 so, the weight of evidence analysis; the non-caner 

14 effects. The mode of action of is neuroendocrine 

15 perturbation of the HPG axis, and we see ovarian cyclicity 

16 disruption, reproductive senescence and other reproductive 

17 effects in the animal tox dataset.

18

19 We also see low birth weight or small for gestational age, 

20 but those are not really related directly to the HPG axis 

21 perturbation, are more likely indicative of just general 

22 toxicity.

23

24 In September 2010 of SAP, the SAP concurred with the 

25 agency's conclusion that the epidemiology data is useful 

26 for hazard ID but not robust for dose-response assessment 

27 or risk characterization. 

28

29 So, we do see some disruptions in the menstrual cycle in 

30 women. We do see, when it comes to menstrual cycles and 

31 estrous cyclicity, it is actually going in opposite 

32 directions in the rodent. We see an early reproductive 
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1 senescence in the epidemiology data. It appears that 

2 there may be a little bit of a delay, and either 

3 reproductive effects are generally testosterone levels in 

4 semen quality, and they are kind of tracking with the 

5 animal data.

6

7 Dr. Christensen just went through the cancer effect, so 

8 I'm just going to go through this very quickly. The 

9 mammary gland tumors in the rats are due to the HPG axis 

10 disruption; a mode of action that has been established not 

11 to be operative in humans. We do not see any other tumors 

12 identified in experimental toxicity data. And to address 

13 Dr. Akana's question earlier, we do not see any adrenal 

14 either in the rats. 

15

16 And at the April 2010 SAP, the panel concluded that they 

17 felt comfortable with retaining the classification of 

18 atrazine as not likely to be carcinogenic to humans, based 

19 on the information available.

20

21 The epidemiology data, we have varying degrees of study 

22 quality and limitations that Dr. Christensen went through 

23 in detail ranging from inconsistency in findings to method 

24 of exposures assessment and the small number of exposed 

25 cases. But the weight of evidence does not support an 

26 association between atrazine exposure and cancers in 

27 general.

28

29 So when we are trying to bring the two lines of evidence 

30 together, what we see is that, for the non-cancer effects, 

31 based on the experimental toxicity data, LH suppression in 

32 the rats appears to be protective of other effects. And 
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1 we have a benchmark dose of 2.56 milligrams per kilogram 

2 per day coming from the Cooper et al. 2010 dataset. The 

3 epidemiology data is useful for hazard ID but not robust 

4 for dose-response or risk characterization. And as Dr. 

5 Christensen mentioned, in the cancer effects, the 

6 available data do not appear to support an association 

7 between atrazine exposure and cancer. And with that, I 

8 will conclude.

9

10 DR. DANIEL SCHLENK: Thanks, Dr. Mendez. Any quick questions 

11 on the integration of the Epi and tox data? Yes, Dr. 

12 O'Byrne?

13

14 DR. KEVIN O'BYRNE: I just have one point of clarification. I 

15 must have missed it in the literature. I was not aware 

16 that there was any evidence of menstrual cycle 

17 disturbance.

18

19 DR. ELIZABETH MENDEZ: That actually comes from the 

20 epidemiology data. There are one or two studies, and I 

21 will defer to Dr. Christensen for that.

22

23 DR. CAROL CHRISTENSEN: Yes, hello. In September of last year 

24 we brought forward to the panel the epidemiology data on 

25 the non-cancer health effects of atrazine and that 

26 included two evaluations, again, within the Agricultural 

27 Health Study; this time on the non-cancer side in which 

28 they looked at the relationship between several different 

29 pesticides including atrazine, and two specific outcomes: 

30 Menstrual cycle characteristics -- and I think, if I 

31 recall correctly, there were five outcomes from long 
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1 cycle, short cycle or regular cycle, that type of thing --

2 and then a separate evaluation on timing of menopause.

3

4 And so those were the only two evaluations that we 

5 identified looking at menstrual cycle characteristics. 

6 They were epidemiology studies in the human population 

7 and, again, among women in the Ag-Health Study who are 

8 among the more highly exposed in the population.

9

10 DR. DANIEL SCHLENK: Okay. Any other questions before we move 

11 on? Okay. Thanks. Our final presentation before lunch; 

12 no pressure, is going to be made by Dr. Chester Rodriguez 

13 who is also at HED and OPP. Thanks.

14

15 DR. CHESTER RODRIGUEZ: Thank you very much. So the title of 

16 the talk is Updates to the Dose-Response Assessment with 

17 Implications for Water Monitoring Frequency. 

18

19 Just to put the presentation in context, this is what the 

20 outline is. I am going to start with a recap of the 

21 September 2010 SAP meeting. The agency proposed using 

22 internal measures of exposure based on radiolabeled 

23 atrazine studies. And then, I am going to move on to 

24 analysis of additional data based on radiolabeled atrazine 

25 studies. Then I'm going to cover some information that is 

26 available on the pharmacokinetics of atrazine in human. 

27 That is not a radiolabeled study. That is a co-study, if 

28 you will.

29

30 Based on those analyses, I am going to go over a 

31 pharmacokinetic modeling approach that the agency is 

32 proposing based on a simplified one-compartment linear 
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1 model. And I am going to go over some model evaluation 

2 exercises.

3

4 So based on that, then, I am going to walk you through a 

5 current understanding of pharmacokinetic behavior, 

6 internal dosimetry and the endpoint of concern, LH 

7 attenuation in rats. 

8

9 And lastly, I am going to go over how we plan to estimate 

10 the human intake of the dose rate through an area-under11 the-curve analysis of water chemographs. And the 

12 application of these methodologies will follow in a later 

13 presentation when we apply them to case studies, so stay 

14 tuned for that.

15

16 So, I am going to start with a recap then of the September 

17 2010 SAP regarding international measures of exposure. 

18 Just as a historical context, in the previous risk19 assessment to support the RED, the risk-assessments were 

20 actually based on a NOAEL and LOAEL approach. The 

21 critical study -- it was a six-month study, and the key 

22 event is actually attenuation of LH.

23

24 At the September 2010 meeting we actually introduced more 

25 sophisticated approaches for doing those response 

26 analyses, and those include benchmark of the dose 

27 modeling, as well as internal measures of exposure.

28

29 Now, for an old chemical like atrazine, there is enough 

30 information, pharmacokinetic information, to depart from 

31 the traditional external dosimetry to an internal dose 
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1 that can help refine the linkage between atrazine exposure 

2 and the endpoint of concern, LH attenuation.

3

4 This is a quote from the National Research Council 

5 regarding the use of internal does, and it says; "The does 

6 at the target site, the internal dose is the ultimate 

7 determinant of risk." So that is a good motivation of 

8 moving towards an internal dose-response analysis.

9

10 In the case of atrazine, the use of internal measure of 

11 exposure is even more relevant because atrazine as a 

12 parent chemical is short-lived in the body due primarily 

13 to being metabolized to other species, which at least some 

14 of them tend to also be active in the endpoint attenuation 

15 of LH.

16

17 At the September 2010 meeting we actually proposed to use 

18 the area under the plasma concentration-time curve as 

19 internal measure of exposure. And the rationale for that 

20 actually follows the endpoint of concern, LH attenuation 

21 which does not seem to be a single dose effect. 

22

23

24 This is a figure from the Cooper et al. 2000 report that 

25 shows that a single does -- and it has to be as high as 

26 300 mg per kg to what you see a decrease in LH -- whereas, 

27 a much lower does, as low as 3.12 mg per kg per day given 

28 to rats over four days, once daily, leads to a nice dose29 response relationship. So based on these findings then, 

30 duration seems to be a critical parameter for the endpoint 

31 in rats, LH attenuation. 

32
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1 So plasma AUC as an internal dose metric does not only 

2 take into account internal dose levels, but also duration. 

3 And a simplified way of thinking about it is that it is 

4 basically the product of how much the exposure is and for 

5 how long. So the AUC that was selected, by the way, was 

6 for plasma triazines based on radiolabeled atrazine 

7 studies. And the reasons for that is as follows: There 

8 is a lack of detailed pharmacokinetic information for 

9 atrazine and its metabolites as it relates to the 

10 endpoint. 

11

12 Radiolabeled atrazine studies are available in rats. The 

13 same species where the endpoint has been characterized, LH 

14 attenuation -- and another reason for selecting 

15 radiolabeled atrazine studies is that these studies, for 

16 the most part, achieve a high degree of mass balance so 

17 you know where the dose is going. Is it getting excreted; 

18 is it getting retained; et cetera?

19

20 Radiolabeled pharmacokinetic studies with atrazine have 

21 been carried now with a 14C radiolabeled triazine ring. 

22 And it is usually labelled at the all the carbons on the 

23 ring. This ring is metabolically stable, will now be 

24 degradable by metabolism. 

25

26 So the radiolabeled atrazine study that was selected was 

27 by Thede 1987, and we selected this study for two reasons, 

28 actually. We wanted to use it to estimate the plasma AUC 

29 for radiolabeled triazines and as well as to learn about 

30 the temporal relationship between atrazine exposure and LH 

31 attenuation in rats. This study was very similar to 
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1 the 4-day LH attenuation study carried by Cooper et al. in 

2 that he actually used intake young female rats. 

3

4 It involved repeated once daily dosing with atrazine for 

5 at least four days, and it covered a wide range of 

6 atrazine doses from one to 100 mg per kg per day. And he 

7 also provided repeat plasma measurements which included 

8 the elimination phase.

9

10 This is what the plasma profile looks like and I just want 

11 to make two points from this slide. Number 1 is that when 

12 you start dosing with atrazine, you get accumulation of 

13 plasma triazines. So the dosing is being done once daily. 

14 And as you can see, by the fourth day of dosing you start 

15 to get a plateau or what I called pseudo steady state 

16 plasma triazine levels. And what I mean by that is that, 

17 the plasma levels stay within a specific range as long as 

18 dosing continues at the same level and frequency.

19

20 This is a hypothetical figure showing how three studies 

21 with different durations can have the same pseudo steady 

22 state plasma levels. So as you can see, we have four 

23 days, we have 14 days and then we even have six months. 

24 These studies, even though they differ drastically in 

25 duration, they will have the same pseudo steady state 

26 level, because as soon as you reach this level and you 

27 continue dosing at the same level and frequency, plasma 

28 levels will remain within that range.

29

30 Plasma triazines will decrease only when dosing actually 

31 stopped, and that is what the elimination phase is. So if 

32 the endpoint of concern is related to pseudo steady-state 
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1 plasma levels then studies of different durations should 

2 have the same LOAEL; and that is what the next slide 

3 shows.

4

5 This is the critical study from the 2003 risk-assessment. 

6 This is the new study by Cooper et al. Two studies are of 

7 different durations. You know, they were performed in 

8 different labs, but the one thing that they have in common 

9 is that the frequency in dosing that was involved were 

10 very comparable.

11

12 Now, as you can see from the LOAELs, they are very, very 

13 similar. So the bioaccumulative profile of plasma 

14 triazines then is consistent with the similar LOAELs for 

15 studies across different durations of atrazine exposures 

16 in rats. So based on these observations, then, we 

17 actually proposed a daily steady state AUC for internal 

18 dose response analysis. 

19

20 Other features that were noted in the behavior of plasma 

21 triazine from the Thede 1987 study is that there is linear 

22 pharmacokinetic behavior. Now, what I mean by that is 

23 that the internal dose metric that we are proposing in a 

24 steady state AUC, it scales directly with atrazine does. 

25 Also, the elimination kinetics that is exhibited by 

26 radiolabeled plasma triazines follows linear behavior. 

27 And I would like to spend some time trying to define what 

28 that is in a conceptual way. Linear elimination kinetics, 

29 it actually means that a change in plasma concentration 

30 or, a change in time, equals -- now, there should be a 

31 minus sign here, by the way.

32
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1 So the change in plasma concentration then will be 

2 directly proportional to the plasma concentration. This 

3 means that the body can adjust to eliminate more or less 

4 without changing anything. But actually, more importantly 

5 though, this is elimination rate constant, kel. 

6

7 When you solve this simple differential equation, what you 

8 get is an integrated form of this expression that relates 

9 plasma concentration as a function of time. Now, as you 

10 can see from this, this is the equation of a line with 

11 slop; kel, the elimination rate constant. So the linear 

12 expression of plasma triazines will have kel as a slope.

13

14 Now, this should say fractional rate of elimination. The 

15 fractional rate of elimination of plasma triazine will 

16 proceed at a rate that is independent of plasma levels. 

17 The fraction or rate will be determined solely by the 

18 elimination rate constant, which is a constant. That 

19 translates to a plasma half-life, a constant plasma half20 life that is predictive for extrapolation across dose 

21 levels.

22

23 This is pretty important because this is one of the most 

24 attractive features of having linear kinetics in that you 

25 will have a constant plasma half-life that is independent 

26 of dose levels. 

27

28 So when we analyzed the elimination phase from the 

29 different groups of the Thede Study, we actually noted two 

30 things; number 1, linear behavior across different dose 

31 levels. But actually, more importantly though, the 

32 elimination rate constant was very consistent for the one, 
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1 three, seven and ten dose groups. And that elimination 

2 rate constant translates into a plasma half-life between 

3 two and three days.

4

5 So now I am going to go over the analysis of additional 

6 animal pharmacokinetic studies with radiolabeled atrazine. 

7 Two of the limitations from the Thede Study that were 

8 noted by the panel at the September meeting was that only 

9 two animals were used per dose group, and the elimination 

10 phase was estimated with only three data points from a 

11 single animal.

12

13 And so, the panel urged caution in actually analyzing this 

14 dataset. So one of the things we wanted to do right away 

15 is to try to find additional support that will support our 

16 findings about plasma clearance from the Thede Study. We 

17 were able to find two additional rats studies with 

18 radiolabeled atrazine; one by Paul et al. in 1993 and the 

19 other one by Simoneaux in 1985. These studies are not 

20 quite ideal to actually compare to the 4-day LH 

21 attenuation study.

22

23 For one thing, these two studies actually involved male 

24 rats. The 4-day study use female rats. But regardless, 

25 we wanted to analyse the plasma clearance from these two 

26 studies to see how they compare from our findings from the 

27 Thede Study.

28

29 In the first study by Paul et al., plasma measurements 

30 resulting from single oral gavage doses of 1 or 100 mg/kg, 

31 radiolabeled atrazine was given to groups of three male 

32 rats. In the Simoneaux 1985 study, plasma measurements 
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1 were taken at various days post-dosing resulting from 

2 doses of either .4 or 4 mg/kg radiolabeled atrazine given 

3 to 36 male rats, 3 per time point for seven days.

4

5 This is the analysis from the Paul et al. Study. This is 

6 what the plasma profile looks like either at 1 or 100 

7 mg/kg radiolabeled atrazine. This is the plasma 

8 elimination analysis from these two dose levels. And as 

9 you can see, the slope of the line, which is elimination 

10 rate constant, is about .01, which is very consistent from 

11 the study from the Thede 1997 report. We did the same 

12 thing for the Simoneaux 1985 study, to see what the plasma 

13 profile looks like. When we analysis the elimination 

14 phase this is what we get. 

15

16 Now, it should be noted that the plasma levels were 

17 actually reported as days post-dosing. This slope that 

18 you have on the first line is elimination rate constant in 

19 per-day units. But when you convert that to per hour, as 

20 the other studies, you again see a very similar 

21 elimination rate constant of about .01.

22

23 So, when you put all these studies together, what you have 

24 is a wide range of atrazine doses that goes from .4 all 

25 the way to 100 mg/kg per day, and they all exhibit a very 

26 similar elimination rate constant. So what we are seeing 

27 is that there is a half-life that is from two to three 

28 days that is exhibited across different studies in oral 

29 doses of radiolabeled atrazine. 

30

31 So, all the studies that are discussed thus far have been 

32 in rats. There is a new study that was recently published 
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1 by Hui et al. in which they examine the pharmacokinetics 

2 or radiolabeled atrazine in non-human primates; monkeys. 

3

4 Rhesus monkeys were actually dosed by oral gavage with 1, 

5 10 or 100 milligrams of radiolabeled atrazine. Plasma was 

6 sampled for radioactivity at various time points. But the 

7 most important observations from this study is that the 

8 internal dosimetry that we are purporting to use also are 

9 scaled in the linear fashion with atrazine dose. And the 

10 elimination of plasma triazines, of radiolabeled 

11 triazines, was also linear. And these are what the 

12 pharmacokinetic parameters that were reported.

13

14 So now I am going to go over some human pharmacokinetic 

15 information that is available for atrazine. So as for 

16 most environmental chemicals, human information is very 

17 limited, like especially controlled human studies.

18

19

20 There is a report from 1985 by Davison where six human 

21 volunteers received a single dose of atrazine at a level 

22 of .1 mg/kg. Whole blood from a single subject was 

23 analyzed for atrazine and its chlorotriazine metabolites 

24 DEA, DIA and DACT. And urine from all subjects was 

25 analyzed for DEA, DIA and DACT. So the results from the 

26 study suggest that only DIA and DACT, two of the 

27 chlorotriazine metabolites for atrazine, were detected in 

28 whole blood. Only DEA, DIA and DACT were detected in 

29 human urine. 

30

31 So it should be noted that the parent atrazine could not 

32 be detected in either blood or urine. But the most 
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1 important thing here is that the three metabolites that 

2 were detected, they all exhibited linear elimination 

3 kinetics, and I cannot emphasize that enough.

4

5 I should also note that in the study, there was a lack of 

6 mass balance. That when they monitored DEA, DIA and DACT, 

7 those three metabolites only accounted for 14.5 percent of 

8 the atrazine dose that was given.

9

10 So in summary, I can say that chlorotriazine -- whether 

11 it's atrazine DEA, DIA or DACT -- when you monitor those 

12 individually, they tend to exhibit linear elimination 

13 kinetics, which is the same when you monitor radiolabeled 

14 atrazine. So we are seeing very consistent 

15 pharmacokinetic behavior in this.

16

17 The human pharmacokinetic parameters that were reported by 

18 Davidson -- and as you can see, there is a linear 

19 elimination very constant, along with elimination of half20 life. Whole blood; like I said before, atrazine was not 

21 detected, and DEA was not detected either. In urine; DEA, 

22 DIA and again, DACT were detected, and those are the half23 lives that were reported. I should also say that these 

24 half-lives were obtained using a one-compartment linear 

25 model for urinary excretion. So this is a quick summary 

26 of the pharmacokinetic information that I just presented. 

27

28 The use of radiolabeled plasma equivalents will include 

29 atrazine in all of its metabolites. We feel that this is 

30 a very conservative approach, given that we do not have 

31 detailed pharmacokinetic information of this species as it 

32 relates to the endpoint.
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1

2 The proposal of use in the area under the curve for plasma 

3 as internal measure of exposure accounts for duration 

4 which seems to be important for the endpoint, LH 

5 attenuation.

6

7 Plasma triazines, they tend to accumulate upon repeated 

8 dosing with atrazine to reach a plateau or pseudo steady 

9 state by the fourth day of dosing. And I should also 

10 note that the endpoint in the 4-day study was also 

11 measured by 4th day or once daily dosing.

12

13 So the proposed internal dose metric then that we are 

14 moving forward with is the daily steady state plasma AUC 

15 for triazines. And I should emphasize once again that 

16 linear behavior of plasma triazines is the most consistent 

17 feature that is observed across different doses of 

18 atrazine in species, including humans.

19

20 There is additional pharmacokinetic information that is 

21 actually emerging. There is a new in vivo PK study that 

22 was carried out in rats that was recently submitted to the 

23 agency by the Registrant. A new PBPK modeling effort 

24 based partly on this new dataset that was also submitted 

25 by the Registrant in collaboration with the Hamner 

26 Institutes. I should note that the agency has not 

27 completed a thorough review and evaluation of this new 

28 PBPK modeling effort, but it is something that we plan to

29 do in the future. 

30

31 So now I am going to go over the pharmacokinetic modeling 

32 approach that the agency is proposing. Pharmacokinetic 
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1 modeling can be very useful in the case of atrazine 

2 because it is a tool that you can use to get an estimate 

3 of an internal dose metric for your endpoint of concern. 

4 It can also be used in the extrapolation of an internal 

5 dose associated with the endpoint to different species, 

6 including humans, life-stages, different exposure 

7 conditions, et cetera. And actually, from the exposure 

8 side you can use the model to relate the human ingested 

9 dose through drinking water to human plasma levels that 

10 can then be compared to a rat plasma point of departure 

11 for the endpoint LH attenuation.

12

13 So the ideal approach for doing all these fancy things is 

14 a PBPK model, okay? Given that we do not have one at this 

15 time; the agency as such is considering other options that 

16 will inform on pharmacokinetic behavior of internal dose 

17 and water monitoring.

18

19 So this is the proposed pharmacokinetic modeling approach, 

20 based on a one-compartment linear model that I should 

21 relate atrazine dose to plasma triazine levels, and is a 

22 single elimination rate constant. This model is actually 

23 based, like I said before, on linear behavior. That is a 

24 very consistent feature of plasma triazines across 

25 different studies, doses and species. This model is also 

26 based on the internal dose that scales directly with 

27 atrazine dose. And the elimination rate constant, which 

28 is a process that is linear that results in a constant 

29 plasma elimination half-life. 

30

31 So this one-compartment linear model is consistent with 

32 previous efforts that have been reported in the past by 
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1 Timchalk et al., by McMullin et al. and Hui et al. Again, 

2 modeling chlorotriazine equivalents; so we are actually 

3 using a similar approach, but based on radiolabeled 

4 atrazine studies. So just a few things about the one5 compartment linear model that is being proposed; the good 

6 thing about having a simplified model like this is that 

7 you only need two parameters, the volume of distribution 

8 and the elimination rate constant.

9

10 I have been talking a lot about elimination rate constant 

11 and the volume of distribution. These two parameters have 

12 to be reflective of the conditions for the endpoint which 

13 is in steady state. So we were able to estimate the two 

14 parameters, the volume of distribution of plasma triazines 

15 as steady state and the elimination rate constant, from 

16 single dose plasma data from the Paul et al. Study. 

17

18 The only nice thing about having a simple one-compartment 

19 model is that you actually have an analytical expression 

20 that relate those rates to plasma levels, and this is what 

21 the expression is. So plasma levels at steady state is 

22 actually equal to the dose rate in mg per kg per day, or 

23 with the volume of distribution or the elimination rate 

24 constant in actually 24 hours.

25

26 So, one of the things that we wanted to do is to see how 

27 the model will behave in predicting pseudo steady state 

28 plasma levels from the Thede Study. As I mentioned 

29 before, this study only use two animals per dose group. 

30 These are the average values that were reported for each 

31 animal. 

32
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1 So we wanted to use the model to see how close we can come 

2 to the values that were measured for plasma radiolabeled 

3 equivalence. And the model does a pretty good job of 

4 actually estimating those levels. In some case, the model 

5 predicts at the halfway, actually, between the two 

6 numbers. That is all very consistent for all of those 

7 groups. So this builds confidence in using the model to 

8 inform a PK behavior of internal dosimetry. 

9

10 So now I am going to talk about what our current 

11 understanding is of pharmacokinetic behavior, internal 

12 dosimetry and the endpoint of concern, LH attenuation. 

13

14 So this is our understanding of internal dosimetry and LH 

15 attenuation is rats. So we know that an oral dose of 

16 atrazine is actually given to rats. It is a good 

17 assumption to say that you will get very close to 100 

18 percent absorption, so you can assume complete absorption, 

19 so this will be your input. That absorbed dose then will 

20 get distributed in a body volume of distribution to result 

21 in plasma triazines. At this point, these plasma 

22 triazines can get eliminated with eliminate rate constant. 

23 That is your plasma clearance. So this will be your input 

24 and this will be your output.

25

26 When the input equals the output is when you have the 

27 condition of pseudo steady state, if you will. So upon 

28 repeated dosing with atrazine, you will get 

29 bioaccumulation until you get to what we call pseudo 

30 steady state, and that will result in your steady state 

31 plasma levels.

32
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1

2 You add duration to that and you have your internal dose 

3 metric that we are proposing to relate to the endpoint of 

4 concern, LH attenuation.

5

6 So what the model is doing, pretty much, is to relate the 

7 atrazine dose to this, okay? And that is what that is. 

8 This is the expression of the one-compartment model for 

9 the adult rats. Like I said, the dose rate to the 

10 internal dose metric of concern, AUC, daily steady state 

11 AUC.

12

13 Now, so the human situation is different. It always is, 

14 right? So, in the case of animal studies, you know, they 

15 have been carried out at a constant dose of atrazine as 

16 well as a constant frequency of dosing. From here 

17 you will get then an internal dose that can be related to 

18 the endpoint of concern, LH attenuation. 

19

20 The human situation is different in that the dose level, 

21 nor the frequency, is likely to be constant. But we think 

22 that this would also lead to an internal dose that can be 

23 associated with the endpoint that has been observed in 

24 rats. So this is how we are relating the two of them. So 

25 this is our current understanding of the human situation.

26

27 The intake rate is going to be determined by the water at 

28 consumption rate in the chlorotriazine levels in water. 

29 The product of these two will give us the absorbed dose 

30 just like we did with animal studies. That will get 

31 distributed in body volume of distribution to result in 

32 the plasma triazine concentration, which can get 
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1 eliminated at this point. And then, once again, you have 

2 the input and the output. When those two are equal, then 

3 you have the condition of pseudo steady state.

4

5 Upon repeated exposures you will also get bioaccumulation 

6 of plasma triazines to reach what we are calling an 

7 equivalent pseudo steady state level. Then, if you add 

8 duration to that, you will have a human-equivalent of an 

9 average daily AUC.

10

11 And the reason why I do not say pseudo steady state here 

12 is because humans are not likely to reach such conditions 

13 in the same way as rats, basically, because we have a very 

14 variable intake dose rate that is determined by the 

15 chlorotriazine in water and in the water consumption rate. 

16 But we think that we can still use the one-compartment 

17 model to relate what we think is the intake dose rate to 

18 the internal dosimetry of concern, which will be the human 

19 average daily plasma AUC.

20

21 So this will be the expression for the one-compartment 

22 model. Once again, so you have an expression that relates 

23 the dose rate to the internal dosimetry for plasma 

24 triazines that will include all the metabolites as well as 

25 atrazine. And in the two parameters that we are proposing 

26 for humans have been allometrically scaled from the rat 

27 values.

28

29 Now I am going to go over how we plan to estimate the 

30 human dose rate. Okay. The way we are proposing to do it 

31 is that we are following a recommendation directly from 

32 this panel. So we are not inventing the wheel here.
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1

2 At the September 2010 meeting the panel recommended the 

3 use of an integrated or daily average internal measure 

4 along with a related drinking water monitoring approach 

5 based on the area and the concentration time curve.

6

7 So, the way we feel that we have addressed this is that 

8 the internal dose, or daily average is our plasma AUC. 

9 And the proposed approached for water exposure would 

10 involve what we are calling a water schemograph AUC, AUC 

11 for water.

12

13 Now, the way we are proposing to do this is that -- let's 

14 take a typical schemograph, a water schemograph, showing 

15 how all triazine levels vary as a function of time. But 

16 the way we are proposing to use this is that we can 

17 estimate the area of this curve for a given duration. So 

18 we can use the old-fashioned sort of rule, so you will be 

19 dividing the duration of concern into trapezoids, which 

20 will be dictated by the sampling frequency.

21

22 So the area of the trapezoid will basically be the area, 

23 at the average level, water times duration. And the AUC 

24 for water then will be the sum of the area of all the 

25 trapezoids. 

26

27 And the way that area is used, is that it actually 

28 provides us to actually come up with an estimate for the 

29 average atrazine level in water. You will take the AUC 

30 for water for a given duration and you will divide it by 

31 the number of days, whether it is 4, 14 or 28, and then by 

32 24 hours.
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1

2 So this expression four here gives a time-weighted average 

3 level of atrazine in water which, along with water 

4 consumption information, can result in an estimate for the 

5 human dose rate, and this is the way it works.

6

7 So after you come up with the average value for atrazine 

8 in water, then that can be incorporated into the one9 compartment linear model expression that will result in an 

10 estimate of the human equivalent average daily plasma AUC. 

11

12 So this is the approach that we are proposing. And here, 

13 I am just going to give you a quick summary. So plasma 

14 AUC seems to be a reasonable internal dose metric since 

15 the duration appears to be important for the endpoint of 

16 concern in rats.

17

18 So the accumulation of plasma triazines to pseudo steady 

19 state is consistent with a temporality of LH attenuation. 

20 Linear pharmacokinetic behavior of plasma triazines is a 

21 feature that is very consistent across different doses of 

22 atrazine, studies and even species, including humans, and 

23 they all support the use of a one-compartment linear 

24 model.

25

26 The extrapolation to adult humans is actually possible 

27 with two parameters, the volume of distribution and the 

28 elimination rate constant being allometrically scaled. 

29 And the human dose rate -- we are proposing that we can 

30 estimate this through an AUC analysis of water chemograph.

31
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1 And the application of these methodologies will follow in 

2 a presentation that we will do later on today. Thanks for 

3 your attention.

4

5 DR. DANIEL SCHLENK: Thank you, Dr. Rodriguez. Questions or 

6 clarification? Dr. Greenwood?

7

8 DR. RICHARD GREENWOOD: I was unable to get hold of either the 

9 Paul report or the Simoneaux report because they were 

10 internal Ciba-Geigy reports, so I have not got all of the 

11 information I need to evaluate those. Can you tell me 

12 what the limits of quantification in these studies were?

13

14 DR. CHESTER RODRIGUEZ: I do not quite recall exactly, but I 

15 believe they were in the range of .01 ppm, but I will have 

16 to double check on that.

17

18 DR. KEVIN O'BYRNE: It was quite difficult for me to follow the 

19 science behind your talk because it is just so foreign to 

20 me. But I was astonished that the plasma levels and the 

21 elimination rates of atrazine and its metabolites were 

22 sort of constant, irrespective of dose. 

23

24 So, this only implies to me that there is no toxicity to 

25 the kidney, even up to 100 milligrams per kilo per day, 

26 which is astonishing. Is this hopeless?

27

28 DR. CHESTER RODIGUEZ: That is very interesting, and it tells 

29 you that the body can adjust up to a point. But if you 

30 get something like kidney toxicity, then you have other 

31 issues. But for the dose range that has been studied, you 

32 see linear kinetics all the way.
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1

2 DR. JAMES MCMANAMAN: Yes. I have a question about the 

3 assumption that it is a single state elimination, because 

4 if you go to slide 22 for humans you will see that the 

5 DACTs get the rate constant for elimination of that is --

6 lets just look at the urine is .060 versus the DIA's .3.

7

8 So, that suggest that both are two elimination rates, one 

9 for each compound, which is still linear but that is fine, 

10 but there are still two elimination rates. So does that 

11 mean that there is a single compartment or -- you would 

12 have to postulate that the rate of elimination of one 

13 would have -- the mechanism would be different from the 

14 other then, right? So it suggests that there may be more 

15 than one compartment.

16

17 DR. CHESTER RODRIGUEZ: Yes. And that is actually one of the 

18 reasons why we are proposing to use a radiolabeled study. 

19 When you look at the radiolabeled, it does not matter what 

20 the metabolite is, just follow the radiolabeled and that 

21 is the elimination rate constant that is actually 

22 estimated here.

23

24 DR. JAMES MCMANAMAN: Well, if you do that, you would have to 

25 know what percentage of metabolite the radiolabeled 

26 represents. So lets say that the majority of your 

27 radiolabeled compound is eliminated as metabolite A, and a 

28 very small minority is eliminated as metabolite B -- so if 

29 you are just looking at radiolabeled, you are going to 

30 pick up the totality, which is going to be mainly 

31 metabolite A. Whereas, the active component may be 
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1 metabolite B and you will not be able to distinguish 

2 between those two using the radiolabeled.

3

4 DR. CHESTER RODRIGUEZ: That is correct. And we feel that this 

5 is very conservative, but in the absence of specific 

6 information about what the contribution of the metabolites 

7 are, I think this is our best option and it is 

8 conservative.

9

10 DR. DANIEL GRIFFITH: When you talk about water consumption, is 

11 it oral consumption? Is there any evidence that there is 

12 absorption through skin?

13

14 DR. CHESTER RODRIGUEZ: The information we have is that the 

15 main route of exposure is drinking water.

16

17 DR. WILLIAM HAYTON: Now, as far as scaling the rate constant 

18 to human, I just found it a little curious that, as you go 

19 from rat to monkey, the half-life actually, for the 

20 radioactivity, gets quite a bit shorter, whereas you would 

21 expect on a body weight, you know, three-quarter par 

22 scaling, expectation that it would get longer.

23

24 DR. CHESTER RODRIGUEZ: Yes. And you are correct, but you have 

25 to go by weight of evidence. You know. You have multiple 

26 radiolabeled studies that give you a very consistent kel. 

27 Also, we did a minor exercise with DACT. We took the 

28 half-life that was estimated from rats and we 

29 allometrically scaled -- so the half-life of DACT in rats 

30 is about seven to eight hours. So we allometrically 

31 scaled that to see if it could predict the measured value 

32 in humans. And I can tell you that the estimate was in 
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1 two-fold. It is in the issue paper. But that is the best 

2 we can do, I think, with the information we have, and you 

3 have to go by weight of evidence. You know. You have a 

4 single monkey study. You have multiple rat studies, so we 

5 do the best we can.

6

7 DR. JAMES MCMANAMAN: Yes. So do you know how the metabolism 

8 of atrazine differs between rats and primates? I mean, 

9 the P-450 system of the rat is pretty good. I am not sure 

10 that the same system is equivalent for the monkeys or 

11 other primates.

12

13 DR. CHESTER RODRIGUEZ: I do not think that information is 

14 known.

15

16 DR. DANIEL SCHLENK: Any other questions before we head to 

17 lunch? I did not want to shut everything off by saying 

18 that, but since it is time -- okay. Let's adjourn for 

19 lunch. And since we are a bit early, let's try to get 

20 back by 1:20, okay? 

21

22 DR. DANIEL SCHLENK: Let's go ahead and get started. Welcome 

23 back to our atrazine SAP re-evaluation of the human health 

24 effects of atrazine; review of non-cancer effects, 

25 drinking water monitoring frequency and cancer 

26 epidemiology. 

27

28 So, we spent the first half of the day talking about the 

29 latter. We are about to talk about some of the former 

30 now, in terms of drinking water monitoring data. And 

31 giving that presentation is going to be Nelson Thurman, 
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1 who is with the Environmental Fate and Effects Division of 

2 OPP.

3

4 NELSON THURMAN: Okay. Good afternoon. Just to try to give 

5 you a very, very brief recap; as part of the 2003 

6 condition of re-registration for atrazine, Registrant 

7 Syngenta began taking weekly samples of both the source 

8 water and treated drinking water at a selected number of 

9 community water systems to ensure the concentrations did 

10 not exceed the toxicological level of concern for a 90-day 

11 exposure period, which is what we had at that time.

12

13 And as we started re-evaluating based on some of the 

14 additional studies that came in and started looking at 

15 potentially shorter durations of exposure for the toxin 

16 points, we started asking how well those weekly samples 

17 were characterized as shorter duration of exposure. And 

18 that led to why we are sitting here with Liz at the table 

19 in that regard.

20

21 We have focused on methods for analyzing the uncertainty 

22 and monitoring for atrazine. And, in actuality, for the 

23 drinking water they are looking at total chlorotriazine. 

24 So, it is not just atrazine that is being measured; it is 

25 the chlorodegradates as well as simazine.

26

27 What we are going to present to you is not an analysis of 

28 drinking water monitoring data itself, but we are taking 

29 the recommendations of the previous SAPs. The methods 

30 that we have been evaluating -- and we are flushing those 

31 out. So we are focusing on the methods and not the 

32 assessment itself, but the methods we had used to analyze 
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1 that data. What we have done is based on the 

2 recommendations from the SAP. We have developed some 

3 proof of concept approaches of various methods. We are 

4 not saying we are zooming in on one as ideal; we actually 

5 are looking at a handful of methods. And when it comes 

6 down to the ultimate assessment, it may depend on which 

7 duration of exposure we end up with and which method we 

8 ultimately use, but we at least want the flexibility of 

9 the tools to get there.

10

11 So, we have looked at a number of approaches and there are 

12 a lot of people who put effort into developing these 

13 approaches. I get to talk, but I want to make sure that 

14 my colleagues who have worked on these are up here and be 

15 ready to answer the questions in some of those.

16

17 Mary Frankenberry had conducted a comparative statistical 

18 analysis looking at the effect of different monitoring 

19 frequencies on estimating concentrations of different 

20 durations. Drs. Jim Hetrick and Jim Wolf evaluated some 

21 of the Geostatistical and stochastic methods that were 

22 used to conduct the time series distribution. It can be 

23 used to fill-in between sampling measurements. A lot of 

24 that is detailed in the Appendix D-2. They went into a 

25 lot more detail than we summarized in the background 

26 paper. 

27

28 Dr. Stephen Wente also looked at a simple watershed 

29 balance model similar to something he worked on when he 

30 was with USGS. That is presented in Appendix G-3. He had 

31 some issues with the flow data available for doing that, 

32 and so we did not fully develop that assessment. We are 
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1 not going to be really talking about it, but if you have 

2 some questions, Dr. Wente is here to respond to those. 

3

4 So the first part of this presentation is going to provide 

5 a brief summary of the monitoring issues as they have 

6 evolved as a result of the feedback we have had from the, 

7 primarily, the April and September 2010 SAPs. The bulk of 

8 the presentation is going to be looking at approaches we 

9 are proposing to characterize the uncertainties and 

10 estimates of the atrazine concentrations in water, 

11 addressing both the day-to-day or short-term variability 

12 as well as year-to-year patterns and the spatial patterns.

13

14 And so, I will begin with a recap. I think, as I pointed 

15 out earlier, some of the questions related to monitoring 

16 between the April SAP and now kind of evolved from; is the 

17 existing weekly monitoring program adequate for the 

18 shorter term duration of exposure to how do we best 

19 characterize the uncertainties in exposure estimates based 

20 on existing monitoring. So, instead of determining do you 

21 need to monitor more, it is what can we get out of the 

22 available monitoring that we have?

23

24 So we are focusing on approaches to characterize the 

25 uncertainties in a way that takes into account the short26 term variability and atrazine concentrations in water that 

27 you expect to be able to address with monitoring. But we 

28 also need to take into account the year-to-year 

29 variability that we will see, as well as spatial patterns.

30

31 So the complexity of the spatial patterns and the temporal 

32 patterns in pesticide concentration are driven by a number 
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1 of factors, some we understand and can predict better than 

2 others, but there are some tools out there that help us 

3 take into account the impact of those factors on pesticide 

4 exposure.

5

6 I will say that they do make it very challenging to design 

7 a monitoring study that takes those into account, and so 

8 as a result, we are often looking at not just the 

9 monitoring data but what models or tools can we bring in 

10 to help better characterize that monitoring data so that 

11 we can use that.

12

13 As we go into this presentation, the background paper, we 

14 have broken the variability down to the components, and 

15 actually we have separated the temporal variability into 

16 your short-term day-to-day patterns as well as year-to17 year patterns. But we are also mindful that the 

18 uncertainties are not necessarily additive in that there 

19 is a lot of integration that needs to go into the final 

20 result. So while I am presenting approaches separately, 

21 what we are going to look at, at the end, is making sure 

22 we do not compound uncertainties where they do not exist.

23

24 So this is going to be a recap of some of the preliminary 

25 monitoring evaluations we did at the last SAP. One of the 

26 emphasis of the SAP is that you really need datasets with 

27 more intensive sampling to evaluate the potential 

28 uncertainty that we have seen in the weekly samples.

29

30 And as we have presented in previous SAPs, we do have some 

31 data available, but they are not community water systems. 

32 We are aware that Syngenta began this year monitoring six 
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1 community water systems on a daily basis, and we look 

2 forward to being able to analyze that data as additional 

3 data to help us as we develop these and evaluate these.

4

5 But in this case, we are looking at a chemograph, which is 

6 the dark blue line which is based on daily monitoring. 

7 There is a dashed blue line there that represents your 4-

8 day rolling average. For community water system that have 

9 weekly samples, you might have a sampling date that hits 

10 that peak. 

11

12 Honestly, with the seven-day sampling intervals, you would 

13 probably have about a 1 in 7 shot of hitting that. You 

14 may not hit that peak. The problem we have is giving 

15 weekly samples we do not know, and that is part of what we 

16 are trying to take a look at.

17

18 One of the reasons we are looking at the daily monitoring 

19 is it helps us develop methods and gives us some feedback 

20 so that when we do apply these methods to the community 

21 water systems we have some confidence in terms of what 

22 they are able to provide for us.

23

24 We have built on the recommendations of those April and 

25 September SAPs. We are not really trying to open up new 

26 ground here, but to more or less tie together the sets of 

27 tools that have been discussed in those previous SAPs and 

28 try to follow up on the recommendations that the panel 

29 have made.

30

31 To evaluate the monitoring data, the April of 2010 SAP 

32 recommends that we consider, first of all, the tox 
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1 exposure duration of concern because that is going to 

2 define how important capturing the peak concentrations 

3 are; the shorter that duration, the more important those 

4 peak concentrations are in there. They also recommended 

5 using intensive monitoring that cover a representative 

6 range of sites. You want monitoring that is sampled more 

7 intensively than what you are evaluating. 

8

9 And one of the comments they made is, when you are looking 

10 at monitoring, you really need to look at methods that can 

11 predict values that are greater than what was measured, 

12 because with some sampling of any duration, you cannot 

13 expect to have captured the highest concentration in every 

14 case.

15

16 As we came back and explored a number of approaches, the 

17 September SAP had recommended taking USGS's watershed 

18 regression for pesticides, combining that model -- which 

19 is regression-based model -- with a deterministic model, 

20 such as the pesticide root zone model that we use in our 

21 drinking water exposures, or SEAWAVE-Q, which is another 

22 USGS model that they have used to evaluate pesticide 

23 trends over time.

24

25 I also noted that most of these models are fairly data26 intensive. And one of the comments was well, it may be 

27 easier to use WARP in combination with some statistical 

28 approach such as kriging. 

29

30 So with that background, I am going to begin by talking 

31 about looking at some of the approaches we have evaluated 
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1 for characterizing the uncertainty monitoring in capturing 

2 day-to-day patterns.

3

4 As I pointed out, the SAP recommended using intensively 

5 sampled monitoring data. We are aware of, in general, a 

6 couple of monitoring datasets that monitor atrazine either 

7 daily or near daily during the periods of time when you 

8 are expected to find atrazine in the water, which roughly 

9 corresponds from April to August/September timeframe, from 

10 the time the corn is planted to well into the season when 

11 you are likely to find the high runoff periods.

12

13 One of these is from Heidelberg University, the National 

14 Center at Water Quality Research. They have collected 

15 data on a number of watersheds of various size in Ohio. 

16 We looked at the Maumee River, which is roughly a 17,000 

17 square kilometre catchment in Ohio.

18

19 Another dataset is the monitoring Syngenta has done on 

20 atrazine for the ecological exposure monitoring. This was

21 a separate monitoring program that focused on, primarily, 

22 headwater watersheds and looking at the impacts on aquatic 

23 plant communities.

24

25 It is a much smaller watershed, but it gives us an 

26 opportunity to look at a range of exposures. They do have 

27 monitoring that covers more than just the spreads from 

28 Ohio well into Missouri and Nebraska, so it gives us a 

29 broader geographic range.

30

31 As we have developed the approaches, we picked two 

32 particular datasets as an evaluation approach. One was 
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1 Maumee River from 1995 and the other one was Missouri-01 

2 from 2007. Missouri-01 had a fairly high peak. Maumee 

3 River had much lower concentrations. It gives us a way of 

4 bracketing that information.

5

6 So one of the first approaches we did was -- let's take a 

7 look at how well we can characterize the uncertainty of 

8 different sampling frequencies in estimating the exposure 

9 varying durations.

10

11 Essentially, we are asking the question; how well do 

12 estimates that are based on sampling at different 

13 intervals compare to the true value, and can we develop a 

14 multiplication factor of some sort based on sampling 

15 frequency and duration of concern?

16

17 To do this, we defined a sampling window across the 

18 datasets. And the sampling window range from 4-day 

19 intervals up to 28 days. Then we used a bootstrapping 

20 simulation to select a random day within each of those 

21 sampling windows to reconstruct a monitoring dataset for 

22 those. And we did that 10,000 times for each of these 

23 four windows that we looked at, ranging from 4-day 

24 intervals to 28-day.

25

26 The community water system is represented by the 7-day 

27 intervals. Then for each of those sampling simulations, 

28 we derived the 1-day maximum, or peak, as well as a 

29 maximum for 7-, 14-, 28- and 90-day rolling average for 

30 each of those. 

31
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1 And then what we did is we took those 10,000 simulations 

2 for each of the sampling intervals and compared the true 

3 maximum concentrations against the 5th percentile of those 

4 estimates. 

5

6 That ratio, we have described in the paper as a bias 

7 factor, which we look at as a potential multiplicative 

8 factor that could be applied to exposure estimate, 

9 depending on the sampling frequency and the duration of 

10 exposure.

11

12 I want to emphasize that this is more of a proof of a 

13 concept based on two examples. It is not an exhaustive

14 analysis. For instance; we have only sampled one. We 

15 only looked at one year in each of these sites. We have 

16 multiple years on those.

17

18 To develop this further, we would obviously be looking 

19 across the years. We would also probably be looking at 

20 sites that represent not just flowing water -- and both of 

21 these, by the way, are flowing water bodies, but we look 

22 at reservoirs, looking at water bodies of different sizes. 

23 So this is more of an illustration purpose of how this 

24 might be applied.

25

26 The two trends I want to point out, and first off, is that 

27 the multiplicative bias factor or the uncertainty 

28 increases as your sampling interval gets wider, which 

29 means that it gives you obviously less data to work with. 

30 The other point is that, as your duration of concern 

31 expands, becomes larger, then your uncertainty factor 

32 decreases.
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1

2 And I want to point out, I would caution too much against 

3 saying, "All right. This is a small watershed; this is a 

4 large watershed and the bias factors are greater for the 

5 small than the large." We are not sure if that is the 

6 case of the watershed or if it is the case of the 

7 magnitude of the concentration.

8

9 And for example, Maumee River -- we used the 1995 data for 

10 this -- in the previous SAP we did some analysis with 2008 

11 data where the maximum concentration was around 50 parts 

12 per billion rather than 14. We would like to do a little 

13 further analysis using wider range of data to see exactly 

14 how that bias factor would play out. And that kind of 

15 leads us to the next approach we were considering.

16

17 Kind of following up on the recommendation of the SAP is 

18 that, when you are not sure what that exposure period is, 

19 you may be better off investing your resources in trying 

20 to capture the pattern of atrazine concentrations, and we 

21 started looking at ways that we might be able to do that.

22

23 The SAP had recommended combining a regression based model 

24 such as WARP with either a deterministic model or a 

25 geostatistical approach. We operated on the philosophy of 

26 lets go simpler -- the simpler, the better -- in terms of 

27 turning things around, and we started looking at WARP in 

28 combination with the geostatistical approach.

29

30 But as we started building on that, what we found is that 

31 -- we found some promising results in constructing the 

32 shape of that time series using the geostatistical 
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1 approaches and conditional simulations with some of the 

2 more intensive sampling.

3

4 And so, this may be a little bit of departure of what the 

5 SAP recommended, and that is one of the reasons why we 

6 want to bring this back to get your reaction to this, 

7 because is it a little bit -- in some cases, we think we 

8 might be able to construct fairly reliable time series 

9 just with a geostatistical analysis. In other cases, it 

10 looks like we would need to move on to add WARP in there.

11

12 So I want to present a little bit about how we went about 

13 approaching this. This is kind of the analysis strategy 

14 that we developed after working around with the data for a 

15 while. This particular figure is in -- I think it is 

16 Figure 6.1 in the background paper. It outlines the 

17 approach we are considering.

18

19 And we began by looking at the time series, the frequency 

20 of sampling. In this case, we basically looked at are 

21 there 15 or more data points in the time series, and that 

22 roughly equates to a sampling frequency of seven days or 

23 more often.

24

25 And what we found in there is that, if you have sufficient 

26 samples, we can provide reasonable time series estimates 

27 by going through a variogram analysis, doing some kriging 

28 of that, and then doing statistic simulations.

29

30 With less frequent sampling, then you need to do something 

31 to fill-in in the dataset. We presented a couple of 

32 options in the Appendix D-2. One was looking at covariate 
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1 to estimate concentration either with something like 

2 another pesticide or with flow.

3

4 We also looked at -- and what we will highlight here is 

5 using WARP to provide a percentile of the time series, and 

6 then using it in relation with flow percentiles to create 

7 the time series. So, to begin with, because we are 

8 looking at weekly samples for the community water systems, 

9 I am going to focus on the approach we did for our 

10 dataset, but would be that robust.

11

12 Now, before we apply these geostatistical methods, we 

13 needed to consider stationarity. And essentially, 

14 stationarity is the assumption that there is an equal 

15 probability of occurrence, in this case, regardless of the 

16 time. 

17

18 This is a typical chemograph over a year for atrazine. If 

19 you were to apply this on an annual basis, then as 

20 stationarity, it is probably not going to hold. There are 

21 times outside the use period where you are not likely to 

22 find atrazine in the waters.

23

24 However, as we took a closer look, if you narrow that 

25 window to what we describe as the runoff period -- and 

26 that basically coincides from the time the pesticides are 

27 going to be applied to the field and after that. You can 

28 define a runoff period within the chemograph where we can 

29 at least make a contention that stationarity will hold 

30 well enough for this particular analysis. For atrazine, 

31 we can come up with a reliably predictable period, 

32 depending on the corn planting season.
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1

2 Now, as we did some of the exploration with the Heidelberg 

3 data with some other pesticides, what we did find is that 

4 is probably going to be more difficult for some of the

5 insecticides that have a less predictable or less 

6 consistent application period. And so, this may work well 

7 for atrazine or other corn herbicides, but it may not 

8 necessarily work for some of these other pesticides.

9

10 For that runoff season, what we did is we analyzed the 

11 monitoring points for a covariate structure in time. We 

12 could have used the correlogram and we chose to use a 

13 variogram, which is what you see here. 

14

15 Essentially, what we did is we took the 1995 Maumee River 

16 data and simulated a 4-day sampling in that, and we also 

17 simulated a 7-, 14- and 28-day samplings, so I wanted to 

18 show you a couple of examples. A couple of terms for 

19 those who may not be familiar; the Nugget describes the 

20 amount of the variance that results from random processes

21 that do not have a temporal correlation, and there would 

22 be such things as measurement error.

23

24 The Sill represents the variance value where the variogram 

25 levels off. And the Range is that time to where you reach 

26 the Sill. Within that Range, the monitoring values are 

27 auto-correlated. So, for Maumee River, that Range of 

28 autocorrelation is roughly 50 to 60 days.

29

30 When we pulled together this analysis we used a visual 

31 process to define the model rather than the least squares 

32 method. And because of that, as we did the kriging, we 
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1 put more emphasis on fitting the nearer points in the 

2 variogram. We also had more data pairs in the nearer 

3 points than the farther points. But that was the approach 

4 we used to fill in between the time series.

5

6 And what you see here are realizations of the atrazine 

7 time series using the variogram models to fill in the data 

8 points. The red line, which you can sort of see on these 

9 is the actual time series from the daily measurements. 

10 The black points are the realizations of the time series 

11 using the variogram models, and the bars around each of 

12 those points represent the standard deviation. 

13

14 A couple of things I want to point out here is that when 

15 you look at -- this is based on 4-day sampling intervals. 

16 This is 7-day sampling intervals. When you look at these 

17 two, what you do see is that the variogram models using 

18 conditional simulations provide a reasonable 

19 characterization of the time series.

20

21 What happens when you started going to 14-day sampling 

22 intervals, your variogram structure started falling apart; 

23 looking fairly messy. Your standard deviations got a lot 

24 larger. We missed the highest sample peak.

25

26 At 28 days, we really could not construct a variogram. 

27 What you see here is for illustration purposes and that

28 was a variogram constructed based on daily flow. And this 

29 is one reason why if we go back to that figure we use that 

30 cut-off, in terms of the amount of data, the approach we 

31 took.

32
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1 Now, if we take a look at the Missouri dataset, what we 

2 did find is that the variogram models do not fit as well 

3 as they did for the Maumee River dataset. And your range 

4 is a lot shorter and it may very well reflect the spiky 

5 nature of the smaller watersheds.

6

7 But as you go from here, these are the variograms from the 

8 4-day sampling intervals and for the 7-day sampling 

9 intervals. I want to show the realizations of the time 

10 series from those variograms. 

11

12 And a couple of points; we still had a fairly good --

13 except for the fact that we did miss the peak -- we did do 

14 a fairly decent recreation of the time series for four and 

15 seven days, and once again, it started falling apart at 

16 the 14- and 28-day intervals.

17

18 And I do want to point out is that, the sampling -- and it 

19 was a 4- and 7-day intervals we simulated -- did miss that 

20 peak concentration. But what we wanted to see -- this 

21 provided us an opportunity to take a look, as we go 

22 through with the assessment, is how much that impact has 

23 from missing that peak concentration as it carries through 

24 in analysis.

25

26 Okay. I am going to jump back to the Maumee River. For 

27 the case study you are going to hear about later this 

28 afternoon, what we did was we generated a thousand 

29 conditional simulations of the time series -- and in this 

30 case for the Maumee River, as well as for the Missouri-01 

31 dataset -- using the variogram model that was constructed 
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1 from the 7-day sampling intervals, which represented what 

2 we are seeing in the community water system. 

3

4 The black line you see here, which you can see a lot 

5 better in the previous, is the known concentration. It is 

6 based on most intensive sampling. So this is the daily 

7 time series. This is the 4-day rolling average that is 

8 constructed from the daily time series; the 14-day and 28-

9 day rolling averages that we constructed from the time

10 series.

11

12 So what we did is, for each of those thousand simulations, 

13 we took at 95th and a 5th percentile, which is what the 

14 upper red line represents; the 95th percentile, those 

15 thousand simulations, and the lower red line represents 

16 the 5th percentile, those thousand simulations. And so, 

17 we did this for the daily time series and we looked at the 

18 constructive 4-, 14-, 28-day rolling averages as well.

19

20 If we take a look at the Missouri-01, as you can see, we 

21 did miss the actual maximum peak and we started looking at 

22 those simulations. In the 95th, we were still 

23 underestimating the maximum peak. But when you move to 

24 something as short as a 4-day rolling average, the 95th 

25 percentile did provide a reasonable upper bound on the 

26 estimate of that exposure.

27

28 We want to examine this in more cases to see whether it 

29 holds true or this one just happened to be a fortunate 

30 circumstance. We are also looking at, ultimately -- what 

31 we are interested in is how well the estimates that we 

32 provide here, based on monitoring, represent what we are 
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1 going to see in the internal dose study to what you will 

2 see in the case study later.

3

4 So let's go back to those 14- or 28-day averages. This is 

5 not an issue with the community water system monitoring 

6 that is going on now because they have done weekly 

7 samplings, but it may be an issue if we wanted to pull in 

8 other monitoring datasets that are likely to be sampled 

9 less frequently, or if ultimately we want to try to apply 

10 this to other pesticides. But typically, if you get 

11 sampling every two weeks or monthly, you are doing pretty 

12 good on a lot of the monitoring. 

13

14 As I pointed out, we evaluated a couple approaches, and 

15 one is a covariate approach using other monitoring data or 

16 flow data. And one of the things I wanted to point out 

17 here -- because this played into how we went from a 

18 percentile time series to a time series distribution -- if 

19 you trying to do covariate analysis between flow and 

20 monitoring data over a long time period, you really do not 

21 find a correlation.

22

23 However, if you narrow that window primarily to the time 

24 whenever your pesticide is applied and shortly after, we 

25 did see some correlations between flow and monitoring 

26 data. So we took advantage of that in the approach that 

27 we did use, and we are going to present to you.

28

29 So we go back to something like the sampling every two 

30 weeks or monthly. We found that the data was too sparse 

31 to really go directly into variogram analysis, so we 
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1 needed some way of filling in that time series in between 

2 those data points.

3

4 So, what we did is we used WARP, pulling together the 

5 characteristics for the monitoring site. We used that to 

6 estimate a time series and that gave us a percentile 

7 distribution of your monitoring time series. Then we 

8 could place the existing monitoring in context with that 

9 percentile distribution.

10

11 Then we took advantage of what we knew about those 

12 percentile distributions and we ranged the flow 

13 percentiles and matched them up with the percentiles from 

14 WARP, and then we could rearrange that based on the actual 

15 time series. So we could reconstruct the time series from 

16 that percentile distribution. And once we did that, we 

17 could proceed through the variogram and kriging and 

18 stochastic simulations. 

19

20 I am going to just show you an example that we presented 

21 in Appendix D-2. When we are looking at the Maumee River 

22 data, we used a 28-day sampling intervals. If you 

23 remember I pointed out, we really could not even construct 

24 a variogram with those 28-day intervals because there was 

25 too little data in between.

26

27 But once we merged that with WARP estimates -- and what we 

28 ended up doing, if you read through that appendix, we did 

29 not have all the WARP parameters we needed for the Maumee 

30 River watershed at the time. We did have a couple of the 

31 atrazine eco-monitoring sites located nearby and we use 
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1 those WARP data parameters to bracket this just as a proof 

2 of concept to see how well this might work.

3

4 I am showing you one that we did with the Ohio '03 

5 monitoring from the WARP site. We used that and related 

6 it back to the flow to reconstruct a time series, and what 

7 you see is that we were able to construct a variogram with 

8 that merged dataset, and we went from that variogram to 

9 the conditional simulations.

10

11 We came up with the realization that we felt was not too 

12 bad of a fit for the time series. You can see there is 

13 the actual peak concentration so we were not too far off 

14 on that. We did miss a little bit there. I think we 

15 are likely to see greater uncertainty bounds around the 

16 estimates from the less robust monitoring data but it does 

17 give us an option for providing a time series estimate 

18 from that dataset.

19

20 So we do have a couple of questions that were related to 

21 that approach. What I would like to do, if we are going 

22 to address this, is to move on to year-to-year patterns, 

23 because if we are going to address the uncertainties in 

24 estimating pesticide concentrations from monitoring data, 

25 we do need to account for that year-to-year variability as 

26 well.

27

28 And once again, we are looking at this separately, but in 

29 the end we are going to have to integrate that to show 

30 that we are not compounding uncertainty that may not be 

31 compounded.

32
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1 We did a quick look at the atrazine monitoring program. 

2 There are more than a hundred community water systems 

3 included in this program that have at least six years of 

4 monitoring data.

5

6 And what we did, as a quick look at those, is that we 

7 looked at the highest maximum annual detection and 

8 compared that to the lowest maximum annual detection for 

9 each of the community water systems that had at least six 

10 years of data. And what we found is that the difference 

11 between the highest measured concentration and the lowest 

12 measured concentration in a given year might be an order 

13 of magnitude or more.

14

15 The issue for atrazine comes down to how many years of 

16 monitoring are really necessary to characterize that 

17 expected range in concentrations from year to year. And 

18 alternately, can we use modeling in addition to monitoring 

19 to provide a characterization to that year-to-year 

20 variability?

21

22 We have presented a few options in the background paper. 

23 One is to take a look at monitoring in spans of longer 

24 periods of time. And a couple are looking at modeling 

25 type approaches that we might be able to tie in with the 

26 existing monitoring to provide some characterization of 

27 the range and year-to-year variability.

28

29 One is a PRZM Hybrid model. I think Syngenta has put a 

30 couple of papers in the docket related to their approach 

31 to the hybrid model. In effect, what you are going to do 

32 is you run PRZM with the watershed-specific inputs, the 
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1 rainfall data for that particular year. I think, in their 

2 analysis they were evaluating the utility of using this to 

3 fill in between monitoring datasets. What we were looking 

4 at is, this may be a way to -- by calibrating this to the 

5 concentration monitoring data for a particular year, then 

6 running that model for additional years, and we might be 

7 able to use this to characterize a range of concentrations 

8 over time.

9

10 With PRZM, we do have weather datasets that run at least 

11 30 years of data that we could use to characterize that. 

12 That is data-intensive in that it does require pulling 

13 together a lot of site watershed-specific data to work 

14 with.

15

16 One other option is to use WARP to provide a way of 

17 characterizing the range concentrations by varying the 

18 temporal patterns. In particular, you can look at a range 

19 of your May/June precip and the atrazine use intensity 

20 over a likely range of values. And by running that, it 

21 may give a way of characterizing that range.

22

23 One thing I will point out is, one of the biggest 

24 uncertainties in input data that we have is atrazine use 

25 intensity, which is a major driver in WARP. And it is 

26 possible that the uncertainty in estimating the use 

27 intensity may override the uncertainty in all the other 

28 parameters. So that is something we are wrestling with. 

29 We laid those options out in the background paper and we 

30 have asked for the panel to provide us some 

31 recommendations and feedback on that approach.

32
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1 And finally, I am going to wrap up with the spatial 

2 patterns, and you will be glad to know that we are not 

3 asking you a question on this. We are actually punting 

4 this to a future SAP that -- since I have got to be 

5 involved in another one, we might as well go with that. 

6 What we are asking is what can we say about pesticide 

7 exposures from one site? How much can we apply that to 

8 another site? Can we link the results from the monitored 

9 data sites to other sites either based on some type of 

10 statistical design or similarities in site 

11 characteristics?

12

13 Now, it is important to point out that the community water 

14 systems that are included in the atrazine monitoring 

15 program are based on compliance monitoring data. In other 

16 words, these were quarterly samples that were taken, and 

17 essentially, Syngenta did an analysis and any community 

18 water system that had annual average atrazine 

19 concentrations from the quarterly samples of 1.6 parts per 

20 billion or greater were included in the monitoring 

21 program.

22

23 As it turns out, this map -- the background you see on 

24 that map -- the dark represents a watershed vulnerability 

25 assessment we did based on WARP and WARP monitored. 

26

27 The darkest blue that you see there are the watershed 

28 areas that we identified as being the most vulnerable, 

29 likely to have the highest atrazine concentrations based 

30 on WARP. The lighter blue is the next highest here. As 

31 it turns out, the community water systems that were 

32 included in the monitoring program all fell within these -
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1 - almost all fell within the highest two vulnerability 

2 tiers based on WARP. 

3

4 While this helps corroborate the vulnerability approach we 

5 used, community water systems do not represent a 

6 statistical sampling across the vulnerability tiers. And 

7 we may not necessarily be able to infer results of these 

8 to other community water systems that occur in the same 

9 vulnerable areas, not without some additional work.

10

11 Now, fortunately, we have had a separate analysis looking 

12 at vulnerable watershed properties for the eco-exposure 

13 assessment, and we have identified characteristics in 

14 those watersheds that are associated with sustained 

15 elevated atrazine concentrations.

16

17 Essentially, you are looking at watersheds that have a 

18 fairly high atrazine use intensity. They are dominated by 

19 soils that have a shallow soil layer that restricts 

20 drainage. And in fact, what you are doing is you are 

21 enhancing runoff and spreading the runoff over a larger 

22 period of time. And related to that, when you set it back 

23 on a national scale, there is a rainfall component that 

24 contributes to that.

25

26 We will be, in 2012, coming back and saying, "All right. 

27 Based on our analysis, based on the monitoring, these are 

28 what we have determined to be the watershed 

29 characteristics and threshold values in those watersheds 

30 that would drive high exposure for the eco-exposure." 

31
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1 It would be simple enough, looking at these community 

2 water systems, to take a look at what the characteristics 

3 are within those to determine whether we can make a 

4 similar relationship in that regard.

5

6 So that is our planned approach for the spatial component, 

7 to put those in context. It is important just to keep in 

8 mind we are confident that what we are dealing with are 

9 community water systems that are in vulnerable tiers, just 

10 making sure we can make inferences to others that may also 

11 reoccur in that tier.

12

13 So I am about to wrap up. This is a simple overview. The 

14 focus of what we are bringing on monitoring to the SAP is 

15 looking at addressing temporal variability. And we have 

16 questions for the panel regarding the methods we use to 

17 address both short-term variability -- in other words, how 

18 well can we address the variability in day-to-day 

19 variations given the sampling interval frequencies we have 

20 -- as well as to look at year-to-year variability.

21

22 So we have looked at potential of taking a multiplicative 

23 bias factor to whatever exposure estimate we have and 

24 using that as a way of characterising the variability, or 

25 alternately, generating, creating a time series analysis, 

26 time series distribution that could be used in that 

27 regard.

28

29 And honestly, we may use both. We may, in the end when it 

30 comes down to it -- depending on the duration of concern 

31 and the tox threshold concentration -- may find that one 
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1 works better than the other, but we would like to have 

2 that option to look at.

3

4 And we have also asked questions about how best to address 

5 that year-to-year variability in terms of how long do we 

6 need to keep this monitoring going? How long do we need 

7 to look at it? And with that, I am going to quit talking 

8 and open this up for questions.

9

10 DR. DANIEL SCHLENK: Thanks a lot. Dr. Coupe. Glad you could 

11 make it. Would you mind just briefly introducing yourself 

12 and where you are from and your area of expertise for the 

13 panel just so that everyone knows who you are?

14

15 DR. RICHARD COUPE: Sure. I am Richard Coupe. I am with the 

16 U.S. Geological Survey. I am working out of the 

17 Mississippi Water Science Center and spent most of the 

18 research time on the fate and transport of agricultural 

19 chemicals. 

20

21 DR. DANIEL SCHLENK: Thanks a lot. Again, let me just remind 

22 speakers to please introduce yourself before speaking into 

23 the microphone so that we know who is talking. So do we 

24 have any questions about water sampling strategies? Yes, 

25 Dr. Lee?

26

27 DR. HERBERT LEE: When you say you used ordinary kriging, does 

28 that mean you estimated an unknown mean?

29

30 DR. NELSON THURMAN: I am actually going to punt this over to 

31 Jim Hetrick who did the detail kriging analysis.

32
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1 DR. HERBERT LEE: So when you say you used ordinary kriging, 

2 does that mean you estimated an unknown mean level for the 

3 function?

4

5 DR. JAMES HETRICK: All I know is that we used the ordinary 

6 kriging that was in GEOEAS. All right?

7

8 DR. HERBERT LEE: Okay. So then my follow-up questions is, 

9 because normally ordinary kriging you are estimating mean. 

10 On slide 16, the Maumee river, the 28-day intervals, they 

11 do not look anything like the real data, and so I am 

12 wondering if you think that may be because it's mean 

13 reverting to the wrong level as opposed to mean reverting 

14 to zero or something like that, or if you have an 

15 alternate explanation for why that does not look anything 

16 like it?

17

18 DR. JAMES HETRICK: First off, if you look at those 28-day 

19 intervals on the -- when we went back and looked at the 

20 variograms for that particular sampling interval, we 

21 essentially had a Nugget. There was no real temporal 

22 correlation. 

23

24 I was trying to squeeze something out of nothing, to be 

25 quite honest with you. So what I did is I went back and 

26 took the flow data that we actually had for that site, fit 

27 a variogram to it and then used that variogram in the 

28 stochastic conditional simulation. So that is probably 

29 one of the reasons why it does not fit, I would guess. I 

30 don't know.

31
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1 DR. NELSON THURMAN: And this is one reason why we knew needed 

2 to do something else for the 14- and 28-day intervals.

3

4 DR. JAMES HETRICK: Actually, Nelson, can you put up the 

5 Missouri site, the conditional simulations? Actually, 

6 that is a representation, a 28-day is essentially a Nugget 

7 for that 28-day, and that is what we would have probably 

8 seen in that 28-day for the Maumee River if we had put a 

9 Nugget model simply in the stochastic simulation.

10

11 DR. KENNETH PORTIER: On that same graph, how does the program 

12 handle potential negative values? I mean, are you just 

13 truncating at zero; are you censoring at zero?

14

15 DR. JAMES HETRICK: Yes. You can truncate at zero. It does 

16 truncate at zero. And in addition to that, which is kind 

17 of interesting -- I do not know that it would be 

18 interesting to get SAP's input on -- you can also 

19 extrapolate to a maximum value and it can be higher than 

20 the value that is in your dataset.

21

22 So one idea, at least in my mind, was to take the bias 

23 factors that we have for a certain sampling interval and 

24 we could multiply that out from the peak and be able to 

25 put in a maximum value to make sure that we are not 

26 underestimating.

27

28 DR. RICHARD COUPE: I am sorry if you have already discussed 

29 this. Let me know and we could talk about it later; but 

30 explain to me how the bias works, how you plan on applying 

31 it.

32
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1 DR. NELSON THURMAN: What we were looking at is exploring 

2 whether we could take a bias factor and -- let's say a 28-

3 day tox window of exposure window. And so, we come up 

4 with -- based on weekly sampling, we estimate here is your 

5 maximum 28-day rolling average concentration. The idea 

6 would be we'd take that bias factor where you'd go to the 

7 table and look up where you have 28-day duration window 

8 and your 7-day sampling intervals and multiply that 

9 exposure by that factor.

10

11 Now, I do want to point out that if we took that approach, 

12 it would be difficult to do what we are doing in a case 

13 study, which is folding that time series into the case 

14 study to get an internal dose. So that is one of the 

15 reasons why we were looking at that as one approach, but 

16 also looking at it in terms of creating that time series; 

17 filling in between. 

18

19 DR. KENNETH PORTIER: If you look at the 4-day interval and you 

20 mentioned that you use common kriging, and I was sitting 

21 there thinking, you know, this is really just time series 

22 modeling, right? In time series modeling; you have a mean 

23 pattern and you have variability. It is structured 

24 variability in this case. You are putting on a variogram 

25 on autocorrelation function. You are saying that the 

26 residuals -- so in my thinking on common kriging, that 

27 mean is usually just a mean. It is not a function of 

28 time. It is just a constant. Am I correct? 

29

30 I was thinking in what they call generalized kriging, you 

31 are actually smoothing, so your mean is a function of 

32 time. You are pulling a little bit of the pattern out and 
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1 then you are looking at residual variability. And if you 

2 were doing generalized kriging, I would think that‟s what 

3 you would get because you're really following pretty much 

4 the general pattern in your simulations. They are all 

5 kind of following the pattern over time with some noise. 

6 So your simulations create a bunch of noisy patterns that 

7 are about that general pattern. So maybe I am kind of 

8 confused between common and generalized kriging.

9

10 DR. JAMES HETRICK: Well, let me go back and tell you how we 

11 did the analysis and then I may straighten -- it may 

12 either cloud up the issue or may clear up the issue; I 

13 don't know. But the bottom line is, what we did is 

14 essentially -- the data that we used to build the 

15 variogram off of is the same data that we used in the 

16 conditional simulation without any type of in-filling. So 

17 the missing data points there are strictly estimated using 

18 a stochastic approach, okay? We did not use the best 

19 linear unbiased estimate for estimating those missing 

20 values. 

21

22 DR. DANIEL GRIFFITH: When you did the variogram analysis, you 

23 did not use the log transformation? You did it on the 

24 original?

25

26 DR. JAMES HETRICK: No. I did a log transformation in the 

27 variogram analysis.

28

29 DR. DANIEL GRIFFITH: Okay. So the back transformations would 

30 not have negative values; is that right?

31
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1 DR. JAMES HETRICK: That is probably true; yes. By the way, 

2 the conditional simulation was done using a z-score 

3 approach, so those were normalized through that program. 

4

5 DR. KENNETH PORTIER: I want to remind Nelson that we talked at 

6 lunch, on the next slide -- I think it is the next slide -

7 - where we talked about the five percent and 95 

8 percentile, and I was going to ask you to kind of remind 

9 us because it is not clear in the write-up exactly what 

10 the five percent and 95th percentiles really mean.

11

12 DR. NELSON THURMAN: Okay. What we did is, we did those 

13 conditional simulations, a thousand simulations. And 

14 essentially what we did is we went across each day of the 

15 simulation to come up with the 95th and 5th percentile. 

16 So, in effect -- we were discussing -- you are going to 

17 end up with a wider range because you are looking at that 

18 percentile for each day.

19

20 DR. KENNETH PORTIER: So they did a thousand simulations, then 

21 you take one day and you look across a thousand simulated 

22 values for that one day and you get the five percentile 

23 and the 95th percentile. And what that tells me is that 

24 it is 95th percentile simulated curve there is not any one 

25 of the patterns that you simulated; it is really the worst 

26 or the worst, right? And the 5th percentile is kind of 

27 the fifth best of the best.

28

29 DR. HERBERT LEE: So, I want to clarify now because I am a 

30 little confused. When you did the conditional 

31 simulations, what scale did you do them on; the log scale 

32 or the original scale, the z-scores?
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1

2 DR. JAMES HETRICK: We used the z-score for the -- that‟s in 

3 the 6M. 

4

5 DR. DANIEL SCHLENK: Any other questions? Clarifications? 

6 Okay. Let's move on to our next presentation. Dr. 

7 Mendez, your third of three there.

8

9 DR. ELIZABETH MENDEZ: Good afternoon. Yes, three of three. 

10 Now I can sit back and enjoy the discussions later on. So 

11 throughout the day, you have heard a lot of discussion 

12 about mode of action, about epidemiology, about 

13 pharmacokinetics, water monitoring now.

14

15 In addition to all of those things that we typically have 

16 to evaluate for all chemicals, for pesticides, we have a 

17 unique situation that we have a statute that speaks to us 

18 about the sensitivity of infants and children. So in this 

19 presentation, what I am going to do is to describe the 

20 state of the science with respect to the potential for 

21 pre- and/or post-natal toxicity and the completeness of 

22 data with respect to toxicity in infants and children.

23

24 So as I just said, we have a statute that governs 

25 pesticide regulation, and perhaps we would start with that 

26 so that we are all on the same page.

27

28 The Federal Food, Drug and Cosmetic Act, as amended by the 

29 Food Quality Protection Act in 1996, requires the agency 

30 to give special attention to the potential risk to infants 

31 and children. Specifically, FQPA instructs EPA in making 

32 its "reasonable certainty of no harm" finding that in "the 
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1 case of threshold effects, an additional tenfold margin of 

2 safety for the pesticide chemical residue and other 

3 sources of exposure shall be applied for infants and 

4 children to take into account potential pre- and post5 natal toxicity and completeness of data with respect to 

6 exposure and toxicity to infants and children."

7

8 Section 408(b)(2)(C) further states that "the 

9 Administrator may use a different margin of safety for the 

10 pesticide chemical residue only if, on the basis of 

11 reliable data, such margin will be safe for infants and 

12 children." 

13

14 This additional margin of safety is referred to as the 

15 "FQPA Safety Factor." So in essence, all these words, 

16 what they mean is, when we start looking at a pesticide, 

17 we start out with a 10x FQPA factor that is automatically 

18 on, and then we have to, based on the data that we have in 

19 front of us, we may reduce it; we may change it but it has 

20 to be, from the get-go, we start with a 10x.

21

22 Now, this talk about both exposure and toxicity, but as 

23 Nelson just finished with his presentation, we are still 

24 dealing with the exposure uncertainty so I am not going to 

25 really address that in this talk. I am going to 

26 concentrate on hazard and toxicity. The hazard 

27 considerations; the important issue is do we have 

28 available data to assess critical life-stages? And if you 

29 remember from my talk earlier this morning, I said that 

30 when we have a neuroendocrine mode of action like the one 

31 we have for atrazine, this becomes a particular critical 

32 issue because the hormonal environment changes 
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1 significantly between life-stages, so that is something 

2 that we really have to be very cognizant of as we move 

3 through our process.

4

5 We have to consider all the relevant information, mode of 

6 action obviously, the animal database of toxicity studies, 

7 the dose response relationships, the human relevance of 

8 the animals findings and, last but not least, epidemiology 

9 findings.

10

11 So back in 2003 when we came to the SAP, in 2000 actually, 

12 the neuroendocrine mode of action was determined to be 

13 relevant for reproductive and development effects, even 

14 though it is not for the mammary gland tumor development. 

15 And during the epidemiology findings that were discussed 

16 in the September SAP meeting, what we heard from the panel 

17 and what the agency concluded was that there is 

18 qualitative information on human relevance of the animal 

19 findings, but that they were not sufficiently robust to 

20 establish a causal associations.

21

22 So, we were seeing things that were somewhat similar in 

23 epidemiology data to what we were seeing in the toxicity 

24 data. So we felt that we could still believe our premise 

25 that these findings were relevant for human risk26 assessment.

27

28 So let's talk about what we do have in terms of 

29 experimental toxicity studies. We have core guideline 

30 toxicity studies. These are a developmental toxicity 

31 studies in two species; rat and rabbit. And for those who 

32 don't know how our guideline studies are done, those 
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1 studies usually start with exposure during the 

2 implementation time, usually around gestation day 6 

3 through gestation day 21 when the animals are sacrificed 

4 and their fetus' are examined. 

5

6 And we have a multi-generation reproduction study. That 

7 study actually -- exposure starts 10 weeks prior to mating 

8 of the animals. It continues throughout mating, gestation 

9 and lactation, and so the pups are exposed in utero and 

10 also through lactational exposure postnatally; and that is 

11 what we typically get from most pesticides.

12

13 But in addition to that, in the case of atrazine, we have 

14 a rather robust dataset of specific and special studies 

15 that concentrate on specific life-stages, mainly 

16 gestation, perinatal, peripubertal and reproductive age.

17

18 And when we last visited the SAP before this re19 evaluation, one of the things that we heard from the panel 

20 was we need a little bit more information on the 

21 peripubertal period. 

22

23 Now, now we have all this data, and thus far, none of the 

24 available studies have provided us an endpoint lower or 

25 more sensitive than the studies on LH attention in the 

26 adult female rat. So I am just going to go quickly 

27 through this.

28

29 Now, we have a lot more studies than these, but I have 

30 just selected the ones that are the most sensitive within 

31 the datasets. And as you can see, we have for gestational 

32 exposure, the lowest NOAEL that we have is 10, with a 
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1 LOAEL of 50, and that is delayed preputial separation 

2 after exposure from gestation day 14 through parturition. 

3 And from Fraites et al., data that was in Davis et al., 

4 data that came in around April of this year, the NOAEL is 

5 20 and the LOAEL is 100. 

6

7 And we have the Fraites et al. is looking at males. The 

8 Davis et al. is looking at females and their siblings. 

9 The endpoints are decreased pup weight and pup viability 

10 and delay in VO, vaginal open in the Davis et al. dataset; 

11 and the exposures were from gestation day 14 through 21. 

12

13 Perinatal exposure we have data from Stoker et al. in 

14 1999, and this is postnatal day 1 through 4 and postnatal 

15 day 6 through 9. And the lowest NOAEL in there is at 12.5 

16 and a LOAEL of 25 and the effect is prostatitis. 

17

18 Peripubertal exposure, which was the period that initially 

19 we needed more information on, we got more information and 

20 then some. But you can see that the lowest that we have 

21 is a delay in preputial separation with a NOAEL of 6.15 

22 and a LOAEL of 12.5.

23

24 Now what I want to remind you is that, when we do a 

25 benchmark dose analysis for the LH attention after four 

26 days of exposure with Cooper et al. data, the BMDL is 

27 2.56.

28

29 In addition to these studies, we recently received the 

30 completed multiple life-stage study from Syngenta. This 

31 is an exposure encompassing prenatal, postnatal, 
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1 peripubertal and adult-life-stages. It evaluated female 

2 offspring only. 

3

4 The doses were 0, 6.5, 25 and 50, and no effects on LH 

5 were seen at doses of 50 milligrams per kilogram or less, 

6 and that is kind of inconsistent with a preponderance of 

7 the peer review literature where we are seeing things 

8 starting to happen with LH around 6-ish or even lower, 

9 depending on the duration, more like 3.

10

11 To be honest, we have looked at that data and we can't 

12 quite figure out why that inconsistency is happening. It 

13 appears to be a well conducted study over all, so we are 

14 still puzzled by that.

15

16 What they do see is a slight delay in vaginal opening or 

17 delay of 1.4 to 2.3 days at 50 milligrams per kilograms 

18 per day. And what is interesting about that is that this 

19 delay is seeing if the exposure started in utero, but not 

20 if it started postnatally. So let me just kind of quickly 

21 flash up the study design so that you can see how that 

22 goes.

23

24 Cohort 1 in Subset A; you have exposure from conception 

25 gestation day 0 through lactation day 0 of the dams, then 

26 from postnatal day 21 through the time of blood collection 

27 and necropsy. So for B you have exposure again from the 

28 day of conception through postnatal day 133, C from 

29 conception to postnatal day 133 and then you have a 

30 recovery period, and then for Cohort 2; D, E and F, 

31 exposure starts on a post-weaning on postnatal day 21, and 

32 you have a similar paradigm. 
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1

2 What is interesting here is that some of these exposure 

3 paradigms are similar to others that have been seen in the 

4 literature where we do see the LH attenuation.

5

6 So the FQPA safety analysis, the summary; what we have is 

7 the data available spanning life-stages from conception to 

8 adulthood. There does not appear to be any evidence of a 

9 unique quantitative susceptibility in the developing 

10 organism. The reproductive and development effects that 

11 we are seeing are consistent with the perturbations of the 

12 HPG axis and the decreases in LH that we are proposing to 

13 use as a sentinel key event in atrazine's neuroendocrine 

14 mode of action that leads to these adverse outcomes in the 

15 rat. 

16

17 The effects that we see, none of the doses are lower than 

18 those eliciting the LH surge attenuation in the adult 

19 female rats with BMDL of 2.56; and with that, just a quick 

20 overview.

21

22 I did not go into a great deal of detail about the 

23 experimental data because we discussed that quite a bit 

24 during the September SAP meeting, so I just wanted to give 

25 a general broad overview. With that, I will stop.

26

27 DR. DANIEL SCHLENK: Thank you, Dr. Mendez. Any questions on 

28 safety factor evaluations? Wow.

29

30 DR. ROBERT GILLIOM: So what was the conclusion about whether 

31 the safety factor was appropriate or not?

32
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1 DR. ELIZABETH MENDEZ: Well, at this point in time, we have not 

2 finished our evaluation and exposure, and the FQPA safety 

3 factor looks at both things. But from a hazard 

4 standpoint, it seems that we don't have any sensitivity in 

5 the young. 

6

7 DR. TRAVIS JERDE: So you repeated again the results of the 

8 Stoker paper from '99, but last year in December the EPA 

9 group had put out a group, lead author was Stanko. Have 

10 you seen that? Because it looked like the atrazine 

11 metabolites were effective in inducing prostatitis semen 

12 as low .87. Are you familiar with that work?

13

14 DR. ELIZABETH MENDEZ: Yes. We are familiar with that. When 

15 you discussed that paper back in September, the agency had 

16 some concerns regarding the conduct of this study and how 

17 reliable it could be for selection of a point of 

18 departure. And during the September SAP meeting the panel 

19 agreed that there were some significant issues with the 

20 paper at that point.

21

22 DR. TRAVIS JERDE: Okay. And yet they published it and it came 

23 out in December. Okay. 

24

25 DR. DANIEL SCHLENK: Okay. Are there any other questions for 

26 Dr. Mendez? Okay. Due to the massive consumption of 

27 caffeinated beverages, I would suggest that we take a 

28 break right now. I have got 2:32 according to my watch, 

29 so let's try to get back at maybe 2:45. Okay? We will 

30 re-adjourn at 2:45.

31

32
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1 DR. DANIEL SCHLENK: Let's go in with our last presentation by 

2 the agency. This will be made by Dr. Rodriguez who has 

3 not elevated his self to Dr. Mendez's status in terms of 

4 three, but is doing number two -- still up there though --

5 and he is going to be talking about some case studies to 

6 sort of put some of these applications in better light 

7 there. Okay? Dr. Rodriguez?

8

9 DR. CHESTER RODRIGUEZ: Thanks very much. I just wanted to 

10 mention that this is going to be a tac-team effort. 

11 Nelson Thurman is going to be speaking with me here. 

12 So the title of the presentation is Case Studies, and this 

13 is where we actually apply the one linear compartment 

14 model to inform water monitoring.

15

16 So this is what the outline is. I am just going to give a 

17 brief summary of how did we get here. And then we are 

18 going to go over the proposed water monitoring durations; 

19 that is going to be done by me. And then I am going to 

20 turn that over to Nelson who is going to be going over the 

21 water exposure estimates based on two datasets that he 

22 previously talked about. 

23

24 Then he is going to turn it back to me and I am going to 

25 be presenting how we take that information and try to come 

26 up with estimates of human plasma area under the 

27 concentration time curve for plasma triazines from his 

28 drinking water exposure estimates. And then I am just 

29 going to end with a quick summary.

30

31 So how did we get here? Well, got here from the four-day 

32 study in the endpoint LH attention, which does not seem to 
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1 be a single dose effect? Okay? It seems to be a repeated 

2 dosing effect. And this is the same slide that I showed 

3 you before with the 4-day study with a LOAEL of 3.12 that 

4 is based on repeated daily dosing for four days. 

5

6 We tried to understand what the pharmacokinetics of 

7 atrazine that would correspond to this study would be, and 

8 for that we turned our attention to the Thede 1987 study 

9 because it actually resembled the 4-day study by Cooper et 

10 al. in that it involved repeated daily dosing for at least 

11 4-days. It used intact young female rats, and you also 

12 had plasma measurements that were taken daily. And the 

13 dosing frequency, I should say, was the same. So these 

14 animals were dosed daily with atrazine, once daily at 

15 different dose levels.

16

17 So one of the things that we noted from the plasma profile 

18 from the Thede study is that we saw linear 

19 pharmacokinetics. And actually, what I mean by that is 

20 that the internal dose metric, plasma AUC, is scaled in a 

21 linear fashion with atrazine dose at all dose levels 

22 tested. And also, we see elimination kinetics for plasma 

23 triazines. And as I said before, we were able to analyze 

24 two additional studies that actually support our findings 

25 for the elimination rate constant or the plasma half life 

26 for plasma triazines.

27

28 So based on these findings, then, we proposed a one29 compartment linear model. It is very simple. It is only 

30 based on two parameters; the elimination rat constant and 

31 the volume of distribution. But we think that it is 
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1 informative because it takes into account information on 

2 body distribution and elimination rate.

3

4 We show in my other presentation that we can predict 

5 plasma levels that will correspond to the rat study using 

6 the one model linear expression. And we also want to 

7 extrapolate this one compartment model to humans using 

8 allometrically scaled volume of distribution elimination 

9 rate constant.

10

11 Now, we are going to walk you through this. And the 

12 critical part more than anything is the estimation of the 

13 human dose rate which, like I showed before, actually 

14 depends on the water levels as well as the consumption 

15 rate. So the product of these two will give us an 

16 estimate of a human dose rate for a given duration. 

17

18 In terms of the range of windows of exposure, we are 

19 proposing three durations. We are proposing 4, 14 and 28 

20 days. And the rationale for these is as follows. The 4-

21 day duration, that duration is based on the accumulation 

22 of plasma triazines to a plateau, or pseudo steady state 

23 level, which seems to be related to optimal LH attention. 

24 It should be noted that this 4-day duration is based on a 

25 constant dose level and frequency. 

26

27 So, based on this condition, the time to steady state in 

28 the rat is four days. Four days just happens to be also 

29 the length of the rat estrous cycle. Twenty-eight days is 

30 within the, at the allometrically scaled time range, for 

31 adult humans of 60 kilograms to reach steady state based 

32 on a constant dose level and frequency. And actually, 28 
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1 days also happens to be the average length of the human 

2 menstrual cycle. So we have four and we have 28 days.

3

4 We also are proposing to include 14 days just to serve as 

5 a mid-point, given that the other two durations are on the 

6 extremes. And it also seems to be reasonable to include, 

7 given that human exposure is neither going to be at a 

8 constant dose level nor constant frequency. So, those are 

9 our proposed durations and I am going to turn this over 

10 now to Nelson.

11

12 DR. NELSON THURMAN: Okay. I should be able to do this pretty 

13 quickly since we just got finished talking about what we 

14 did here. 

15

16 We took the two monitoring datasets that we talked about 

17 earlier, the Maumee River, the 1995 monitoring dataset, 

18 and the Missouri-01 from 2007. We simulated the 7-day 

19 sampling intervals for this data, then we used the 

20 geostatistical methods to develop a variogram model that 

21 captures the covariance of monitoring data based on the 7-

22 day sampling intervals.

23

24 We ran the 1,000 conditional simulations of that variogram 

25 model to estimate the daily time series from the 7-day 

26 sampling. We bracketed it with the 5th and 95th 

27 percentiles. As Dr. Portier pointed out is that we 

28 calculated those percentiles and we looked each day, so 

29 they may be a wider range than you may have if you looked 

30 at the overall simulation, but that was one thing we 

31 wanted to take a look at.

32
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1 Once again, you just saw this in the last present, the 

2 Missouri-01. We want to point out, we did the daily time 

3 series. We derived the 4-day rolling averages, which is 

4 what we used for representing the 4-day window. Those 

5 would be used for representing the 14- and 28-day windows 

6 in that regard.

7

8 So there's the Missouri sites, there is the Maumee River 

9 sites and those are in your slides so you do not have to 

10 leap back through mine for a reference. And at this 

11 point, I am going to turn this back over to Chester and 

12 let him carry it the rest of the way through.

13

14 DR. CHESTER RODRIGUEZ: Thanks. So this is how we come up with 

15 estimates of human equivalent plasma AUC from atrazine 

16 concentration in drinking water. We basically applied the 

17 expression that I showed before, based on a one18 compartment linear model.

19

20 So what you have here is the average atrazine level for a 

21 given duration, whether it is 4, 14 or 28. We have a 

22 water consumption rate which, in this case, we are 

23 assuming two liters per day for an adult human, a body 

24 weight of 60 kilograms. And we have the elimination rate 

25 constant in the volume of distribution, both of which have 

26 been allometrically scaled from the adult rat values.

27

28 It should be noted that when you take this estimate of the 

29 human plasma average daily AUC and you compare that to the 

30 rat plasma, point of departure value for the endpoint LH 

31 attention, you will get an estimate of the margin of 

32 exposure that will be based on internal measures now.
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1 So this is similar to what we are used to doing when we 

2 have a NOAEL here and we compare that to human exposure. 

3 It is actually very similar, but in this case it is going 

4 to be based on internal measure of exposure.

5

6 So what you have on the Y axis here are the human plasma 

7 AUC estimates based on the water exposure values that 

8 Nelson showed. It is not surprising that the pattern is 

9 actually the same, because what you have is a linear 

10 relationship between water exposure and human plasma 

11 values. It is a one compartment linear model, so it is 

12 not surprising then that you have a similar pattern.

13

14 Just for reference, the dash lines here represent the BMDL 

15 rat plasma AUC with a combining certain factor of either 

16 300x or 100x. And as you can see, that the general trend 

17 here for the 2007 Missouri-01 dataset is that, as a 

18 duration of exposure gets shorter from 28 to 14 to 4, then 

19 you have a higher probability of exceeding this reference 

20 values based on this dataset.

21

22 For the Maumee 1995, this is the results that we get. 

23 Basically, the water levels that Nelson actually estimated 

24 were actually very low and the human plasma values are 

25 actually correspondingly very low, so they will not exceed 

26 the rat point of departure value. There is a linear 

27 relationship.

28

29 So this is just a quick summary. This is a short 

30 presentation. I wanted to summarize just a few points 

31 about the approach that we are using. It is actually 
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1 based on an internal dose metric, plasma AUC, which is 

2 consistent with temporality of the endpoint, LH attention.

3

4 One of the reasons why we selected this internal dose 

5 metric is that it accounts for magnitude of exposure as 

6 well as duration. Duration seems to be critical for the 

7 endpoint and we feel that the one compartment model is 

8 actually based on the best available science in the 

9 absence of a fully calibrated and evaluated PBPK model. 

10

11 We think that this model is simple and incorporates an 

12 average dose rate for a given duration, it incorporates 

13 body distribution information as well as plasma clearance. 

14 So we think this is the best science right now that we can 

15 use.

16

17 And it is not surprising that the water levels, you know, 

18 the higher the predicted human plasma levels for a given 

19 duration, because what you have is a linear relationship, 

20 is a one compartment linear model. And with that, I am 

21 going to stop talking and try to address questions.

22

23 DR. DANIEL SCHLENK: Thanks, Dr. Rodriguez and Mr. Nelson; 

24 appreciate it. Any questions or clarification? Yes, Dr. 

25 Hayton?

26

27 DR. WILLIAM HAYTON: The human volume of distribution, you 

28 scaled that but did you use the rat? -- liter per 

29 kilogram, you assumed it to be the same, right?

30

31 DR. CHESTER RODRIGUEZ: Yes, you are correct. Yes. So we took 

32 the rat volume of distribution value on a liter per 
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1 kilogram body weight basis, and we scaled that to the 

2 corresponding human body weight.

3

4 DR. WILLIAM HAYTON: Okay. And then the elimination rate 

5 constant value was about 1/4th of the rat value in the 

6 human, something like that. That is about how it is 

7 scaled out. But the monkey -- you know we talked about 

8 that this morning. The monkey actually went the other 

9 way. It got bigger; not smaller, but I guess you do not 

10 use that information in this scaling.

11

12 DR. CHESTER RODRIGUEZ: But the monkey study; I mean, you just 

13 have to go by what the science gives you. And what it 

14 gives you in the monkey is that the internal dose metric 

15 is like it scales directly with atrazine dose and the 

16 elimination is linear. 

17

18 We cannot explain why it goes the other way, but you have 

19 to go by weight of evidence. We have multiple rat 

20 studies. Rats are a species where the endpoint has been 

21 characterized, not in monkeys. So based on that, we do 

22 the best we can.

23

24 DR. KEVIN O'BYRNE: I mean, I like the simplicity of your one 

25 compartment model but do we have some idea as to where 

26 atrazine and its metabolites sit within that? I mean, 

27 does it sit in fat? Does it sit in some other tissue?

28

29 DR. CHESTER RODRIGUEZ: Yes, indeed. So let me just say that 

30 the condition of pseudo steady state also supports the one 

31 compartment model which, by definition, assumes that all 
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1 the other compartments, all the tissues are at 

2 equilibrium. So do you follow that?

3

4 DR. KEVIN O'BYRNE: But is that realistic? I mean, I don't 

5 know. Does it have a tensity to sit in fat, for example?

6

7 DR. CHESTER RODRIGUEZ: Well no, actually. But the site of 

8 action is in the brain and we think the plasma is a good 

9 surrogate because that is how chemicals travel in the 

10 body.

11

12 DR. KEVIN O'BYRNE: The brain is full of fat.

13

14 DR. CHESTER RODRIGUEZ: Yes, but there is no accumulation in 

15 fat. There is no significant accumulation in fat.

16

17 DR. KEVIN O'BYRNE: So that is known?

18

19 DR. CHESTER RODRIGUEZ: Yes. Let me just say that there is 

20 accumulation in red blood cells, you know, apparently up 

21 to 1.5 percent of the dose actually accumulates in red 

22 blood cells, but there is no red blood cell toxicity that 

23 we can talk about.

24

25 So, it is like we think that, at this point in time, this 

26 is what the science has actually given us. This feature 

27 of linear kinetics I think we should take advantage of 

28 because it simplifies everything. You know. What you 

29 have is a constant half-life that is independent on those. 

30

31 DR. DANIEL GRIFFITH: If you go to slide number 9. Clearly in 

32 the 1-day, and even in the 4-day, you see that there is a 
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1 breaking of the threshold line. So why wouldn‟t you be 

2 adjusting that threshold line as you do increasing 

3 smoothing to try and make an adjustment for what is 

4 happening with the smoothing of the data?

5

6 DR. CHESTER RODRIGUEZ: If I understand your question 

7 correctly, you are asking about the reference values that 

8 we are showing, correct?

9

10 DR. DANIEL GRIFFITH: Right. 

11

12 DR. CHESTER RODRIGUEZ: Okay. That is just for a reference 

13 value just to compare how values will become relative to 

14 the rat point of departure.

15

16 DR. ELIZABETH MENDEZ: Let me just clarify a little bit about 

17 the BMDL and the 300. The BMDL comes from the point of 

18 departure from the study, so that is standard throughout. 

19 The 300 factor comes from the fact that, in the current 

20 risk-assessment from 2003, the uncertainty factor is 300, 

21 10x for intraspecies, 10x for interspecies and the 3x that 

22 was retained as an FQPA factor showed where those 

23 reference values come from.

24

25 DR. NELSON HORSEMAN: On that point there; you said you are 

26 using a 3x FQPA, not the 10x? 

27

28 DR. ELIZABETH MENDEZ: Correct.

29

30 DR. JAMES MCMANAMAN: So I have concerns about using the rat 

31 data as a model, and I understand what you are trying to 

32 do. At least, I think I understand what you are trying to 
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1 do, is trying to see how well you can model both the water 

2 modeling and the animal pharmacodynamics. But maybe the 

3 point of departure and dose for a human is not the same as 

4 it would be for a rat, but it might be closer if you chose 

5 a monkey model.

6

7 And so, if you are going to use this as a way of setting 

8 policy, I am a little concerned about using those values. 

9 And I am also concerned about the fact that you really do 

10 not know very much about how the atrazine is metabolized. 

11

12 I mean, you know a lot about it in the rat but not so much 

13 in a primate model. So, all those things are going to go 

14 into helping you set standards, but it seems to me you are 

15 lacking critical information and, from my perspective, I 

16 would urge you to try another animal model, in addition 

17 to the rat, to see if you can get a more closely modeled 

18 what is going to be going on in the humans.

19

20 And then, in regards to the water, there is a huge 

21 variation in the Maumee data and the Missouri data in 

22 terms of the Y axis. I mean, it looks like there was a 

23 four- or five-fold difference. Presumably, that was 

24 because of where the catchment area was, so wouldn't that 

25 also have to be factored into your model as having a 

26 standard catchment area at the headwaters or wherever? 

27 And maybe I just did not understand that. But I think 

28 that all that would have to go into whether you would 

29 break the bar in terms of being in a critical range to 

30 potentially have some adverse outcome.

31
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1 DR. NELSON THURMAN: Let me clarify. I mean, first of all, 

2 these are not drinking water monitoring. Those take a 

3 look at how well the simulations would play into this. 

4 And I think what we would be doing is we'd be looking at 

5 each individual community water system in terms of 

6 providing those estimates for each of the community water 

7 systems, so it wouldn't be breaking down a catchment size 

8 in that regard. We would actually apply this approach to 

9 the weekly sampling for each system. 

10

11 DR. ROBERT GILLIOM: So you helped a lot explaining where the 

12 benchmarks came from for illustration and I might've just 

13 missed something in what is written, but how are you going 

14 to actually determine what the actual threshold from all 

15 this information is? Because you kind of were right on 

16 the brink of just saying, based on all this stuff, this is 

17 what the threshold is and therefore here is what the water 

18 level, but you are not quite saying it. So I'm just 

19 curious where that fits in.

20

21 DR. ELIZABETH MENDEZ: All right. Let me try and see if I can 

22 explain this a little bit more clearly. So at this point 

23 in time, we have our point of departure that is pretty 

24 solid. Now, in regards to the uncertainty factor, what 

25 you saw today from Nelson and from Chester is a proposal 

26 in a case study. We have an enormous amount of data that 

27 we need to go through before we can make a final call on 

28 what the factor is.

29

30 Now, in this particular instance, what you are seeing in 

31 this slide for instance is, if your critical window of 

32 exposure is four days -- let's just say that -- and you 
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1 are sampling every seven days, then there are some 

2 instances, not very many, when you are going to be 

3 exceeding that benchmark dose divided by a factor if the 

4 factor were to be retained at 300 as it is right now.

5

6 If once we have looked and done the full-blown analyses on 

7 the exposure, from the hazard standpoint, it appears that 

8 we may not need to factor. But that could change if new

9 data were to become available. But at this point in time, 

10 we are just going with the 300 that is currently, so to 

11 speak, on the books, but we will make that determination 

12 once we have fully analyzed all of the data.

13

14 DR. CHESTER RODRIGUEZ: I just want to go back to the monkey 

15 study question that you raised. You know, this is state 

16 if the science right now, but there is more data coming 

17 out for atrazine. My understanding is that the Syngenta 

18 is actually doing monkey study with radiolabeled atrazine. 

19 So in the future we should be able to get more 

20 information. This is all we have right now.

21

22 DR. RICHARD GREENWOOD: On this slide that is on the screen at 

23 the moment, is my interpretation correct that where the 

24 red line, which is the area under the curve, breaches the 

25 limit, then that is when you have exceeded the critical 

26 exposure for the effect? 

27

28 So when you've managed to get the peaks fairly well 

29 defined when you do daily sampling, then it is 

30 approximately 10 days out of that whole sampling period 

31 that it has exceeded the critical exposure, but only for 

32 10 days. So if the critical exposure for humans is longer 
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1 than 10 days, you have not actually exceeded it. Is my 

2 interpretation correct?

3

4 DR. CHESTER RODRIGUEZ: Yes. Let me try to make sense out of 

5 this. So all the lines here are actually human plasma AUC 

6 estimates, okay? This line here is a 95 percent 

7 confidence interval. That is the worse case scenario: 

8 someone said worst of the worst, you know.

9

10 This lower line here is a 5th percentile and the black 

11 line is the actual data based on 4-day rolling averages, 

12 14, 28, et cetera. So the trend that we are seeing here 

13 is that the shorter the duration of concern -- whatever 

14 that is -- the higher the likelihood of exceeding a given 

15 reference value. That is a take-home message.

16

17 DR. ROBERT GILLIOM: Sorry, one last thing to make sure I have 

18 it right. So because it is a simple linear model linking 

19 plasma to water, if you translate that graph into water 

20 concentrations, which is on the previous slide there, you 

21 are talking about roughly 50 micrograms per liter as the 

22 4-day moving average, something like that. It does not 

23 matter the exact amount.

24

25 So where this would end up going, whether the 300x stays 

26 the same or whatever you do to policy-wise change that, we 

27 could just visualize that being translated back into a 

28 particular rolling average duration level like 50 or 40 or 

29 30 or whatever you come up with.

30

31 DR. NELSON THURMAN: Yes, that is correct. And once again, we 

32 are just pointing out that this is not community water 
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1 system monitoring. The concentration that we have here 

2 are -- except for a couple of sites where the monitoring 

3 was in a source that does not necessarily feed into --

4 these concentrations are higher than what we have seen in 

5 the community water system. 

6

7 It was a scoping exercise in that regard to see what 

8 happens if we push something that we think may be at or 

9 around a duration concern. By doing this analysis, would 

10 be able to catch that if it did indeed occur? So I look 

11 at it more of a scoping exercise. 

12

13 From my point of view, looking at it a different way, is 

14 that if the duration of concern -- the window tox exposure 

15 is four days, then I need to pay more attention to the 

16 frequency of sampling than if it is 28 days. So that is 

17 another way for us to look at it in that regard as well.

18

19 DR. DANIEL SCHLENK: Any other questions, clarification? At 

20 this point, what I will state to the panel is what we 

21 would like to do is, maybe tomorrow after we have had a 

22 night to stew on the plethora of data, that you guys would 

23 be available for a final questioning period before we 

24 actually begin the charge questions, if that is okay. 

25 All right, with that I guess we will conclude the agency's 

26 presentation and move on to the public comment period. 

27 Our first presenter is the Syngenta Group, which I believe 

28 Dr. McFarland is going to sort of lead off with an intro, 

29 I believe.

30

31 DR. DANIEL SCHLENK: Just for the panel's knowledge, you are 

32 going to get copies of part of the presentations, 
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1 initially, and more will be flowing in as the presentation 

2 goes on, so we are getting hardcopies of the 

3 presentations. Dr. McFarland, are you going to introduce 

4 everybody or -- just make sure that they introduce 

5 themselves as they go through; that'd be great.

6

7 DR. JANIS MCFARLAND: Yes. Thank you. Thank you, Dr. 

8 Schlenk and thank you very much to the panel. I am Janis 

9 McFarland, head of Regulatory Affairs North America for 

10 Syngenta Crop Protection. Syngenta Crop Protection is a 

11 research and development company that discovers and 

12 develops herbicides, fungicides and insecticides.

13

14 We are also a world's producer and breeder of seeds. Many 

15 people don't know we are the number one Pansy flower 

16 producers around the world, and we also produce sweet corn 

17 seeds to make tomato seeds. And we introduce about a 

18 hundred new ornamental flower varieties every year.

19

20 We would like to thank the panel for the opportunity to be 

21 here. We greatly appreciate, and we'd like to express our 

22 sincere thanks to both the panel for their work as well as 

23 to all the scientists at EPA for the amazing amount of 

24 work that has gone into the atrazine assessment over the 

25 past year and a half, and then also prior to that.

26

27 Syngenta has listened closely through the SAP process to 

28 the questions, the suggestions, and also to the 

29 recommendations that the science advisory panels have 

30 provided. And we have responded by conducting many new 

31 studies and assessments that we provided to EPA and 

32 through the docket process of the Science Advisory Panel.
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1

2 The basic research that has been conducted on atrazine 

3 both recently and over the last two decades have led to 

4 several improvements in methodologies, databases, study 

5 designs and also risk-assessments.

6

7 As part of the SAP process in 2010 and 2011, Syngenta, 

8 with the help of several university experts as well as 

9 external scientific experts, have submitted 15 new 

10 toxicology and mode of action studies, more than two dozen 

11 reports with different statistical analysis and water 

12 monitoring, and we have also developed at new PBPK model 

13 that we will be discussing later on today and that was 

14 spoken about earlier by EPA.

15

16 We have developed the framework and published an 

17 assessment of how to assess epidemiology and toxicology 

18 data, and we have provided several summary reports on a 

19 broad range of the key aspects of toxicology and exposure.

20

21 At the same time, we are continuing to monitor 88 

22 different community water systems with weekly monitoring 

23 data, and we have continued a second year of an extensive 

24 and comprehensive ecological monitoring in smaller 

25 streams, first- and second-order streams for the 

26 ecological assessment which involves daily monitoring, and 

27 some of that you also saw earlier in the EPA presentation.

28

29 We have several stewardship projects that are going on in 

30 both environmental, putting in buffers. One of our 

31 projects, we have planted a million and a half trees 

32 along the side banks of streams in Iowa and Illinois 
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1 working with a non-profit to reduce runoff of pesticides, 

2 fertilizers and segment into water ways and improve water 

3 quality.

4

5 And in additional, we have developed many different 

6 environmental databases and modelling that you will hear 

7 about soon from Dr. Hendley and his team. We really look 

8 forward to the next several days and listening to the 

9 advice and suggestions on the risk-assessment of atrazine.

10

11 We are excited about the progress that has been made both 

12 by the EPA studies and our research in the various areas 

13 of mode of action, risk and exposure. And with the 

14 comprehensive database we have, we look forward to any 

15 recommendations on how to statistically analyze that.

16

17 We did start six new water systems, daily monitoring in 

18 community drinking water systems this year voluntarily, in 

19 order to aid in the modeling and statistical analyses.

20 The conclusion that we see when looking at this 

21 comprehensive database is, with the toxicology and 

22 exposure, there are wide margins of safety. And the 

23 current regulatory standards are protective. And we look 

24 forward to advice on how to advance its overall area of 

25 science from the panel. And with that, I will go through 

26 what we are going to cover for Syngenta in the next slide, 

27 please. 

28

29 And I am going to introduce Dr. Paul Hendley. He is our 

30 senior research science fellow with Syngenta and he will 

31 be introducing his team to discuss the atrazine occurrence 

32 in drinking water and the statistical analyses.
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1

2 We decided to actually start with the water area in line 

3 with the first batch of questions, and then we will follow 

4 up with the various toxicology and modeling of the risk5 assessments; so with that -- Paul?

6

7 DR. PAUL HENDLEY: Okay. Thank you, Janis. Thank you, Mr. 

8 Chairman. Paul Hendley, a senior fellow at Syngenta. I 

9 would like to introduce the two gentlemen on my right; Dr. 

10 Chris Harbourt from Waterborne Environmental, Dr. Wenlin 

11 Chen from Syngenta, and on my left, Dr. Paul Mosquin from 

12 RTI International and they will be answering questions. 

13

14 We are looking forward to this presentation and I am very 

15 pleased to say it is highly complementary to the 

16 presentation you‟ve heard from Nelson Thurman, and I think 

17 we've got some exciting things to show you.

18

19 So the overall statement -- and we have talked about this 

20 before -- is that atrazine exposure is exceptionally well 

21 characterized due to the database. The database sample 

22 numbers provide high confidence on the exposure. In that, 

23 we mean they help us understand what the peak shapes look 

24 like. They help us understand the upper percentiles of a 

25 distribution because of the magnitude of the number of 

26 samples in the database. They help us understand and 

27 differentiate between community water systems.

28

29 On the community water system side we are going to talk 

30 about how we have learned more about them and 

31 differentiated between them, in terms of their watersheds 

32 and their sources. And that is important because that 
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1 leads on to how you use these bias factors. And the bias 

2 factors, we have seen two examples in the earlier 

3 presentation. 

4

5 We are going to show you bias factors from a large number 

6 of additional case to give us a better sense of the 

7 distributions, et cetera. And that is going to show how 

8 we have actually developed a synthetic chemograph for a 

9 margin of exposure assessment for TCT that Dr. 

10 Breckenridge will be finishing the presentation with later 

11 on this afternoon.

12

13 Now, I am particularly excited to be able to talk to you 

14 about a modeling development, PRZM-Hybrid which is showing 

15 great promise at being able to help supplement 7-day 

16 monitoring data. And then we will talk a little bit about 

17 how the year-to-year variation is defined well for 

18 atrazine by the database and how there are opportunities. 

19 Obviously for atrazine we've got a lot of measured data, 

20 but there are opportunities to learn a lot from that 

21 database for understanding monitoring questions, in 

22 general.

23

24 So let's turn to a database which is, of course, 

25 extensive. And when I say that, what I mean is, there is 

26 approximately 340,000 surface water samples in the 

27 database; that‟s not talking about the 200,000 additional 

28 ground water, safe drinking water, samples that are in 

29 there. 140,000 of those samples approximately come from 

30 drinking water related programs, and 200,000 also from 

31 non-drinking water.

32
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1 The biggest contributor for non-drinking water is actually 

2 NAWQA program with USGS which covers enormous range of 

3 years, grounds and is very valuable for helping to 

4 calibrate.

5

6 We are actually going to focus most of our attention today 

7 on the three highlighted groups, and let me just sort of 

8 explain what these initials mean. The AEMP is the 

9 Ecological Monitoring Program that the Missouri-01 site is 

10 from. This was probably now 60 or more sites that we have 

11 investigated, 180 plus site-years of data.

12

13 Some of these -- Missouri-01, for instance -- they are 

14 very small. They are 11 square miles. You can drive 

15 through them very quickly and they are largely 

16 agricultural. And they are, of course, not drinking water 

17 related. It is about 15,000 samples, and the last two or 

18 three years have been daily, as Dr. McFarland mentioned. 

19

20 The next one of importance is the dataset from which the 

21 Maumee came from, which is the NCWQR, as Nelson explained, 

22 the National Center for Water Quality Research, again 

23 about 15,000. That is interesting because the AEMP -- the 

24 eco-program has a lot of sites with not as many years in 

25 between three and six or seven, whereas the NCWQR has 

26 fewer sites but far more years. So we have got a temporal 

27 and spatial match or complementation there.

28

29 The other dataset that we are going to talk about, as Dr. 

30 McFarland mentioned, we started daily monitoring at six 

31 community water systems, so we have actually gone back and 

32 found out what the variation is at six real community 
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1 water systems just to build some confidence that our 

2 models are appropriate.

3

4 However, all of that is based on what I call the 

5 fundamentals which is; the monitoring we have from the 

6 Safe Drinking Water Act which started in 1993, about 

7 55,000 finished water samples from about 4,000 community 

8 water systems -- quarterly samples -- which is served as a 

9 screen to identity community water systems that move into 

10 higher frequency programs. Initially, that was the 

11 voluntarily monitoring program, which operated between '94 

12 and 2003. That‟s got about 22,000, maybe a few more, 

13 finished water samples, and then that transition to the 

14 atrazine monitoring.

15

16 Nelson gave the perfect description of that. All samples 

17 that have exceeded 1.6 ppb annual average atrazine 

18 concentration, from 1997 on, were put into that program in 

19 2003. Any subsequent community water systems that have 

20 exceeded that have moved into the frequent monitoring 

21 program. And so, that‟s why the AMP program is looking at 

22 those community water systems, if you like, at the 

23 pentacle of a pyramid, which is a model I have shown you 

24 before.

25

26 In addition to that, there is a couple more additions. 

27 One is the database of environmental vulnerability 

28 factors, which is pulling together the information we have 

29 been gathering because of the eco-program to understand 

30 watershed behavior, and in addition, a database of 

31 community water system characteristics and watershed 
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1 information. So those are the new tools we have to play 

2 with.

3

4 When we looked at the challenge before we panel, it was 

5 interesting because there's two types of question: One is 

6 retrospective and one is prospective. The retrospective 

7 is sort of, what can we say based on the data about the 

8 specific atrazine question?

9

10 For example, from the magnitude of the data we can say the 

11 95th upper confidence interval of a 99.9th centile for 

12 finished water from the frequent monitoring is 41.6 ppb. 

13 We can be very precise because of the number of samples 

14 involved. 

15

16 We can also use 7-day data to look at bias factors to 

17 understand shorter endpoints by the same approach that 

18 Nelson explained very clearly. And we can use that to 

19 help us understand modeling. We can look at that for 14-, 

20 20-day, 28-day intervals as well.

21

22 We also have used a database year-to-year variation, 

23 residue patterns and trends, but there is a prospective 

24 element of how might we understand bias factors for other 

25 sorts of water bodies? What about future assessments 

26 presumably for molecules with less frequent monitoring, 

27 and how would you go about setting upper bounds on the 

28 year-to-year variation?

29

30 Nelson made the point clearly that those were exciting 

31 questions if you liked the scientists, but for atrazine, 

32 the database already actually tells us. We have answered 
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1 a lot of these questions by sheer volume of data that are 

2 measured.

3

4 And for example, 14-, 28-day in tools, while interesting, 

5 philosophically, when you've got a great database of 7-day 

6 data; those are the ones we need to focus on when we look 

7 at bias factor, et cetera. The existing database also 

8 gives us the modeling clues. Okay.

9

10 Moving on to differentiating community water systems; 

11 we've set up a new database, 200 plus community water 

12 systems. We've been looking at their source water types, 

13 their watersheds. Interestingly, there are 375 intakes 

14 associated with that. Quite a lot of community water 

15 systems have multiple intakes. I'll show you an example. 

16

17 The database is dominated by static water bodies. 224 of 

18 those intakes are static compared to 100 flowing. And 

19 most of the static intakes are on on-channel reservoirs 

20 with the stream running through it, but some of them are 

21 off-channel where the water is stored, and it has to be 

22 pumped to get in. They don't have a watershed of their 

23 own that is storage units.

24

25 Once you have characterized the watershed you can use that 

26 environmental database I mentioned in order to find the 

27 environmental parameters, soils data, cropping data, 

28 vulnerability. And equally, the community water system 

29 characteristics like the atrazine monitoring data, 

30 summarized, is all pulled into one convenient location, 

31 and that allows categorization. 
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1 Now that sounds nice and fun, but why are we doing it? It 

2 is the help us understand how to use things like bias 

3 factor, because community water systems are individual and 

4 you need to target your thinking and your learning 

5 appropriately to different community water systems.

6

7 So here, for example, is a community water system that the 

8 red line, which is a little hard to see coming down here, 

9 is a rather tiny creek. It is a two square mile 

10 watershed. But the water they store -- and you can see 

11 five ponds here -- there is actually another one off to 

12 the side we cannot see in this image, so five intakes from 

13 five separate units collecting from one watershed. That 

14 is the level of complexity the database has to cope with 

15 to be useful to understand what might be going on when we 

16 are looking at the residues from this database.

17

18 We talked about dividing up community water systems by 

19 source and watershed size last time. We have taken a 

20 simple approach; small, medium and large, and basically 

21 50-squre mile cut off for small. And the for the statics, 

22 a medium to large cut off of a 1000 square miles for 

23 flowing 800 square miles. And you can see that the 

24 database is dominated by small static water bodies. 143 

25 which is half of all the intakes we are looking at here 

26 with small static water bodies with watersheds less than 

27 50 square miles.

28

29 When you look at the flowing you see, actually, the 

30 majority of them come from large watersheds, which when 

31 you think about the hydrology immediately makes sense.

32
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1 I'm not going to dwell on this, but once you have 

2 categorized them you can start looking at the statistics 

3 and the environmental parameters associated with the 

4 different groups. That is the reason why we have been 

5 doing this categorization and spending the time developing 

6 the database.

7

8 And example is shown here, and I'm coming back. The royal 

9 blue color here, this is a distribution of areas for the 

10 eco-watershed. And you can see that there is overlap in 

11 terms of watershed areas with the small flowing community 

12 water systems, and there are 16 of those, but there is 

13 almost no overlap with the rest of the medium and large 

14 community water systems on flowing watersheds. So the 

15 eco-datasets are very useful for understanding the 

16 hydrology and temporal behavior but they are not relevant 

17 to the vast majority of community water systems on a scale 

18 basis.

19

20 So moving on to bias factors and supplementing what you've 

21 learned from Nelson, we have looked at many locations and 

22 years. And using exactly the same nomenclature that the 

23 95th centile of the error ratios -- and the error ratios 

24 were the ratios between the true from the daily data and 

25 the simulated value from the 4 of a 7-day simulation, and 

26 the 95th centile of those ratios makes up the bias factor.

27

28 Syngenta use systematic sampling and I am going to spend a 

29 minute on that because it is a question that has been 

30 asked of the panel. Systematic sampling means if it is 

31 four days, you've just got four ways of doing it. You can 

32 do it on day 1, day 2, day 3, day 4; seven days just the 
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1 same, Sunday, Monday, Tuesday, Wednesday. And we use that 

2 rather than stratified approach which will be a random 

3 selection from period 1 and then period 2 and could come 

4 up with a range of different intervals. 

5

6 However, we have gone back and looked at what the actual 

7 guys taking samples in the community water systems have 

8 done, and 66 percent of all those samples were taken spot9 on the 7-day intervals. They got used to a weekly 

10 routine.

11

12 Twenty-four percent of those were actually taken with one 

13 day of that; Thanksgiving falls July the 4th, or whatever. 

14 And, so 90 percent of those samples were being taken 

15 within one day of the systematic point. And what my 

16 statistical colleagues are showing me is when you've got 

17 systematic sampling in the field, systematic simulated 

18 samplings is an appropriate way to look at it.

19

20 The downside of using systematic sampling is if you've got 

21 four or seven measurements, error ratios, it is difficult 

22 to come up with a 95th centile. So that is why Syngenta 

23 calculated a 95th centile on the group. And when I say a 

24 group, this is not a random group. This is all the eco 

25 sites pulled together, or six community water systems with 

26 daily monitoring or all the years for an NCWQR site, so it 

27 is grouped by logic. 

28

29 Moving on; the data we see is that -- here is 34 eco-sites 

30 with one to two years. So that is our spatial 

31 information. There are four NCWQR sites with 15 or 16 

32 years, so that is our temporal dimension. And then St. 
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1 Louis, which is finished water from a real but not very 

2 flashy community water system.

3

4 Just using 7-day simulated sampling and looking at 4-day 

5 average for, let's say Honey Creek, the bias factor will 

6 be 2.73. If you look at it as you move across, as you 

7 move -- as Nelson pointed out -- from shorter durations to 

8 longer durations, the bias factor drops; the amount by 

9 which it drops decreases.

10

11 I would stress here, this is adding to what Nelson showed 

12 us for two examples. This is giving us a good sense from 

13 a distribution of bias factors. And you are going to see 

14 the individual ratios in a minute.

15

16 We have also done the monitoring at six community water 

17 systems. Those are flowing water; they've got a wide 

18 range of areas. And the preliminary data that I'm 

19 reporting here goes up to the end of June. It covers the 

20 peak atrazine season and, as many of you know, it's been 

21 quite a high runoff season.

22

23 We did, again, the same 7-day and 4-day simulated 

24 sampling. Bias factors estimated in just the same way and 

25 we will see some of the individual error ratios. The bias 

26 factor that results and you can see in the table, very 

27 similar between raw and finished water, and the bias 

28 factor is actually four within the range that we saw from 

29 the temporal dimension NCWQR sites. 

30

31 Here's an example where, for clarity, we've spread out 15 

32 years of Honey Creek and of Maumee from the NCWQR so we 
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1 can see the individual variations of the seven error 

2 points when we are using the 7-day simulated sampling to 

3 look at 4-day rolling averages. 

4

5 You can see there that the red lines are the group means 

6 for all those years for bias factor, the 95th centile. 

7 The open circle is those points above the 95th centile. 

8 And you can see the larger watershed, Maumee, which is 

9 6300 square miles, has a lower bias factor, as you might 

10 expect, than the smaller watershed.

11

12 Here, what we've done is something slightly different and 

13 the X axis is logged area. So we are looking at the 

14 distributions across the whole range of things we've 

15 looked at, CWS error ratios, for cross area.

16

17 What you can see is St. Louis, which it has the whole 

18 Missouri which is 500,000 square miles and is finished 

19 water. You've got Maumee, Sandusky which is 1200 square 

20 miles, Honey, Rock Creek, and the gold ones are the six 

21 community water systems with daily monitoring, and the 

22 gold line is their combined bias factor, and the gray dots 

23 are the ones from the eco-programs, from these very small 

24 non-drinking water watersheds.

25

26 What you can see here is a difference as you have an area 

27 with larger watershed areas, has generally lower bias 

28 factors. So what we've drawn from our look at bias 

29 factors is that, from 110, 116 sets of error ratios, 

30 systematic sampling is an appropriate way because of the 

31 way the AMP samplers did it. Raw and finished is showing 

32 similar ratios. Bias factors decrease as the averaging 
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1 period increases, and they also decreases the watershed 

2 area increases. 

3

4 There is a simple way, perhaps, we could look at this. 

5 For smaller watersheds you might want to pick a larger 

6 bias factor than for larger watersheds. And just 

7 something simple; not some regression equation but just 

8 pick a couple of categories that match perhaps the 

9 categories for dividing up community water systems.

10

11 Once more, there was another point, and I'll show you an

12 example. With what we know about the atrazine database, 

13 we can put firm figures on some of these upper centiles. 

14 So, to use a bias factor and create numbers that fall into 

15 highly improbable areas wouldn't make a lot of sense. 

16 That is another factor to consider when you apply bias 

17 factor to a chemograph.

18

19 We've got a good sense from this wide range of years and 

20 sites about flowing waters. What about static? And I 

21 think the panel agreed last time that static is generally 

22 less flashy than flowing water systems.

23

24 The one thing we do know is the flowing water bias factors 

25 would be conservative if we were looking at some static 

26 water bodies. This is a way of applying factors to come 

27 up with a TCT margin of exposure synthetic chemograph, a 

28 worse case chemograph. And to do this, we took the 149 

29 community water systems between 2006 and 2010. And the 

30 reason why we chose that was because every sample was 

31 analyzed for the components of TCT. And the 4-day rolling 

32 averages were calculated and ranked, and the 17 community 
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1 water systems that had the highest 4-day rolling was 

2 selected as an indicated dataset of the uppermost tier of 

3 4-day rolling averages.

4

5 What we did was, we took a worse case assumption, and 

6 atrazine runoff event happened between every pair of 7-day 

7 samplings. And that will generate an unrealistic number 

8 of atrazine events and it probably distorts the peak 

9 shapes. 

10

11 What we chose was to use a three-fold magnifying factor 

12 that‟s greater than the maximum of two adjacent residues. 

13 Now, what I mean by that; you can see the red points here 

14 are describing the measured values. The linearly 

15 interpolated chemograph that would describe that site for 

16 that year is shown there.

17

18 Now we put in these three x factors between each of those 

19 pairs of points and we create the new chemograph, and you 

20 can see the new daily points marked on that. And so, 

21 that‟s what has gone into the margin of exposure 

22 assessment that Dr. Breckenridge will discuss. 

23

24 Because that was meant to be a worse case assessment, we 

25 did not apply the upper bound cap I just mentioned. But 

26 what you would normally think about doing is using what 

27 you know about statistics of an enormous database to 

28 remove highly improbable values, which would have the 

29 effect of capping those synthetic peaks at some number 

30 justified by the sampling statistics.

31
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1 Moving to PRZM-Hybrid -- and I would like to give credit 

2 to Dr. Miller of Waterborne who's actually driven this 

3 piece of work resourcefully and relentlessly, I think is 

4 the phrase. And I think it's a particularly clever piece 

5 of work he's done.

6

7 The phrase to remember here is PRZM-Hybrid is site and 

8 local event specific. It uses PRZM code, which EPA and 

9 the industry are familiar with, atrazine Efate data. The 

10 only regional information used is crop reporting data 

11 which comes in groups of 10 counties from the NASS 

12 service.

13

14 But if you are simulating a monitoring year, you have 

15 watershed and year-specific data for soils, for local 

16 rainfall of the year from the radar maps we see every 

17 night on the TV. 

18

19 So actually, the rainfall that was falling across the 

20 whole area of the watershed is taken into account and the 

21 cropping of the year from the NASS mapping, which maps all 

22 the fields by crops across the nation now. The output you 

23 get is a watershed scaled concentration time series. It 

24 uses available data. 

25

26 There is no model-specific calibration for each site. 

27 This is applied in the same way to all sites using code. 

28 And it uses conservative edge-of-field concentrations.

29 What I am trying to say here is it is driven by rainfall 

30 events, which is what happens in the field. You can see 

31 here, rainfall events coming down from the upper line in 
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1 the graph, flow figuratively below. There are uniformly 

2 sampled points. That is our linear interpolation.

3

4 PRZM-Hybrid runs; every time there's a rainfall big enough 

5 to cause a runoff, we get a PRZM-Hybrid estimated 

6 concentration. What we do then is say, "Is that 

7 concentration higher than the linear interpolated value?" 

8 If so, you use it in a schemograph; if it isn't, like 

9 point D, you don't use it. So again, you are retaining a 

10 worse case element in this assessment.

11

12 Just for the record, we did look. When we were trying to 

13 do this, we thought flow would be an attractive way of 

14 getting into trying to fill in the gaps. We didn't find 

15 useful correlations during the period of high atrazine 

16 runoff when we did it, but the PRZM-Hybrid seems to be a 

17 better way from the way we are looking at things.

18

19 The reason why things have improved -- the report that you 

20 have in your docket uses a growing degree day approach. 

21 The simulation was good but it wasn't great. 

22

23 There is a new algorithm that accounts for land 

24 workability; can I get a tractor on the land, and 

25 distribution across time; how many people apply even if 

26 it's a day they could apply. And what that takes account 

27 of is the local response of a soils to rainfall. Is it 

28 too wet to go out? And the wariness of some farmers, 

29 frost concerns -- if it's a field that isn't no till, to 

30 create seedbeds before you can go out and spray. And the 

31 equipment capacity; in some watersheds you simply can't 

32 spray all the fields in a day even if you'd like to. And, 
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1 of course, the possibilities of post-emergent treatments -

2 - there are quite a number of atrazine treatments applied 

3 post-emergent.

4

5 So PRZM-Hybrid chose considerable promise for 

6 supplementing 7-day data. It reflects reality. The peaks 

7 are only predicted when runoff events are likely to occur, 

8 unlike some of the models we've seen. And it does tend to 

9 over-predict at bit at greater than 20 ppb. There is no 

10 need anymore because of its algorithm improvement from 

11 distribution matching we thought we might have to do. 

12

13 Here is a before picture. This is the growing degree day 

14 approach. This is a site. The black marks are the PRZM15 Hybrid predicting concentrations. The opened triangles 

16 are the measured values; not a bad fit but could do 

17 better. 

18

19 Here you see what we have with the workability approach. 

20 And what I'd like to do is go back to this slide and just 

21 talk about the way we simulated the application across the 

22 watershed. These green bars show that we applied a chunk, 

23 about 15 percent of the chemical that was going to apply 

24 on four occasions, and we filled in in-between with daily 

25 add-in loads of atrazine. And that made sure there was 

26 always a little bit of fresh atrazine present if it was a 

27 runoff event.

28

29 Using the workability approach, you find all the workable 

30 days in that period and you divide the application equally 

31 as a fraction between those. And so, you can see here it 

32 identified a whole bunch of workable days. The chemical 
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1 went down and we caught that first peak rather well. We 

2 also, because of those days, managed to catch the second 

3 peak quite well, so we are matching reality with this 

4 technology.

5

6 There's a couple more here. The top one is one of the 

7 less good ones. We thought we really ought to put some 

8 less good ones in as well as the better ones, but it's 

9 still pretty good. I'm still quite pleased with it. And 

10 of course, here's another one where we're picking the 

11 peaks up in time. And as the modelers will tell you, the 

12 problem is modeling and catching this in time on a 

13 watershed scale. 

14

15 We realize this is sort of rabbit out of a hat data coming 

16 in at the last minute because it is hot off the press. We 

17 want to get this out in the open literature and as a 

18 report into an EPA very soon. There are still some more 

19 tweaks on the workability approach, but the challenge is 

20 perhaps on larger watersheds because I don't think we do

21 quite a well for that.

22

23 There are a number of ways we could do it. We do not need 

24 to dwell on it, but I actually think that is where moving 

25 towards the regression models may be, as Dr. Nelson 

26 suggested, an attractive way of tackling larger 

27 watersheds.

28

29 So moving on to the last key point, year-to-year variation 

30 is well defined, and the database is useful for general 

31 purposes.

32
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1 Atrazine is a near-ideal case to look at the year-to-year2 variation. It is applied to a high fraction of a major 

3 crop nearly every year at uniform rates. It has a great 

4 length of monitoring to understand the results and look at 

5 modeling and it covers a wide range of scenarios for all 

6 of the data in NorAqua and our programs.

7

8 We have a direct answer for year-to-year measurement of 

9 variation for atrazine, but the data offer an opportunity 

10 to answer questions about monitoring for other compounds.

11 And in simple summary -- and there is actually, the back 

12 on the handout I think you'll find a few examples showing 

13 the variation -- but there is high variation of residues 

14 and error ratios across years.

15

16 The primary driver for that is the interrelationship 

17 between rainfall inducing runoff timing and application. 

18 And so, basically, even if you don't have the extensive 

19 database we have for atrazine, even medium term 

20 prospective monitoring is probably not going to answer the 

21 question of putting bounds around year-to-year variations. 

22 And that‟s why, for years, for ecological modelling, we've 

23 used probabilistic approaches of many weather years.

24

25 So we think that where monitoring is required, which isn't 

26 always, one approach would be exactly as Nelson suggested, 

27 using PRZM-Hybrid calibrating for local monitoring data 

28 and then extending that to a probabilistic environment. 

29 And when I say that, I mean this schematic will show the 

30 PRZM-Hybrid approach.

31
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1 We run that for a site for two or three years of 

2 monitoring maybe. We check that the model is behaving 

3 itself. Then we move into a probabilistic environment. 

4 And that environment link here, the key link, is taking 

5 the watershed parameters that we know we are fitting and 

6 then playing the year-to-year variation of rainfall data 

7 in a probabilistic sense on that watershed shown to work 

8 dataset.

9

10 You could also chose to variate the crop treated percent, 

11 the crop rotation and perhaps the rate. But the key 

12 driver here will be the weather, and that will give you a 

13 host of PRZM weather years of 365 days of predicted 

14 environmental concentrations which can be looked at in 

15 terms of whatever you want in variation; magnitude 

16 duration, peak shape; that all comes out the PRZM daily 

17 record. So, we think that‟s quite an attractive approach. 

18

19 In summing up, atrazine exposure is exceptionally well20 characterized. It is more than sufficient for analyses of 

21 exposure, magnitude and variation.

22

23 The database numbers give us high confidence. We know the 

24 high centiles, shapes, trends. The community water system 

25 differentiation allows us to know what's out there and 

26 think what would be the appropriate way of dealing with 

27 filling in the 7-day record for that community water 

28 system.

29

30 The daily or near daily data validate analyses. We've 

31 looked at the wide range of bias factors so now we've got 

32 a sense for how those vary across area and across time. 
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1 That‟s been backed up with daily work at six true 

2 community water systems. And we've used this for 

3 synthetic chemographs for 7-day TCT record, as Dr. 

4 Breckenridge will show you.

5

6 The database has provided a test bed for looking at 

7 modeling and monitoring. PRZM-Hybrid is promising. The 

8 application algorithm has moved us forward, and that 

9 approach is actually giving us a reality-based way of 

10 coming up with simulated peaks that depend on rainfall, 

11 which is the driver.

12

13 Year-to-year variation is characterized by the atrazine 

14 monitoring data. Probabilistic modelling is an attractive 

15 way of looking at other questions beyond atrazine. And we 

16 think it is a smart way of doing it using the new tools. 

17 So that‟s the summary. I thank you for your attention.

18

19 DR. DANIEL SCHLENK: Thank you, Dr. Hendley. Let's go ahead 

20 and have some questions for he and his group right now for 

21 the panel if you have questions for clarification. Yes, 

22 Dr. Coupe?

23

24 DR. RICHARD COUPE: On the PRZM model, does that generate 

25 hydrology also? So, like, you have a point on a stream 

26 you'll get flow through that stream?

27

28 DR. PAUL HENDLEY: Simply put, no. PRZM is an edge of field 

29 model. And particularly, in this case, we are using it as 

30 an edge-of-field sense. In the EPA normal methodology, 

31 it's linked to the exams model to simulate entry into a 

32 pond system. And there are various implementations that 
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1 can link it to a river system, but because we in PRZM2 Hybrid simulate the watershed in terms of area waiting, 

3 the different soil runoffs run under PRZM, the edge-of4 field numbers is simulating edge-of-watershed, if you 

5 like. And it is quite surprising that the fit is as good 

6 as it is without having that dilution factor from a 

7 stream, but very satisfying when it does that. Does that 

8 answer the question?

9

10 DR. RICHARD COUPE: Yes. Thank you. But did you show real 

11 data versus your simulated data?

12

13 DR. PAUL HENDLEY: Yes. The open purple triangles were the 

14 real data, daily data coming from the ecological 

15 monitoring program from those sites, and it's been run for 

16 the 34 sites that have been in the program, so probably 48 

17 to 50 site years.

18

19 DR. RICHARD COUPE: I like the PRZM, it's really good. I read 

20 the paper on PRZM and how we triggered planting under the 

21 growing degree day, so how did we trigger planting with 

22 the new workability algorithm?

23

24 DR. PAUL HENLEY: Can I pass that one to Dr. Harbourt?

25

26 DR. CHRIS HARBOURT: There is a couple of different ways that 

27 we're doing it, and right now, the main characteristic of 

28 workability is soil moisture, so we are modeling soil 

29 moisture and figuring out when is the soil trafficable. 

30 You know, when can a tractor enter the field; defining 

31 that as the workable days. We are using temperature 

32 windows to set the time. And then we can also, if we 
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1 choose to, use some of the characteristics of the GDD 

2 model and use the growing degree days as an end 

3 characteristic to kind of limit the window in time to 

4 where the corn is at a height where it's no longer 

5 feasible to apply herbicide or where it's off label.

6

7 DR. PAUL HENDLEY: And there is actually a description of that 

8 at the back of the slide set.

9

10 DR. KENNETH PORTIER: Dr. Hendley, this was all pretty quick 

11 and I followed most of it. I just wanted to double check. 

12 When you do the PRZM in-fill, you're not filling everyday 

13 though. You're filling event days between the 7-day 

14 sampling, is that right? So you'd have a rainfall event 

15 or something that triggers PRZM to compute an estimate 

16 that would go into between two sample points to pick a 

17 peak; is that correct?

18

19 DR. PAUL HENDLEY: That‟s absolutely correct. And rather like 

20 those simulated peaks, it creates a new schemograph.

21

22 DR. KENNETH PORTIER: Well it supplements the schemograph by 

23 adding higher peaks that hopefully reflects some aspect of 

24 variability between the sample points. Have you looked at 

25 other statistics, the maximum, in terms of duration at a 

26 concentration? One of the other things we were talking 

27 about -- we'd been talking a lot about peak, but actually 

28 for the AUC you are more interested in duration at a level 

29 of concentration. And I wondered if kind of the 7-day 

30 supplemented graphs, how they would compare to the 

31 everyday picture in terms of that kind of statistic? You 
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1 know, is it five days at 10 parts per million? You 

2 understand what I'm asking?

3

4 DR. PAUL HENDLEY: Yes. You've got it dead right. And what 

5 we've actually got -- and the report is in draft -- is an 

6 analysis of the eco-sites, the NCWQR and a lot of NorAqua 

7 sites, as well, in terms of looking at the peak wits at 

8 different concentration ranges for exactly that reason. 

9 My suspicion -- and I don't have the data to back it up at 

10 the moment -- is if in fact there is one event between 

11 seven days -- well, I'm certain, for sites like Missour12 01, which are very small, very flashy sites, that we are 

13 making the peaks must broader than they would be in 

14 reality. But if you went to Maumee, which is a very big 

15 watershed, the peaks are broad. So it's a watershed 

16 scaled dependent thing and that is another factor we are 

17 looking at in this analysis of the database.

18

19 DR. KENNETH PORTIER: So when you're looking at those periods, 

20 statistics, are you thinking to relate them to water site 

21 characteristics so that you'd be able to say, well, a 

22 large water slope might have a distribution of durations 

23 that look different than a flat small -- I don't know if 

24 you follow what I'm saying.

25

26 DR. PAUL HENDLEY: Yes. However, let's not expect -- I think 

27 we are looking for simple relationships here because there 

28 are so many things that sort of complicate -- one of the 

29 biggest ones is the duration of rainfall events because 

30 you can have very flashy watershed, but if you get two 

31 days of solid rain it is going to look like a broad peak, 
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1 so there are some confounders in there that you really 

2 have to think hard about.

3

4 DR. ROBERT GILLIOM: Staying on the PRZM theme first. So in 

5 terms of size of watershed limits that you are thinking 

6 about from what you see so far, I mean how big are you 

7 thinking it's comfortable to go, given no adjustments for 

8 the flow system?

9

10 DR. CHRIS HARBOURT: We looked at some base flow calculations 

11 and looking at break points in different streams to try 

12 and see is there a scale at which routing is necessary. 

13 And one of the thoughts -- and Paul had it in one of the 

14 slides -- about 800 square miles as being a break point; 

15 we are fairly confident that between 800 to 1000 square 

16 miles or smaller. When we're talking about time of 

17 concentration in watersheds on the order on the scale of a 

18 day, and our monitoring data is on the scale of a day, it 

19 will do fairly well with PRZM, a daily time-step model. I 

20 would think much larger than that, we are going to need to 

21 do some sort of routine to be accurate in the short-term. 

22 At some point you will catch the peaks, but we'll mimic 

23 what happened in reality, potentially not, depending on 

24 routing, and then depending on the complexity of the scale 

25 of storms.

26

27 When you get to a scale of 800 or 1000 or 5000 or 50,000 

28 square miles, it can rain in part of the watershed and not 

29 the other. And if we are diluting that across the 

30 watershed average, as an example, you may not see the 

31 response that you would see in a local portion of that, 

32 and that somehow needs to be reflected in the modeling 
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1 scheme as you go up in scale with some of these drinking 

2 water supplies that are much, much larger, potentially.

3

4 DR. ROBERT GILLIOM: I just got a couple more just to follow5 up. In terms of the readiness of the method to be applied 

6 geographically, am I understanding right that the basic 

7 method can now be applied to any watershed in the US? Or 

8 is it only for the drinking water intakes you have looked 

9 at?

10

11 DR. CHRIS HARBOURT: The underlying data -- and that‟s one of 

12 the strengths of PRZM-Hybrid. I mean, in traditional 

13 modeling, setting up a model, parameterizing it and 

14 calibrating it is a challenging exercise. What we have 

15 done here instead is process data nationally. We have set 

16 up datasets. The underlying SSURGO soils, the weather, 

17 all that stuff. It's setup and ready to go in a way that 

18 can run everywhere.

19

20 One of the challenges though with the PRZM-Hybrid is that 

21 it corrects itself back to a real time series with 

22 measured points. The hybrid concept of them using it in a 

23 probabilistic fashion, the thought there is using a few 

24 years where you have sampled monitoring data -- you know 

25 the crop rotation history of the watershed from national 

26 land cover data, and we are able to expand that or 

27 extrapolate that to multiple years, 50 years of rainfall 

28 record and do some estimates. 

29

30 So there are opportunities, I would think, outside of 

31 areas where there is monitored data looking at that 

32 because it's an un-calibrated model, you know, comparing 
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1 one where you have monitored data and you check it, but 

2 it's realistic and its behaving property and you could 

3 compare that side-by-side to one where you have no 

4 monitored data; maybe not in a realistic sense but side5 by-side within the model perhaps in comparison.

6

7 DR. ROBERT GILLIOM: And I have one last non-PRZM one. On the 

8 earlier part of the presentation we were talking about the 

9 bias factors for every year from the AMP data, and 

10 basically, when you do the weekly sampling frequency you 

11 have the seven values because you used the systematic 

12 sampling. I know, you made the argument for pooling those 

13 because you only have one year from each. Did you look at

14 how those pooled values compared to assuming a reasonable 

15 frequency distribution for the individual years? I know 

16 you only have seven values and you may have to estimate a 

17 95th, but using something like a Weibull or a lognormal --

18 you know where I'm going -- is some way to get at the 

19 individual site.

20

21 DR. PAUL MOSQUIN: No. We did not do that but we provided the 

22 plot so you can see what the underlying sampled values 

23 are.

24

25 DR. WENLIN CHEN: I just want to add to what Paul said. We did 

26 some comparisons, not to the normal distributions, but 

27 actually compared to the sort of stratify it or random 

28 something. Actually it generate fairly small differences; 

29 not a whole lot.

30

31
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1 DR. DANIEL SCHLENK: Okay. Any other questions, Dr. Hendley? 

2 Okay. Thanks, Dr. Hendley. We'll move on to the next 

3 group.

4

5 DR. JANIS MCFARLAND: Thank you. And we'll now bring on our 

6 toxicology and pharmacokinetic modeling team. Dr. 

7 Breckenridge will be leading that discussion. I am 

8 introducing Dr. Charles Breckenridge. He is a senior 

9 science fellow with Syngenta Crop Protection and has been 

10 intimately involved in several years of the development of 

11 the mode of action and toxicology of atrazine as well as 

12 basic research on many of our other products. And Dr. 

13 Breckenridge will be introducing his other experts that 

14 will be discussing the biology and the modeling of 

15 atrazine.

16

17 DR. CHARLES BRECKENRIDGE: Good afternoon, ladies and 

18 gentlemen. Thank you again for having us and listening to 

19 our long presentations. We greatly appreciate the

20 opportunity to discuss our data and concepts relevant to 

21 EPA processes on atrazine.

22

23 I would like, while I'm introducing the people that are 

24 available at the table and in the room, if we could 

25 advance that slide set to slide 39. I'm sorry I just did 

26 not have the linkage slide at the beginning of the 

27 presentation. We'll come back to this after.

28

29 The people that we have here today are experts in the 

30 endocrine effects of atrazine are Dr. James Simpkins from 

31 the University of Texas, Dr. Tony Plant from Pittsburgh, 

32 Bob Handa from Arizona. 
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1

2 And we've complemented that team with a group that are 

3 involved with the pharmaco PBPK modeling, principally, the 

4 folks at Hamner Institute. Harvey Clewell will be marking 

5 the presentation but Mel Andersen is also in the room and 

6 he was intimately involved in the creation of the first 

7 atrazine PBPK model while he was at the university in 

8 Colorado. And we also have Jerry Campbell who implemented 

9 the code for the PBPK model which we will illustrate 

10 today. 

11

12 Finally, as part of the PBPK modelling exercise, we 

13 coupled it to a simulation program, and Dr. Bob Silken did 

14 all of that work. And so, he's in the room as well to 

15 answer detailed technical questions about how that 

16 simulation was conducted.

17

18 I'm going to start with a simple schematic that tries to 

19 capture the essence of what we are going to be discussing 

20 today. On the one hand, we want to have a brief 

21 discussion of some key factors relating to the selection 

22 of the point of departure, and for that, Dr. Simpkins and 

23 Dr. Plant will lead that discussion.

24

25 Largely speaking, we want to bring up the point, in fact, 

26 that we believe that pulsatile GnRH release is the more 

27 relevant endpoint for human risk-assessment. And we 

28 acknowledge and we recognize the difficulty of dong 

29 studies in that arena, and so the LH surge stands as a 

30 convenient endpoint for point of departure consideration. 

31 Dr. Plant will have that discussion with you.

32
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1 The PBPK modelling, such as we've implemented it, permits 

2 us to take into account distributed dose versus bolus dose 

3 such as has been done in the rodent studies, age-dependent 

4 sensitivity, animal model sensitivity, functional 

5 outcomes; those are the topics we'll cover under point of 

6 departure discussions.

7

8 We then go backward and pick up the information that Dr. 

9 Hendley introduced the concept of residues in water are 

10 fluctuating dynamically over the time, and coupling that 

11 with a water intake record that permits one to enter those 

12 factors into a PBPK model.

13

14 Dr. Harvey Clewell will introduce the work we have done to 

15 develop that model and to characterize it for you so that 

16 you can see then when we go to apply the model for margins 

17 of exposure calculations, you will have some understanding 

18 of where that‟s coming from.

19

20 And I should just say that, in fact, we did take an 

21 initiative to commence cynomolgus study, a 

22 pharmacokinetics study. Effectively, that has begun in 

23 April and is still going on. We are not administering 

24 14C-atrazine. We're administering cold material and we 

25 are doing a rather comprehensive characterization of 

26 metabolites in plasma, urine, feces, cage-wash. We are 

27 trying to achieve the mass balance that people were 

28 envious of with the 14C, and also some postulated 

29 metabolites that were introduced into the discussion just 

30 recently.

31
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1 So our intent is to actually take the rodent model that 

2 we've developed, scale it to humans, take analogous data 

3 from the cynomolgus monkey and scale it to humans and see 

4 how we do so that there will be continuous development on 

5 this front.

6

7 I think, with that, I will stop and I'll turn the topic 

8 over to Dr. James Simpkins. Thank you.

9

10 DR. JAMES SIMPKINS: Okay. We need to go back to the first 

11 slide, please. I'd like to thank you for the opportunity 

12 to present -- Tony Plant and I will be very brief. We've 

13 identified five issues that we think are key for 

14 consideration in decisions about point of departure 

15 concentrations of atrazine. Those five features are 

16 listed here.

17

18 We will discuss four of those features. The fourth one 

19 that is developing animal less sensitive than the adult to 

20 atrazine. We fully agree with the EPA's position that the 

21 developing animal is not more sensitive than the adult and 

22 we'll have no more to say about that.

23

24 We will provide a summary of the presentation that Dr. 

25 Plant did in September of 2010, relative to the role of 

26 pulsatile LH secretion across species in comparison to the 

27 LH surge, the mechanism of which does not cross species 

28 well.

29

30 In addition to that, we will discuss every so briefly the 

31 concept of distributed dose, and that being more relevant 

32 to the manner in which humans are exposed to atrazine. 
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1 And we will show you pharmacological data, and then later 

2 in the presentation Dr. Clewell will show you kinetic data 

3 showing that distributed dose produces a remarkably 

4 different response as well as exposure than does bolus 

5 dosing with atrazine.

6

7 We will show you data that we have that functional 

8 endpoints do not appear to be affected by the atrazine9 induced modest decline in LH surge suppression, and 

10 finally show you data that supports our opinion that the 

11 Long-Evans animal may not be the appropriate animal to 

12 look at because of the instability of its estrous cycle. 

13 With that, I will quickly turn this over to Dr. Plant and 

14 he will talk about comparison of pulsatile and surge LH 

15 secretion

16

17 DR. TONY PLANT: Thank you, Jim. So in September I did present 

18 some data to this panel, and basically, the conclusion 

19 from that talk was, understanding the way atrazine 

20 interacts with pulsatile LH secretion is more relevant to 

21 understanding or translating data on the rodent to the 

22 human. 

23

24 Those slides are in your docket. I'm not going to go 

25 through those slides again. I'm just going to sort of hit 

26 the bullets for sake of time, and so I won't have time to 

27 go through any caveats which, of course, there are to any 

28 scientific discussion.

29

30 The first point I want to make is that, the pulsatile mode 

31 of secretion is a distinct mode of secretion versus the 

32 surge mode of secretion. So I think you are all familiar 
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1 with pulsatile LH secretion in the human female. This 

2 result from brief episodes of secretion of LH from the 

3 pituitary, results in a small increase in circulating LH 

4 levels, and these decay exponentially over a matter of 15, 

5 30 minutes, 60 minutes.

6

7 Surge secretion, on the other hand, gives you this massive 

8 discharge of LH, which spans in the human female maybe two 

9 or three days. And these modes are regulated by different 

10 hypothalamic mechanisms. 

11

12 If we look first at pulsatile LH secretion -- as we've 

13 talked -- and I think it's well recognized that this is 

14 driven by a corresponding pulsatile patent of GnRH release 

15 from the hypothalamus, and this occurs in both rats and 

16 humans. A neuro-mechanism, which we call a hypothalamic 

17 GnRH pulse generator, which even in 2011 is still somewhat 

18 of a black box that is responsible for this pulsatile GnRH 

19 release, that of course is present in both rat and human 

20 hypothalamus.

21

22 In both species there is an increase in the activity of 

23 the GnRH pulse generator as puberty is entered. However, 

24 I know that you're interested in lifestyle effects. And 

25 one difference I do want to point out between the rodent 

26 and the human is that, GnRH pulse generator activity in 

27 the human infant is robust, and this is again, a species 

28 difference with the rat. And that leads to gonadotropin 

29 secretion. In fact, in the infantile human male you have 

30 testicular testosterone secretion and elevated blood 

31 levels during human infancy. So that is a very different 
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1 endocrine environment from childhood and juvenile 

2 development in the human.

3

4 Now from what we know, the neuromechanisms that are 

5 responsible for GnRH pulse generator appear to be similar 

6 across mammalian species. So what you learn probably in a 

7 rat, what you learn in a sheep about the GnRH pulse 

8 generator is probably translatable to the human. 

9

10 The other two points I want to make is that you all focus 

11 on ovulation in the LH surge, but pulsatile LH secretion 

12 together with that of FSH is absolutely critical for 

13 folliculogenesis. And if you don't have health follicle 

14 or health follicles you may have deficits in ovulation. 

15 You may have deficits in the corpus luteum. And pulsatile 

16 LH secretion also plays a major role in maintaining the 

17 corpus luteum and progesterone production in the human 

18 female. 

19

20 So now, what about the surge mode of LH secretion? So, in 

21 the rat the LH surge is short. It's entrained by the 

22 light/dark cycle. It has a critical period. It fires on 

23 the afternoon of pro-estrous and it is sensitive to 

24 barbiturate.

25

26 In the human and primate on the other hand, as I 

27 mentioned, this is a protracted event. There is no 

28 critical period and it's photoperiod and barbiturate 

29 insensitive. 

30

31 In both the human female and the rat, the LH surge is 

32 initiated by a positive feedback of estradiol, which is 
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1 produced by the Graafian follicle. It matures and results 

2 in increasing blood levels of estradiol, which talks to 

3 the hypothalamus in the pituitary. So, again, the ovarian 

4 signal is the same.

5

6 Now, a key side of this estradiol action in the rodent, in 

7 the rat, is in the rostral hypothalamus, and this is a 

8 major difference. In the human female, this side of the 

9 positive feedback action occurs at the level of pituitary. 

10 Now, the positive feedback action in the rat, essentially 

11 it opens the gate to the circadian signal in the rat, so 

12 this can now be relayed to the GnRH surge generator, which 

13 then in turn triggers the LH surge.

14

15 And interestingly, it appears the GnRH pulse generator 

16 during this LH surge is actually decelerated or arrested, 

17 and that‟s an important point. And again, it emphasizes 

18 the difference underlying the hypothalamic control of 

19 these two modes.

20

21 In the human female, on the other hand, the LH surge 

22 results by an interaction of GnRH pulses, pulsatile 

23 stimulation of the pituitary and this action of estradiol 

24 we call a positive feedback action to amplify the response 

25 to the pulsatile stimulation. And in the human female, 

26 there is no evidence for a GnRH surge. On the other hand, 

27 GnRH pulse generator activity is maintained throughout the 

28 surge in the human female. 

29

30 And so, this difference in the hypothalamic control of the 

31 LH surge in the rat it involves a GnRH surge generator and 

32 a suppression, it appears, or a block of the GnRH pulse 
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1 generator; whereas in the human female there is no GnRH 

2 surge generator. All you have is a GnRH pulse generator, 

3 which is maintained, and the action of estradiol is at the 

4 pituitary.

5

6 Because of this key hypothalamic difference, I think if 

7 you want to understand the mechanism of action of atrazine 

8 or anything else using a rodent model, it's okay if you 

9 are translating the effects on GnRH pulse generator. But 

10 I think you're going to run into trouble if you're going 

11 to translate effects that you study in the rat on LH 

12 surges to the human female.

13

14 DR. JAMES SIMPKINS: Despite our opinion that the pre-ovulatory 

15 LH surge in rodents is not relevant to non-cancer 

16 endpoints in humans, the pre-ovulatory LH surge can and 

17 has been used to gain information about mode of 

18 administration of atrazine, duration of administration and 

19 functional outcomes of presumed reductions in LH.

20

21 I would like to point out to you that the mode of 

22 administration of atrazine has pharmacological 

23 consequences when looking at the LH surge. A 4-day 

24 atrazine administration by a distributed dose in Sprague25 Dawley rats does not reduce pre-ovulatory LH surge if you 

26 administer the same or equivalent doses by bolus. You do 

27 get a high dose of atrazine induced reduction in LH 

28 secretion, so there is a remarkable pharmacological 

29 difference there.

30

31 If one does chronic distributed dose administration in 

32 Sprague-Dawley rats, six months exposure to atrazine, in 
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1 the neuroendocrine aging animal results in prolonged 

2 estrous, and it has been agreed that that is the mechanism 

3 by which tumors in Sprague-Dawley animals are induced. In 

4 Fischer 344 rats, the same distributed dose of atrazine 

5 has no effect on LH secretion. I will quickly show you 

6 datasets relative to that.

7

8 This is a 4-day bolus dosing with atrazine in Sprague9 Dawley rats looking at the pre-ovulatory LH surge, so this 

10 is the afternoon of the day that the animal shows an LH 

11 peak. These are the doses of atrazine that were 

12 administered by bolus (gavage) once per day, and these are 

13 the resulting LH secretions. As you can see, we can dose 

14 up to 50 milligrams per kilograms, which causes an 

15 approximate 50 percent reduction in peak LH secretion with 

16 bolus dosing. In contrast to that, if we do feeding at 

17 roughly equivalent atrazine doses for the same 4-day 

18 period in Sprague-Dawley animals and then monitor their 

19 pre-ovulatory LH surge, we see no effect of atrazine 

20 treatment on the pre-ovulatory LH surge. And again, 

21 Harvey Clewell will present pharmacokinetics data that we 

22 think is relevant to this difference. 

23

24 This is six months distributed dose atrazine in Sprague25 Dawley and Fischer 344 rats. In Sprague-Dawley rats, 

26 high-dose atrazine feeding four/six months markedly blunts 

27 the pre-ovulatory LH surge; whereas in Fischer 344 rats, a 

28 similar feeding paradigm is without effect. So there's 

29 strained difference in the response to feeding.

30

31 In addition to that, we asked the question whether or not 

32 the modest LH suppression achieved with bolus (gavage) 
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1 dosing -- this is in Sprague-Dawley or Long-Evans animals 

2 over broad dose ranges -- or distributed dose feeding of 

3 atrazine over these dose ranges, which are equivalent to 

4 the higher doses given by bolus administration, had 

5 effects on outcomes that would be expected if LH is 

6 suppressed. 

7

8 So we looked at percent of animals that became pregnant 

9 when exposed to a male number of corpora lutea that were 

10 formed, number of implantation sites, number of fetuses. 

11 And the data here are expressed with the zero at 100 

12 percent. As you can see in both Sprague-Dawley and Long13 Evans animals, there is no effect on any of these outcomes 

14 parameters of gavaging or feeding any of the doses of 

15 atrazine. So we are not entirely sure there is a 

16 functional outcome of the modest LH suppression that is 

17 achieved.

18

19 I will not present the development data because we fully 

20 agree with EPA's position. And then the final issue we 

21 addressed was the animal that‟s been proposed for the 

22 point of departure determination, and that is the young 

23 adult female Long-Evans rat, and we have a bit of a 

24 problem with that for a couple of reasons. 

25

26 In a CODAR study recently submitted to the EPA, greater 

27 than half of the Long-Evans animals in the study, before 

28 they were treated with atrazine, failed to show normal 4-

29 to 5-day estrous cycles. And it has been known for some 

30 time that treatments with high doses of atrazine by gavage 

31 results in a further disruption of estrous cycles in Long32 Evans animals. 
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1

2 So we have an animal we think that is not stable for doing 

3 these kinds of studies. These are the data -- and I 

4 apologize for the small size. I'll point to what I think 

5 is the relevant issues. These are the Long-Evans animals, 

6 and again, these are prior to exposure to atrazine. 38 

7 percent of the Long-Evans animals; 306 total animals, 38 

8 percent had normal 4-day estrous cycles. 62 percent had 

9 abnormalities of one or another type. In contrast to 

10 that, the Sprague-Dawley animals, about 60 percent of the 

11 animals had normal estrous cycles. So we think the Long12 Evans rat has an unstable estrous cycle.

13

14 So to summarize, we do believe that Pulsatile LH secretion 

15 is more relevant to do non-cancer endpoint assessments 

16 than the LH surge, more relevant to humans. Distributed 

17 dosing is more like the exposure to which the vast 

18 majority of people are exposed to atrazine and it has not 

19 been routinely studies. In fact, distributed dosing is 

20 not being used in setting the point of departure. We are 

21 failing to find the functional outcome of the suppression 

22 of LH that occurs with bolus dosing of atrazine. 

23 Developing animals are indeed not more sensitive than 

24 adults to atrazine. And finally, Long-Evans animals, we 

25 think, have a very unstable estrous cycle. And with that, 

26 I'll stop.

27

28 DR. DANIEL SCHLENK: Okay. Any questions for the panel? Jan, 

29 Dr. Chambers?

30
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1 DR. JANICE CHAMBERS: Dr. Simpkins, do you have a reason to 

2 suggest for the difference between the Fischers and the 

3 Sprague-Dawley rats in their responses?

4

5 DR. JAMES SIMPKINS: Responses to functional outcomes or 

6 estrous cycle?

7

8 DR. JANICE CHAMBERS: You showed a graph where the Sprague9 Dawleys responded and the Fischer 344 did not. Is there 

10 any reason that you have for the difference there?

11

12 DR. JAMES SIMPKINS: Well, what we know, this study is 

13 essentially a combination of chronic exposure to atrazine 

14 while animals are aging. And so, we know for example, in 

15 Sprague-Dawleys that by the time of this LH observation, 

16 many of those animals have gone from normal estrous cycles 

17 to persistent estrous. That is, they are not able to 

18 mount a pre-ovulatory type LH surge probably because the 

19 neuromechanism is no longer working sufficiently.

20

21 We also know that in the Fischer 344 rats, those animals 

22 can age to as long as two years, and in our hands, have 

23 absolutely normally regulation of LH secretion. The 

24 problem they experienced during their aging is they cannot

25 suppress prolactin secretion so they suffer from a 

26 hyperprolactinemia, that causes retention of corpus 

27 luteum, and they go into a persistent diestrus state.

28

29 But their regulation of LH secretion is preserved even 

30 into very late life, and we think that‟s the explanation 

31 for why atrazine is not adversely affecting LH secretion 

32 in the Fischer. They just have a more robust regulatory 
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1 system for LH secretion, whereas the Sprague-Dawley animal 

2 is actually breaking down over their regulation of LH 

3 secretion, is aging, and then you superimpose the atrazine 

4 insult on that, and that happens earlier in their life and 

5 so we detect it by the six months of feeding.

6

7 DR. JAMES MCMANAMAN: So let me get this straight. You believe 

8 that the LH surge in the rat is totally due to a 

9 hypothalamic effect, and the human, it would be due to a 

10 pituitary effect, and that atrazine, you believe, acts 

11 solely at the level of the hypothalamus, therefore would 

12 not effect the LH surge in the human because that is 

13 through the pituitary; is that correct?

14

15 DR. TONY PLANT: Yeah. In the rat, clearly it's a hypothalamic 

16 action of estradiol which, together with the circadian 

17 cycle, triggers the GnRH surge.

18

19 In the human, you have a hypothalamic component. You have 

20 to have GnRH pulsatility. Without that, you will have 

21 amenorary cyclicity. So it's a combination of a pulsatile 

22 hypothalamic input and an effect of estrogen at the level 

23 of the pituitary.

24

25 Now, we are told though -- or I'm told -- I mean, I'm not 

26 an expert on the rat estrous cycle, but that the effect of 

27 atrazine is hypothalamic, is a brain effect. I think that 

28 was quoted recently. And what effects does atrazine or 

29 any of these compounds have on the GnRH pulse generator is 

30 much less clear, but there is no reason why it shouldn't

31 have an action. That‟s what should be investigated for 

32 relevance to the human.
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1

2 DR. JAMES SIMPKINS: If I can add, Dr. Handa, do you want to 

3 respond to that question?

4

5 DR. ROBERT HANDA: We've done studies looking at the pulse 

6 generator in the rat and atrazine has an effect at very 

7 high doses in lengthening the pulse period, but that 

8 doesn't occur until 200 milligrams per kilograms in our 

9 hands. We have also looked at pituitary effects of 

10 atrazine and we've found no effects of atrazine on 

11 pituitary sensitivity to GnRH even at very high levels.

12

13 DR. JAMES MCMANAMAN: I just want to clarify why it's the 

14 pulsatility versus the surge component that you thought 

15 was the most relevant, and I think that you've answered 

16 the question but I just want to double check on that.

17

18 DR. TONY PLANT: Well, all the evidence for the human female 

19 suggests there is not a GnRH surge. Now that‟s indirect. 

20 But if you don't have a GnRH surge then you can't 

21 translate the findings in the rodent where the LH surge is 

22 driven by a GnRH surge to the human female.

23

24 DR. KEVIN O'BYRNE: I'm delighted to see the distribution 

25 dosing. It took a long time for that to come. But why 

26 wasn't it put in the water, the atrazine?

27

28 DR. CHARLES BRECKENRIDGE: The problem of achieving a high dose 

29 in water is the water solubility limit of the compound, so 

30 it can put approximately 20 part per million. And so, you 

31 cannot actually get to an effective dose at a solubility 

32 limit. So that is essentially the reason that we needed 
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1 to go higher. And by incorporating it in the feed, you 

2 can actually get a distributed dose and have the animal 

3 consume considerably more.

4

5 DR. KEVIN O'BYRNE: This separates toxicology from physiology.

6

7 DR. SUSAN AKANA: Could you remind me with the distributed drug 

8 dosing whether all the animals voluntarily ate the diet 

9 with the atrazine? 

10

11 DR. CHARLES BRECKENRIDGE: We've done many studies with feeding 

12 of atrazine to animals, and there is an initial body 

13 weight suppressive effect of the compound and that‟s 

14 coupled with a food intake reduction. There is a 

15 reasonably rapid recovery of that food intake reduction 

16 that happens, and it stays stable throughout. There is a 

17 possibility that those effects on body weight and food 

18 intake are actually mediated through some direct action in 

19 the hypothalamus, but it seems like the principle interest 

20 was on the endocrine response. We don't understand that 

21 transient change, but we note it does occur regularly, and 

22 that happens even if you give it in bolus dose 

23 administration.

24

25 DR. KATHERINE ROBY: And what will be known about a change or 

26 differential absorption given in feed versus either water 

27 or a bolus?

28

29 DR. CHARLES BRECKENRIDGE: We're exploring that question in our 

30 monkey study, effectively. We're interested not, 

31 obviously, in feed, so we're trying to move away from 

32 that. Typically in the bolus dose scenario, people use 
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1 five percent or one percent CMC suspension, and 

2 effectively, one would anticipate at high concentrations 

3 that could modify absorptions. We have evidence that that 

4 actually is occurring.

5

6 In a second phase of our primate study, we took atrazine 

7 and put it in five percent ethanol water and we were able 

8 to achieve a hundred part per million under those 

9 circumstances and be able to compare the pharmacokinetics 

10 of aqueous mediated formulation as apposed to a CMC. It's 

11 our intent to try to do that same study in the monkey with 

12 only 20 part per million in water.

13

14 You start to get to limits of detection issues in terms of 

15 picking up metabolites in plasma, but we think we can 

16 actually achieve that by virtue of the volume of water we 

17 might administer to those animals, and the highest 

18 achievable water concentration. And by that way, we can 

19 hope to get around the question of absorption and 

20 presumably what we observe when atrazine is in water, is 

21 that kinetics of absorption are rapid. And we suspect 

22 that we eliminate the potential for gut metabolism, which 

23 can further confound the question, which I think I had 

24 come up at some of the previous SAPs. So that‟s our 

25 strategy to try to understand the impact of the vehicle on 

26 kinetics of absorption.

27

28 DR. JAMES MCMANAMAN: So just let me clarify because I think 

29 I'm hearing maybe different things. Syngenta's position 

30 is that atrazine does have an effect on the hypothalamus 

31 or does not have an effect on the hypothalamus?

32
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1 DR. CHARLES BRECKENRIDGE: You know, you're asking a very 

2 difficult question as to exactly where the molecular 

3 target is, and we don't know where the molecular target 

4 is. We believe it is in the hypothalamus for probably the 

5 pulse generator and the LH surge, but we have been 

6 pursuing that over the years and we still do not have that 

7 knowledge.

8

9 DR. JAMES MCMANAMAN: A follow-up on that then: So the weight 

10 loss effects, you wouldn't attribute that to an LH effect, 

11 would you?

12

13 DR. CHARLES BRECKENRIDGE: No, I wouldn't, but I wouldn't 

14 discount the possibility that it is somehow affecting 

15 food-intake centers in the brain. I mean, that‟s always a 

16 conceivable outcome. It could also reflect just general 

17 toxicity.

18

19 DR. JAMES MCMANAMAN: So, if it is affecting the food intake, 

20 the food regulatory centers and the hypothalamus, then 

21 perhaps by focusing solely on the LH, we might be missing 

22 an important effect of atrazine.

23

24 DR. CHARLES BRECKENRIDGE: I imagine the simple answer to that 

25 is the dose is whereby the food intake effect disappear or 

26 are substantially higher than the doses, were the LH 

27 effects are observed. So to that extent, the agency's 

28 choice of the LH surge is protected.

29

30 DR. JAMES MCMANAMAN: So in regards to the neonates whereas the 

31 LH may not be anymore sensitive or less sensitive -- any 

32 difference in sensitivity than the adults -- that might 
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1 not be true for food intake. For instance, it's known 

2 that the food regulatory mechanisms develop, at least in 

3 rodents, earlier in life, and it develops through the 

4 hypothalamus, so that might be a key thing to be concerned 

5 about.

6

7 DR. CHARLES BRECKENRIDGE: We have probably extensive data on 

8 reproductive measurements of food intake in neonatal pups. 

9 And again, I think then the point of reference becomes 

10 evidence of pulse generation change in those developing 

11 animals. And most people believe that vaginal opening and 

12 preputial separation are coupled to the operation of a 

13 pulse generator. We see effects on that pulse generator 

14 in the range of 10 to 15 milligrams per kilograms. Food 

15 intake effects, I believe, tend to occur at much higher 

16 doses. So I think that could be looked at carefully, just 

17 to explore that question. Thank you.

18

19 DR. TONY PLANT: Just to comment on the infant and the maturity 

20 of the hypothalamus; I mean, the infant primate, the GnRH 

21 pulse generator is robust, it's functioning. And this is 

22 a fundamental difference in the ontogeny of the 

23 reproductive axis between a rodent and a primate.

24

25 DR. KEVIN O'BYRNE: Could you possibly comment on the 

26 distribution of atrazine in the various tissues in rodents 

27 and primates?

28

29 DR. CHARLES BRECKENRIDGE: Thanks for that question. I think 

30 it's going to be answered by the next speaker, and so, 

31 perhaps that would be a nice segway into our next speaker. 

32
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1 DR. DANIEL SCHLENK: Thanks. We'll move ahead with Dr. 

2 Clewell, is it?

3

4 DR. HARVEY CLEWELL: Okay. Thank you. In the September 

5 meeting, David Kim who was with Syngenta at the time 

6 presented a number of pharmacokinetics studies that had

7 been performed by Syngenta and promised that at the next 

8 meeting he would present a PBPK model based on those data. 

9 However, David is now in Africa. He lives and works in 

10 Africa doing completely different work, and so I'll be 

11 presenting in his place.

12

13 It is actually an effort that has been carried out between 

14 several of us at the Hamner, Mel Andersen, Jerry Campbell 

15 and myself along with David and Charles and others at 

16 Syngenta to try to design studies that are informative for 

17 a model and then develop the model on that basis.

18

19 So, I'm going to briefly talk about the questions relating 

20 to the nature of a pharmacokinetic model that could be 

21 used to help inform the relationship between animal and 

22 human dosimetry for atrazine in support of risk 

23 characterizations, and then I'll actually go through the 

24 steps in the development of the refined model.

25 

26 It's called a refined model because an initial atrazine 

27 model was developed in Colorado State by Tammy McMullin 

28 and Mel Andersen and others, and we began with that as our 

29 starting point. It was a rat model. We have expanded it 

30 to be also a model for monkey and human, and I'll just 

31 take you through those steps. And then at the end, I'll 

32 very briefly mention how this model can be used and has 
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1 been used by others to evaluate margins of internal 

2 exposure.

3

4 So first of all, comparing what seems to be called EPA's 

5 simplified model; the total radioactivity-based estimates 

6 of pharmacokinetics with a PBPK approach. I've spent many 

7 years trying to justify why I go through the trouble of 

8 developing PBPK models when you could use a compartmental 

9 model instead, so I'll try to kind of take you along on 

10 that.

11

12 This slide shows the known metabolites of atrazine. I've 

13 put red boxes around atrazine and the metabolites that are 

14 considered to be active in the sense they still have the 

15 chlorine in the key position so that they are part of the 

16 common mechanism group, if you will.

17

18 Those are the desethyl, desisopropyl, initial metabolites 

19 which are subsequently metabolised to DACT. All the other 

20 unboxed metabolites are subsequent to glutathione 

21 conjugation which replaces the chlorine with the 

22 glutathione conjugate and are considered, generally, 

23 considered inactive.

24

25 The box in the top on the left, which is called other 

26 oxidative metabolites? -- is because Ernie Hodgson's lab 

27 recently looked at atrazine metabolism in microsomes and 

28 identified a couple of oxidative metabolites of atrazine 

29 apart from the three that had been known historically. 

30 But those have not yet been identified in vivo. So we do 

31 not actually know if, quantitatively, they are produced in 
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1 vivo in amounts that are significant, and I'll show you 

2 later why I don't think so.

3

4 Finally, I want to emphasize this dotted arrow over here 

5 from DACT to protein adducts. One of the things that we 

6 were reminded of by Mel Andersen, who remembers well 

7 because he was involved at the time, is that Colorado 

8 State looked carefully at the impact of protein adducts of 

9 DACT on the concentration of radioactivity in the blood 

10 and plasmas, in the red cell in plasmas, so I'll show you 

11 that data.

12

13 This shows studies that were done in actually Tammy 

14 Mullin's first paper, looking at red cell binding, and 

15 they determined that -- and I think it's been mentioned 

16 earlier that as much as two percent of the oral dose binds 

17 to the hemoglobin in the red cells of the rat. The red 

18 cells in the rat are different from those of other species 

19 in having a cysteine that is very open to adduction.

20

21 DACT does react with that cysteine-125 and this is, as I 

22 said, an example, as dimethyl arsenic also binds in this 

23 way to rat red cells. And so, that is a consideration for 

24 the disposition. But as long as you are measuring plasma 

25 samples, then it doesn't really matter in terms of the 

26 dose metric measurements. But a similar effect occurs in 

27 the plasma. DACT was also shown by Colorado State to 

28 react with cysteine and albumin and in similar fashion to 

29 that with hemoglobin. And they actually showed nice proof 

30 of it. It turns out that the half-life of the adduct is 

31 consistent with the turnover of albumin in rat plasma, so 

32 that‟s undoubtedly what you're actually seeing.
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1

2 Tammy McMullin did a nice simulation when she took 

3 Timchalk's data on the total 14C in the plasma and then 

4 ran the model to show what the total chlorotriazine 

5 concentrations would be. And you can see that the tail is 

6 due to the bound material that DACT bound to the plasma 

7 element.

8

9 You can see that is responsible for the terminal half10 life, which is on the order of 60 hours, as I recall. And 

11 that terminal half-life does not occur for the free 

12 compounds. DACT has got the longest half-life that‟s on 

13 the order of six or eight hours, as I recall.

14

15 So that‟s kind of important as it turns out for 

16 understanding what would be a good dose metric. And so, 

17 looking at the 14C atrazine-equivalence in plasma, it's 

18 been pointed out that they account for total mass of 

19 atrazine derived metabolites, atrazine derived 

20 metabolites. So that‟s a blessing and a curse.

21

The problem is that 14 22 C includes compounds that are active 

23 and compounds that are inactive. It's not necessarily 

24 conservative to lump everything in to a measurement. In 

25 fact, in order for it to be an accurate basis for cross26 species extrapolation, it would have to be true that there 

27 was a similar fraction of relationship of active to 

28 inactive metabolites in both species. And so, it could be 

29 conservative or anti-conservative depending on whether the 

30 glutathione pathway versus the oxidative pathways had 

31 different relationships, which they very often do.

32
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1 The terminal half-life has the disadvantage that it is 

2 driven actually by the covalent binding in the plasma, 

3 which means that it's really reflecting albumin turnover 

4 in the plasma, and so it is not a good metric for target 

5 tissue exposure to active compounds.

6

7 Another disadvantage is that there is no human 14C data. 

8 I like to say that risk-assessment is a ratio business. 

9 If you want to relate human toxicity to animal studies, 

10 you got to have information in both. So if you don't have 

11 human 14C data which, of course, is hard to collect, then 

12 you have to use default allometric scaling. And I myself 

13 am not against default allometric scaling. This is what I 

14 would've done in this situation.

15

16 I like to use the term chemical-specific adjustment factor

17 which is what IPCS likes to call these kinds of cross18 species relationship factors. And so the animal to human 

19 kinetic relationship using default allometric scaling, 

20 which is body weight to the three-quarters, is on the 

21 order of three, and that‟s what EPA used.

22

23 And they say in their report that, basically, for the same 

24 applied dose, then you would have roughly a three-fold 

25 higher steady state plasma level predicted in a human 

26 compared to the rat. 

27

28 We developed the PBPK model basically to get away from the 

29 problem of a dose measure where you don't know to what 

30 extent it's based on active materials. And so, what we 

31 worried about doing was accounting for the majority or the 

32 active species. And one of the things we tried to do is 
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1 determine are the four that have traditionally been listed 

2 as the active species, do they seem to actually account 

3 for the metabolism of atrazine.

4

5 It's difficult to assure that kind of mass balance because 

6 of the complexity of the glutathione conjugates and down7 stream metabolites were captured at 16 conjugates. I mean 

8 it is very messy, so it's hard to look for all those 

9 things. 

10

11 But actually, Syngenta is very aggressive in trying to do 

12 that study in the monkey and Mel and I are both really 

13 pleased that -- you know, we basically have asked for an 

14 awful lot here and they are going to try to do a very, 

15 very thorough study to identify metabolites in the monkey.

16

17 There is data in both rat and human. And one of the 

18 things we in risk-assessment have to deal with more and 

19 more is that the chemicals that were studied to death in 

20 the human are being taken care of and we are stuck now 

21 with the chemicals where there is not a lot of human data. 

22 And so you cannot calibrate a human model based on a 

23 number of in vivo studies; you actually have to use more 

24 indirect ways of doing it.

25

26 But there is actually a human study where the half-life 

27 for DACT, which is almost all of the exposure was 

28 measured, and we have used the model that I'll describe to 

29 you to look at the cross-species equivalence. What we 

30 found is that it indicates similar internal exposures at 

31 roughly the same ingested dose rate. So the CSAF is about 
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1 1.5. In other words, the human has about 50 percent 

2 greater plasma level at the same dose.

3

4 So what we included in the model were, of course, atrazine 

5 and its oxidative chlorometabolites, so that‟s desethyl, 

6 desisopropyl and DACT. Then we are now trying to improve 

7 our description of the conjugation metabolites, the 

8 glutathione conjugates and downstream metabolites in order 

9 to be able to use the model to evaluate mass balance. And 

10 we are adding description of the adduction to plasma 

11 proteins so that we can actually do some simulations to 

12 compare 14C-based dosimetry with active compound-based 

13 dosimetry under different exposure situations.

14

15 So now I'm going to go through a description of the model 

16 that we've developed. We had a hepatic metabolism from in 

17 vitro studies in the rat, human, and now the monkey. We 

18 had Jeff Fisher when he was at Georgia determine the 

19 partition coefficients in vitro. There is not a very high 

20 distribution of atrazine of its metabolites into tissues. 

21 It's a fairly even distribution around the body.

22

23 We have beautiful datasets on the kinetic differences of 

24 bolus dose and distributed dietary dosing in the rat. 

25 There are also studies in the monkey that are going to 

26 look at different vehicles, comparing water and ethanol 

27 and CMC slurry, as Charles mentioned. The monkey data, in 

28 particular, is going to, as I said, have very extensive 

29 characterization of the various metabolites. 

30

31 This shows the in vitro data that was a study that David 

32 Kim designed. You can see atrazine, DEA, DIA and then 
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1 DACT. The red lines are the rat. The blue curves are the 

2 human. There is a difference in the relative split 

3 between the two intermediate metabolites across the 

4 species. Two different concentrations were used. We used 

5 this data to estimate the metabolic parameters, a nice 

6 spacing of the data across the four-hour period of the 

7 study.

8

9 So the description that we used, we actually had to model 

10 that in vitro data, so we modelled the in vitro system, 

11 including competitive metabolic inhibition, which had been 

12 described earlier at Colorado State. And Jerry Campbell 

13 did this work and he actually had to account for the loss 

14 in viability of the rat hepatocytes during the assay 

15 because four hours is really kind of long to have a 

16 hepatocyte functioning. 

17

18 So, by adjusting for the viability of the hepatocytes, we 

19 were able to actually obtain good data even on the DACT, 

20 which takes a while to appear. And so, then we used 

21 affinity constants from Tammy McMullin's study which were 

22 at higher concentrations and so were better for 

23 determining a KM. And then we re-estimated the Vmax's for 

24 the conversion of atrazine and the metabolites.

25

26 This shows the model simulation of the rat hepatocyte 

27 data. You can see it correctly describes the loss of 

28 atrazine, the production of the DIA and DEA and their 

29 subsequent conversion into DACT. 

30

31 I really want to emphasize the fact that -- so the model 

32 has no compartment per other metabolites. So this 
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1 demonstrates that there is a mass balance between the loss 

2 of atrazine and the production of DIA, DEA and DACT in 

3 this in vitro system. That‟s really all that was going 

4 on. There's not a mystery metabolite that everybody has 

5 missed. 

6

7 This shows the same analysis with the human data and, 

8 again, you can see that we were able to coherently 

9 describe the conversion of atrazine to DIA, DEA and their 

10 subsequent conversion to DACT quantitatively with the 

11 model.

12

13 So then, that gave us our in vitro metabolism. We had our 

14 partition coefficient physiological parameters, and then 

15 this is the study that was performed for actually 

16 evaluating the model's prediction of in vivo kinetics. 

17 There was three different dietary concentrations, three 

18 different gavage doses and a very, very high rate of 

19 sampling that actually required parallel groups because 

20 you cannot actually sample animals that frequently, so two 

21 groups were used to go back and forth and get a very dense 

22 spacing for the data.

23

24 Four-day study; you can see here a pulse each day of 

25 atrazine for the gavage in red, and it has broaden peaks 

26 for DACT because there is longer half-life. You can see 

27 for the distributed dietary dosing is the blue lines, and 

28 it's a much flatter profile. You can see here the DACT 

29 comes up to about half of the peak value. 

30

31 You can't see the Y axis values, but I can tell you that 

32 DACT accounts for almost all of the exposure. It is very 
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1 rapid metabolism of atrazine and the intermediate 

2 metabolite is 2 DACTs and then DACT has a longer half3 life. 

4

5 You saw this LH surge data before. What I wanted to point 

6 out was that it is interesting to see that you have an 

7 apparently greater potency for gavage than for diet. And 

8 if you look at the kinetic time courses for those two 

9 administrations, you can see very high peaks exposures, 

10 particularly looking at the DACT which, as I say, is most 

11 of the exposure. And Colorado State shows the DACT is 

12 roughly equal potent with atrazine for the LH effects.

13

14 So the peak values with the gavage study are roughly a 

15 factor of two greater than for the dietary study, even 

16 though the area under the curves, are similar. So this 

17 suggests a nonlinear relationship between the kinetic of 

18 target tissue dosimetry and the nature of the response; 

19 actually suggest perhaps time above a critical 

20 concentration might be a good metric. But one of the 

21 things we like to do with the kinetic data is to try to 

22 get a good idea of the nature of the dynamic relationship.

23

24 So, just to review, we took the PBPK model of Tammy 

25 McMullin and added physiological parameters for the monkey 

26 and the human from the literature, added additional target 

27 tissue compartments, used the metabolic rates from our in 

28 vitro modelling and partition coefficients from Jeff 

29 Fisher, and then we simplified the description of oral 

30 uptake because we were modelling lower doses of 

31 administration where the kinetics of oral uptake was not 

32 as complicated and it makes for a simpler description.
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1

2 This is the model. It looks complicated because you have 

3 four different compounds you are tracking simultaneously, 

4 which are interconverted by metabolism in the liver. The 

5 oral uptake was modeled as a slurry portion and a soluble 

6 portion. Below about 20 milligrams per kilogram, I think 

7 it is, that it all ends up being just in the soluble dose. 

8 So for human exposures, for example, it's all in the 

9 soluble compartment. And then there's also glutathione 

10 conjugation in the liver.

11

12 This shows the predictions of the in vivo data. In this 

13 case, this is the single dose data. We used this data to 

14 estimate the oral uptake parameters. So we had the 

15 metabolism petitioning disposition, was already determined 

16 from in vitro studies. The only thing we could not do in 

17 vitro was oral uptake. So we estimated the rate of oral 

18 uptake for atrazine and its DIA and DEA. And we were able 

19 to get a nice reproduction with the model of the time 

20 course for the four materials. So this is in vivo then 

21 for a single gavage.

22

23 Now, what we did then was to use that model for the 4-day 

24 administration data, a much richer dataset. And you can 

25 see DACT, as I said, is the major contributor, and the 

26 model does a beautiful job of reproducing the time course 

27 for DACT in the rats over the 4-day exposure period.

28

29 What I was really impressed by was, however, it was the 

30 dietary administration. When they did the study they 

31 actually measured the food ingestion during the dark and 

32 light cycle so that we could input that into the model 
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1 which produces the diurnal cycles of DACT concentration 

2 which perfectly mimics the data. As you can see, that 

3 wave effect is because each day the animals are eating 

4 more at night than they are during the day.

5

6 So this is extrapolation; it's not root to root, I guess. 

7 It's administration form to administration form from 

8 gavage to a dietary. No parameters in the model were 

9 changed in order to predict the dietary as opposed to the 

10 gavage. We are not done with the model. In particular, 

11 we're really focusing on the monkey now, but as I had 

12 mentioned, we are going to incorporate plasma protein 

13 binding. We are doing more work on the formation of 

14 glutathione conjugate in vitro and verifying partition 

15 coefficients that were done in vitro with in vivo tissue 

16 disposition data just to confirm them.

17

18 Then with the monkey study, we will be looking 

19 particularly at the glutathione conjugates and 

20 mercapturates, and the developing data by which we can 

21 estimate with the mild urinary and fecal elimination rates 

22 and try to calculate with the model an in vivo metabolite 

23 mass balance. 

24

25 In particular, we will be -- we -- I won't be doing any of 

26 the work on this study but I'll be modeling it later. We 

27 will be looking for the newly postulated oxidative 

28 metabolites from Ernie Hodgson's lab to see whether they 

29 are produced in any quantity in vivo.

30 And then, the major work that we have ahead of us once we 

31 finish with this monkey elaboration is to do a model32 sensitivity uncertainty analysis to characterize the 
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1 propagation of the uncertainty from the model inputs to 

2 the prediction of dose metrics for the animal and the 

3 human.

4

5 Finally, what we're going to do with this model -- so we 

6 have a model for the rat, monkey and human. The monkey 

7 really is to help us define the human model with a more 

8 appropriate surrogate than the rat, since you cannot 

9 collect as much data in the human as you can in the monkey 

10 or rat.

11

12 We have already had an initial evaluation of this model by 

13 Battelle, Pacific Northwest Laboratories. We asked them 

14 to follow the new World Health Organization's guidance on 

15 evaluation of PBPK models for risk assessment. They 

16 actually are the contractor for EPA NCEA for evaluation of 

17 PBPK models so they know what they're doing. They 

18 concluded that the model was credible, reliable and 

19 applicable for this. That it is "fit for purpose" is 

20 another way of putting it. Everybody has their favorite

21 terms for just saying it works.

22

23 We are still improving the model, but I think that right 

24 now I am comfortable that the model accounts for the 

25 active forms of atrazine and its metabolites and that that 

26 is a better way to do the cross-species dosimetry. 

27 What you'll hear about next is the model is used to 

28 predict human plasmas total chlorotriazines, the four 

29 compounds, resulting from drinking water exposures in 

30 order to get margin of exposure for effects in the rat 

31 studies.

32
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1 DR. DANIEL SCHLENK: Okay. Thank you, Dr. Clewell. Let's go 

2 ahead and address questions of the model right now because 

3 I think we're going to be getting into more of the risk4 assessment component after this. So let's go ahead and do 

5 that; yeah, so, Dr. Greenwood?

6

7 DR. RICHARD GREENWOOD: If you look at slide 30 where you were 

8 looking at the area under the curve for the two, I really 

9 do not believe that the area under the gavage curve is the 

10 same as the area under the distributed dosing curve. Have 

11 you actually measured them or is it just --

12

13 DR. HARVEY CLEWELL: Oh, yes; we characterized those. I don't 

14 actually remember. Frankly, I don't pay much attention to 

15 the non-model-based comparisons. 

16

17 DR. RICHARD GREENWOOD: I would say it is at least double. 

18 You have a look at that. I have looked at DIA, and if you 

19 take one of those peaks and then you lay that flat, two of 

20 those peaks, you're at least going to cover the blue 

21 lines. So if you are thinking in terms of the area under 

22 the curve, I suggest that that is something that really 

23 ought to be checked. Before you make a statement like 

24 that you really ought to measure them. I don't believe, 

25 just looking at those, that that‟s going to be the case, 

26 that they're similar.

27

28 DR. HARVEY CLEWELL: Okay. 

29

30 DR. RICHARD GREENWOOD: So I think that really is something 

31 that needs checking. The other thing that hit me, because 

32 I was really impressed with the fit, on slide 35 I was 
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1 really impressed with the fits there for the dietary 

2 exposure. I think those are excellent, but at the end you 

3 are actually under-predicting quite markedly, not just for 

4 DACT; I could understand that because I think well maybe 

5 it is binding, as you said. I think that depends on 

6 turnover of albumin. But what about DIA and DEA, because 

7 they are similarly over-predicted at longer times by the 

8 model? The fit is wonderful up to there. Have you any 

9 explanation?

10

11 DR. HARVEY CLEWELL: Actually, I forgot to point out what that 

12 horizontal line in each of these plots is. That‟s the 

13 limit of quantification. So actually we are fighting with 

14 the question of is that a real measurement or not. We see 

15 this, and very often that we get data that is below or in 

16 the vicinity of the limited quantification and then we're 

17 really not sure what it's meaning is. So in the case of 

18 DACT, this would not be because of the binding to albumin 

19 because this is free DACT that is being measured, and so 

20 the covalently bound would not be included, so even in 

21 that case it's not clear what that reflects. Charles, did 

22 you want to say something?

23

24 DR. CHARLES BRECKENRIDGE: Yes. I think that explains a lot. 

25 It's very difficult to put a number below the level of 

26 quantification. Thank you. 

27

28 DR. DANIEL SCHLENK: I have a quick question for you. On slide 

29 26 and 27, the rat hepatocyte and human hepatocyte data in 

30 your statement there is that atrazine disappearance 

31 completely accounted for by production of the three phase 

32 1 metabolites. If you are doing hepatocytes wouldn't you 
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1 expect to see the glutathione adduct as well being 

2 produced in that?

3

4 DR. HARVEY CLEWELL: I believe it's a matter of rate. The 

5 oxidative metabolism happens very fast. 

6

7 DR. DANIEL SCHLENK: So would the GST though. I mean, you 

8 should get the primary metabolite off of atrazine. You 

9 should get that adduct immediately.

10

11 DR. HARVEY CLEWELL: You can see the slight decrease in DACT, 

12 later on, most likely reflects glutathione conjugation. 

13 The rate was rather slow. 

14

15 DR. DANIEL SCHLENK: Well, again, if you go back to metabolic 

16 scheme, for example, if you go straight down from your 

17 atrazine to your first GST adduct, that doesn't require 

18 any oxidative metabolism and that should take place 

19 immediately. If I understand it, I am pretty sure that is 

20 the primary metabolite of atrazine metabolism, isn't it?

21

22 DR. HARVEY CLEWELL: No, I don't believe so, actually. When 

23 you have high affinity, oxidative metabolism like this, 

24 the concentrations of the compound in the liver stay very 

25 low, and the glutathione pathway generally is actually 

26 quite slow.

27

28 At very high concentrations, you would expect, as you 

29 saturate the oxidative metabolism, you would expect more 

30 production by the glutathione pathway. But we are trying 

31 to characterise the glutathione metabolites better now, as 

32 I mentioned. It does appear that most of it is DACT, 
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1 glutathione conjugates, which makes sense because DACT 

2 isn't subsequently oxidatively metabolized, so it 

3 circulates while it's being conjugated.

4

5 And we will be running cytosol experiments looking 

6 especially for rates or reaction with glutathione, both 

7 with and without GST. So we will be in a better position 

8 to address that. Unfortunately, there really has only 

9 been identification data and really not quantitative 

10 characterization of which are the key glutathione 

11 conjugate metabolites.

12

13 DR. DANIEL SCHLENK: Yes. It just seems a bit overblown that 

14 you say it completely accounted for production. That just 

15 doesn't jive with what you would expect to see in 

16 hepatocytes, I guess, that‟s my whole point.

17

18 DR. CHARLES BRECKENRIDGE: Dr. Schlenk, could I perhaps try to 

19 address that question? We are doing cytosolic fraction 

20 studies with hepatocytes so that we can remove oxidative 

21 metabolism from the picture altogether. Like Dr. Clewell 

22 mentioned, we can actually then evaluate the rates of 

23 formation there. So that‟s the first line of attack.

24

25 We also note that, as you cryofreeze hepatocytes for the 

26 purposes of processing, you actually deplete the GSH, and 

27 perhaps that is maybe complementing to the fact of not 

28 observing. The hepatocytes are obviously taken from whole 

29 animals and provided to the lab for assessment and they 

30 have to be provided in that manner. So it perhaps is a 

31 component of depletion of GSH that could be happening.

32
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1 So we're trying to address the rate by actually looking at 

2 freshly harvested livers and looking at the cytosolic 

3 fractions.

4

5 DR. DANIEL SCHLENK: Great; thanks. Dr. Chambers?

6

7 DR. JANICE CHAMBERS: Two questions, Dr. Clewell. Do you 

8 consider any of the protein binding to be reversible or is 

9 it all covalent? 

10

11 DR. HARVEY CLEWELL: That would probably be a better question 

12 for Mel Andersen, if we could get him to come up here. 

13

14 DR. MEL ANDERSEN: Mel Anderson from the Hamner Institutes in 

15 North Carolina. The binding that‟s been observed in the 

16 red cells and in the plasma is covalent. That has been 

17 actually evaluated by direct evaluation of the adducts. 

18 We don't have any evidence. I have done kinetic models 

19 for things that bind strongly. I have no evidence with 

20 atrazine that any of this is strongly bound, but 

21 reversible. 

22

23 DR. JANICE CHAMBERS: Thank you. And the second question is, 

24 do you have enough information about the partition 

25 coefficients to estimate how much is getting into the 

26 hypothalamus or the parts of the brain?

27

28 DR. MEL ANDERSEN: At this point, it appears that there is a 

29 fairly equal distribution of atrazine throughout the 

30 tissues, and it has a partition of about one. What you 

31 see in the blood is about what you see in the tissues. 

32 That's true of all the tissues that have been looked at.
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1

2 DR. CHARLES BRECKENRIDGE: I'd like to add to that. 

3 Effectively in the study where we did the pharmacokinetic 

4 characterization of atrazine, those same animals provided 

5 us with tissue samples on the last blood collection, so we 

6 have the pituitary hypothalamus adrenal gland and we're in 

7 the process of analyzing for the amount of atrazine, DEA, 

8 DIA and DACT, in those tissues, and we already know what 

9 the plasma concentrations are. So that was what we meant 

10 by in vivo characterization of partitioning will verify 

11 the components that were derived from an in vitro modeling 

12 system.

13

14 DR. JAMES MCMANAMAN: Yes, two quick questions. One is that 

15 you show human -- and this is on slide 24 -- human and 

16 rats cells. What cells were those? I mean, they were 

17 hepatocyte, but presumably liver-derived somehow, or are 

18 they cell lines, or what exactly were they?

19

20 DR. CHARLES BRECKENRIDGE: These were human donor samples that 

21 we obtained there. I think there were three different 

22 donors, so they are real obtained, fresh and 

23 cryopreserved.

24

25 DR. JAMES MCMANAMAN: The second question is, do you see any 

26 other protein adducts, for instance, with these cells? 

27 Your primary ones may be albumin or some of the others, 

28 but are there protein adducts within the cell themselves? 

29

30 DR. MEL ANDERSEN: In the original work that was done at 

31 Colorado State to pay for that was published in 2003, we 

32 looked at these terminal half-lives that you see in the 
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1 plasma, as well as we saw them existing in tissues. It 

2 appears that atrazine, at a slow rate, cannot react with a 

3 variety of protein cysteines so you get can some level of 

4 adduction.

5

6 So after the identification of persistence of binding in 

7 the red cells and in the plasma and in tissues, another 

8 student at Colorado State, Greg Dooley -- so Tammy 

9 McMullin did her PhD at Colorado State with me and Bob 

10 Handa, and Greg Dooley with John Tessari and others in the 

11 Department of Biochemistry. He actually evaluated 

12 directly the binding, both the hemoglobin to albumin. And 

13 then they looked at, I believe it was hypothalamic, the 

14 hypothalamic binding. They found a variety of binding 

15 sites in the hypothalamus all associated with cysteine 

16 adducts being formed in the tissue.

17

18 DR. JAMES MCMANAMAN: So they were associated with cysteine 

19 adducts, but did they localize where in the cell those 

20 cysteine adducts were? Were they in the endoplasmic 

21 reticulum? Were they in the mitochondria? Where? Do you 

22 know that information?

23

24 DR. MEL ANDERSEN: It wasn't done by isolation within the cell; 

25 it was done by identification of the proteins to which the 

26 adducts had formed. And they were not proteins that were 

27 expected to be found in any one particular sub-cellular 

28 compartment but more uniformly distributed.

29

30 DR. SUSAN AKANA: Short question. I am back on slide 30. 

31 These animals - were these the first four doses that they 

32 saw either by gavage or the four nights of feeding?
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1

2 DR. CHARLES BRECKENRIDGE: Are you referring to the effects 

3 slide of that slide?

4

5 DR. SUSAN AKANA: Well, what I wanted to know is had they been 

6 maintained on the gavage and the diet for a number of days 

7 before the sampling started or were these the very first 

8 four doses that you are characterising?

9

10 DR. CHARLES BRECKENRIDGE: Again, on the right hand panel, they

11 represent the progression of doses for the animal. So 

12 that animal went on study for blood collection, it was 

13 dosed everyday for four days. And if it was scheduled to 

14 have a blood collection in the latter part of the study, 

15 then that was after having had four days of dosing.

16

17 You cannot bleed the animals continuously through so you 

18 have to have sub-fractions of animals for the purposes of 

19 blood collection. The LH part are the animals that were 

20 treated for four successive days and look for LH surge 

21 suppression at that point in time.

22

23 DR. SUSAN AKANA: My question is, were they being given the 

24 atrazine when there is a known acute effect on food intake 

25 and body weight?

26

27 DR. CHARLES BRECKENRIDGE: So on the first day of 

28 administration, as indicated by the plasma data, that 

29 would be the first day they would have received the 

30 compound. They would have not received the compound prior 

31 to that, they're being gavaged. The other animals are 

32 being fed it. We actually were trying to achieve equal 
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1 doses by means of gavage versus feeding, and we had to 

2 estimate the likelihood that they would reduce their food 

3 intake and we increased the food concentration 

4 appropriately.

5

6 We slightly undershot -- and you can see that 40 mg per kg 

7 was an attempt to achieve a 50 mg per kg oral gavage dose, 

8 and that is attributed to the fact that we miss-guessed 

9 how much we needed to put in the feed, given the reduction 

10 in food consumption that was occurring as a result of the 

11 dosing. So it was our best attempt to achieve equivalence 

12 of dose. We were slightly under in that particular case.

13

14 DR. DANIEL SCHLENK: Okay. If there are quick questions we can 

15 go. My plan is that we would like to finish. We have one 

16 more presentation to go, and then if you guys are 

17 available tomorrow, which I am sure you are, that we could 

18 begin to tomorrow morning with questions as well because 

19 obviously we've had a lot to ponder. So if you can hold 

20 it to tomorrow that would be great. If you'd rather go it 

21 now we can go ahead. Okay. Go ahead, Dr. Greenwood.

22

23 DR. RICHARD GREENWOOD: Going back to slide 30, an explanation 

24 for the sort of behaviour if those areas really are 

25 similar, is that it's not total areas under the curve but 

26 area under the curve over some sort of critical threshold. 

27 Is there any evidence that there is a critical threshold 

28 for this action of atrazine? 

29

30 DR. CHARLES BRECKENRIDGE: It's a very attractive hypothesis. 

31 We haven't yet figured out how to test that hypothesis. 

32 You move from correlation to causation, and that, as you 
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1 know, is always a difficulty and we haven't yet got there. 

2 Thank you.

3

4 DR. DANIEL SCHLENK: Okay. So let's go ahead. Again, we will 

5 have an opportunity tomorrow morning. You guys will be 

6 first off in the morning, if that‟s okay with you. And if 

7 anyone has questions over this evening to come back to the 

8 team, I'm sure that they would be happy to answer those.

9

10 DR. RICHARD GREENWOOD: Okay. And I'll move through this quite 

11 quickly and I'll try to setup a framework in which you can 

12 be able to understand the results that we are now going to 

13 be showing to you. 

14

15 So we're using the model to -- and I'll go to the 

16 schematic on the following page -- we're using the model 

17 that we've generated, and which Dr. Clewell has describe, 

18 to provide a mechanism of calculating internal plasma 

19 concentrations following human exposure to atrazine and 

20 drinking water. So, the water consumption records that we 

21 see on the bottom effectively represent the details that 

22 Dr. Hendley spoke of. That is to say we have intermittent 

23 exposures to different concentrations as the DEA, DIA and 

24 DACT in atrazine and water.

25

26 We've taken those systems that were the most highly 

27 exposed, the 17 CWSs that were selected by the strategy 

28 that he described, and we coupled that schemograph intake 

29 profile with a survey of water intake record.

30

31 There were 885 individuals that were part of a 7-day water 

32 intake survey whereby they reported on an hourly basis 
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1 their water consumption records. And so, we took those as 

2 inputs into the PBPK model and derived from there, tracked 

3 the individual metabolites but calculated the total 

4 chlorotriazine concentration. 

5

6 At the same time, we took a point of departure from the 

7 animal model, such that was described by Dr. Rodriguez, 

8 and converted into an internal plasma concentration for 

9 TCT. So now we have a reference point of no effect 

10 compared to exposure, and that magnitude difference is a 

11 margin of exposure, so it's a ratio of those two numbers. 

12 And we will be reporting throughout those data.

13

14 I won't go through this tiered strategy, but let me just 

15 say that we're actually using the 99.9th percentile of the 

16 MOE distributions. For each simulation we had roughly 

17 10,000 iterations of the calculations of the MOE. We do 

18 that in the one hand by using a standard case with 28 

19 subjects calculating a rolling 4-day average, 

20 sequentially, through the entire year. So there's 362 

21 values, and from there you calculate distributions of 

22 MOEs, and we are going to report to you the 99th 

23 percentile of those distributions.

24

25 The standard case that we used was chosen as a point of 

26 reference so that other points of departure could be 

27 considered and we did a series of sensitive analysis. 

28 I'll try to move quickly through this. This is the 17 

29 CWSs that we selected, the most highly exposed CWSs, and 

30 we are reporting now. In the top part you'll see the 

31 distributed dose NOAEL we picked from our feeding study 

32 that had no effect on the LH surge, 50 milligram per kg.
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1

2 We used two metrics for characterization. One was a TCT 

3 peak and the other was TCT area under the curve. The 

4 second one you see is a bolus dose NOAEL from our Sprague5 Dawley rat LH study. That was 10 mgs per kg. The next 

6 dose higher than that was 12 mgs per kg and we had an 

7 effect at that level.

8

9 We were pretty confident that we had isolated the point of 

10 break between effect and no effect on the LH surge. And 

11 the third endpoint was the EPA proposed point of 

12 departure, 2.56, based on the bolus dose experiment, 

13 Cooper in 2010.

14

15 So we selected those three different endpoints and 

16 compared or did the margin of exposure calculation. And 

17 here you see for the 17 CWSs, those margins of exposures; 

18 the mins, the maxes and the averages.

19

20 We then proceeded to investigate the sensitivity of those 

21 margins of exposure to the interpolation of peaks such as 

22 that was described by Dr. Hendley where between two 

23 measured peaks, seven days apart, and synthetic peak was 

24 inserted and we ran those chemographs through the model 

25 using -- on the left hand panel of the green bars, you'll 

26 see the linear model -- that‟s the linear interpolation 

27 between measured values -- and then, on the right-hand 

28 side you'll see the synthetic peak and the impact of that 

29 on the margin of exposure calculation. You can see there 

30 is an obvious reduction. It's roughly a three-fold 

31 reduction in the margins because, in fact, it was roughly 

32 a three-fold increase or the inserted peaks. 
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1

2 In any case, that adjustment factor that he applied was 

3 approximately three-fold and we confirmed that. If you 

4 want to look at that on the right-hand side you'll see the 

5 MOEs are less in the other circumstance where we didn't 

6 have that interpolated peak.

7

8 In the next slide, we looked at the -- well, this is just 

9 the same information showing in a variety of different 

10 ways, and I'll slide through that.

11

12 In the next slide, we asked the question what would be the 

13 impact of calendar year, a year-to-year variation so that 

14 from the highest peak that was observed in a particular 

15 year to a year that had the lowest peak, so that‟s what 

16 this comparison is doing. 

17

18 You should realize that when we do the simulation in a 

19 standard case, we pick that year which has a highest 

20 value, and in this particular instance we compared it to 

21 the year that had the lowest value for the TCT in drinking 

22 water. 

23

24 And you can see that there's about on average a ten-fold 

25 difference between the variances from year to year and 

26 that‟s due to environmental factors that drive residue 

27 concentrations in water. The CWS-96 actually we know was 

28 an off-labeled use, so that one year that maximum

29 difference that was observed is attributed to, in fact, 

30 that high value. But overall there are generally very 

31 large margins, and in some years those margins falls below 

32 10,000.
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1

2 The next slide, we asked the question -- and all of this 

3 is in the form of sensitivity analysis and you can see the 

4 standard case we're comparing to the 10 mg per kg NOAEL 

5 using TCT area under the curve.

6

7 We asked the question, what would happen if you modify the 

8 rolling average duration over which you're trying to 

9 integrate the internal dose; so one day of dose, two days 

10 of dose, three, four, seven, 14, 21, 28 and 90 days. You 

11 can see that it's virtually no impact of modifying the 

12 duration of the averaging period until you get past 28 

13 days, and then there's a modest increase in the margins of 

14 exposure as a result of averaging over a longer period of 

15 time when you're simulating from the CWSs.

16

17 The next slide tests the question of; well what is the 

18 magnitude of the difference between the distributed dose 

19 versus the bolus does, so this is comparing the 50 

20 milligram per kg dose translated into an internal dose 

21 metric for the rat; and comparing then the exposures of 

22 those individuals in the CWSs between those two metrics. 

23 You can see there's roughly a five-fold difference in 

24 terms of if you chose to use, for regulatory purposes, a 

25 bolus dose versus a distributed dose, and that implies a 

26 conservative judgement then that leads to a more 

27 conservative risk-assessment.

28

29 The next slides show the impact of choosing between a 

30 particular strain of animals versus another strain of 

31 animals. So the Sprague-Dawley rat, we did an extensive 

32 and clear characterization of the LH suppression following 
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1 four days of atrazine administration and the NOAEL was 10. 

2 Dr. Cooper's point of departure was 2.56 and you can see 

3 the magnitude of that choice of animal strain, relative to 

4 point of departure, is about a 3.6 fold difference in 

5 regard to the impact on the margins.

6

7 The next slide shows, well, what's the impact of actually 

8 using the peak versus the area under the curve, and this 

9 is kind of coming to the question of, well, what is the 

10 appropriate dose metric. If we don't know, at least we 

11 can measure what the consequences of choosing one versus 

12 the other. Intuitively, area under the curve makes some 

13 sense. Area under the curve above a critical 

14 concentration probably makes more sense, but we are just 

15 trying to determine the magnitude of that impact. It 

16 looks like, in fact, if you use area under the curve you 

17 are slightly more conservative by an order of 1.5 fold.

18

19 Then we assessed a number of other variables that actually 

20 did not make much difference. All of these standard 

21 models were done with females aged 13 to 19 using 

22 databases from CSFII, the Continuing Food Intake Survey 

23 for Individuals. The water consumption records, some body 

24 weight records, and so on, came from there. The frequency 

25 of water intake and the amount of water consumed came from 

26 a Bayer survey that had been embedded in those models for 

27 assessing the risk.

28

29 But as you can note there, at that age there is hardly any 

30 consequence between choosing 13- to 19-year-olds versus 

31 20- to 49-year olds, so that variable didn't seem to make 

32 much impact.
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1

2 We also evaluated the consequences of just including 

3 direct water where people reported having consumed 

4 directly water versus, in some of these other databases 

5 there is information about indirect water. That is to say 

6 if you cook food in water that has atrazine in it, that 

7 could get incorporated in your food and you'll get 

8 indirect water consumption through your food. By adding 

9 that variable, you can actually, again, decrease margins 

10 of exposure because you are effectively increasing 

11 exposure to the compound. So that was a sensitivity 

12 analysis around that aspect.

13

14 And finally, we did two types of simulation. The standard 

15 case was where we took 4-day rolling averages and we just 

16 moved that rolling average day-by-day throughout the 

17 entire year, so we generated 362 samples or margins for 

18 the individual we were simulating. Those are dependent 

19 MOEs, obviously, because what you see in the last on is 

20 not related to what you are going to get in the next one.

21

22 There was a certain desire by a staff at EPA to have that 

23 continuity of exposure so that that is what individuals 

24 will do in the real world. They will have a water supply 

25 and they will get residues that are coming through. 

26

27 The other approach was to actually randomly draw from the 

28 CWS 4-day averages and just calculate the margins so it is 

29 a random draw, and that has an impact. It actually makes 

30 the analysis more conservative, as you can see, 4,000 

31 versus the 1,700. So those were the forms of sensitivity 

32 analysis we have done, and we have done a lot of other 
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1 interesting things relative to predicting the kinetics in 

2 relationship to the dynamics. 

3

4 We have not had a great insight out of all of that yet. I 

5 am kind of enamored by my modeling friends here, because 

6 what takes me six months to do an experiment, it takes 

7 them -- although Bob Silken (phonetic) will disagree with 

8 me -- it takes him half a day to do an experiment. So, in 

9 that sense we can do sensitivity analysis and actually 

10 find out things that might be useful for us to measure in 

11 experiments.

12

13 So to sum up, I would just say that all of the highly 

14 selected 17 CWSs, in terms of this MOE distribution 

15 analysis at the 99.9th percentile, we note in general that 

16 the MOEs are large and higher than a thousand. We note 

17 that there is roughly a three-fold impact of the synthetic 

18 chemographs on those MOE distributions. We note that the 

19 MOE distribution seem to be insensitive to the duration of 

20 the averaging period up to about 28 days, and then after 

21 that it makes a significant difference, or it at least 

22 increases those margins. 

23

24 We believe that this modeling exercise can be used to 

25 inform the risk-assessment process. We are not making a 

26 risk-assessment here. We presented it in a neutral way by 

27 just discussing margins of exposure. And individuals and 

28 people who are responsible for those interpretations can 

29 judge the extent to which the factors that we have studied 

30 in our sensitivity make a difference or should be 

31 incorporated in such a regulatory decision.

32
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1 We do note though, however, that as one makes 

2 progressively sequential judgements of conservative 

3 nature, you're effectively concatenating the margins or, 

4 shall I say, the protective factors. And in this way, we 

5 can actually quantify what you are doing by means of those 

6 judgements when you pick this animal strain versus that 

7 animal strain or this endpoint versus that endpoint. And 

8 really that was the purpose of that investigation was to 

9 assess in a quantitative way the impact of those 

10 judgements. 

11

12 So I think, with that, I will stop and we could take 

13 whatever questions you want relative to this simulation 

14 part or anything else that you would wish to discuss.

15

16 DR. DANIEL SCHLENK: Thank you, Dr. Breckenridge. Just by a 

17 show of hands, how many actually have questions right now? 

18 Did I intimidate everybody? Okay. So what we'll do, if 

19 you don't mind, we'll start off with you in the morning 

20 and let the panel incubate a bit over the information that 

21 they've gotten. And then we'll hit you guys first thing 

22 in the morning with some questions if they're around, and 

23 then we'll move on from there. 

24

25 DR. CHARLES BRECKENRIDGE: Thank you, Mr. Chairman, on behalf 

26 of Syngenta.

27

28 DR. DANIEL SCHLENK: Okay. Thanks. With that, let me turn it 

29 over to Joe. And the panel is going to meet for a few 

30 minutes in the coffee room afterwards. 

31
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1 JOSEPH BAILEY: Actually, I just want to thank everybody. I 

2 have no other closing comments. Just note that the 

3 meeting tomorrow morning starts at 8:30, so we'll be here 

4 then.

5

6 July 27, 2011 - 8:30 a.m. Day 2

7 JOSEPH BAILEY: Good morning everyone, just want to welcome 

8 you back to the second day of the FIFRA Scientific 

9 Advisory Panel. This is the Re-evaluation of the Human 

10 Health Effects of Atrazine: Review of Non-Cancer 

11 Effects, Drinking Water Monitoring Frequency and Cancer 

12 Epidemiology.

13

14 Just want to make a note about public comment; you are 

15 getting some handout this morning. Syngenta is going to 

16 have a few clarifying slide on questions that were raised 

17 yesterday. There was one other comment that was in the 

18 docket from the Physicians for Social Responsibility; you 

19 should have that now. There is one comment from Triazine 

20 Network; we may have additional handout for them, I am 

21 not sure at this point. 

22

23 Finally, there is the Center for Regulatory 

24 Effectiveness‟s comments that you should also have. Just 

25 a reminder, please state your name into the microphone 

26 when you make any comment, and with that I will turn it 

27 over to Dr. Schlenk.

28

29 DR. DANIEL SCHLENK: Thank Joe. Let‟s go around the room one 

30 quick time, just have each panel member introduce 

31 themselves, where they are from and their area of 

32 expertise, for the general public. I will begin; my name 
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1 is Dan Schlenk. I am a Professor of Environmental 

2 Toxicology at the University of California Riverside. My 

3 expertise is in Fate and effects of Emerging contaminants 

4 and Pesticides and aquatic organisms.

5

6 DR. KENNETH PORTIER: Good morning, I am Ken Portier, Managing 

7 Director, Statistics and Evaluation at the American 

8 Cancer Society in Atlanta. I am a Bio-Statistician. 

9

10 DR. JANICE CHAMBERS: I am Jan Chambers, with the College of 

11 Veterinary Medicine at Mississippi State University. I 

12 am a Pesticide Toxicologist and a member of the permanent 

13 panel.

14

15 DR. STEPHEN KLAINE: I am Steve Klaine, Clemson University. I 

16 am an Aquatic Ecotoxicologist and I am a member of the 

17 permanent panel. 

18

19 DR. ELLEN GOLD: I am Ellen Gold. I am from U. C. Davis where 

20 I am a Professor of Epidemiology.

21

22 DR. FRANK BOVE: I am Frank Bove. I am with the Agency for 

23 Toxic Substance and Disease Registry. I am a Senior 

24 Epidemiology in the Division of Health Studies.

25

26 DR. HEATHER YOUNG: I am Heather Young, George Washington 

27 University, Department of Epidemiology, specializing in 

28 cancer reproductive outcomes.

29

30 DR. NELSON HORSEMAN: Nelson Horseman from the University of 

31 Cincinnati in the Department of Molecular and Cellular 

32 Physiology and I am an Endocrinologist.
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1

2 DR. JAMES MCMANAMAN: Jim McManaman, University of Colorado, 

3 Department of Obstetrics and Gynecology.

4

5 DR. DANIEL GRIFFITH: I am Daniel Griffith, Ashbel Smith 

6 Professor of Geospatial Information Sciences, University 

7 of Texas at Dallas. I am a Spatial Statistician.

8

9 DR. HERBERT LEE: I am Herbie Lee, University of California, 

10 Santa Cruz where I am a Professor of Statistics and the 

11 Vice Provost for Academic Affairs and my research areas 

12 includes spatial statistics and deterministic Computer 

13 Modeling.

14

15 DR. ROBERT GILLIOM: Bob Gilliom, U. S. Geological Survey. I 

16 direct our pesticides studies for the National Water 

17 Quality Assessment Program and my expertise is in 

18 primarily the hydrology and water quality monitoring 

19 aspects of this problem.

20

21 DR. RICHARD COUPE: Richard Coupe with the U. S. Geological 

22 Survey out of Mississippi Water Science Center and I am a 

23 researcher on the fate and transport of agricultural 

24 chemicals.

25

26 DR. SUSAN AKANA: I am Susan Akana. I am currently at City 

27 College of San Francisco, which is my second career. I 

28 have retired from the University of California – San 

29 Francisco where I had a career as a research physiologist 

30 in stress and energy balance.

31
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1 DR. KEVIN O'BYRNE: My name is Kevin O‟Byrne. I am from 

2 King‟s College London. I am a Professor of Reproduction 

3 and Endocrinology and I am passionate about what controls 

4 luteinizing hormone secretions.

5

6 DR. KATHERINE ROBY: I am Kathy Roby from the University of 

7 Kansas Medical Center and my expertise is reproductive 

8 endocrinology. 

9

10 DR. BARRY TIMMS: I am Barry Timms, Professor of Basic 

11 Biomedical Sciences, Sanford School of Medicine, 

12 University of South Dakota with a specialty in 

13 reproductive biology and prostate biology.

14

15 DR. TRAVIS JERDE: I am Travis Jerde, Indiana University 

16 School of Medicine, Assistant Professor of Pharmacology, 

17 Toxicology and Urology and I specialize in prostate 

18 biology.

19

20 DR. PENELOPE FENNER-CRISP: I am Penny Fenner-Crisp, private 

21 consultant living in Charlottesville, Virginia and a 

22 member of the state‟s Pesticide Control Board. My area 

23 of expertise is toxicology and human health risk 

24 assessment.

25

26 DR. BETTE MEEK: I am Bette Meek. I am at the McLaughlin 

27 Center of the University of Ottawa and my background is 

28 in regulatory risk assessment and toxicology.

29

30 DR. RICHARD GREENWOOD: I am Richard Greenwood. I am an 

31 Emeritus Professor at the University of Portsmouth and my 
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1 expertise is in mode of action of pesticide and 

2 pharmacokinetics.

3

4 DR. WILLIAM HAYTON: I am William Hayton, Professor Emeritus, 

5 College of Pharmacy at Ohio State University, expertise 

6 in pharmacokinetics.

7

8 DR. DANIEL SCHLENK: Thank you everyone, I appreciate that. 

9 We are going to start out this morning; we have the 

10 Syngenta team up again this morning. They have provided 

11 some additional slides as Joe had mentioned. What I 

12 would like to do if it is okay, if we can maybe in five 

13 minutes go though a list of what those additional slides 

14 are and then open it up to the panel for additional 

15 questions. I think there were some follow up questions 

16 maybe that you may have and we will go from there. So 

17 Dr. McFarland if you can provide the list of the 

18 additional material that would be great.

19

20 DR. JANIS MCFARLAND: Thank you Dr. Schlenk and good morning 

21 everyone. Thanks again for all the time yesterday. We 

22 have three areas of clarification, based on questions we 

23 received yesterday afternoon, in the very brief handouts. 

24 The first area is some of the questions on food intake. 

25 The second area is on the pharmacokinetics – slide 30 

26 questions with the area of under the curb. The third is 

27 areas of water from assessment of the upper centiles 

28 concentration in finished drinking water. 

29

30 Attached to that are some of the reasons for a very few 

31 number of daily monitoring samples that were missing from 

32 the daily monitoring assessment that was provided to the 
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1 panel. So out of over 500 samples the reasons, for 

2 instance when the power went off at a particular site. I 

3 will turn it over to Dr. Breckenridge to quickly go 

4 through the slides on food intake.

5

6 DR. CHARLES BRECKENRIDGE: Good morning ladies and gentlemen, 

7 my name is Charles Breckenridge. I am a toxicologist 

8 with Syngenta. There were some questions yesterday 

9 afternoon relating to the impact of atrazine on food 

10 intake and body weight. 

11

12 I selected one study to represent that impact; this was a 

13 multi-generation reproduction study in atrazine. It is a 

14 guideline study. The schematic of the study is laid out 

15 here where you see the animals beginning dosing early 

16 young adult, male and female F0 generation. They are 

17 treated for several weeks and they are mated in driving a 

18 second generation. 

19

20 So in those kinds of studies we track body weight and 

21 food intake on a weekly basis. This graph is a 

22 representation of food consumption in the male animals in 

23 F0 and F1 generation. You will note that there is an 

24 immediate and sustained impact on food intake in the 500 

25 part per million (ppm) dose group at 40 mg/kg. The 50

26 ppm dose group has no affect on food intake in the male 

27 or F0 and F1. The same data is shown here for the female 

28 animals. Those asterisks below the data sets are 

29 indicating statistical significance, and again the 500 

30 ppm group is having an effect whereas the 50 ppm is 

31 comparable to control. 

32
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1 There is a consequence of that relative to body weight

2 progression over the course of that treatment interval, 

3 and you can note on the left-hand side there is a 

4 progressive slow separation of treated animals from the 

5 controls at the 500 ppm where there is significant 

6 reduction in body weight gain throughout the course of 

that continuous feeding regimen; for the males F0 and F17

8 generations and the same is observed in the female‟s. 

9 The indication where the elevation occurs, where the 

10 females are mated and they are developing a litter. So 

11 that body weight gain is associated with that event. 

12

13 So in summary then, the continuous feeding-type studies 

14 suppressed body weight, suppressed food intake, and the 

15 same experience occurs with respect to gavage dose. The 

16 gavage dose is instantaneously on a day of treatment, 

17 impact food intake and body weight progression. Those 

18 effect and no effect level for those kind of studies are 

19 documented in probably the short-term experiments. The 

20 best one would be the CODAR study 0639081. If the panel 

21 is interested in seeing those data summarized, we could 

22 quickly do that and provide it to the panel as a handout 

23 later. But for now let‟s just say that there is clearly 

24 affects on body weight and food intake of atrazine and 

25 there is clear dosage that have no effect as well. I 

26 will stop there and pass the topic over to the kinetic 

27 question, unless there are any questions about that 

28 particular data set.

29

30 DR. DANIEL SCHLENK: Okay, any questions we have.

31
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1 DR. SUSAN AKANA: I am interested in the group that had a 

2 lower body weight, lower food intake. It is likely their 

3 body composition is going to be different. Have you 

4 measures of Leptin for instance?

5

6 DR. CHARLES BRECKENRIDGE: No, we have not measured Leptin in 

7 any of those studies.

8

9 DR. DANIEL SCHLENK: Okay, let‟s go ahead and move on.

10

11 DR. HARVEY CLEWELL: Harvey Clewell, from The Hamner 

12 Institutes. I just was going to show a couple of slides 

13 about the question regarding the AUCs, because we did not 

14 have the quantitative information yesterday; I could not 

15 remember the details. 

16

17 This is that same study that I described yesterday with 

18 the three gavage and three dietary dosages; and this is 

19 the slide that I showed. This is a slide I did not show 

20 and it shows the area under the curb versus dose with red 

21 being gavage, blue being diet. The DACT is on the right. 

22 The area under the curb is similar, slightly lower for 

23 the diet but then the mg/kg per day dose for diet was 

24 slight lower than the gavage. What you may not be able 

25 to see is the Y-axis on these. The DACT is about sixty 

26 times higher area under the curb compared to the DIA, and 

27 DEA and atrazine are even smaller. So the DACT really 

28 dominates the area under the curb exposure for these four 

29 compounds. 

30

31 As I suggested yesterday, the area under the curb 

32 exposure in these dietary and gavage studies was pretty 
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1 similar and what the most striking difference is the 

2 pulsatile nature of the gavage exposure as opposed to 

3 more stable concentrations achieved with the dietary 

4 intake and the very striking difference in the LH 

5 suppression with the diet versus gavage is possibly, we 

6 think, associated with this more pulsatile nature and the 

7 higher concentration achieved.

8

9 DR. DANIEL SCHLENK: Okay, thanks. Any questions related to 

10 that?

11

12 DR. SUSAN AKANA: Yesterday there was some discussion of the 

13 fact that the atrazine and their compounds can bind to 

14 red blood cells and to albumin. Have you attempted to 

15 look at free circulating, unbound compounds in the 

16 experiment you just showed?

17

18 DR. HARVEY CLEWELL: That is what was reflected; there were 

19 free compound, not covalently bound compound, so that is 

20 what was measured, yes.

21

22 DR. SUSAN AKANA: And not bound to albumins, so totally free.

23

24 DR. HARVEY CLEWELL: That is correct. I mean the binding is 

25 covalent and so when you do the analysis, you don't get -

26 - the coloring is gone, it doesn't come off. 

27

28 DR. DANIEL SCHLENK: Okay, last but not least, we have Dr. 

29 Hendley.

30

31 DR. PAUL HENDLEY: Okay, thank you very much. Good morning, 

32 Paul Hendley, Syngenta. There are just a couple of quick 
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1 slides here, thankfully. The first one is just pulling 

2 one of the tables from Dr. Mosquin's report of April. 

3 This is really just pointing out the raw and finished 

4 data from the Safe Drinking Water Act and the frequent 

5 monitoring VMP and AMP. Just simply to give you some 

6 reference values of what those high centile concentration 

7 were in raw and finished from over 48,000 finished water 

8 samples, frequent monitoring. That 99.9 centile; it is 

9 22.66 and the report will also show you what the bounds 

10 around that are. 

11

12 The other thing we wanted to point out, one of the panel 

13 members had asked the question – very incredibly 

14 observantly – about some of the missing data. What we 

15 normally do when we submit data and we pull data together 

16 is, we have a spreadsheet of values and in this case, it 

17 is 500+ raw, and 500+ finished. We have another 

18 worksheet that says metadata, and the metadata includes 

19 this table. In the report, because it is only a 

20 preliminary data report because there are more samples 

21 being collected, we have not put the metadata statement 

22 in the appendix. Had it been the full year‟s report, you 

23 would have seen this table and I believe the questions

were focus on June 29th 24 , an auto-sampler failure. These 

25 auto-samplers are sitting in water treatment plants and 

26 sometimes folks either turn off the water streams to them 

27 or the electric sockets. 

28

29 But just for the record that stretch in the middle for 

30 number 54 came from that dreadful period when the levees 

31 were being opened and that is why it was not just a minor 
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1 flood on the road; there was a foot of water shifting 

2 around. 

3

4 So for the record, there was a good reason why those 

5 samples were not taken. Thank you.

6

7 DR. DANIEL SCHLENK: Okay, any questions for Dr. Hendley? Any 

8 final questions for the Syngenta team and the panel as a 

9 whole?

10

11 DR. RICHARD COUP: Can I ask a PRZM question from yesterday? 

12 It has to do with kind of how you scale it up to larger 

13 watershed size. Now, my understanding is this is an 

14 edge-of-field model. So you have a 500 square mile basis

15 and you are treating that 500 square mile as an edge-of16 field now. So everything is instantaneously transported 

17 in like one day to a point that you are using as your 

18 measurement. How would you ever account for transport 

19 through streams and hydrology or are you planning on 

20 scaling it up so you can go larger.

21

22 DR. PAUL HENDLEY: I think this is why I said this was the 

23 outstanding issue that maybe needs to be tackled, because 

24 it was originally a technology design for the ecological 

25 monitoring program where we were thinking of watersheds 

26 that were 10 to 50 square miles. We found it is fine 

27 working with some of the AMP watersheds, the smaller 

28 ones. But the challenge is working out exactly where 

29 some sort of routing comes in or weather. 

30

31 I was trying to make the point yesterday, in fact, the 

32 moment you get to an area where averaging starts taking 
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1 into account a lot of what is going on in the 

2 environment, whether regression approach may be easier in 

3 terms of a simple way forward. 

4

5 So we are still looking at how to account for the 

6 routing. You may have notice in one of the tables we did 

7 put time of concentration in for the various sizes of 

8 water bodies, which is exactly why we are looking at that 

9 problem. So work in progress and it is a good question 

10 and it needs to be address. But if you remember from 

11 many of the key watersheds the dominant, for example, 143 

12 static watersheds are small, less than 50 square miles. 

13 And to be blunt, those are the ones that tend to have the 

14 highest residue so those are the ones that I think are of 

15 primary interest at the moment.

16

17 DR. JAMES MCMANAMAN: This is a question for Dr. Breckenridge. 

18 This is on the feeding question. So I notice that during 

19 pregnancy, there is really no effect of atrazine on food 

20 intake during pregnancy, but there is a difference in 

21 weight gain. Do you think that is significant? If you 

22 could comment on that please.

23

24 DR. CHARLES BRECKENRIDGE: We see an impact at higher doses on 

25 the weight gain in the mothers and it also finally 

26 reflects itself in the up weight at birth. There has 

27 always been a question in my mind about the food 

28 utilization efficiency that perhaps in periods of high 

29 demand that consequence on food intake becomes more 

30 important to the animal. So I cannot really say much 

31 more than that, but we do know that for the same amount 

32 of food in consumption, the relative body weight gain is 
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1 proportionally less. That is to say, food efficiency is 

2 less in atrazine treated animals. 

3

4 DR. TRAVIS JERDE: This question is probably best addressed to

5 Dr. Simpkin. You showed a slide yesterday where serum 

6 and therefore probably tissue concentrations of atrazine 

7 metabolite, particularly DACT, can reach levels of a 

8 micro-molar to ten micro-molar after a dose. But the 

9 doses you gave are fairly high, 3 mg/kg to 50 mg/kg and 

10 water exposure is about a hundred or less, but you only 

11 gave one dose. Are you planning studies to look at lower 

12 and repeated dosage and tissue concentration of what are 

13 metabolite gains? Because when you start to get to 1 --

14 10 micro-molar concentration that is the concentration 

15 where pharmacologic and toxicological effects can happen. 

16 I am just wondering what your thoughts are on that.

17

18 DR. JAMES SIMPKIN: That is a Syngenta research planning 

19 question and I think it is best handled by Dr. 

20 Breckenridge. 

21

22 DR. TRAVIS JERDE: Okay, that is fine. 

23

24 DR. CHARLES BRECKENRIDGE: When we go to quantification and 

25 the presence of analytes in plasma or tissue, we run into 

26 limits of the quantification issue. So as we roll down 

27 to really lower dosages, we start to now not be able to 

28 measure. And I think that our intent, relative to the 

29 monkey study, is in fact to try a 20 part per million 

30 administered dose so we will achieve relatively low 

31 concentrations in plasma. 

32
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1

2 We think we will be able to measure DACT at that point, 

3 but we doubt we are actually going to detect the mono, 

4 the acolytes or the parent with that low level of an 

5 inputted dose. But we are trying to move down the region 

6 of relevance to actual possible human exposure and that 

7 is even a thousand-fold higher than the average of two 

8 part per billion. 

9

10 So in some ways we start to run into analytic sensitivity 

11 questions as we are trying to quantitate a small dose 

12 entering a volume of distribution with rapid metabolism. 

13

14 DR. RICHARD GREENWOOD: Yesterday you talked about two phases 

15 of elimination of DACT. One, which was rapid with a 

16 half-life which was in hours and the slower process, was 

17 tens of hours which you put down to the time to turn over 

18 the plasma protein. Those figures must have been derived 

19 from the in vivo data, I guess. 

20

21 DR. CHARLES BRECKENRIDGE: I will take a first answer at this 

22 and then Dr. Clewell can comment more on the modeling 

23 part. But those observations came from two types of 

studies. One was the 14 24 C study in monkeys where we are 

25 looking for the urinary elimination rates and we note 

26 just that you can do a one compartment model, but in fact 

27 it seems like a two compartment model is better to 

28 characterize that. And that was also observed in the 

29 modeling of the human urinary elimination with the single 

30 .1 mg/kg dose as an input dose for those individuals. 

31
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1 So I believe that it is a matter of how to best 

2 characterize the rate of elimination. It seems like it 

3 fell into two different compartments.

4

5 DR. HARVEY CLEWELL: I just wanted to add that the work at 

6 Colorado State was some years ago while with Tammy 

7 McMullin is what identified the nature of the longer 

8 half-life being the plasma binding. Simulation was used 

9 in order to come up with the timeframe for that and it 

10 does coincide close to the turnover rate for plasma 

11 albumin but it has not been completely demonstrated I 

12 would say.

13

14 DR. RICHARD GREENWOOD: Just to follow up on that. So you 

15 have evidence from the monkey study but also the previous 

16 study in rats. And again you had sufficient data and 

17 details to get a good fix on that, I take it.

18

19 DR. DANIEL SCHLENK: Any other questions.

20

21 DR. SUSAN AKANA: A general point I hope you can clarify. 

22 With the atrazine, on one hand what I understand it is 

23 not that soluble in water so it is difficult to 

24 administer in fluids or to inject. On the other hand, 

25 when we talk about the metabolism of it, the patrician 

26 coefficient, if I recall correctly I think Dr. Rodriguez 

27 said it is a patrician coefficient of what. And that the 

28 one compartment model was appropriate; that there was 

29 not, for instance, a preferential uptake or storage of 

30 the atrazine compounds in fact. 

31
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1 Now, can you recount those two facts? One, that it is 

2 not very soluble in water; on the other hand you have a 

3 one compartment model?

4

5 DR. HARVEY CLEWELL: A problem with the one compartment model 

6 is that the half-life, the plasma kinetics is clearly not 

7 single compartment because of that long terminal half8 life. And so there is a transition from the rapid

9 clearance of the compounds themselves, which is reflected 

10 in the total radioactivity and then the longer half-life 

11 for clearance of the albumin adducts. So it is not 

12 actually a good candidate for single compartment 

13 modeling, if you are using the total radioactivity data. 

14

15 You might be able to use a single compartment model for 

16 the total chlorotriazines. I have not really tried to do 

17 that. We started with the PBPK approach, but the problem 

18 with that is that does not consider the flow limited 

19 metabolism in the liver. So you would not correctly 

20 describe the presystemic clearance of the compounds in 

21 the liver before reaching the blood. So it is a good 

22 candidate for more physiological descriptions. 

23

24 DR. WILLIAM HAYTON: That previous question triggers this 

25 thing that I wonder about, and that is the one 

26 compartment kinetic parameter value. Following the 

27 distribution numbers are, I think up around five or six 

28 liters per kilo. And I believe I heard you say, Dr. 

29 Clewell, that the distribution is fairly uniform across 

30 all of the tissues. I am wondering if there is any 

31 reconciliation of that.

32
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1 DR. HARVEY CLEWELL: That is an artifact of the use of the 

2 terminal half-life, which is really based on one very 

3 small portion of the total radioactivity that does not 

4 really reflects the distribution of the vast majority of 

5 the compound. So that is the trouble with volumes of 

6 distribution of course, is they do not have a 

7 physiological meaning. So I think that that very high 

8 level is just because you have a very slow clearance and 

9 low blood levels and so then you have to impute a very 

10 high volume of distribution in order to put it into a one 

11 compartment description. So I think it is just 

12 artificial.

13

14 DR. DANIEL SCHLENK: Okay, thanks to the Syngenta team; 

15 appreciate that. Our next public commenter will be 

16 Wendelyn Jones from CropLife America. Dr. Fenner-Crisp?

17

18 DR. PENELOPE FENNER-CRISP: While they are changing folks 

19 around, I have a question Joe. Will the presentations 

20 that have been made by the agency and the commenters be 

21 available on regulations.gov during the course of this 

22 meeting? 

23

24 JOSEPH BAILEY: They should be actually. I think they are on 

25 docket now; they are just waiting to be posted. The EPA 

26 presentations... yes.

27

28 DR. PENELOPE FENNER-CRISP: Since Harvey reminded me it is 

29 hard to see some of those numbers. My eyes are getting 

30 kind of old and I would like to blow them up.

31
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1 JOSEPH BAILEY: I looked last night and the docket had not 

2 posted.

3

4 DR. PENELOPE FENNER-CRISP: They were not there last night. 

5

6 JOSEPH BAILEY: Yes, but they are there, we just need to get 

7 to the docket and tell them to please put it up. So we 

8 will get a note out to them and they should be available 

9 very shortly.

10

11 DR. PENELOPE FENNER-CRISP: Thank you.

12

13 DR. DANIEL SCHLENK: Do you have a presentation?

14

15 WENDELYN JONES: You know, there are no slides.

16

17 DR. DANIEL SCHLENK: Amazing.

18

19 WENDELYN JONES: There are no reading materials.

20

21 DR. DANIEL SCHLENK: Okay, thanks.

22

23 WENDELYN JONES: Good morning, I am Wendelyn Jones and I am 

24 here today to represent CropLife America, and on behalf 

25 of our organization, we respectfully encourage the 

26 EPA/OPP and the SAP to remember their science background 

27 and ensure a science-centric path such that decisions are 

28 made on valid and reproducible science. CropLife America 

29 is a not-for-profit trade organization representing the 

30 nation‟s developers, manufactures, formulators, and 

31 distributors of plant science solutions for agriculture 

32 and pest management in the United States. 
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1

2 We are committed to the safe and responsible use of the 

3 industry‟s products in order to provide safe and abundant 

4 food as well as control for insect and plant disease 

5 vectors for the protection of human health and providing 

6 valuable benefits back to the consumer. Crop protection 

7 products require extensive data development for initial 

8 and continued registration. We respectfully note that 

this is the 11th 9 SAP on atrazine since 2000. Atrazine was 

10 reregistered in 2006 based on a 12-year EPA review and 

11 the input from multiple SAPs. 

12

13 This current SAP is part of a series scheduled by EPA 

14 beginning in 2009 to reevaluate atrazine. Both Syngenta 

15 and EPA have responded to this reevaluation of atrazine 

16 by providing many additional studies to characterize the 

17 toxicological characteristics and exposure potential. A 

18 robust scientific data base supports the continued use of 

19 this valuable product. A valid and reproducible sound 

20 science should always lead EPA‟s decision making. 

21

22 The atrazine safety package represents one of the most 

23 advance sciences for the acquisition of hundreds of 

24 thousands of drinking water samples, detailed mode of 

25 action studies, and cutting-edge pharmacokinetic 

26 pharmaco-dynamic characterizations. Such a rich database 

27 should provide confidence in the safety of this product. 

28

29 We encourage EPA/OPP to lead the way in utilizing advance 

30 scientific approaches such as evident by atrazine 

31 research being reviewed at this SAP. Such leadership 

32 would align with two recent NAS reports, Toxicology 
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Testing in the 21st 1 Century and Science and Decisions: 

2 Calling For a Major Scientific Change in How Toxicology 

3 and Risk Assessments Are Done. 

4

5 Among the cardinal principles of regulations of drinking 

6 water under SDWA, is providing the public with accurate 

7 and informative human health risk information and 

8 avoiding unnecessary public alarm by false allegations of 

9 threats to the safety of its drinking water. As 

10 respected scientists, we ask that the SAP exercise clear 

11 judgment in the report and recommendations that it 

12 provides in order to help assure that the work reflects 

13 the best scientific principles and avoids creating 

14 unwarranted public concerns. 

15

16 Additionally, we encourage the EPA and the SAP to 

17 carefully consider the mode of action studies and the 

18 pharmacokinetic studies. EPA defines mode of action as a 

19 sequence of key events and processes, starting with the 

20 interaction of an agent with a cell, proceeding through 

21 operational and anatomical changes and resulting in the 

22 adverse affect. 

23

24 Previously the agency has noted in certain experimental 

25 rodent strains, that atrazine induces changes in 

26 luteinizing hormone secretion without any adverse 

27 consequences on reproduction. Within the white paper for 

28 this SAP, the agency is proposing to continue to use the 

29 change in LH secretions as the basis of the atrazine risk 

30 assessment. 

31
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1 We also note that the duration of exposure is an 

2 important parameter considered in evaluating the 

3 relationship between dose and attenuation of the LH 

4 surge. The ability to confidently compare dose response 

5 and exposure in rats to humans is extremely important. 

6 We are therefore very encouraged to see that the agency 

7 has paid particular attention to the elucidation of the 

8 pharmacokinetic behavior of atrazine. The integration of 

9 this understanding and other new studies is key to enable 

10 OPP‟s leadership and we encourage for the refinement of 

11 this approach. 

12

13 Lastly, we would like to highlight a recent paper that 

14 was issued as part of the Agricultural Health Study. The 

15 AHS is a long-term research project that has been 

16 tracking the health of nearly 90,000 people, certified 

17 pesticide applicators and their spouses in Iowa and North 

18 Carolina since 1994. 

19

20 The report recently published in environmental health 

21 perspective concluded that overall there was no 

22 consistent evidence of an association between atrazine 

23 use and any cancerous site. No one cares more about the 

24 safety of crop protection products including pesticides 

25 than the farmers who use them on their crops and soils 

26 where are own children play. Farmers have an important 

27 stake in keeping their land, rivers and ponds safe for 

28 their families, their neighbors and their communities. 

29

30 If valid reproducible sound scientific research finds 

31 that any agricultural pesticide cannot be used safely, we 

32 will be the first to agree with increased regulations. 
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1 Sound science has found repeatedly that atrazine is safe 

2 when used responsibly and according to label 

3 recommendations. The comprehensive dataset on atrazine, 

4 over the years, including the more recent research, 

5 reaffirmed the safety of atrazine. Thus, COA supports 

6 this product as it helps us raise our crops affordablely 

7 and sustainablely. Thank you for your time and 

8 attention.

9

10 DR. DANIEL SCHLENK: Thanks. Any questions from the panel? 

11 Okay, thank you very much. Our next public commenter 

12 will be Scott Slaughter. I believe there is a handout 

13 associated with the comments.

14

15 SCOTT SLAUGHTER: I have been here before and it is always a 

16 pleasure to appear before the Science Advisory Committee. 

17 Hi, my name is Scott Slaughter and I am commenting today 

18 on behalf of the Center for Regulatory Effectiveness. I 

19 will be citing a number of documents. There are links to 

20 all the documents I refer to in the written materials you 

21 have. And I would like to first address the Cooper Study 

22 on LH surge. EPA has validated at least four tests for 

23 measuring the effects of atrazine on LH surge. Dr. 

24 Cooper‟s LH attenuation study differs significantly from 

25 the four LH tests that EPA has validated. 

26

27 One difference is that the Cooper Study only tests Long28 Evans rats. The four validated tests only use Sprague29 Dawley rats. The Cooper Study on Long-Evans rats is also 

30 inconsistent with EPA‟s standard procedures for assessing 

31 potential endocrine effects of all pesticides including 

32 atrazine. In its Endocrine Disruptor Screening Program, 
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1 affectionately or unaffectionately known as EDSP, EPA 

2 used Sprague-Dawley rats to validate tests for potential 

3 pesticide endocrine effects on female pubertal 

4 development. The endpoints of these validations test in 

5 EDSP included vaginal openings and estrous cycling. 

6 Atrazine was utilized as a control in the validation of 

7 this EDSP assay. 

8

9 EPA‟s report for this validation study adopts Sprague10 Dawley rats as the strain to use in testing pesticide 

11 endocrine effects on development and reproduction. And I 

12 quote from EPA validation study in the EDSP as follows, 

13 “In summary, EPA is aware of the potential for 

14 differences between strains and therefore expresses a 

15 preference for standardization using Sprague-Dawley rat.” 

16 If EPA still wants to rely on Dr. Cooper‟s study and its 

17 non-standard use of Long-Evans rats to regulate the 

18 endocrine effects of atrazine, then we ask that EPA first 

19 validate that study. 

20

21 Validation of the Cooper Study on LH attenuation should 

22 comport with first, EPA‟s guidance for validating 

23 endocrine disruptor tests; second, EPA‟s Information 

24 Quality Act Guidelines; and third, guidance produced by 

25 the NTP Interagency Center for the Evaluation of 

26 Alternative Toxicological Methods (NICEATM) and the 

27 Interagency Coordinating Committee on the Validation of 

28 Alternative Methods (ICCVAM). Among other things, the 

29 validation process should determine whether Dr. Cooper‟s 

30 study results are reproducible by other independent 

31 laboratories. 

32
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1 As EPA noted earlier, Syngenta submitted studies that 

2 generated different results on LH attenuation than Dr. 

3 Cooper‟s study. The validation process should determine 

4 whether the NOAEL and LOAEL from Dr. Cooper‟s study are 

5 reproducible by other independent laboratories. The 

6 validation process should also determine whether the use 

7 of Long-Evans rats in tests for potential endocrine 

8 disrupting compounds is scientifically justified. If so, 

9 the EPA should reconsider its Endocrine Disruptor 

10 Screening Program. 

11

12 I would like to briefly now address some of these 

13 specific charge questions. I am not going to repeat all 

14 of the comments in my written testimony, in the interest 

15 of saving time. Please I refer you all to those. 

16

17 The next subject I would like to address is on page four 

18 of my written comments, which are Mode of Action and 

19 Adverse Outcome Questions, which I identified, perhaps 

20 incorrectly, as five, six, seven, eight and nine to the 

21 SAP. 

22

23 To begin with EPA‟s Issue Paper, which was presented to 

24 you for this SAP states, and I quote EPA. “This Agency 

25 is using the 33% LH surge attenuation after a 4-day 

26 exposure as a precursor event to protect for other 

27 adverse outcomes including estrous cyclicity disruption, 

28 and delays in sexual maturation occurring at higher doses 

29 in laboratory animals.” 

30

31 Using the 33% LH surge attenuation after a 4-day 

32 exposure, like EPA proposes to do, is not based on any 
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1 adverse human health effect. In fact, the September 2010 

2 SAP explained in its written minutes that 33% surge 

3 standard is not based on any adverse rat event. And I 

4 quote from the minutes of the 2010 SAP, “Greater than 80% 

5 attenuation of the LH surge, in any given 4-day estrous 

6 cycle, would be needed to observe deleterious effects in 

7 the reproductive systems in rats.” And another quote 

8 from the 2010 SAP minutes, “Attenuation of the LH surge 

9 has no adverse effect on reproductive function and does 

10 not prevent ovulation until about 80% attenuation. 

11 Therefore, the proportion of animals and the latency to 

12 exhibition of delayed cycles might constitute a better 

13 endpoint or 'adverse response' for determining the effect 

14 of atrazine than is attenuation of the LH surge.” 

15

16 There are other quotes from the 2010 SAP minutes, which I 

17 will not repeat here but they are in my written 

18 documents. And it is quite clear that what EPA proposes 

19 to do is not directly connected to any adverse health 

20 effect that has been observed in a rat or a human. 

21

22 I would like to close briefly by pointing out another 

23 quote from the September SAP. The introduction part is, 

24 Members of the September SAP “expressed the opinion that 

25 there doesn‟t seem to be strong evidence of adverse 

26 health effects from atrazine exposures at the levels 

27 found in surface waters; because of this, these Panel 

28 members believed it was unfair to ask the registrant to 

29 increase their sampling efforts.” We agree. I will try 

30 to answer any questions you might have now. 

31

32 DR. DANIEL SCHLENK: Any questions from the panel.
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1

2 SCOTT SLAUGHTER: Thank you very much.

3

4 DR. DANIEL SCHLENK: Thank you Mr. Slaughter. The next public 

5 commenter will be Jere White from the Triazine Network 

6 and I believe there are several folks involved in that 

7 one. I think there are some slides.

8

9 JERE WHITE: Good morning Mr. Chairman, members of the panel, 

10 my name is Jere White. I am the executive director of 

11 the Kansas Corn Growers Association and the Kansas Grain 

12 Sorghum Producers Association. I serve as Chairman of 

13 the coalition that was formed in 1995 somewhat in 

14 response to the initiation of the special review of the

15 atrazine. The goal of the Triazine Network, since its 

16 formation, was simply to see a scientifically based 

17 conclusion to the special review. We obviously are a 

18 coalition that represents the user community, if you 

19 will. And as such, we obviously have a keen interest if 

20 there are safety issues related to the use of the 

21 product. We represent over 30 commodities grounded in 

22 over 40 states and certainly commodities grounded in 

23 almost every state that has agricultural production, 

24 which is obviously most of the states in the country. 

25

26 We believe the scientific weight of evidence continues to 

27 show that atrazine is both safe and effective and that is 

28 certainly the kind of tool that a farmer needs to have. 

29 We do look forward to a science based conclusion of the 

30 review of the use of atrazine on our farms. And it is 

31 not because of their uncertainty with the product but 

32 because of seemingly continuous review of the product has 
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1 literally surpassed the career of many at EPA; I assume 

2 probably some on the SAP and certainly several of our 

3 growers. In fact, I am wondering if it will surpass my 

4 career as well. This is also, as noted earlier; it is 

the 11th 5 SAP since 2000. Network members have 

6 participated in every SAP since the special review began. 

7 Quite frankly at this point, Joe Bailey and I have 

8 observed a longer relationship than I have with my wife 

9 and I think she is starting to ask questions.

10

11 JOSEPH BAILEY: You did not need to state that publicly Jere.

12

13 JERE WHITE: As you know atrazine has been used for some 50 

14 years by farmers; it has been used by more farmers in the 

15 US than any other herbicide. It is used on over half of 

16 the corn, 2/3 of the sorghum, 90% of the sugarcane. It 

17 is an important product and I think that has been well 

18 established, but it still bears repeating I guess 11 

19 times since 2000. We use it because it is efficacious 

20 for wheat; it is cost effective; we believe it is safe. 

21 It is also a key tool that farmers use in conservation 

22 tillage and controlling soil erosion. 

23

24 So we are here again today, and our sense is that much of 

25 the activist clamor that led to this re-review post 2009, 

26 has been properly vetted out by the agency and the SAP, 

27 and that we are moving on. However, we simply cannot 

28 disregard some of our specific concerns with certain 

29 Agency positions that are being discussed at this SAP. 

30 And that is why we have asked Dr. Lamb to join us again 

31 as he did in 2010. We certainly have shared concerns 
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1 with the SAP regarding some of these studies and Dr. Lamb 

2 will address those again. 

3

4 We were pleased but not really surprised that the new 

5 version of the Agricultural Health Study adds further 

6 confidence to with the EPA has already established, that 

7 atrazine is not likely to cause cancer in humans and 

8 indeed other organizations and government agencies from 

9 around the world have concluded much the same. 

10

11 In some of the previous discussions on, I guess what we 

12 commonly refer to as the Cooper Study, there were 

13 discussions about the appropriateness of the strain of 

14 rats, the appropriateness of the gavage technique. And I 

15 guess one of the things I took away from the earlier 

16 discussions was this whole issue of solubility and it 

17 seems pretty basic, it is the concept of saturation I 

18 guess is the same concept that we learn as elementary 

19 kids making rock candy. You super saturate the liquid 

20 and it forms the sugar crystals. It just seems like a 

21 basic concept that if you cannot get that much product 

22 into the water, then the exposure to humans or normally 

23 even to animals would be through the water and we are 

24 talking about regulatory discussion of water that is kind 

25 of a basic concept. Probably my understanding is limited 

26 to making rock candy, but hopefully Dr. Lamb can help 

27 communicate some of our concerns and I think others have 

28 addressed that. 

29

30 Monitoring results clearly indicates that atrazine 

31 levels, even when they are detected in drinking water, 

32 are extremely low; do not exceed thresholds for human 
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1 health effects. Finished drinking water, in our opinion, 

2 should be the only water used in drinking water 

3 assessment, not raw water. This is the same requirement 

4 for all other potential contaminants including many with 

5 known health concerns at levels possible in the 

6 environment, again, not limited by solubility issues. 

7 There is no scientific justification to single out 

8 atrazine as being unique or different. 

9

10 In addition, the use of Eco sites such as Missouri O-1 

11 for risk assessment is just simply not appropriate. I

12 believe in past SAPs there has been a lot of pictures 

13 shown of the Missouri O-1 site. One time it was a 

14 construction site and those of you that served for years 

15 on the SAP will remember the pictures of earthmovers in 

16 action. 

17

18 There is simply no basis to assume that you could derive 

19 a minimum safe yield from these sites, it would be 

20 appropriate to site them for community water systems. 

21 There is no reason to believe that Missouri O-1 in and of 

22 itself would be appropriate for water supply and 

23 moreover, if you were to consider that you would build a 

24 reservoir. By doing so you would change the 

25 characteristics of what the exposure pattern would be 

26 from that water. 

27

28 At this point, I would like to have Dr. Lamb share a 

29 review that he continued to do for us on behalf of the 

30 Triazine Network looking at the Cooper work and its 

31 appropriateness for this use.

32
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1 DR. JAMES LAMB: Thank you Jere. Some of you, it is good to 

2 see you all again. I appreciate your taking the time to 

3 hear me, really I do. I am Jim Lamb, I am Director of 

4 Toxicology and mechanistic Biology and Exponent and I am 

5 here on behalf of the Triazine Network. I am going to 

6 comment specifically on the use of the Point of Departure 

7 that is proposed, which is the suppression of the LH 

8 Surge in the Long-Evans rats. 

9

10 There are several major issues; this is going to be quite 

11 different actually, than the last time I spoke where I 

12 got into some of the nuts and bolts of the study. This 

13 is more about the science policy and the use of this 

14 endpoint and the use of the Long-Evans rat, the bolus 

15 dose, the LH surge for risks assessment purposes, which 

16 is really an important part of your charge. Should the 

17 reduction in LH be treated as an adverse effect? And I 

18 have got some bullets here but I am going to go into them 

19 more detailed further in the study, so I am not going to 

20 read them to you now. 

21

22 I would contrast the study with the typical risk 

23 assessment study and the studies that already exist on 

24 atrazine, for which we all know there is a huge database. 

25 Specifically, this particular study, meaning the Cooper 

26 Study on the LH surge, it is an unusual selection of 

27 animals in this study design in order to measure very 

28 specifically effects on the LH surge. This was designed 

29 by Dr. Cooper for a specific mode of action purpose; it 

30 was not really designed to be a risk assessment study and 

31 you can see the way he designed and conducted the study 
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1 that that really was not the intention of this study. 

2 And for many reasons I do not believe it is appropriate. 

3

4 It is a collection of three blocks of animals based on 

5 several requisitions of animals over a course of a year. 

6 It is a large complex study; it involved gavage 

7 administration for four days so that they could very 

8 precisely evaluate, in a subpopulation of animals, a 

9 change in the LH surge. And it required a group of very 

10 precise selection criteria. There is in addition, a very 

11 precise two-hour window in that surge, and you can see 

12 from his data how this is set up. It is not a 

13 conventional design; it is not validated; I do not expect 

14 as a study design there is any need that it ever would be 

15 validated. But I think some form of replication is 

16 critical if it is going to be used in a risk assessment. 

17 And for many reasons I do not believe there are serious 

18 issues with waiting to see that replicated, if indeed it 

19 is going to be used. And I will talk too about some of 

20 the questions about whether or not it even should be used 

21 for risk assessment. 

22

23 I mentioned exclusion criteria... basically this study 

24 protocol called, at the beginning, for about 1000 

25 animals; of which 359 were used. And the exclusion 

26 criteria, first and foremost was if the animals did not 

27 have a regular 4-day estrous cycle over the course of a 

28 couple of week, two or three weeks, they were not to be 

29 used in the study. So more than half of the animals or 

30 about half of the animals were eliminated from that 1000 

31 at the very beginning of the study.

32
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1 Then at the time of kill, after the 4-day dosing and the 

2 collection of tissues and samples and measurement of 

3 hormones, three additional criteria were applied. Did 

4 the animal have a proestrus smear; was there an increase 

5 in uterine weight, beyond a half of gram; was there 

6 elevated progesterone? All of these had to be satisfied 

7 to include the animals in his evaluation. So you go from 

8 1000 to approximately 500 – I will have the numbers in 

9 just a second; and then from that 500 to 359 on that 

10 second set of criteria. Again, this is after four days 

11 of bolus dosing and he did not dose the animals that did 

12 not have a regular cycle. And then sample collection was 

13 every two hours you could not use an animal more than 

14 once because you were killing them every two hours. 

15

16 I apologize for the size of the numbers on here, but this 

17 is a listing, by requisition, of the animals in the 

18 study. Go to the bottom line where it says total; there 

19 are 861 animals that we could identify in the information 

20 on the docket. The protocol call for 1000 but I never 

21 could figure out exactly how many animals started in that 

22 last group. And also could not tell how many were 

23 excluded after dosing. So there are some question marks 

24 here that you need to be aware of. So this numbers, 

25 especially the 861, is the total end is low. There are 

26 other animals because ultimately out of that group 31 

27 were used, which you can see in the last group. 

28

29 So you have total number you start with, followed by how 

30 many of those actually had the 4-day regular cycle and 

31 how many then were excluded after four days of gavage 

32 dosing and necropsy. So after the data is in hand, how 
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1 many did you then excluded and the total number included 

2 at the end, that we could calculate, was 359 animal. 

3

4 It is an extreme design for a very specific purpose, and 

5 that is to answer questions particularly about Long-Evans 

6 rats. Dr. Cooper has done a tremendous of important work 

7 on atrazine and Long-Evans rats and his hypothesis, as I 

8 understand it, was to explore that mode of action. So 

9 this had a very particular design. But one thing that 

10 happened is, by removing animals after the data are 

11 collected, you are eliminating some of the variances in 

12 the study. The variance is still reasonably high in the 

13 included animals but if you, again, before you yell at me 

14 that this is unreadable, I am going to pull out part of 

15 this slide. What this is, is this is each of the seven 

16 groups, control and six treatment groups from the study 

17 and what are called excluded animals. These are excluded 

18 after dosing; this does not include the animals, which 

19 were eliminated for lack of an estrous cycle. 

20

21 Then, included animals are on the last chart. I am going 

22 to just show you the control data from this and since you 

23 have the entire chart in the file, you can look at it at 

24 your leisure, which I am sure you will enjoy doing. You 

25 can see that the times for collection were – I guest 

26 Ralph calls it “rat time” – 1200, 1400, 1600, 1800 and 

27 2000. And you see the variance in the animals excluded. 

28 Again, these are excluded after they have collected the 

29 data. 

30

31 So he has LH numbers on these but they did not meet his 

32 criteria. Most of the animals were eliminated because 
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1 their uterine weight was below a half of gram. The other 

2 two criteria really came into play much less often. But 

3 you can see the variance in looking at the standard 

4 deviation versus the mean; in the included animals, it is 

5 much lower. If he had had to include these other 

6 animals, which he had removed for a purpose and through 

7 criteria, the variance would have been much higher. So 

8 you have a really well selected subpopulation of a 1000 

9 animals. 40% of those animals -- less than 40% -- 35% 

10 ended up being used in this study. So this is not a 

11 study that represents even the entire population of Long12 Evans rats, much less other rats or humans, for various 

13 reasons. 

14

15 I do not believe this study is really designed to set a 

16 point of departure. I also think there are serious

17 problems with the bolus exposure to these animals and the 

18 precisely timed sampling if you want to use this for risk 

19 assessment purposes. Other studies support the use of LH 

20 suppression as an endpoint, but probably at higher dose 

21 levels or longer exposure. There are various effects, 

22 vaginal opening, preputial separation, and other effects 

23 that indicate that some effect on LH may be a sentinel 

24 effect. I am not sure that the effect on LH surge in the 

25 Long-Evans rat though is a good sentinel effect for a 

26 human risk assessment. 

27

28 This really repeats the point just made that you can link 

29 LH suppression, but probably the pulsatile LH suppression 

30 to adverse effects. The mechanism is really not that 

31 well sorted out at this point but on water exposure and 

32 solubility, an issue that has come up several times, this 
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1 chart shows a line and that is the limit of solubility, 

2 thirty parts per millions, of atrazine in water. And the 

3 reason you cannot do a drinking water study is because 

4 the top dose is actually right about where the current 

5 point of departure is, two and a half milligrams per 

6 kilogram per day. That does not even consider whether 

7 they are palatability problems or other reasons that they 

8 would not consume this water. So that is the highest you 

9 could go, theoretically it may be higher than you really 

10 could go in running the study. So we are sort of trapped 

11 back into either dietary or gavage studies. 

12

13 As far as mode of action, the mode of action for the LH 

14 surge was described really well yesterday and it is 

15 dramatically different in the Long-Evans rats compared to 

16 humans. In rats atrazine does affect, in Long-Evans

17 rats, the pre-ovulatory -- and probably Sprague-Dawleys 

18 as well -- the pre-ovulatory surge of LH, by blocking 

19 GnRH secretion in the hypothalamus. Ralph has shown this 

20 in his Long-Evans rat studies; I think this is very real; 

21 it is true for the Long Evans rat. That surge occurs 

22 over about two hours. But it does not alter the ability 

23 of the rat pituitary to respond or to produce LH; it 

24 really is a hypothalamic effect. And the in vitro study 

25 shows that atrazine does not seem to affect the response 

26 of the pituitary to GnRH. 

27

28 Now, effects of atrazine then for the GnRH surge, appear 

29 to originate in the hypothalamus. In humans though, that 

30 is not really relevant. The pre-ovulatory surge, first 

31 of all, is two or three days not two or three hours. It 

32 is not triggered by GnRH surge. You have pulsatile GnRH 
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1 and estradiol positive feedback have been identified as 

2 resulting in that surge of LH. The atrazine exposure 

3 that inhibits the LH surge in certain strains of rats, 

4 whether it is Long-Evans, but not Fischer 344, basically 

5 this is not relevant to other strains rats and this mode 

6 of action is not relevant to humans. 

7

8 Again, the timing in this study was very precise; it was 

9 gavage. You saw pictures of basically, even when you 

10 look at DACT, the saw-tooth pattern of daily gavage on 

11 the concentrations of DACT in these animals. It is not a 

12 steady state; it is not even really much of a pseudo13 steady state. It is highly variable numbers based on 

14 daily gavage; they go up, they go down fairly quickly. 

15 Using the bound atrazine as noted by Harvey Clewell and 

16 the information he showed changes dramatically how steady 

17 you think this pseudo-steady state may be.

18

19 Also, human drinking water exposure is not like gavage. 

20 It is not modeled well by gavage; it includes many uses

21 of tap water intake throughout a given day. It is not a 

22 single dose for most of us. Temporal considerations that 

23 I think are also important are – dosing is daily, but we 

24 are talking about through the 4-day cycle. It was not 

25 four days necessarily because of the cycle; it was again 

26 coming back to that pseudo-steady state as I understand 

27 it. But they did treat for the 4-day cycle and if it 

28 were relevant to humans, it should be compared to the 28-

29 day human cycle. 

30

31 When you look at an effect on a two-hour surge, well the 

32 LH surge is 48 hours, so if you are comparing the timing 
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1 of the effects you need to make some adjustments. 

2 Really, the 4-day study may relate better to a human 28-

3 day exposure. But susceptibility is really dependant on 

4 the mode of action, and they are different between rats 

5 and humans. Humans are unlikely to be susceptible to 

6 changes in the GnRH surge. Typically, in a conventional 

7 risk assessment we are going to use a no observed adverse 

8 effect level or a lower confidence of a benchmark dose, 

9 or some equivalent number in a guideline study. But 

10 mechanistic studies do play a significant role in risk 

11 assessment. They typically do not involve the selection 

12 of a subpopulation, which this study does. 

13

14 There are various endpoints relevant to adverse effects 

15 that have been studied over the years for atrazine. And 

16 no observed adverse effect levels have been established. 

17 In the end, the design of this study limits its 

18 usefulness in risk assessment; it was done for a very 

19 particular scientific purpose, and he answered his 

20 question. But such changes should not be treated as 

21 adverse effects relevant to humans. Other data are

22 really already there that are more important for the 

23 atrazine risk assessment, but if EPA is going to regulate 

24 on such an unusual research study, it really needs to be 

25 independently replicated. That there is time to do this 

26 in that all the margins – we do know, you heard from 

27 Syngenta, you heard from EPA, the margins of exposure are 

28 sufficient. If you need to repeat or replicate this 

29 study, there is time and there are mechanisms by which 

30 EPA can demand that the study be done, and I have had 

31 great experience in the past working with Dr. Mendez and 

32 Dr. Cooper and others on unusual study designs for 
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1 regulatory purposes. There are ways to get these 

2 repeated if this is an endpoint that really is going to 

3 be used for risk assessment. With that, I will conclude, 

4 pass it back to Jere, or answer any questions. Thank 

5 you.

6

7 DR. DANIEL SCHLENK: Do you have any further comments Jere?

8

9 JERE WHITE: No.

10

11 DR. DANIEL SCHLENK: Any questions from the panel? 

12

13 DR. PENELOPE FENNER-CRISP: Okay Jim, so if you were tasked to 

14 do the atrazine risk assessment, and you have available 

15 the current dataset that exists, what would you select as 

16 the appropriate dataset and study to derive a NOAEL and 

17 LOAEL or benchmark dose to use at the point of departure?

18

19 JIM LAMB: Good question, and I think I would rely on vaginal 

20 opening or preputial separation, which have no observed 

21 adverse effect levels point of departure at about six and 

22 a quarter milligrams per kilograms per day. 

23

24 DR. DANIEL SCHLENK: Any other questions? 

25

26 JIM LAMB: Thank you very much.

27

28 DR. DANIEL SCHLENK: Thank you. Our next public commenter 

29 will be Sarah Gallo from the National Corn Growers 

30 Association. We also have a handout for that.

31
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1 SARAH GALLO: Good morning. My name is Sarah Gallo. I am the 

2 Director of Public Policy for the National Corn Growers 

3 Association, and I appreciate the opportunity to be here 

4 this morning. I am providing comments on behalf of the 

5 National Corn Growers Association, which represents more 

6 than 3600 members in 48 states, and 47 affiliated state 

7 organizations with more than 300,000 corn farmers who 

8 contribute to state check-off programs across the 

9 country. 

10

11 Our members are proud to be a part of a sector that is 

12 one of the few bright spots in our country's balance of 

13 trade. USDA forecasts agricultural exports to reach a 

14 record $137 billion for this fiscal year – including a 

15 $44 billion trade surplus, which is the highest it has 

16 ever been. Our corn farmers represent an important part 

17 of these economic strengths. The United States is the 

18 world's largest producer and exporter of corn, and one of 

19 the key inputs that makes that possible is atrazine. 

20

21 For more than 50 years, corn farmers have relied on 

22 atrazine to fight weeds effectively and affordably. It is 

23 applied on well over half of all corn acres in this 

24 country. By EPA's own estimate, atrazine saves corn 

25 farmers as much as $28 an acre and has reduced herbicide 

26 costs and increased yields. 

27

28 Our confidence in this vital tool for corn farming has 

29 been bolstered by more than 6,000 studies and nine 

30 reviews conducted by the EPA. Just this past May, 

31 atrazine got another "all-clear" from a comprehensive 

32 study. A new report from the Ag Health Study - a massive, 
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1 government-sponsored epidemiological study of

2 agricultural workers that has been on-going since 1994, 

3 found no association between atrazine worker exposure and 

4 any form of cancer. 

5

6 This latest report studied more than 57,000 licensed 

7 pesticide applicators from 1994 to 2007. It is just the 

8 latest in a series of studies conducted by governments 

9 and international organizations that have found that 

10 atrazine is not a health risk. In 2007, the World Health 

11 Organization reviewed atrazine and concluded it is "not 

12 likely to pose a carcinogenic risk to humans." The World 

13 Health Organization is so confident of the safety of 

14 atrazine, in fact, that in 2010 it raised its acceptable 

15 drinking water recommendation from two parts per billion 

16 (ppb) to 100 ppb. That's far higher than the EPA limit of 

17 three ppb. 

18

19 Over the past ten years, atrazine has been reviewed all 

20 over the world, in Britain in 2000-2003, Canada in 2004 

21 and again in 2007, Australia in 2008, and the state of 

22 Minnesota again last year. In all of these cases, it has 

23 been favorably reviewed from a human health standpoint. 

24 Of course, the EPA itself re-registered atrazine in 2006, 

25 after a 12-year review. 

26

27 The safety of atrazine, to people and the environment is 

28 clear. It has been verified by thousands of studies. The 

29 economic importance of atrazine is just as clear. It has 

30 been vouched for by corn farmers all over America. At a 

31 time when so much of the US economy is struggling, we 

32 cannot forget that agriculture is one of the few areas 
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1 that is competing better than ever; creating good 

2 American jobs right here at home in the heartland of 

3 America. Rather than do anything that would hurt our 

4 farmers' ability to compete, we should do all we can to 

5 ensure that America's farm exports remain strong in world 

6 markets. Atrazine helps us do that. Thank you.

7

8 DR. DANIEL SCHLENK: Any questions from the panel? 

9

10 DR. KEVIN O’BYRNE: What is your major concern, anxiety?

11

12 SARAH GALLO: What is mine personally? We just want to make 

13 sure that this is a product that our producers are able 

14 to continue to use and just want to convey how important 

15 it is as an important tool for our growers. 

16

17 DR. KEVIN O’BYRNE: So where is the stumbling block? What is 

18 causing the anxiety in your members?

19

20 SARAH GALLO: I think just the concern that there would be 

21 something that would prevent them from using the product. 

22

23 DR. KEVIN O’BYRNE: Coming from what source?

24

25 SARAH GALLO: Nonscientific data or unwarranted concern.

26

27 DR. KEVIN O’BYRNE: Is that from government agencies or is 

28 that just...

29

30 SARAH GALLO: I am not entirely sure, I can...

31
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1 DR. KEVIN O’BYRNE: Or is it from anxiety within the general 

2 population that have little or no understanding of what 

3 goes on in the fields.

4

5 SARAH GALLO: Well, sir, yes of course that is a concern of 

6 our, that people are misinformed. 

7

8 DR. KEVIN O’BYRNE: So does your organization do anything to 

9 educate the general public?

10

11 SARAH GALLO: Yes, absolutely.

12

13 DR. KEVIN O’BYRNE: What is the nature of that?

14

15 SARAH GALLO: Both nationally and within all of our state 

16 organizations, Jere being one of them, we certainly have 

17 public outreach campaigns to education people about corn 

18 farming, about kind of the modern practices that our corn 

19 farmers have adopted to reduce herbicide use and 

20 transform their practices to be both economically and 

21 environmentally beneficial.

22

23 DR. KEVIN O’BYRNE: Thank you.

24

25 SARAH GALLO: I have no personal anxieties – I‟m good.

26

27 DR. DANIEL SCHLENK: Thanks Ms. Gallo. Any other questions 

28 that relate to the panel charge questions? Let me ask 

29 that. Any questions related to the charge questions we 

30 have been given? Okay. Thank you very much. Our next 

31 public commenter is Tyler Wegmeyer from the American Farm 
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1 Bureau Federation. And we do have a handout as well for 

2 this.

3

4 TYLER WEGMEYER: Good morning everybody. My name is Tyler 

5 Wegmeyer and I am Director of Congressional Relations for 

6 the American Farm Bureau Federation. I am also a fourth 

7 generation farmer, growing mostly specialty crops in 

8 Western Loudoun County, Virginia. 

9

10 The American Farm Bureau Federation is the country's 

11 largest general farm organization. Farm Bureau members 

12 grow, produce and raise the food and fiber and energy 

13 sources that feed, clothe and fuel the U. S. and the 

14 world. Our farms and ranches are found in all 50 states 

15 as well as Puerto Rico, and we represent producers of 

16 every size and scale of operation. 

17

18 The American Farm Bureau Federation welcomes this 

19 opportunity to speak to the benefits of atrazine and what 

20 it means to the American farmer. Having access to 

21 important crop protection products is vital to the 

22 success of providing a safe and abundant food supply. I 

23 appreciate this opportunity to be able to express our 

24 views before this Scientific Advisory Panel. Atrazine has 

25 been in use for more than 50 years and has proved to be a 

26 safe, valuable and a cost-effective herbicide that 

27 farmers across the country use to manage the spread of 

28 weeds that rob crops of nutrients. 

29

30 Today, US farmers safely and successfully use this 

31 herbicide on over 50% of corn, 90% of sugar cane and two32 thirds of sorghum acreage. Corn is a base commodity for 
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1 inumerab1e food products, and corn and sorghum are key 

2 feedstocks. If these sectors are undermined, the 

3 repercussions would be felt throughout the US food 

4 industry, including weakening the economic health of the 

5 America's farmers and ranchers. No degree of economic 

6 dependence would matter if atrazine were a problem, but 

7 it's not. We believe sound science shows it to be safe 

8 for use. 

9

10 Atrazine has been the subject of intense scrutiny since 

11 it has been on the market and has been the Subject of 

12 eleven SAPs since the year 2000 by the EPA. Recently, as 

13 you know, the Agricultural Health Study, a large

14 government-sponsored study of agricultural workers, going 

15 on since 1994, found no association between atrazine 

16 worker exposure and any form of cancer. 

17

18 In addition, the World Health Organization raised its 

19 acceptable drinking-water recommendation from two parts 

20 per billion (ppb) to 100 ppb, far higher than the EPA 

21 limit of three ppb. Atrazine has been examined by the 

22 international organizations and countries including the 

23 World Health Organization, the United Nations Food and 

24 Agriculture Organization, the governments of Great 

25 Britain, Canada and Australia, and the state of 

26 Minnesota, which have all deemed it safe for use. 

27

28 The American Farm Bureau Federation has participated in 

29 every scientific advisory panel convened to examine 

30 atrazine's safety since the first special review in 1994. 

31 More than 6,000 studies on atrazine have been 

32 commissioned since its introduction to the market, and it 
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1 is one of the most complete scientific databases of any 

2 crop protection product. Our members look at EPA's 

3 recent actions with dismay and frustration. Farmers are 

4 deeply concerned that this process will result in an 

5 unjustified restriction or elimination of an important 

6 crop protection tool. 

7

8 At a time of continuing high unemployment, and enormous 

9 trade deficits, agriculture is providing a much needed 

10 bright spot in our economy. And yet, we find ourselves 

11 fighting off ill-considered proposals, such as the one 

12 before this panel, that have the potential of making 

13 farming more difficult, less efficient and more 

14 expensive. 

15

16 We hope that this atrazine review process is not being 

17 subjected to an unseemly rush to take unwarranted action 

18 and we urge that you ensure that the principles of sound 

19 science remain our way forward. Again, we appreciate 

20 this opportunity to submit our comments. Thank you.

21

22 DR. DANIEL SCHLENK: Again, any questions that relate to our 

23 charge questions.

24

25 DR. HEATHER YOUNG: I just want to comment since this is the 

26 second public commenter that has made the comment about 

27 the Ag Health Study; have you reviewed the 2011 Beane28 Freeman study that show the four-fold increase risk with 

29 thyroid cancer? Because, you are stating here that they 

30 are showing no association with any forms of cancer and 

31 we have the 2011 study that shows us the four-fold 
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1 increase risk with thyroid cancer. I am wondering what 

2 your thoughts are on that?

3

4 TYLER WEGMEYER: Thanks for bringing it up. I have not looked 

5 at that specifically but I will.

6

7 DR. DANIEL SCHLENK: Any other questions? Thank you, Mr. 

8 Wegmeyer. The last public commenter that I have on my 

9 list is Stephanie Whalen from the Hawaii Agriculture 

10 Research Center. I believe she has a couple of slides. 

11

12 STEPHANIE WHALEN: My name is Stephanie Whalen. I am the 

13 Executive Director of the Hawaii Agriculture Research 

14 Center, and some of you may be wondering, what is someone 

15 from Hawaii doing here. And so I thought I would put 

16 that a little in perspective. 

17

18 HARC, our organization is over 100 years old. It is a 

19 private agricultural organization that supports 

20 agriculture in Hawaii. It began with a dominant 

21 agriculture product of sugarcane and then pineapple, 

22 along came coffee, macadamia nuts, papaya and now more 

23 vegetable production. Currently we have very little 

24 sugar left and our work is involved with the diversity, 

25 which includes those that I named, plus herbs, seed

26 products, cacao tea, et cetera. 

27

28 Our organization has been focus on scientific based 

29 information and technologies to transfer to our client, 

30 essentially the farmers in Hawaii. We have been 

31 delivering that type of information and it has been very 
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1 science-based. We are an organization that is very focus 

2 on science-based information. 

3

4 My personal responsibility, throughout my almost 40 years 

5 with the organization, has been a background in pesticide 

6 residue work and I was around at the beginning of EPA 

7 when it was transferred over from HEW, and was involved 

8 in their original training operations. So that is where 

9 my background comes from, basically pesticide residue 

10 work, which then involved working with all the chemical 

11 manufactures throughout my last 40 years. 

12

13 I also then was responsible, as EPA developed in more 

14 regulatory areas in air, water, clean water act, safe 

15 drinking water act, non-source pollution and then 

16 chemicals with pesticides, and manufacturing them, 

17 basically because the sugarcane industry was vertically 

18 integrated from the field to the table, so all those 

19 regulatory statues affected operations. That has been my 

20 history in terms of why I am here. 

21

22 It is very obvious why I have been involved in the 

23 atrazine process; because of my history and my 

24 responsibility to the industry in Hawaii. And so I have 

25 been involved in all of the SAPs, I believe, except one. 

26 I provide the comments for -- I think it was the 1988 --

27 where the special review was first announced by EPA, and 

28 they asked for comments and got over 80,000 comments for 

29 that. I think that was the highest in their history up 

30 to that point. 

31
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1 A little bit about sugarcane in Hawaii, we were a 250,000 

2 acres in its history; we are now down to 40,000 acres. 

3 Partly or mainly due to the stable price for the last 

4 three decades while the input cost increased over that 

5 time, and regulatory being just one of those cost and 

6 atrazine being a major concern. 

7

8 In Hawaii, we estimate there is about 10% loss on 60% of 

9 our acres if we were to lose atrazine; that works out to 

10 be $130 per acre or about $2.3 million. Using Hawaii‟s 

11 numbers and then take that across the nation to 

12 Louisiana, Florida and Texas; their cost would be about 

13 $280 an acre and they are looking about a $90 million 

14 loss if we lost the use of atrazine. One reason of why

15 there are more restrictions maybe the manufacture would 

16 say, Okay, we are going to just get rid of the sugarcane

17 tolerance or the ability to use it for sugarcane because 

18 corn and sorghum maybe higher and therefore, we will give 

19 the agency sugarcane and that is why we have been at the 

20 table from the very beginning with the manufactures also, 

21 making sure they save our use as well as the more major 

22 uses. 

23

24 And just to let you know that as new chemistry are found 

25 we, part of our role has always been testing all of the 

26 new chemistries for the industry in Hawaii, and so far we 

27 have always had to add and most of the new products have 

28 a little bit of atrazine still left in them to make them 

29 at least equivalent to the regular effective use of 

30 atrazine. There just has not been one that came along 

31 that could totally substitute for it. 

32
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1 I was going to talk a little bit about the Agriculture 

2 Health Study, but I think you have heard enough about 

3 that so I am not going to do that. So I will look at 

4 that first slide, because the National Corn Growers 

5 Association and the Farm Bureau talked about other 

6 countries and I thought it might be useful for you to 

7 have that in a table format, and so there it is. It is 

8 just put in a nice format that you can look at to see 

9 what the other countries have done about atrazine. 

10

11 Then the bottom row there is for the water levels. Just 

12 to emphasis those; in Europe it is 14 parts per billions, 

13 that is a health-based standard; Australia 40 parts per 

14 billions. And institutional research center there is not 

15 applicable; they do not set that. US EPA has the 

16 lifetime MCL at three ppb. And I was very involved in an

17 early period before that and it was 25 parts per million. 

18 It was a health advisory and we happen to have an area in 

19 our state that was close to three and so we voluntarily 

20 stopped the use of that compound in that particular area 

21 because there was this concern which was not fully 

22 fleshed out that three would be the new level set, which 

23 it eventually was. Though we did not hit the three; we 

24 were always at two and went down from there, but we are a 

25 very conservative industry when it comes to environmental 

26 issues. 

27

28 Also under the US EPA they have the DWLOC of 12.5, which 

29 is regulated under now to 68 parts per billion. World 

30 Health Organization, which was pointed out already, went 

31 from two from prior to 2010 to now 100, based on the same 

32 data that is being reviewed here. 
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1

2 So my next slide, I just out of curiosity did a little 

3 calculation based on some of the data that has been 

4 presented in the last couple of days. Anyway, so I did a 

5 calculation for the 2.56 milligrams/kilograms/day which 

6 was the number that came out of the Cooper Study, and 

7 converted it to ppm in water for adult females, 60 

8 kilograms instead of the 70 kilograms male and then the 

9 consumption rate of 2 liters per day. 

10

11 If you put those numbers in -- and then for the 

12 uncertainty factor on the bottom there -- that is the ten 

13 times ten for the standard intra and interspecies safety 

14 factors. Then three has been talked about here for the 

15 FQPA number and then the two liters per day. And you 

16 will get a level of 256 parts per billion and it is my 

17 understanding is we have seen some of the monitoring 

18 data, which we did some of ours ourselves very early on, 

19 no numbers have even come near that. I think the highest 

20 in the community water system is something like in the 

21 high 60‟s to 70‟s. 

22

23 The only other thing I wanted to say was that since I do 

24 really have a long history with EPA in terms of just 

25 paying a lot of attention to regulations and statues that 

26 are done by Congress, I wanted to express the agriculture 

27 community‟s appreciation of the deliberative and 

28 transparent and open process that now allows dialog and 

29 input from all of the stakeholders. 

30

31 Now really almost previous to the atrazine thing, the 

32 growers just sat back and let the EPA, the Agency and the 
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1 registrant just do their thing and come out with the 

2 numbers and then we followed the labels. But, in the 

3 atrazine, it was really hitting many of the growers hard 

4 in that they had worked with this compound 50 years -- or

5 back then it was probably only 40 years or 30 years, 

6 since we have been doing this for almost two decades --

7 and felt that, gee they are really the first target of 

8 any toxicity that comes about. So they were very 

9 concerned about this and wanted to follow the process a 

10 whole lot more than we had in the past. 

11

12 So we are pleased to be able to be at the table as a 

13 stakeholder in this process and we are glad that the 

14 process has developed to that, because that is not where 

15 it was before. So we recognize the hard work and long 

16 hours everybody involved has put into the regulatory 

17 process, including yourself, and regardless of the topic, 

18 not just about pesticides but all the environmental 

19 statutes. And we appreciate that through the open 

20 process the Agency may often feel like a bull‟s eye, 

21 which everyone is taking shots at. And surely we have 

22 heard some of that this morning but all of you are 

23 scientists, you go to the conferences and workshops and 

24 that is where we have dialog and we are able to talk 

25 about experimental designs and the rest of it, and this 

26 is really the process that is set up for regulatory 

27 things, which does not allow that same kind of stuff that 

28 the scientists do. And so unfortunately this is the way 

29 that we can have scientific discussion and although it 

30 may be difficult sometimes, you feel that people are 

31 being critical unfairly, unfortunately, this is the 
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1 process we have and we are glad that we have some 

2 process. 

3

4 So again, I want to express the growers‟ gratitude to you 

5 folks and to the Agency that opens up the process for 

6 input. In the end, we believe that we will get to a fair 

7 and reasonable policy and safe and effective pesticide 

8 use that is based on science. Thank you.

9

10 DR. DANIEL SCHLENK: Thank you. Any questions, clarification? 

11 Thanks. This concludes our public commenting period and 

12 I would like to thank each of the participants in that 

13 for coming forward with their comments. At this point in 

14 time what we would like to do before the break, if 

15 possible, if we could have the agencies come forward. We 

16 mentioned yesterday that we would have them come forward 

17 for those of you that have any questions that sort of 

18 came up over the evening from the plethora of 

19 presentations that took place yesterday. And, give you 

20 guys the opportunity to ask one final sort of batch of 

21 questions or clarification if you had any. So I thought 

22 it would be good before we got into the charge questions 

23 that we could ask any questions or clarification if 

24 anything came up through some of the oral presentations.

25

26 DR. HEATHER YOUNG: I think the epidemiology group would just 

27 like some clarification to make sure that we are on the 

28 same page. One of the charge questions that we are being 

29 ask is to look at the descriptor for the cancer risk 

30 assessment and so last night we looked online and we 

31 found that using the 2005 classification. And so I 

32 wanted to make sure that our choices would be inadequate, 
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1 not likely, suggestive; are those the categories that we 

2 are making recommendations as to?

3

4 DR. ELIZABETH MENDEZ: With regard to cancer classification 

5 within the Agency, that is indeed how we do it, 

6 suggestive, not likely, et cetera.

7

8 DR. HEATHER YOUNG: Okay. Thank you.

9

10 DR. DANIEL SCHLENK: Dr. Greenwood...

11

12 DR. RICHARD GREENWOOD: I wonder if you could give me a little 

13 bit of help on some of the pharmacokinetic data because I 

14 have not been able to get hold of some the original 

15 reports. It is just about the methodology, if you could 

16 just help me. I wonder can you tell me whether when they 

17 measured the total radiolabeling in the plasma, was that 

18 just taken at spinning down the red cells and then 

19 combusting the plasma or was it whole blood?

20

21 DR. CHESTER RODRIGUEZ: Yes, that is my understanding. So the 

22 cellular component of blood like it was removed, like it 

23 was spin down, like you said, to actually isolate a 

24 plasma component. I should also mention that the 

25 Simoneaux 1995 study that you requested it was sent to 

26 Joe Bailey so he should be able to provide that.

27

28 DR. RICHARD GREENWOOD: Thank you for that because I think 

29 interpreting the data, the methods that we used from the 

30 fraction that was actually counted I think you will 

31 realize there is a big difference. 

32
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1 DR. DANIEL GRIFFITH: For the monitoring group, why did you 

2 choose GEOEAS for your semi-variogram modeling? 

3

4 DR. NELSON THURMAN: This is Nelson Thurman. I am going to 

5 let Jim Hetrick who used GEOEAS come up and explain why 

6 he chose GEOEAS. 

7

8 DR. JAMES HETRICK: It's a simple answer actually. We used it 

9 because that is the software package we had available. 

10 How is that?

11

12 DR. DANIEL SCHLENK: Any other questions or clarification.

13

14 DR. KEVIN O’BYRNE: I just have one, yesterday you were 

15 talking about the pseudo plasma steady state levels; in 

16 the rat it is four days and it has a 4-day cycle and it 

17 takes four days of atrazine to reduce LH secretion. Then 

18 in the human, it takes 28 days and they just happen to 

19 have a 28-day cycle. It all seem terribly simplistic to 

20 me. Is that because women are bigger than rats? It is 

21 body weight that leads to that?

22

23 DR. CHESTER RODRIGUEZ: Basically, the 28 days comes from a 

24 range of values. For a human body weight of 60 

25 kilograms, we came up with an estimate that ranges from 

26 21 to 30 days based on allometric scaling or the rat 

27 elimination rate constant. So we decided to just use the 

28 value within that range and it just makes sense to use 

29 28, which just happens to be the human menstrual cycle. 

30 So that was our thinking. But it was a range it was not 

31 28 days exactly.

32
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1 DR. KEVIN O’BYRNE: Yes, but in your slide you had 28. It's

2 just terrible emotive. If we turn the clock back and 

3 think about the mammoths, assume they are like elephants; 

4 then what would you predict for them? 112 days? 

5

6 DR. CHESTER RODRIGUEZ: The scaling that we have done is based 

7 on body weight, and that is the best information we have 

8 available. In the absence of specific human information, 

9 that is the best choice we have.

10

11 DR. TRAVIS JERDE: This question is for Dr. Cooper, regarding 

12 mechanism of action. It seem most of the research has 

13 been on luteinizing hormone and some on GnRH, and yet 

14 there is also affects on prolactin and the description 

15 has been that we have differing modes of action 

16 potentially. But one could also imagine that there would 

17 be a single or similar mode of action at the molecular 

18 level. I am wondering what kind of studies are being 

19 undertaken or proposed to look at signaling mechanisms or 

20 imprinting mechanisms, changes in DNA, things of that 

21 type that might help us assess more subtle effects 

22 particularly in the low dose range that seem to be 

23 overpassed in a lot of these studies. 

24

25 DR. RALPH COOPER: There have been a number of them 

26 undertaken, not by us but by different laboratories, both 

27 in academia and elsewhere. And we still get bits and 

28 parts, but I can speculate a little bit on the 

29 suppression of prolactin by atrazine. 

30

31 It is curious that in the ovariectomy estrogen-treated 

32 animal you can see a clear affect on prolactin 
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1 suppression. It is also clear that prolactin regulation 

2 during nursing, there is an effect. The reason that we 

3 even evaluated it, at the time I was reviewing a 

4 manuscript depicting the unique control of prolactin 

5 during lactation. However, when you go back into the 

6 cycling animal it is difficult for us to see -- under 

7 this condition that we have examined prolactin -- to see 

8 changes in the intact animal prolactin release. 

9

10 Then the last part of that question is if there is a 

11 common mechanism. The one thing that we see consistently 

12 in the brain -- although I am not a big believer in the 

13 catecholamines driving any of this -- is under the acute 

14 experiments anyway, there is an increase in dopamine. 

15 That one possibility would be that it could influence 

16 GnRH neuronal activity, especially at the axonal level 

17 for the GnRH neurons, and then also of course dopamine 

18 being a prolactin inhibiting factor. 

19

20 I am not aware of other studies looking into that. We 

21 are in our lab looking at other peptides in those things 

22 but we have limited resources. 

23

24 DR. PENELOPE FENNER-CRISP: I guess this is for Dr. Rodriguez, 

25 since you were the one that raised the issue about the 60 

26 kilogram person; what was the selection criterion for 

27 that? 

28

29 DR. CHESTER RODRIGUEZ: None actually, it was just a typical 

30 body weight that we selected. But the good thing is that 

31 you can use any body weight that you think is appropriate 

32 for an adult human. It was just arbitrary. 
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1

2 DR. PENELOPE FENNER-CRISP: Once upon a time, it was estimated 

3 that the average female weighed about that, but if you 

4 look at the CBC data and the recent NHANES data, the 

5 average female in the US now weighs 74 kilogram. So the 

6 question becomes if you used the current average female 

7 as your sentinel for determining a number, it may not be 

8 28 days anymore. 

9

10 DR. DANIEL SCHLENK: All right, well thank you. We will go 

11 ahead and take a break now and begin the charge questions 

12 after the break; let‟s be back at 10:30.

13

14 DR. DANIEL SCHLENK: Everybody please take a seat. We are 

15 going to get started on our 14 questions. Before we get 

16 started, I think Dr. Fowle has a few comments.

17

18 DR. JACK FOWLE: Yes, I just wanted to just kind of review the 

19 biding for the purpose of the Scientific Advisory Panel. 

20 I admit I could be reading this wrong, but my sense of 

21 hearing some of the comments we heard this morning is 

22 basically that we are presenting with you a final risk 

23 assessment. 

24

25 I just wanted to note that we are not coming to you 

26 today, to the Scientific Advisory Panel, and not sharing 

27 with the public, frankly, because we do not have it yet, 

28 this is not a final risk assessment. It is not even a 

29 preliminary risk assessment. We will not have a 

30 preliminary one until late 2012 or 2013. 

31
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1 What the purpose of this is to come to the Scientific 

2 Advisory Panel and share with you some of the conclusions 

3 we are coming to; some of the methods and models that we 

4 will be using and these will be inputs into the risk 

5 assessment that we will come up with. So we are coming 

6 to you to try to get your scientific advice and guidance 

7 as to our thinking; are we on the right track, or are 

8 there other things that we should be considering what you 

9 view as the tools; the strengths and weaknesses of tools 

10 and models and the types of endpoints we are thinking 

11 about right now. 

12

13 Also, with respect to the epidemiology, we are really not 

14 asking for a judgment of the overall epidemiology risk. 

15 Basically what we are doing is saying, in terms of 

16 thinking about the considerations we have that go into 

17 our evaluation of the various studies, and think about 

18 how we might come to an overall judgment of the 

19 epidemiology data; also, more importantly, how we might 

20 integrate that with the toxicity information to come up 

21 with an overall weight of evidence approach. 

22

23 We have tried to share with you, as best we could, what 

24 our thinking is, our line of reason, our logic and that 

25 kind of things. We would like your feedback on that. 

26 Also, to the extent that we are trying to move -- as some 

27 of you heard in May -- we are trying to move more towards 

implementing the toxicity testing in the 21st 28 century, 

29 "Approach to Toxicity". We mentioned it would not happen 

30 fully for 15, 20, perhaps more years, but we try to do 

31 this incrementally as we went along. 

32
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1 We are using an adverse outcome pathway as a basis to try 

2 to lay out what we know, however much or however little 

3 in terms of toxicity, kind of use that as a framework. 

4 So we have given you, as best we could, what we know 

5 about atrazine in that context. So if you could give us 

6 feedback about that as well, that is the kind of thing 

7 that we are looking for in these charge questions, not a 

8 risk assessment, per se.

9

10 DR. DANIEL SCHLENK: Thank you very much. With that we will 

11 go ahead and start the reading in of the questions and as 

12 we discussed, if it is okay with you guys we are just 

13 going to read the letters of the questions rather than 

14 the whole question. Nelson, you are going to read the 

15 questions.

16

17 DR. NELSON THURMAN: Given the example dataset, we presented a 

18 matrix approach for deriving bias factors. So the 

19 questions we have related to that approach. 1. a) Given 

20 that the factors are likely to vary based on watershed 

21 size and water-body type, please comment on the level of 

22 detail we would need to develop for that. In other 

23 words, flowing water versus reservoir, and small versus 

24 medium versus larger watershed area. How many datasets 

25 would we need to analyze to provide a reasonable 

26 representation of a bias factor for each category? Then 

27 part b) Please comment on the advantages and 

28 disadvantages of deriving bias factors based on analyses 

29 of individual sites and years compared to taking 

30 percentiles of averages across sites and years.

31
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1 DR. DANIEL SCHLENK: I guess you guys have the choice of doing 

2 this separately or together. Did you want to separate 

3 them into a and b or did you want to do them both 

4 together?

5

6 DR. ROBERT GILLIOM: I have it all together. I mean it is a... 

7 b, but it is sequential. 

8

9 DR. DANIEL SCHLENK: Okay, so lead discussant on that is Bob 

10 Gilliom.

11

12 DR. ROBERT GILLIOM: So at risk of being a bit more boring 

13 because I read this, I would like to get it all down as I 

14 wrote it; so bear with me. 

15

16 As a context for answering this question, the bias factor 

17 approach is probably best viewed as an early step in the 

18 type of systematic process that you show in Figure 22 of 

19 the issue paper, albeit with some different methods in

20 the different steps. 

21

22 Application of a bias factor to exposure statistics 

23 calculated from simple linear interpolation of sparse 

24 monitoring data is a potential simple and practical 

25 approach to evaluating data from a variety of monitoring 

26 frequencies to get either unbiased or conservatively 

27 high-biased preliminary estimates of exposure metrics, 

28 depending on how the factor is derived. The approach is 

29 primarily applicable to sites with moderate frequency 

30 monitoring data, such as weekly or biweekly, so that

31 initial biased sample estimates are more or less in 

32 statistical control. 
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1

2 Quarterly data, for example, would be too sparse for use 

3 for short duration sample estimates. This said there is 

4 not a simple answer to the questions posed, because there 

5 are not enough data over an adequate range of sites and 

6 years to reliably organize the entire problem. The 

7 reality, as shown by the available calculations of site8 year bias factors, is that each individual site has its 

9 own characteristics that govern the adequacy of different 

10 frequencies. And here are some observations about those. 

11

12 Each site year has a different concentration distribution 

13 compared to other sites that same year and to other years 

14 for the same site. These site to site and year to year 

15 differences in the temporal distribution and magnitudes 

16 of concentrations also apply to the specific exposure 

17 statistic of interest, such as a particular maximum 

18 rolling average of 4, 7, 28-day levels. 

19

20 Both the annual maximum of specific rolling averages and

21 the temporal distribution of rolling averages, including 

22 total area under the curve for selected intervals, may 

23 turn out to be important. The implication of this is 

24 that focusing only on a bias factor for annual maximums 

25 may not fit all the needs for future risk assessment. 

26 Sparser sampling as compared to the actual population of 

27 interest, for example daily values, results in 

28 uncertainty in estimates and a tendency toward low bias 

29 for estimating high-end statistics. 

30

31 Bias tends to be low because short-lived high32 concentration events have a higher likelihood of being 
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1 missed with sparser sampling. Some broad differences 

2 among different types of sites are evident, such as 

3 according to basin size and reservoirs versus streams, 

4 but we really do not have adequate sample sizes across 

5 the gradients of all these conditions to quantify the 

6 relations with a sophisticated approach. 

7

8 Potential Approaches that could be taken to address the 

9 problem include, and I will just name three. The first 

10 is evaluation of “relatively homogeneous” groups to 

11 develop a categorical system of bias factors. And this 

12 is the approach that is really referred to in the charge 

13 question. And if there really are useful groups, as 

14 opposed to a continuum of conditions, then – to give you

15 a specific answer – perhaps on the order of 30 sites per 

16 group, each with several, 5-10 years, worth of data, 

17 might be the kind of adequacy for approaching on that 

18 level. 

19

20 Reservoirs, however, which account for a large proportion 

21 of the community water supplies, will probably be 

22 difficult or impossible to categorize because of the 

23 highly variable characteristics, such as volume and 

24 residence time, which are not readily attainable. 

25

26 An alternate approach to this categorical one that is 

27 mentioned would be to basically use a regression of bias 

28 versus explanatory variables, such as basin 

29 characteristics and water-body type, thus expressing bias 

30 as a continuum governed by specific characteristics. 

31 This approach could be promising for at least certain 

32 parts of the problem, such as watershed size for flowing 
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1 streams, but more data would be needed for multiple years 

2 at selected sites and at additional sites with 

3 intermediate basin sizes. 

4

5 A third approach is a worst case group approach, such as 

6 small basins, to yield a conservatively high bias factor 

7 for protective screening that then would trigger 

8 monitoring. This could be a practical approach that can 

9 be used now, because we are relatively confident that 

10 flowing water sites with small basins, such as the AEMP 

11 sites and other small-basin sites, define the worst case 

12 bias factors, both for larger flowing streams and also 

13 probably for reservoirs, at least regarding short-term 

14 duration concentrations. 

15

16 There are a significant number of community water 

17 supplies with the watershed size range within the range 

18 of the AEMP sites. A remaining weakness overall for 

19 these approaches is the lack of sufficient multi-year 

20 data. This is a problem for approaches other than the 

21 worst-case group approach above, because extremes do not 

22 happen every year. So that is the answer to part a). 

23 Should I stop here?

24

25 DR. DANIEL SCHLENK: Yes, let‟s stop here and we will split 

26 them up. Dr. Coupe...

27

28 DR. RICHARD COUPE: The only additional comment I have is just 

29 to reemphasize what Bob said was that I do not know that 

30 you could really develop categories of these community 

31 water systems. I think there are enough variables in 
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1 there that you would have a category of one for every 

2 water system.

3

4 DR. DANIEL SCHLENK: Okay. Dr. Lee...

5

6 DR. HERBERT LEE: I do not have too much to add to that, I 

7 largely agree fully. I did want to comment on one thing 

8 that Syngenta mentioned, they said “database sample 

9 number provide high confident on exposure”. I just want 

10 to add in another piece of uncertainty that we mostly 

11 have been glossing over, which is measurement error. We 

12 do not have a good idea about what the magnitude of 

13 measurement error is. If you say go and take multiple 

14 samples at the same time, how similar did they turn out; 

15 or if you have multiple people taking samples at the same 

16 time, how similar did the turn out. And how similar is 

17 it if you have a person taking measurements versus an 

18 auto-sampler. 

19

20 These are probably going to be relatively small compare 

21 to the big peaks, but if we are looking at extended 

22 durations of exposure, these sort of errors could add up. 

23

24 DR. DANIEL SCHLENK: Any comments from the other panel 

25 members? Okay, you want to go ahead and do b)?

26

27 DR. ROBERT GILLIOM: So part b) to remind folks is, please 

28 comment on the advantages and disadvantages of deriving 

29 bias factors based on analyses of individual sites and 

30 years compared to taking percentiles of averages across 

31 sites and years. 

32
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1 My answer is the fundamental unit of exposure assessment 

2 is the site-year combination. And each community water 

3 system site has a unique watershed with corresponding 

4 hydrologic behavior, pesticide use, etc. A unique 

5 population of people served, and every year is different. 

6 Generally, analysis needs to focus on each individual 

7 community water supply as a unit. The condition of 

8 greatest concern is when the maximum of a selected 

9 rolling average duration exceeds a level of concern, yet 

10 to be defined, and this tends to be more likely in high 

11 use seasons during years when runoff after applications 

12 is high. Commonly, the most extreme conditions happen 

13 one or more times every few to several years, as 

14 exemplified by the Honey Creek and Maumee River multi15 year results submitted by Syngenta. 

16

17 Bias factors, to the extent they are used for screening18 level analysis, should be developed with the objective of 

19 identifying sites that merit direct monitoring. In this 

20 application, they can be biased in the conservative 

21 direction and used to identify individual sites with an 

22 unacceptable likelihood, yet to be defined, of exceeding 

23 threshold, based on the available sparse monitoring data. 

24 These sites would then be monitored more intensively to 

25 more accurately assess the actual condition. 

26

27 The bias factors may also be useful as a simple and 

28 transparent approach to estimating exposure for sparsely 

29 monitored sites for other purposes, such as for large30 scale risk assessments or correlation with 

31 epidemiological results. In these applications, the 

32 starting point for analysis and the endpoint of interest 
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1 is the individual site, not groups of sites. However, 

2 there may be certain data analysis approaches that use 

3 data from groups of sites to make inferences for 

4 individual sites. This can be done as long as the 

5 uncertainties in predictions for individual sites are 

6 properly represented. 

7

8 DR. DANIEL SCHLENK: Dr. Coupe...

9

10 DR. RICHARD COUPE: I do not have any additional comments.

11

12 DR. DANIEL SCHLENK: Dr. Lee...

13

14 DR. HERBERT LEE: I just want to clarify that – I agree with 

15 Bob – but want to say that it is important to look at 

16 each site by year when looking at the bias. So if we 

17 have comparison daily data and then we look at sub18 sampling weekly or 14 or 28 days, compute by 

19 interpellation, compute the bias factor. You want to do 

20 that for each site for each year and then look at the 

21 distribution of bias factors, say across sites or across 

22 years. And you can gain information by pooling that way. 

23 But for computing the individual points in the 

24 comparison, you want to do it by site by year.

25

26 DR. DANIEL SCHLENK: Other panel members... All right, moving 

27 right along. Mr. Thurman, are you clear with what you 

28 have? 

29

30 DR. NELSON THURMAN: I think we are very clear with what we 

31 have.

32
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1 DR. DANIEL SCHLENK: Okay. Let‟s move on then to question 

2 two, and again you can feel free to read the sub-headings 

3 on that. 

4

5 DR. NELSON THURMAN: Question number two; please comment on 

6 the Agency‟s method of estimating time series using 

7 conditional simulations of variograms for monitoring data 

8 sets such as the AMP community water system monitoring 

9 that have 7-day sampling frequencies. And part b) is; 

10 based on the US EPA‟s analysis using WARP with longer 

11 duration sampling intervals, what advantages does the SAP 

12 see of including WARP modeling in this approach, i.e., 

13 better estimation of the daily maximum value?

14

15 DR. DANIEL SCHLENK: Okay. Dr. Griffith, our lead discussant.

16

17 DR. DANIEL GRIFFITH: I have addressed these sequentially, so 

18 I will present part a) and then part b) separately. I 

19 wanted to prefix this with two comments. One is that the 

20 other discussants and I realize that probably some of 

21 what we will raise, the EPA scientists are fully aware 

22 of. Second, I do have tables in my report, which I will 

23 summarize. 

24

25 So, this methodology acknowledges the serial correlation 

26 latent in time series data. Note that Table D1.1 NCWQR 

27 1995 Maumee River Data Set contains substantial temporal 

28 autocorrelation. Conventional Box-Jenkins type ARIMA 

29 models require uniform spacing in time, but more 

30 effectively address seasonality. As an aside, the daily 

31 measures for 2011, the Syngenta report 2001301-03, imply 

32 that, for finished water, an ARIMA (1,1) model adequately 
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1 describes these data. And with those data that were 

2 released, most recently the ARIMA (1,1) model, the 

3 autoregressive term, was consistently above .9 suggesting 

4 that perhaps even differencing would be effective and the 

5 moving average term was roughly around -.4 across those 

6 six data sets. 

7

8 Also, CWS-71 had a suspicious correlagram, but it could 

9 be a result of some of what we saw in the metadata this 

10 morning. Restricting attention to the days of interest 

11 appears to handle the stationarity issue in an effective 

12 way, but the Table D1.1 sample atrazine data implies that 

13 Julian days 101-200 may be the wrong time interval; the 

14 start time seems to be closer to Julian day 130 for that 

15 time series, and seems to go beyond Julian day 200. 

16

17 The most recent Syngenta data support this contention for 

18 some of the other watersheds. And in fact, we saw a map 

19 presented yesterday and there was a similar map that was 

20 in one of the background material reports that showed the 

21 variation in latitude, which may well correlate with 

22 different start times and support this geographic 

23 variation consideration. 

24

25 The complication here may well be that different CWSs 

26 will have different Julian day time periods; in other 

27 words, geographic variation in the windows across these 

28 CWSs. 

29

30 Any methodology that focuses on mean responses, such as 

31 moving averages, the rolling averages, will tend to 

32 underestimate peak atrazine concentration. Expectation 
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1 maximization imputations are conditional expectations; in 

2 other words, they are means. The presence of 

3 autocorrelation implies that these conditional means are 

4 locally adjusted. Substituting conditional means into a 

5 time series for missing data values suppresses variance; 

6 they only represent a trend line. This is one reason for 

7 the underestimation of a 1-day maximum concentration, 

8 while obtaining reasonable estimates of rolling average 

9 concentrations.

10

11 This variance suppression also raises questions about 

12 assuming that standard time series developed by 

13 unadjusted kriging are representative of true daily time 

14 series. Virtually all software packages report standard 

15 errors for the case of random sampling. The assumption 

16 that they are the same for systematic or stratified 

17 random sampling, or for the observed non-probability 

18 sample of monitored days and I think diagnostics should 

19 be performed to evaluate the assumption of a pseudo20 random unequal probability design, which appears to be at 

21 odds with their voluntary, truncated and mixed water 

22 gathering nature, may well seriously impact upon 

23 uncertainty assessment. 

24

25 In addition, assuming that un-sampled days are missing at 

26 random seems questionable. In contrast, assuming missing 

27 years for any CWS are by design, and hence eliminating 

28 those years from the population of interest, seems 

29 reasonable. Perhaps assessments within the context of 

30 mixed modeling could furnish insights here. And I will 

31 come back to this in terms of pooling of time series. 

32
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1 Other principle issues of concern include the following: 

2 (1) impacts of misspecification on the semi-variogram 

3 model – a wide range of forms should be examined; (2) 

4 impacts of assuming that atrazine concentration data 

5 conform to a log-normal rather than another extreme value 

6 distribution, which can be dramatic in terms of the 

7 estimation of 95th percentiles; (3) impacts of ignoring 

8 spatial autocorrelation, which are less on mean patterns, 

9 and much more on 95th percentile estimates through 

10 effects on variance; (4) impacts of assuming a linear 

11 relationship between atrazine concentrations and 

12 covariates; again, potentially more dramatic on 95th 

13 percentile estimates, and add to uncertainty rather than 

14 improve estimatability; and, (5) impacts of not 

15 performing a sufficient number of simulations to really 

16 establish the uncertainty distributions; again, 

17 especially on 95th percentile estimates rather than on 

18 mean patterns. 

19

20 Issue (1) here is of particular concern. Empirical semi21 variograms estimated with sample data can be extremely 

22 variable and unstable. Subsequent SAS 9.22 PROC 

23 VARIOGRAM results include standard error estimates for 

24 semi-variogram model parameters. Many geo-statistical 

25 software packages fail to report these values, because, 

26 for example, some use visual curve fitting. Those 

27 reported here highlight this degree of variability. 

28

29 One option is to exploit spatial autocorrelation by 

30 pooling data for similar watersheds when estimating semi31 variograms; the subsequent discussion addresses this 

32 topic, too. 
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1

2 Finally, relatively large nugget effects tend to overly 

3 smooth rolling averages; in the absence of any 

4 autocorrelation, the E-M solution is the sample mean. 

5 Kriging produces the best linear unbiased predictors, and 

6 is one way to deal with irregularly spaced data through 

7 time; treating it like a linear geographic landscape, as 

8 well as a time series with a sizeable amount of missing 

9 data. For instance, the selected subset of Table D1.1 

10 data has 43% of its values missing. It also is 

11 substantially better than the simple linear interpolation 

12 used in some of the preliminary research, and I have seen 

13 it in some of the more recent research since I have been 

14 here, although some substitutions, such as the one with 

15 PRZM model are conditional. But the implemented 

16 methodology appears to suffer from a number of 

17 weaknesses. 

18

19 One drawback is considerably restricted candidate set of 

20 semi-variogram models available in GEOEAS, which no 

21 longer is a state of the art software package. It has a

22 few exponential Gaussian, spherical; semi-variogram trend 

23 lines portrayed in Figures D-3 and D-27 appear 

24 unconvincing. A mis-specified model here is another 

25 source of the nugget effect. 

26

27 In other words, you get a non-zero intercept value 

28 arising simply because of specification error. The 

29 autocorrelation in the selected subset sample time series 

30 is considerable, and appears to be much better described 

31 by a Bessel function, which more directly links to an 

32 ARIMA (1,l) model that is reflected in the most recent 
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1 data; if not a stable function, which is similar to a 

2 Gaussian function, but with an exponent other than two. 

3

4 These models, as well as other valid semi-variogram 

5 models, can be estimated with ArcInfo's Geostatistical 

6 Analysis module, which I note the software supported the 

7 research for Report MRID 48470008. They also can be 

8 estimated with SAS 9.22 PROC VARIOGRAM. And they also 

9 can be estimated with modules from the R project, which 

10 are free and can be downloaded. 

11

12 These two latter software packages furnish analytical 

13 rather than visual model estimation routines. SAS 

14 quantifies uncertainty associated with the semi-variogram 

15 model estimation, which is alluded to in the reports, but 

16 without specificity, and differs from kriging prediction 

17 error. 

18

19 For the log-normal transformed atrazine example time 

20 series data; I did an estimation with SAS PROC variogram 

21 for the spherical Gaussian, which are two models that 

22 were reported, and the Bessell and the spherical clearly 

23 is not the best descriptor of the data. And depending 

24 upon criteria that you use, the Bessell and the Gaussian 

25 are competitive for that one-time series. 

26

27 When I repeated this analysis, for the six most recent 

28 daily time series, in all six cases the Bessell function 

29 dramatically outperformed the spherical and the Gaussian. 

30 So those are two tables that I am just summarizing here. 

31 And it outperformed it on both criteria that are reported 

32 for goodness-of-fit. 
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1

2 Comparable results for other semi-variogram models can be 

3 obtained with SAS, PROC and NLIN – so you can actually 

4 program these for a couple dozen possible semi-variogram, 

5 but as I argued I think the most consistent one is the 

6 Bessell function – and it uses weighted nonlinear least 

7 squares. 

8

9 A second weakness is the log-normal distribution 

10 assumption. Although the three-parameter logarithmic is 

11 the best Box-Cox power transformation enabling the 

12 selected subset of the Table D1.1 data to mimic a normal 

13 frequency distribution, the transformed data still are 

14 far from bell-shaped. The Shapiro-Wilk statistics, which 

15 are normalcy diagnostic statistics, improved from .9 to 

16 .93; so there is some improvement but there is still 

17 quite a bit of deviation entailed. This same description 

18 also applies to the six recent daily sampled CWSs. Of 

19 the six, only one achieves something that is 

20 indistinguishable from a bell shape curve with the three 

21 parameter log-normal distribution. 

22

23 The log-normal conceptualization describes an outcome 

24 that may be viewed as the product of many positive-valued

25 independent random variables. It has been used to 

26 analyze extreme values of, for example, rainfall 

27 quantities and river discharge volumes, and often is 

28 acknowledged as being a heavy or fat-tailed distribution. 

29

30 One of the following extreme value distributions, which 

31 mean that their probability distributions have extreme 

32 deviations from their medians, may well be more 
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1 appropriate: the Weibull, the generalized extreme value, 

2 Gumbel, and the Frechet. The selected subset of Table 

3 D1.1 data better conforms to a Weibull distribution than 

4 a log-normal and slightly better conforms to an extreme 

5 value distribution, but the difference between the latter 

6 two is not very much. 

7

8 Nevertheless, in all three cases, evidence exists 

9 suggesting that the empirical distribution still differs 

10 significantly from their counterpart‟s theoretical 

11 distributions. The largest extreme value goodness-of-fit 

12 appears to suffer from too many low values in the 

13 beginning of the selected subset time series. I think 

14 one of the reasons these goodness-of-fit are not coming 

15 out better is because there was a set day 101 that the 

16 time series started and if you inspect the time series, 

17 what you see is that that varies quite a bit. Which is 

18 what I was talking about earlier, a variation in the

19 stationary part of the time series start and end date. 

20

21 One stated ultimate goal of the methodology is to be able 

22 to predict values greater than those sampled. The 

23 Weibull, or perhaps another extreme value distribution, 

24 offers more potential for doing this than does the log25 normal distribution. The most recent Syngenta report 

26 T001301-03 furnishes data for an additional six CWSs, and 

27 these data yields, almost across the board, support for 

28 the Weibull distribution over the log-normal distribution 

29 although there are some cases in which the log-normal 

30 distribution does slightly outperform the Weibull 

31 distribution. 

32
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1 These findings support the contention that atrazine may 

2 be better described by a Weibull distribution. They also 

3 suggest that such a characterization may be watershed 

4 specific. A third weakness is the overlooking of spatial 

5 autocorrelation. This is somewhat surprising because 

6 geo-statistics was developed to handle this data feature, 

7 and because of the extensive relevant discussions in 

8 Report MRID 48470008. 

9

10 Many geographically distributed variables within a 

11 watershed exhibit spatial autocorrelation. Time series 

12 for different watersheds also may be correlated. 

13 Depending upon such parameters as planting timing and the 

14 occurrence of storm events, watersheds of similar size 

15 and similar characteristics may well generate similar but 

16 perhaps lagged time series of atrazine concentration. 

17

18 If so, information can be borrowed from one time series 

19 to help complete another time series. And, information 

20 in comparable time series may be pooled to better 

21 estimate the semi-variogram models. 

22

23 Planned research apparently seeks to address a forth 

24 weakness, namely the use of covariates, which are called 

25 soft data in the reports. Co-kriging allows inclusion of 

26 additional information. One concern here is the 

27 assumption of linear relationships between atrazine and 

28 selected covariates: scatterplots appearing in Figures D29 23 and D-24 do not support this assumption. The 

30 furnished reports state a number of R square values 

31 without including scatterplots: a random scatter of n-1 

32 points of approximately the same coordinate pair 
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1 accompanied by an extreme outlier can produce similar 

2 results. 

3

4 Some linear regression analyses involve too few points. 

5 Linear regression with four, eight, or 15 observations I 

6 think tend to yield questionable results. Results have 

7 been obtained with analytical routines from Microsoft 

8 Excel; various analysts have shown many Microsoft Excel 

9 routines to be unreliable. 

10

11 Conditional simulations are an efficient and effective 

12 way to produce confidence intervals for the atrazine time 

13 series. A fourth weakness, which is easily remedied, 

14 pertains to these simulations. Simulation experiments 

15 exploit the Law of Large Numbers and the Central Limit 

16 Theorem. Those based upon 10,000 replications should be 

17 sound. Those based upon 1,000 replications could be 

18 bolstered. Those based upon ten replications, for 

19 instance Figures D-28 and D-29, are unacceptable. Except 

20 in extenuating circumstances, the number of replications 

21 should be the same across all simulation experiments. 

22

23 DR. DANIEL SCHLENK: Wow... Thank you Dr. Griffith. Dr. Lee, 

24 anything to add to that? 

25

26 DR. HERBERT LEE: Not much, he has already incorporated pretty 

27 much what I had to say. I do want to just get on to the 

28 record a conversation I had with Nelson Thurman‟s group 

29 yesterday, after his presentation. 

30

31 I think they are estimating an overall mean when they are 

32 doing what they are calling ordinary kriging, and that it 
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1 is skewing their results. The particular data structure 

2 here, when nothing is happening it returns to zero rather 

3 than to some overall mean level that is nonzero, which 

4 would be the normal case with spatial patterns. But here 

5 it returns to zero and so if there is a way to set the 

6 mean to zero, rather than estimating the mean, I think 

7 that will improve their results especially for the 

8 confidence bands. 

9

10 For example, on their slide 16, looking at the Maumee 28-

11 day results; those are sort of wondering around and they 

12 just have the wrong mean there I think is the main 

13 problem. On the 4-day average simulation on slide 19, 

14 for the Maumee and then the daily 4-day, 14-day averages 

15 for the Missouri -01, you see these weird bubbles that 

16 show up early on in the confidence bands and the 

17 confidence bands tends to be stretching higher than I 

18 think they really should be. And I think that is all 

19 because of having a mean that is nonzero; and that moving 

20 the mean to zero will help with a lot of the results. 

21

22 DR. DANIEL SCHLENK: Thank you. Dr. Portier...

23

24 DR. KENNETH PORTIER: I should point out that we did 

25 communicate on Dr. Griffith‟s report before the meeting 

26 and so it really represents the three of us kind of 

27 thinking through this and we iterated it a couple of 

28 times. 

29

30 I just have one additional issue that I want to bring up 

31 at this point, with using a 1D geospatial approach to 

32 model what is essentially a non-stationary time series. 
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1 And that is the assumption is made with the geospatial 

2 approach that observed data are known without error. And 

3 the impact of this assumption is most evident in figure 

27-A, where the 95th and 5th 4 percentile curves from the 

5 conditional simulations, coincide every seventh day at 

6 the known sample points. And if you look at the curve it 

7 is kind of weird; it goes up and than down... up and down. 

8

9 We know in fact that these values are really estimates; 

10 for grab samples, they are simply snapshots of 

11 concentration at the time of sampling in the location 

12 that is actually being sampled. Looking at the actual 

13 data from, for example, NCWQR 1995 Maumee River dataset 

14 in Appendix D, section 1.1, we see that for some dates 

15 multiple samples were taken and that there is substantial 

16 variability evident in these estimates. 

17

18 I think, although I am not certain of this – I did talk 

19 to my colleagues and I think they agree – that this 

20 variability is over and above the variability modeled by 

21 the nugget effect in the kriging model; this kind of 

22 needs to be confirmed and then incorporated into the 

23 model. Doing so will add additional variability to the 

simulations making 95th percentile curbs higher and 5th 24

25 percentile curbs lower and having variability at the 

26 sample points. 

27

28 DR. DANIEL SCHLENK: Any other panel members want to weigh in 

29 on this? 

30

31 DR. ROBERT GILLIOM: I did not directly collaborate on the 

32 answer and I wanted to add that I think in this panelist 
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1 member‟s view I do not think kriging is the way to go to 

2 fill in this and a lot of the problems brought up maybe 

3 reinforce that. 

4

5 I still feel that the better approach is to use a 

6 statistical time series model that links the temporal 

7 patterns of occurrence to some predictive factors like 

8 precipitation and stream flow and so forth, which is 

9 exemplified by the SEAWAVE model that was talked about in 

10 previous SAPs and recommended for this purpose. And I 

11 think, if I understood it right from the comments in the 

12 issue paper, it was kind of too much in the timeline to 

13 really get to that and try it. But my encouragement 

14 would be to still try that approach as a way to fill in 

15 data. 

16

17 And the advantage it may have is an addition to being 

18 able to produce realistic time series that are unbiased 

19 for sampling frequencies like seven days and so forth. 

20 It can also be spread across wider sampling frequencies 

21 and more variable conditions with one tool. So you would 

22 have the advantage of having one singe tool be able to be 

23 used across a much wider range of circumstances where as 

24 with kriging you are going to have to have 7-day or 

25 better data probably to make it work. Thanks.

26

27 DR. DANIEL SCHLENK: Dr. Portier...

28

29 DR. KENNETH PORTIER: Bob, I was thinking kind of the same 

30 way. The only problem is the real non-stationarity of 

31 these time series, the fact that they have this jump up 

32 patterns and then decline for an event. And that is kind 
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1 of hard, unless you have a factor in the environment that 

2 mimics that kind of effect; say like stream flow, which 

3 we are going to talk about in a few minutes. It is going 

4 to be very hard to capture that with traditional time 

5 series modeling, and I do not know of any kind of 

6 approaches that would easily do it. But, in general, I 

7 kind of agree with you. The kriging is nice and it is 

8 taking into account all of this temporal autocorrelation, 

9 but like you I am kind of not convinced, and I think we 

10 will get to that in the subsequent questions.

11

12 DR. DANIEL SCHLENK: Anyone else in the panel on this? Okay, 

13 let‟s go ahead and go to b). 

14

15 DR. DANIEL GRIFFITH: The WARP, which is the watershed 

16 regression on pesticides model, furnishes predictions of 

17 the distribution of atrazine concentrations in specific 

18 watersheds. Its input includes the following variables: 

19 atrazine use intensity, precipitation and rainfall 

20 intensity, a soil erodibility factor, percent runoff, and 

21 watershed size. 

22

23 Competing models include: PRZM, which is the pesticide 

24 root zone model, EXAMS, which is the exposure analysis 

25 modeling system, and mass-balance. WARP model-generated 

26 output synthetic data for a 1-day temporal resolution 

27 would allow the use of co-kriging to secure missing 

28 atrazine concentration data imputations in a time series. 

29

30 These supplemental data would need to be properly 

31 integrated with monitoring data. The reported experiment 

32 reveals that conditional simulations of merged WARP 
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1 model-generated and sampled monitoring data are highly 

2 dependent on the WARP-based imputations. If these data 

3 are equivalent to conditional expectations, then the 

4 associated imputations will have considerably less 

5 variability; in other words, the varian variation is 

6 suppressed, which given the large percentage of missing 

7 values, could overweight these portions of a time series. 

8

9 In other words, WARP estimates do not really add the 

10 additional variability that would be expected in ambient 

11 measurements. Potential impacts include compromising the 

12 upper percentiles of observed concentrations, as well as 

13 reducing the likelihood of observing 4-day, or any x-day, 

14 rolling averages of concentration above some threshold 

15 value. 

16

17 Perhaps one way to temper this effect is to add random 

18 noise to the deterministic values in such a way that they 

19 are indistinguishable from the observed monitoring data. 

20 One ultimate goal is to establish an upper percentile 

21 threshold that is not excessively conservative, in other 

22 words, orders of magnitude beyond the observed data. 

23

24 The final percentile should furnish adequate protection, 

25 but not far more protection than is necessary, which 

26 could cost society benefits of atrazine while really not 

27 significantly improving the likelihood of avoiding 

28 adverse health effects. Within the context of this goal, 

29 error propagation merits evaluation to see whether or not 

30 compounding occurs, with this evaluation being balanced 

31 against returns on an investment of resources in such a 

32 study. 
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1

2 Conceptual arguments in terms of plausibility may be 

3 sufficient to dismiss some propagation possibilities. 

4 Sources of error meriting consideration range from 

5 merging spatially gridded field data that are 4x4 

6 kilometers for rainfall with 85x74 kilometers for 

7 temperature, to raster images of rainfall totals from 

8 historical radar weather data – all of which may involve 

9 raster-to-polygon conversions - to the numerous 

10 assumptions employed by model-based imputation, for 

11 example, the 1:1 relationship between relative 

12 percentiles of flow and atrazine in the WARP model. 

13

14 One concern expressed in the reports is the need for a 

15 priori knowledge about reasonable upper limits for peak 

16 concentration estimates. Although such figures furnish 

17 checks for synthetic results, percentages of these peaks 

18 are not being estimated. Furthermore, because 

19 imputations are conditional means, estimation of extremes 

20 is unlikely. Replacing a log-normal probability model 

21 with an extreme value probability model may help 

22 remediate this situation. 

23

24 Research establishing a valid auto-Weibull type of model 

25 might be useful. For example, the autocorrelation trend 

26 in the specimen atrazine data suggests a Weibull 

27 distribution with a shape parameter of roughly 3.2, which 

28 implies that it approximately mimics a bell-shaped curve. 

29 This may be one of the reasons why, for that specimen 

30 dataset, the log-normal distribution performs well. 

31
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1 With an accompanying scale parameter of 2.3 and a 

2 suitable autocorrelation factor, which was estimated from 

3 the observed data, the resulting daily time series 

4 resembles the observed atrazine time series. Based on a 

5 simulation with 10,000 replications of 100 draws from the 

6 auto-Weibull distribution; the average almost perfectly 

7 replicates the base time series. And the approximate 95% 

8 confidence intervals based on 10,000 replications of 100 

9 draws gives an upward bound of roughly 23.5 for the 

10 maximum observed concentration value of 14.058. This 

11 latter result supports the need for a priori knowledge of 

12 reasonable upper limits for peak concentration estimates, 

13 as well as indicates that imputations based upon a 

14 deterministic model, such as the WARP model, combined 

15 with an extreme value distribution, such as the Weibull, 

16 could allow prediction of values much greater than those 

17 sampled. 

18

19 This example also illustrates that imputed values tend to 

20 be highly dependent upon the deterministic model 

21 predictions. In this case the synthetic temporal 

22 autocorrelation component employed accounted for roughly 

23 87% of the variance in the observed time series, allowing 

24 much less variability for the stochastic component. 

25

26 In closing, recognizing that EPA seeks reasonable 

27 estimates of exposure to atrazine from limited data, 

28 fine-tuning of the Agency's current approach may yield a 

29 number of benefits, whereas diminishing returns in 

30 additional accuracy of atrazine estimates almost 

31 certainly will set in as the complexity of its 

32 methodology increases. Furthermore, as methodological 
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1 complexity increases, chances of user error also 

2 increase. The final methodology needs to be 

3 implementable by various EPA scientists with a diverse 

4 set of expertise. In other words, EPA must establish 

5 acceptable trade-offs between the theory and the practice 

6 in these assessment too.

7

8 DR. DANIEL SCHLENK: Dr. Lee...

9

10 DR. HERBERT LEE: He has already incorporated all of my 

11 remarks. Thanks.

12

13 DR. DANIEL SCHLENK: Dr. Portier...

14

15 DR. KENNETH PORTIER: I just wanted to make one more point. 

16 Discussion of these fill-in models has centered on the 

17 potential of producing series with maximum closer to the 

18 single-day expected maximum. WARP PRZM combined models 

19 could help inform the estimate of the single-day maximum 

20 concentration in those sampling situations where the 

21 maximum has a low chance of being observed in the sample. 

22

23 There was relatively little, if any, discussion in the 

24 white paper, on the ability of these simulated series to 

25 recreate the distribution of what I will call “durations 

26 of time exceeding a specified threshold”; in other words, 

27 estimating the likelihood that the concentration series 

28 produces a pattern with x-days in a row above some 

29 threshold concentration. 

30

31 This to me seems to be a much more important statistic 

32 than the single-day daily maximum. Primarily because it 
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1 is more directly related to the regulatory decision, as 

2 we have had discussions about area under the curve and 

3 days of exposure. 

4

5 Concentration time series with WARP PRZM infills, seem 

6 much more likely to more accurately estimate this 

7 distribution then would be simply using weekly samples or 

8 just modeling from the sample data rather than taking 

9 into account the basin and meteorology data that WARP and 

10 PRZM would do. It remains to be seen whether a running 

11 average time series properly scaled would produce a 

12 better estimate of this distribution than would a WARP 

13 PRZM infill series. And I think that remains an area of 

14 research. 

15

16 DR. DANIEL SCHLENK: Thank you. Any other input from the 

17 panel? 

18

19 DR. ROBERT GILLIOM: So I guess in further comment on the 

20 application of the WARP model is that I view it as its 

21 most valuable role as in applying to sites that you have 

22 no monitoring data for or data that is so sparse that you 

23 cannot fit a time series model or equivalent fill-in 

24 method. In that role what it is doing is simply giving 

25 you a prediction of the central tenancy of a chosen

26 concentration statistic for all similar basin in a 

27 region. 

28

29 So it give you an approximation of what to expect for 

30 that basin that could be used as has been mentioned to 

31 reconstruct a synthetic time series for sites with no 

32 data, basically. If you have actual data, just to stress 
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1 again, it should be used as opposed to trying to use the 

2 WARP regression model. 

3

4 I guess the last thing I would say is that as the target 

5 statistic of interest get refined, such as a 4-day moving 

6 average or 14-day moving average or whatever, you can 

7 pretty readily refit a model like the WARP model to make 

8 that the dependent variable and just directly predict it. 

9 and directly predict the 4-day max based upon the 

10 watershed characteristics, and put confidence bounds on 

11 it and then that gives you a direct way to get right to 

12 the problem rather than having to reconstruct the whole 

13 time series. That is basically how we have applied it to 

14 date. So I will just leave it at that and we can follow 

15 up later if you like. Thank you.

16

17 DR. DANIEL SCHLENK: Any other input from the panel on this? 

18 Okay, we will go back to Nelson. Did you have everything 

19 you need on these? Do you need clarification at all?

20

21 DR. NELSON THURMAN: I think we have what we needed. Well...

22

23 DR. JAMES HETRICK: I guess I want a little bit of guidance 

24 here. Because I am hearing that if we are going to 

25 continue down this path of doing variogram analyses we 

26 have to upgrade our software, correct?

27

28 DR. DANIEL GRIFFITH: I think so. You can do it with R if you 

29 wish.

30

31 DR. JAMES HETRICK: Okay, that is fine. The other thing is I 

32 would like to just maybe in a little bit more plain 
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1 English here ask the question, are we on the right track 

2 as far as the conditional simulation approach?

3

4 DR. DANIEL GRIFFITH: I think you are. One of the concerns 

5 that I see is that if you do the imputations and use 

6 those – I saw several statements in the background 

7 materials that were completing a time series and now we 

8 have this time series of atrazine values. Well, all of 

9 those imputations are like the trend line and so you 

10 tremendously suppress the variance especially if in most 

11 of these cases you are estimating nearly half of the 

12 data. 

13

14 And I realize that if you go into the spatial domain, 

15 that will estimate 90% of the data and there is

16 controversy in the spatial domain about using that as 

17 well. So if you look at the missing data literature, 

18 people like Schafer, what they do is they then sample 

19 from – if it is a Weibull distribution, I would take this 

20 as my mean now that I have and I would draw a sample from 

21 it, with that mean. And then you might do that so many 

22 times to get some idea of the variability. 

23

24 What I did in my example simulation, was I did 10,000 

25 replications and so I had the basic imputed time series 

26 that was the trend lines and so I was sampling at each 

27 point from a Weibull distribution with that auto28 correlated mean and then I get my upper and lower bounds. 

29 So it is a conditional simulation in that sense. 

30

31 But I think that if you just impute and than use those 

32 imputations as though they are real values, you have 
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1 dramatically underrepresented the variation that you have 

2 in an actual time series. And you can even compare that 

3 with these daily time series that are available now; 

4 would give you some sort of benchmark to get an idea 

5 about that with.

6

7 DR. DANIEL SCHLENK: Dr. Portier and then Dr. Lee.

8

9 DR. KENNETH PORTIER: I think you are and as I was looking at 

10 what we were talking about, what you have not done here 

11 is really helped us split out uncertainty in variability. 

12 And it is something we keep coming to in front of the 

13 panel. But we are dealing with a time series and 

14 typically there we are talking about variability, right. 

15 We are trying to really explain that variability, the 

16 auto-correlation structure, making sure that we are not 

17 losing sight of the fact that yesterday‟s estimate has 

18 some information on what we expect today. 

19

20 The other thing is the uncertainty. We are sampling from 

21 these systems. We are fitting models; the models 

22 themselves have uncertainty. Some of what Dr. Griffith 

23 talked about is uncertainty in estimating the semi24 variogram, which has a big impact as it propagates 

25 through the model predictions. And probably in the next 

26 iteration of this, you really need to be kind of laying 

27 that out maybe a little bit more clearly. When are you 

28 addressing variability, which is a model component, and 

29 when are you addressing uncertainty, which is really a 

30 component of this simulation; to a certain extent, what 

31 you are capturing in the simulation.

32
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1 DR. HERBERT LEE: As much as I really like kriging in general, 

2 in this particular case, as we have discussed, there is a 

3 lot of uncertainty. So I want to ask explicitly, are we 

4 getting any better results then just doing a linear 

5 interpolation and using a bias factor in terms of being 

6 able to predict accidence over a certain threshold of 

7 time. 

8

9 It may be that in terms of the accuracy of our results, 

10 because of all of the uncertainty, we may be able to do 

11 just as well with the linear interpellation and the bias 

12 factor; that would be a lot easier and a lot simpler and 

13 probably cheaper to do in practice. And so I want to ask 

14 explicitly, is it worth the extra effort to do the 

15 kriging – as much as I like kriging in general.

16

17 DR. DANIEL SCHLENK: Okay, did you get your clarification?

18

19 DR. JAMES HETRICK: Yes, I think we are on the right track and 

20 I know where we need to go at least.

21

22 DR. DANIEL SCHLENK: Great. All right I think we have time 

23 for maybe one more question we can move in through here 

24 to get to lunch. Let‟s move on to charge question number 

25 three. And Nelson I will let you decide how much you 

26 want to read of that one. If you do not want to read the 

27 whole question, or just the subheading that is fine.

28

29 DR. NELSON THURMAN: This question is relating to some of the 

30 modeling approaches and methods we looked at applying to 

31 less frequent sampling intervals. And hopefully some of 

32 this is a spillover from what was discussed earlier. 
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1 Please comment on these additional modeling approaches, 

2 that we have presented both in the background paper and 

3 in our presentation, for interpreting sparse monitoring 

4 sets; in other words, sampling less frequently than 

5 weekly.

6

7 DR. DANIEL SCHLENK: Lead discussant on that, Dr. Lee.

8

9 DR. HERBERT LEE: So in some ways this is a continuation of 

10 the previous question but it has some different flavor to 

11 it. I do want to repeat when the data is sparse, you 

12 just cannot fit a variogram; you cannot get accurate 

13 results solely from kriging or from linear interpolation. 

14

15 So various approaches have been explored; one of them is 

16 to use flow as a covariate. But the initial result has 

17 not been particularly promising. There are more complex 

18 relationships than just a simple linear relationship with 

19 flow; it depends on also the application timing and it is 

20 about transported materials rather than just the outright 

21 flow. So instead of thinking about flow itself, one 

22 direct improvement is to think about WARP, which is 

23 actually developed to model the situation; and so it 

24 makes more sense to use WARP rather than try and reinvent 

25 WARP. 

26

27 Alternatively, we have looked at some other approaches. 

28 Syngenta‟s approach using PRZM appears promising. 

29 Looking at infilling points particularly around 

30 precipitation events; filling that in and using that to 

31 predict peak areas. You could set up a fairly 

32 conservative approach using PRZM. They also looked at 
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1 some methods using sort of the three times infilling, 

2 saying you expect a point, as a conservative approach, 

3 probably not more than three times what is observed 

4 nearby; and then you can infill using that. You can also 

5 look at building time series models or the SEAWAVE model, 

6 specifically for time series. So there are a number of 

7 different ways that this can go that I think are better 

8 than just looking at flow as a covariate. 

9

10 I want to say sort of ideally, we want to set up a regime 

11 such that sites can move between different frequencies of 

12 monitoring. Right now we have some sites that are 

13 monitored weekly, during the application season, and 

14 others that are just monitored quarterly; and that is a 

15 really big gap. And it is unclear exactly how much 

16 information we are losing in there, but on the other hand 

17 if a site has been relatively clean, it does not 

18 necessarily need to be monitored as frequently. 

19

20 So there needs to be a good way for moving up and down in 

21 terms of frequency, perhaps with something intermediate 

22 between weekly and quarterly like monthly. And using 

23 these sorts of different models at different levels would 

24 be a way to help guide when sites needs to move between 

25 levels. When you have weekly data you can do things like 

26 kriging or linear interpellation with a bias factor. 

27 When you have monthly or quarterly data we are going to 

28 need these models to help guide, do they need to be 

29 looked at more closely.

30

31 DR. DANIEL SCHLENK: Okay. Dr. Griffith, you are next.

32
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1 DR. DANIEL GRIFFITH: The WARP, watershed regression on 

2 pesticides model, furnishes predictions of the 

3 distribution of atrazine concentrations in specific 

4 watersheds. Its input includes the following variables: 

5 atrazine use intensity, precipitation and rainfall 

6 intensity, a soil erodibility factor, percent runoff, and 

7 watershed size. 

8

9 The PRZM predicts chemical movement in surface soil, 

10 yielding a daily time series of potential runoff event11 based concentrations, and requires more input, for 

12 instance temperature, land use, and soil type than the 

13 WARP model. It uses spatially specific NEXRAD radar 

14 data, requiring additional data merging. The EXAMS model 

15 predicts the fate, transport and exposure concentration 

16 in surface water by combining chemical loadings, 

17 transport, and transformation into a set of differential 

18 equations using the law of conservation of mass as an 

19 accounting principle. 

20

21 Its data inputs include fundamental chemical properties 

22 of atrazine, and up to 32 different segments for a given 

23 watershed, for each of which up to 28 different 

24 substances may be simulated. The EXAMS model also 

25 requires more input than the WARP model. 

26

27 Finally, the mass-balance model, which describes 

28 variations in atrazine concentration as a series of 

29 storm-event associated peaks that taper off over time, 

30 produce atrazine discharge mass quantities that are often 

31 different by orders of magnitude in neither a positive or 
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1 a negative direction. Consequently, WARP appears to be a 

2 reasonable choice for obtaining supplemental data. 

3

4 Output from that model yielding the best estimate of 

5 daily atrazine concentrations should be employed as the 

6 covariate in kriging. If output for no single model 

7 appears best, perhaps a weighted average of daily model 

8 output could be utilized. Reconsidering the semi9 variogram models for the specimen atrazine data, 

10 including the synthetic spatial autocorrelation factor as 

11 a covariate for co-kriging produces considerable 

12 smoothing of the daily experimental variogram. 

13

14 In addition, the resulting goodness-of-fit diagnostics 

15 improve for all candidate model specifications, and 

16 furnish additional evidence that the Bessel function may 

17 be the preferred model. These results corroborate that 

18 imputed values will tend to be highly dependent upon 

19 deterministic model predictions used as covariates. The 

20 lack of a stochastic component for the imputations will 

21 tend to suppress variability; deterministic model 

22 predictions are similar to conditional expectations. 

23

24 Theoretically, if no relationship exists between the 

25 model-generated data and the observed monitoring data, 

26 then the deterministic values do not impact upon the 

27 kriged values. As the relationship between the 

28 deterministic model output and the monitoring data 

29 increases in strength, increasingly more information can 

30 be borrowed from the deterministic model output to 

31 complete each daily time series. 

32
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1 This procedure is far superior to linear interpolation. 

2 One principal weakness of using deterministic model 

3 output arises from the assumptions involved. Because 

4 values between observed monitoring points in time are 

5 unknown, they may not coincide with model output, even if 

6 the observed data perfectly align with the corresponding 

7 subset of model output. 

8

9 This weakness furnishes a strong argument to employ a 

10 time-interval stratified random sampling design, rather 

11 than a systematic design with a random start weekday. 

12 Sensitivity analyses, especially with regard to error 

13 propagation, could shed light on the magnitude of impacts 

14 of certain assumptions. 

15

16 Critical ones include the following: 1. in the PRZM 

17 model 60% of atrazine is applied at four uniformly 

18 distributed major pulses, with the remaining 40% being 

19 applied uniformly across all other days; 2. movement 

20 through a watershed is indexed to the longest shortest 

21 path between its outlets and its headwaters; 3. growing 

22 degree days are defined by – and they state a formula –

23 difference between temperature extremes divided by 2-50 

24 and that is in Fahrenheit. 4. all corn and sorghum crop 

25 areas are treated; 5. for PRZM all watershed farmers use 

26 atrazine in a similar way to those in the baseline CRC 

27 survey; 6. for PRZM the atrazine use rate is uniform 

28 across all soil types; 7. watersheds experience no 

29 conservation practices, and have good hydrologic 

30 conditions; and, 8. the non-random sample of monitoring 

31 data can be treated like a random sample. 

32
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1 Assumptions that most likely have little adverse affect 

2 on results include: 1. results for an irregularly space 

3 time series can be adjusted by weighting each value by 

4 50% of the time distance between its preceding and its 

5 subsequent value; 2. multiple sample values for a day can 

6 be represented by their geometric mean; 3. values 

7 substituted for those quantities less than the detection 

8 limit; 4. designing analyses in such a way that 

9 concentration estimates tend to be conservative; and, 5. 

10 the half-life of atrazine is 61 days in a watershed 

11 although evidence exists suggesting that it has a much 

12 longer half-life in subsurface soils. 

13

14 In the end, a meaningful model is only as good as the 

15 ability of its assumptions to mirror the real world. One 

16 notable trade-off is between the expenditure of resources 

17 to collect reliable sample data, in a design-based

18 context, and the use of model-based techniques that 

19 require resources to assemble massive amounts of 

20 ancillary data properly and then convert them into sample 

21 data equivalences. Physical sample collection requires 

22 retrieval followed by storage of specimens, and is 

23 plagued by instrument malfunctions as well as human 

24 error. Model generated results suffer from data 

25 availability as well as human error. One cannot go back 

26 in time to correct the former; updates followed by model 

27 re-executions allow corrections to be made for the 

28 latter.

29

30 DR. DANIEL SCHLENK: Okay, next discussant, Dr. Portier.

31
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1 DR. KENNETH PORTIER: Thank you. So I will start by saying I 

2 do not have a lot of experience with this kind of 

3 modeling, but that do not usually stop me from 

4 commenting. I should say that that experience is evening 

5 worst when I sit down with Dr. Gilliom and Dr. Coupe and 

6 start talking about what PRZM, EXAMS, SEAWAVE, and WARP, 

7 really produce in getting a better understanding of that. 

8 So some of this I may have to change as my understanding 

9 changes from the discussion. But I‟ll go on. 

10

11 I can only assume that the WARP or PRZM, EXAMS, or the 

12 SEAWAVE models have the potential to produce a daily time 

13 series that could be used as a covariate time series, for 

14 example, in a co-kriging approach. That if sufficiently 

15 correlated with concentrations, would allow one to 

16 essentially fill in mean concentration pattern between 

17 sampling dates and the concentration time series. I am 

18 assuming that the estimated variogram, for the 

19 concentration time series, would possibly demonstrate 

20 better properties because it would not have to be 

21 accounting for as much of the total variability as it had 

22 to do without the covariate. 

23

24 So I think if we get the right predictions from these 

25 deterministic models, it is quite possible we can get 

26 much better synthetic kymographs. So that is the first 

27 thing. But I wondered, as have some of the previous 

28 discussants as I read this section a number of times, if 

29 a simpler model might result in similar results and 

30 actually be easier to understand. For example, what 

31 happens if one were to simply regress the sample 

32 concentration time series on the PRZM or other model 
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1 predicted time series, using some kind of nonlinear 

2 function, a polynomial or something else or use some kind 

3 of robust smoothing approach such as low est. and 

4 properly lagging these results, whether we would get just 

5 as good a prediction. I do not know if we have really 

6 looked at that. 

7

8 If one finds that the resulting R square was pretty high,

9 close to one, I would suggest that the PRZM model or 

10 other model would be a good predictor of the 

11 concentration time series for the un-measurable time 

12 points. This approach might work quite well for what 

13 Syngenta referred to as the small AMP size classes, 

14 because the smaller areas allow for only one or a few 

15 fields to be impacting the water concentration and time 

16 lags would be reasonable on the order of a day or a 

17 couple of days. 

18

19 So those small size classes where we have things that are 

20 really spiky, some of these model that might actually 

21 work because it is a close connection between what is 

22 going on in this small watershed and what is happening at 

23 that sampler in the community water system. 

24

25 For larger CWS size class areas, incorporating many more

26 fields and longer transit time, it is more likely that 

27 the concentration time series is some kind of weighted 

28 sum of variably lag WARP model prediction for all the 

29 fields impacted by significant rainfall event. So the 

30 larger the area the more kind of random of incidents 

31 occurring that you are trying to add up and integrate 
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1 through to get to a concentration at a community water 

2 system. 

3

4 And all I could think of is for one thing this would be a 

5 very challenging model to fit; that is a statistician way 

6 of saying it is impossible. But then on the other hand I 

7 keep thinking this is some kind of sum of correlated 

8 series model. There must be some kind of limit theorem 

9 going on here that says when you get a number of these 

10 things happening and they are lagged, you would expect to 

11 see some kind of log-normal type pattern or something 

12 like this. 

13

14 So for the medium to larger systems you are having to 

15 deal with less of the spikiness of the pattern and it is 

16 much more of a modulated pattern, which I would expect 

17 from this kind of sum of correlated series kind of thing 

18 in that. So you might be really looking at two different 

19 kinds of modeling scenarios. 

20

21 This kind of regressed time series approach do not 

22 preclude the introduction of further autocorrelation 

23 structure in the residuals of the model fit. So the 

24 regression is fitting kind of the long-term pattern and 

25 then you have correlated noise around it as well and you 

26 could add that; some kind of again nonlinear ARIMA model 

27 or where the mean is not constant. 

28

29 To some extend Syngenta is taking this approach by using 

30 the modified PRZM model to fill in the extreme events 

31 between sample concentration value. But if you are going 

32 to fill in the extreme events, why not go the whole way 
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1 and use WARP to fill in the rest of the sequence. This 

2 is not what Syngenta is doing because if you look at 

3 slide 20 of the Syngenta „Occurrence in Drinking Water‟ 

4 presentation in the meeting docket, it clearly shows they 

5 are using linear interpellation between the WARP inspired 

6 maximum and the observed sample points. One other issue 

7 with the Syngenta approach is that they assume an 

8 atrazine runoff event occurs between every pair of 

9 sampling days. 

10

11 So for a 7-day sampling this essentially assumes a runoff 

12 event every week and from some of the date we have seen 

13 in previous SAPs, and I will have to go back and look and 

14 see whether it was the February or April 2010 SAP, where 

15 we looked at a lot of these patterns, we know that for 

16 the most part it is one or two event a season that any of 

17 these things observes; so why would we even assume a 

18 priori a maximum every week. We really should be saying, 

19 “Oh, we will throw one somewhere in here” and that is a 

20 more reasonable model. And for small watersheds I might 

21 throw that maximum in closer at the beginning of the week 

22 and give myself time for that decay that would match the 

23 next sampling point, right; a very simple modification of 

24 their method. 

25

26 Are there other time series that could be used as an 

27 explanatory model in this kind of regression approach? I 

28 think most hydrologist would agree that water flow would 

29 not be expected to be highly correlated with 

30 concentration except in possibly the very smallest 

31 basins; and even then both concentration and flow would 

32 be highly correlated with rainfall or irrigation patterns 
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1 and all of that is assuming atrazine has been recently 

2 applied in the field. There has to be some material 

3 there to flow off. 

4

5 So if I have anything else I would say for small fields 

6 we really need to look at rainfall and irrigation 

7 patterns; that would be the only other time series that I 

8 could think of that might have any effect. And I will 

9 stop at this point.

10

11 DR. DANIEL SCHLENK: Any other panel member have anything to 

12 add.

13

14 DR. ROBERT GILLIOM: I guess one thing I would add is that 

15 personally I would encourage the continue development of 

16 the PRZM approach and especially the underlining data 

17 that is needed to drive it at a national level. Because 

18 the transfer value from that whole methodological 

19 approach from data to model will have a lot of transfer 

20 value to other chemicals and it is a good investment to 

21 make I think. But, on the other hand my other comment 

22 would be is, I would not get bogged down by holding off 

23 on all of the related decisions for a compound like 

24 atrazine while you work through the whole process, which 

25 might take a while of seeing how an edge-of-field model 

26 applies to different size systems and so on and so forth 

27 that we have not all got in to. So I guess I am in the 

28 camp of continuing to move ahead on development, but do 

29 not let it get in the way of progress.

30

31 DR. KENNETH PORTIER: This morning we had some conversation 

32 like this. So there is the atrazine issue and then there 
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1 is a longer term risk assessment paradigm for EPA and we 

2 see this kind of PRZM modeling time series stuff as part 

3 of a longer term paradigm that EPA is going to need 

4 nationally to be able to make these kinds of decisions. 

5 It just happens to be you are doing it with atrazine 

6 because it has this fantastic dataset and a lot of 

7 information, and you have a little bit of a push now to 

8 incorporate that. But I tend to agree with Dr. Gilliom 

9 in that we do not want that longer term goal to hold up 

10 your shorter term decision-making process.

11

12 DR. DANIEL SCHLENK: Any other panel input on question three. 

13 Okay we will go back to Nelson, do you have everything; 

14 do you need any clarification on this?

15

16 DR. JAMES HETRICK: In our attempt to try to infill that in 

17 that example that we have in the white paper in the 

18 appendix, we use flow; flow may not be the best covariate 

19 to use to try to take the WARP estimates and put them 

20 into a time series. Do you have any other suggestions of 

21 other covariates – I was thinking like nitrate or 

22 nutrients that might be something to look at – that you 

23 would suggest looking at as a covariate? 

24

25 DR. ROBERT GILLIOM: I think certain aspects of flow will work 

26 if our time series modeling is an indication. So if you 

27 are within the seasonal window and you focus on anomalies 

28 of flow conditions from normal, for that season, then I 

29 think you will find them to be predictive; in the same 

30 vein I think daily precipitation will prove to be 

31 predictive as a explanatory variable and a time series 
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1 model. And then I guess those are the two main ones I 

2 think of and I can let you know if I think of others. 

3

4 DR. DANIEL SCHLENK: All right, everybody good on number 

5 three?

6

7 DR. KENNETH PORTIER: When I looked at the flow data and you 

8 plot it out, it is very clear you have kind of a bi-modal 

9 thing going on. You have these periods of low 

10 concentration and high flow; so the stream is flowing but 

11 there is nothing in there. There is no chemical in there

12 and that is because there probably was no chemical in the 

13 field or previous rainfall events washed out whatever was 

14 available to be washed out. But on the other hand there 

15 is a period where concentration is correlated to flow and 

16 so you kind of get this bi-modal process going on. And I 

17 do not know how to model that because the information on 

18 how you switch between this one and that one is 

19 essentially what PRZM is providing you. 

20

21 PRZM integrates that field level fertilizer application 

22 and stuff like that. So just kind of naively using flow 

23 is not going to help you because I think you are adding a 

24 lot of variability because of what we looked at there. 

25

26 When you look at other agrichemicals like fertilizers, 

27 clearly they are not put down at the same time as 

28 atrazine is, atrazine being a pre-plant. Sometimes they 

29 put fertilizer down, I guess, for some crops; some crops 

30 they make a separate trip at a different time through the 

31 field. I really do not know whether looking at those 

32 things are going to help you – especially nitrogen. 
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1

2 When you said that I had a flashback to some time series 

3 data I looked at of North Florida nitrogen flow in these 

4 clay areas, and the data was a nightmare because I could 

5 not predict when you would get these spikes. This was 

6 off of the chicken farms where they spread the fertilizer 

7 out in the pasture and then you are wondering when it is

8 going to hit the stream. And it was not correlated with 

9 rainfall; we needed a PRZM model to begin to understand 

10 why it showed up today and it did not show up yesterday 

11 or a week ago. 

12

13 DR. DANIEL SCHLENK: Okay, any other comments on that? You 

14 guys good with that?

15

16 DR. NELSON THURMAN: Yes, I think so.

17

18 DR. DANIEL SCHLENK: Okay, debating on whether to go on to 

19 four, but I think since we are so far ahead I think we 

20 will go ahead and just take a lunch break until 1:00. 

21 Let‟s go ahead and do that and we will reconvene at 1:00. 

22

23 DR. DANIEL SCHLENK: Welcome back. Let‟s go ahead and move on 

24 to our next charge question which is charge question #4 

25 which is, I think, the last in terms of our water 

26 sampling section and Nelson if you want to read that into 

27 the records that would be great. 

28

29 DR. NELSON THURMAN: The preamble relates to our focus on 

30 trying to characterize year to year variability. Part A: 

31 Please comment on the sufficiency of existing 

32 atrazine/triazine monitoring data available to the Agency 
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1 – in particular the Atrazine Monitoring Program (AMP) 

2 coupled with the earlier Voluntary Monitoring Program 

3 (VMP), which conceivably span from 1993 to the present 

4 for some community water systems (CWS) – for use in 

5 characterizing the likely range in year-to-year 

6 variability in atrazine or total chlorotriazine (TCT) 

7 concentration. 

8

9 Part B: Please comment on the Agency‟s suggestion for 

10 using a PRZM hybrid model, calibrated on the current 

11 years of monitoring, to provide estimates for a wider 

12 timeframe by modeling additional years using weather data 

13 that span a 30- to 50-year period. 

14

15 And Part C: What other possible approaches can the SAP 

16 recommend for capturing year-to-year variability? 

17

18 DR. DANIEL SCHLENK: Okay thanks. Our first lead discussant 

19 is Dr. Coupe.

20

21 DR. RICHARD COUPE: Thank you. This is Richard Coupe. Sadly 

22 I think everything I am going to say has already been 

23 said, at least once in our first three questions but I am 

24 going to say it anyway just because I have the floor. 

25

26 Before I address the question directly, though, I want to 

27 make a couple points. They have been made by other 

28 people but I kind of want to put them together. One is 

29 that atrazine is an extremely important economic 

30 chemical. The other one is that atrazine is found in the 

31 source water from a number of community water systems. 

32 Material that was supplied by Syngenta indicated that 
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1 during the period 2001 to 2009, greater than 1.5 million 

2 of our fellow citizens were exposed to concentration of 

3 atrazine greater than 3.0 in their drinking water and 

4 that was just from quarterly samples. 

5

6 I think these two facts are why we are here. In my mind 

7 it is right and appropriate that we discuss this. There 

8 have been some comments about how many SAPs there have 

9 been over the last 10 years or so but again, in my mind, 

10 it just shows how important the topic is. 

11

12 I want to mention that when I was first asked to be on 

13 the SAP last year, I called Bob and asked him whether I 

14 should do it or not. He said sure, it is fun, come and 

15 watch government sausage being made, it will change your 

16 life. 

17

18 So the question presupposes that you can never stop 

19 collecting observation data. By using historical data we 

20 can characterize the distribution of atrazine in drinking 

21 water in the future or that would mean for that to happen 

22 we would have to be able to say that what happened in the 

23 past is what is going to happen in the future. 

24

25 So some of this data that we are looking at now is 

26 collected from as early as 1993 and that is getting close 

27 to 20 years old now and the question that comes up is, 

28 has anything changed over that time that would make you 

29 think that perhaps the delivery of atrazine to the water 

30 shed had changed? Of course there is a whole list of 

31 things that make that true and one is conservation 

32 tillage. We have a whole lot more conservation tillage 
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1 than we did before and the other one was the introduction 

2 of genetically modified crops, specifically for atrazine 

3 and some of the other crops; I mean for corn, for 

4 glyphosate, so this changed how atrazine is used. 

5

6 So if you consider the drivers of what makes atrazine 

7 appear in your water, basically you have kind of three 

8 drivers. One is hydrology, one is flow path and the 

9 other one is use. All three of these work in combination 

10 to move atrazine into your service water. Then the 

11 question that comes up is, if you want to stop your 

12 sampling or if you are going to change your sampling 

13 pattern and rely on historical data is, will these change 

14 in the future? Well, hydrology kind of reflects rainfall 

15 and we do not have to look too far past this last May to 

16 see how much rainfall can change over time. 

17

18 All you have to do is look at some of the WEB sites and 

19 show how many sites in the Midwest during the month of 

20 May had historic periods of record in their flow. It was 

21 just tremendous. So we had a record flood in May right 

22 there in our application time period. And you can ask 

23 these questions of flow path. When we talk about flow 

24 path, flow path is how water and atrazine moves over the 

25 landscape or under the landscape; how it moves into the 

26 stream and then into the drinking source. And, has that 

27 changed over time or can it change over time? 

28

29 And then in reality it can change and it is going to 

30 change probably in the future. Conservation tillage is a 

31 big part of it. I mean, one of the big drivers for 

32 atrazine in service water is how it flows over the 
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1 surface and so if your conservation tillage leaves a lot 

2 more material on the surface it slows down the water so 

3 this has probably changed a little bit how the atrazine 

4 appears in surface water. 

5

6 We are also now having -- in the Midwest especially -- we 

7 are having subsurface drainage which is being directly 

8 run to the surface of the soil. So we are having a flow 

9 path that is now changed. We are not running off into 

10 the stream now, we are running off into a low depression 

11 on the field. It is going into the service drain and is 

12 moving down through this drain and then into the stream. 

13 So that could actually elongate the distribution of 

14 atrazine in the stream. 

15

16 We could have changes in flow path. All this is just to 

17 say that we could have changes in the future. And then 

18 use; could we look at use? Can use change in the near 

19 future? That is again true. If we just take a look at 

20 what happening with glyphosate now, we are having 

21 resistance appear for glyphosate for the chemical roundup 

22 and that could change how atrazine is used. The use of 

23 atrazine could increase quite a bit in the future because 

24 of resistance to glyphosate. 

25

26 Also the biofuel‟s initiative, although it does not 

27 directly increase the amount of atrazine use, it 

28 increases the incentive for farmers to grow corn which 

29 does actually increase the amount of atrazine use. So we 

30 could be having big changes in all three of the major 

31 drivers for our flow path. 

32
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1 So then the question was is do we have enough data or 

2 when will we know we have enough data. Well, you are 

3 never going to have enough data is my conclusion on that. 

4 But do you have to sample everywhere all the time. Well 

5 no, of course you do not have to. And we have this 

6 atrazine monitoring program, which has a way into it and 

7 a way out of it which is really pretty good. 

8

9 I do not think it is completely predictive of all the 

10 systems because to get into it you look at a quarterly 

11 sampling and have to exceed -- I forget what it is -- to 

12 exceed, 1.6. So we can probably use something like a 

13 WARP or a PRZM model to kind of look at places we do not 

14 have which are more vulnerable, but we do not have 

15 exceedances in their STWA samples.

16

17 In addition, you could use those to move them out of AWP 

18 or the monitoring program as you can use that in 

19 combination with your sampling to indicate that 

20 conditions have change, that you will not have the issues 

21 with the distribution that you had. I think that was all 

22 my comments on that part.

23

24 DR. DANIEL SCHLENK: Thanks. Bob Gilliom.

25

26 DR. ROBERT GILLIOM: Mine are just a few specific things to 

27 point out regarding adequacy of the historical multiyear 

28 data. 

29

30 One is just along the lines of... and I am not trying to 

31 parse out which ones have which, but the total 

32 chlorotriazine were not measured in all of them so that 
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1 is just one thing to keep in mind. Also, in particular, 

2 simazine has been, as an example, some of the long term 

3 trends has been more up-trending in use and 

4 concentration. So those kinds of things always have to 

5 be watched out for as Richard alluded to for applying 

6 historical data to the future. 

7

8 I did think that the VMP data, the voluntary monitoring 

9 thing that is weekly but since the early 90‟s, does 

10 present a valuable database to examine year to year 

11 trends as long as the bias and the short duration of 

12 statistics is either avoided or accounted for. It still 

13 does provide a really valuable database that has 

14 multiyear data for over 100 sites. 

15

16 In terms of sufficiency of annual data, you know, 

17 sufficiency depends on the application; I guess is the 

18 glib way to say it. What we have now is sufficient for 

19 some purposes and not for others. If the objective is to 

20 estimate it conservatively, protective bias factor to 

21 apply to sparse monitoring data, and you can be 

22 comfortable with a conservatively high estimate from the 

23 data arranged, it is probably adequate. If you want to 

24 estimate actual within a narrow bound of uncertainly like 

25 plus or minus 20 or 50%, then it is not. 

26

27 I think the reliability criteria just need to be 

28 stipulated and used to gauge whether accuracy is adequate 

29 or not. I think that is all I have.

30

31 DR. DANIEL SCHLENK: Thanks. Dr. Lee.

32
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1 DR. HERBERT LEE: I just want to add one quick old detail in 

2 that there is a lot of year to year variability but 

3 particularly in, say, if you are looking at the 

4 magnitude. There is a lot of year to year variability. 

5 I would expect the shape to be more similar year to year. 

6 So things like the modality, is it one peak, it is 

7 multiple peaks, what is the correlation structure as far 

8 as estimating variograms? I would hope that that would 

9 be more consistent year to year but the overall magnitude 

10 of these peaks may be highly variable depending on these 

11 other factors that have already been discussed. 

12

13 DR. DANIEL SCHLENK: Okay. Dr. Portier?

14

15 DR. KENNETH PORTIER: I guess I tend to agree with Syngenta 

16 that for atrazine we have a very rich database, one that 

17 should provide more than adequate information on year to 

18 year variability in atrazine and TCT concentration. The 

19 issue is more how to effectively utilize this database; 

20 how to extract the necessary statistics with the 

21 appropriate models to be able to produce, what I call, 

22 less bias estimates with within year and among year 

23 variability. Bottom line, I think we have a very rich 

24 database; we just have to figure out how to use it. 

25

26 DR. DR. DANIEL SCHLENK: Okay, other panel comments? Yes, Dr. 

27 Griffith.

28

29 DR. DANIEL GRIFFITH: This links back to some of the other 

30 questions that we discussed before lunch as well. In 

31 part, when I think about... and this question, right at the 

32 beginning, characterizing overall uncertainty and 
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1 exposure... It seems to me that one of the critical 

2 questions -- and I commented briefly about it before and 

3 it was discussed in your presentation to us as well --

4 one of the critical issues is if we start looking at all 

5 the sources of variability or error, do they compound? 

6 How does error air propagate through all of the analyses 

7 and in part there has been focus on sampling error in 

8 terms of frequency in time and to some degree in space in 

9 terms of doing the sampling. 

10

11 There is going to be, sort of, inherence stochastic 

12 variability in atrazine and that is going to be through 

13 time, that is going to be over space and that also is 

14 going to be how it gets into the water supply, which I 

15 think links back to the type of water shed, et cetera. 

16 We have talked about measurement error and I have not 

17 seen anything about the degree of measurement error in 

18 the water assay, but certainly imputations are going to 

19 introduce measurement error and type of sampling. 

20

21 If it is the auto sampling which, at least what was 

22 described in the background material, is sort of 

23 averaging across the day because there is a little bit 

24 taken at different times of the day versus a one-time 

25 scoop. There is going to be, what really is measurement 

26 error there because it is a different type of sampling. 

27 And then I noted the specification error in terms of the 

28 semi-variogram model, but specification error also could 

29 enter through, is WARP the right model to use, should it 

30 be a different type of model and so there could be 

31 specification error there. 

32
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1 And these are some of the basic sources of error and I 

2 think there needs to be some type of a conceptualization 

3 as to how these interact, if they interact, and do they 

4 compound so that if in the end the 95... well I guess they 

5 are 90% confidence intervals if what you are really 

6 establishing, I would prefer 95%, but 90% confidence 

7 intervals, if they are based just on the sampling error, 

8 are they adequate. 

9

10 If you assume that all of these actually compound, you 

11 could end up stretching those dramatically and 

12 unnecessarily. And so I think that there needs to be 

13 some consideration about what is going to compound and 

14 what is not in order to go back to this characterization 

15 of overall uncertainty. I think the answer to this 

16 question probably focuses a bit more on specification 

17 error in that whole topology. 

18

19 DR. DANIEL SCHLENK: Thanks, Dr. Griffith. Any other comments 

20 on letter A, question 4? Okay let‟s go on to B. Dr. 

21 Coupe.

22

23 DR. RICHARD COUPE: Richard Coupe. Let‟s talk about the PRZM 

24 model. I just wanted to say that I really like what 

25 Syngenta has done with it. It is a very innovative 

26 approach. I particularly like the process-based work so 

27 you will incorporate whether atrazine use and the 

28 degradation characteristics for atrazine and the 

29 partitioning of atrazine and use soil characteristics to 

30 kind of predict how atrazine moves off. Their initial 

31 results certainly look promising. 

32
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1 On the other hand, PRZM really was not specifically 

2 developed to look at surface water runoff. It was 

3 developed as a root zone model and hydrology is really 

4 not model directly within the model so I am not sure how 

5 useful the model might be in the long run if you wanted 

6 to look at... say you wanted to look at what process was 

7 moving your atrazine along. If you do not have hydrology 

8 models specifically, you cannot really eliminate or look 

9 at it. 

10

11 And then that kind of restricts your watershed size 

12 unless you figure out a way to handle that. Otherwise, I 

13 think it is a really good and worthwhile effort to move 

14 forward with and we need to have some sort of model, such 

15 like this process base, like the WARP does or PRZM, it 

16 looks really good for future results. That is all I have 

17 for that.

18

19 DR. DANIEL SCHLENK: Okay. Bob Gilliom.

20

21 DR. ROBERT GILLIOM: I think the only thing that I want to add 

22 is that the added benefit of continued development of 

23 PRZM is in relation to the database that supports it and 

24 that also may help support moving into also try to SWOT

25 the STA model, which may have more applicability to the 

26 watershed scaling up in the long run. I think the 

27 overall effort is really good in that it is just going to 

28 enhance over time the ability to make the modeling 

29 approaches a more sophisticated addition to the tool box. 

30

31 DR. DANIEL SCHLENK: Dr. Lee.

32
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1 DR. HERBERT LEE: I concur and have nothing to add.

2

3 DR. DANIEL SCHLENK: Dr. Portier.

4

5 DR. KENNETH PORTIER: Sounds like a good idea if we can make 

6 it work. 

7

8 DR. DANIEL SCHLENK: Any other panel members for B? Okay. 

9 Letter C then. Dr. Coupe.

10

11 DR. RICHARD COUPE: I really do not have much to add to this. 

12 We have been given an awful lot of ways to fill in data 

13 to kind of look at how to predict this. I do not want to 

14 overwhelm us too much. 

15

16 DR. DANIEL SCHLENK: Okay. Bob.

17

18 DR. ROBERT GILLIOM: I would just return to the old topic of 

19 what kind of time series model to approach for fill in. 

20 I think if you use a more deterministically-based 

21 statistical model that has predictors in it like precip 

22 and flow anomalies and so forth, those could actually 

23 prove useful to reconstruct historical records from 

24 historical climate data and historical use data. That is 

25 one of the reason I favor that direction as oppose to a 

26 pure statistical fill in. 

27

28 DR. DANIEL SCHLENK: Dr. Lee.

29

30 DR. HERBERT LEE: I just wanted to add in another comment that 

31 I was trying to figure out where to fit in and this is 

32 the best place I could figure to fit it. We have had 
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1 some discussion brought up by other presenters yesterday 

2 about quotes from previous SAPs. I want to note that 

3 previously, for example, in April 2010 we were asked to 

4 consider exposures as short as single day maximums and 

5 there was a lot of emphasis on trying to estimate a 

6 single day maximum from a weekly sample. 

7

8 And then even in September of 2010 we were considering 

9 ranges. But at the time that we were discussing the 

10 hydrology and the water monitoring aspects we were still 

11 keeping single day maxima within the scope of what we 

12 might be trying to estimate. And at the conclusion of 

13 that panel we were moving towards area under the curve 

14 for a minimum of a 4-day moving average and that seems to 

15 be where we are moving now. 

16

17 So going forward I think we are interested more in time 

18 periods of four or more days. So some of what we have 

19 suggested in previous SAPs may not be as applicable 

20 anymore now that the time period has shifted. We were 

21 trying to be flexible in the earlier SAPs depending upon 

22 what came out the biological side and it seems like there 

23 is some convergence now on the biological side that it is 

24 not really a daily maximum that is of interest to human 

25 health, maybe not conclusively, but that seems to be 

26 where we are headed. Some of what we said in earlier 

27 SAPs should be taken in the context that that has 

28 changed. 

29

30 DR. DANIEL SCHLENK: Thank you. Dr. Portier.

31
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1 DR. KENNETH PORTIER: So kind of building on that theme, 

2 you know, all of the approaches discussed up to this time 

3 have centered on the full period of interest, time series 

4 or concentration, right? And the focus has been on being 

5 able to simulate this time series with all of its 

6 correlated temporal value. Well when you actually stop 

7 to think about it, the periods of most interest are 

8 really event related; that is those day of high 

9 concentrations that we know are related in some way to 

10 the timing of chemical application and location and 

11 duration of significant rainfall events. 

12

13 So what happens if you only look at these significant 

14 events? Forget the times of base flow where nothing 

15 interesting is happening. Let‟s look only at the 

16 significant events. I am thinking about this in terms of

17 -- I am going to get to year to year variability but I 

18 think my bottom line is -- it's one thing to try to 

19 simulate 20 years of time series and it is another thing 

20 to think of 20 years of events, of significant flow 

21 events that are going to lead to some kind of exposure 

22 that we are going to be interested in. 

23

24 So one is a time series... 20 sets of time series and 

25 another one might be 35 events in those 20 years that you 

26 are trying to understand. Can we identify how many and 

27 when significant rainfall events occur in the basin of 

28 interest. With the historical meteorology data, the 

29 answer to this is likely yes. Can we identify which of 

30 these events is likely to product a concentration 

31 increase event at the monitoring station or at the 

32 community water system? 
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1

2 If we can, then we are able to say something like this 

3 rainfall event in this area would produce this kind of 

4 schemograph at the community water center. This next 

5 rainfall event, coming say three days later, would add 

6 this additional bump to that schemograph. Those two 

7 rainfalls relate to one event that I am calling a 

8 concentration increase event. 

9

10 When we can identify each of these concentration increase 

11 events, we can use them to develop distribution such as 

12 maximum daily concentration or duration of days above 

13 threshold concentration during the season or over a 

14 period of record. Can we relate site specific weather 

15 station time series data to these significant rainfall 

16 events? We are thinking initially spatially... spatial 

17 rainfall events relate to an event that happens at the 

18 community water system. 

19

20 Now I am taking one step further back and saying, well, 

21 really the long-term data we have is rainfall gauge data. 

22 We have 50, 60, 70 years of daily rainfall gauge data. 

23 You kind of like to look at that in terms of year to year 

24 variability. Well that means you have to kind of relate 

25 this point specific rainfall gage data to some area 

26 rainfall so you can pass it through some kind of PRZM 

27 model to figure out an event at community water system, 

28 or, some kind of regression model. 

29

30 To me that is going to be the hard thing to do, is to 

31 take that point data that we have, that rich rain gage 

32 time series data and try to relate that to something that 
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1 is really happening on a rainfall event on a basin-wide 

2 basis. I do not know anybody who has been successful in

3 doing that. That is kind of where being able to tackle 

4 year to year variability falls down. 

5

6 Now on the other hand, we had an SAP not too long ago 

7 where we looked at climate change impacts on risk 

8 assessment. I am sitting here thinking a lot of what we 

9 concluded there was that yesterday‟s rainfall is not 

10 necessarily tomorrow‟s rainfall in our current scenario. 

11 Maybe the 20 years of meteorological data that we have 

12 right now is a better tool for looking forward than 60 

13 years of rain gauge station data. Maybe we should not be 

14 wasting our time trying to think that far back and just 

15 use the 20 years, and use that moving forward as our 

16 baseline because the last 20 years are probably a much 

17 better picture of the next 20 years than is the last 60 

18 years a picture of the next 60 years. 

19

20 And I think I will stop at that point. That is my 

21 thinking. The basic idea being an ultimate approach to 

22 year to year variability might be not to look at it from 

23 a time series but look at it as a set of events and break 

24 it up into how many events can happen in a year and of 

25 what magnitude and duration of those events. 

26

27 DR. DANIEL SCHLENK: Okay. Other panel members? Okay. Let‟s 

28 go back to you Mr. Thurman, do you have any questions or 

29 clarification?

30

31 DR. NELSON THURMAN: Okay, it‟s Nelson Thurman. Actually I 

32 want to followup on a comment Dr. Portier had made about 
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1 using the existing database. I think one of the comments 

2 in terms of either taking the bias out of it or 

3 accounting for the bias and you said something along the 

4 lines of we need to figure out how to use it. Do you 

5 have any suggestions on how? Because we are wrestling 

6 with that, how do you use that? 

7

8 DR. KENNETH PORTIER: You're talking the last 20 years 

9 where we have pretty good meteorological area temporal

10 spatial data on rainfall from radar?

11

12 DR. NELSON THURMAN: I am actually talking about Part A. Your 

13 response to Part A which was on the atrazine monitoring 

14 database and you were...

15

16 DR. KENNETH PORTIER: Oh yeah, okay. Well I think that is 

17 what we have been talking about all along in question 3 

18 and 4. It is going to depend on the size of the basin, 

19 right, and in the small basins we might be able to use 

20 PRZM to really feed some information to what these events 

21 look like and as the basins get larger somehow we are 

22 going to have to integrate that information and PRZM may 

23 not be an acceptable model for doing that kind of 

24 integration. 

25

26 I keep thinking, and my understanding from talking to 

27 Coupe and Gilliom, is that they are really field-level 

28 based models and so you either have to run it on every 

29 field, which is something Syngenta showed us in a 

30 previous SAP that they could do with sufficient computing 

31 power and given enough time, they can run every field in 

32 the corn belt of the US and run it for the season and
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1 accumulate all that information and all I could think of 

2 at the time is more power to them. I would love to see 

3 them do that, that maybe one approach. 

4

5 And then you are god, with a small “g”, you know 

6 everything. Then you can aggregate, you can compute, you 

7 can do everything that you want assuming the model is 

8 correct, right. Assuming the model works and you are 

9 doing all those kinds of things. 

10

11 I think that is kind of what we have talked about today 

12 and what we have talked about in previous SAPs on this. 

13 We are still working on the same premise that we need 

14 these kinds of field-based models to do the integration 

15 for us. There is no kind of simple in between. And when 

16 you get the year to year variability that is just another 

17 level of uncertainty that we are having to factor in, and 

18 right now the only way to tackle that is year-to-year 

19 measurement of some type. 

20

21 DR. DANIEL SCHLENK: Okay? You have everything you need Mr. 

22 Thurman?

23

24 DR. NELSON THURMAN: I think we have plenty. I do appreciate 

25 the thought and effort that the panel members have put 

26 into these questions. It has been helpful or us.

27

28 DR. DANIEL SCHLENK: Fabulous. Let‟s move on then to question 

29 5 and change out the table there. Dr. Mendez were you 

30 going to read that? You do not have to read that whole 

31 paragraph. You can just read the little A if that suits 

32 you. 
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1

2 DR. ELIZABETH MENDEZ: Good afternoon. So we are going to 

3 have a little bit of a shift now in the types of 

4 questions you are going to be hearing. We have been 

5 talking a lot about the drinking monitoring program and 

6 now we are going to start talking a little bit about the 

7 hazard characterization and the neuroendocrine mode of 

8 action that we have been working with for the past few 

9 years. 

10

11 Before I get started with that I wanted to reiterate 

12 something that Dr. Fowle said earlier this morning. We 

13 are at this point in time looking at these data. We are 

14 not really doing the risk assessment. What we are trying 

15 to do is come to you to seek your advice on how we are 

16 approaching, where the approach is that we are proposing, 

17 and how we are interpreting these data as we may 

18 eventually start applying them to our risk assessment 

19 process. So with that I am going to ask Dr. Cooper to 

20 join me at the table and then I will start asking the 

21 questions.

22

23 Charge question 5 has to do with the neuroendocrine mode 

24 of action and the disruptions to the HPG access that has

25 been the basis of our risk assessment in the past. And 

26 the first question that we have is currently available 

27 data show that in the rat, a brief exposure, as brief as 

28 four days to low levels of atrazine can elicit decreases 

29 in LH. Please comment on the biological plausibility of 

30 these brief changes leading to an adverse outcome taking 

31 into account typical variability and how long and how 

32 much an LH surge reduction is needed to cause the 
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1 observed adverse effects; i.e., disruptions in cyclicity, 

2 delayed puberty and prostatitis. 

3

4 DR. DANIEL SCHLENK: Our first lead discussant on that is 

5 Dr. O'Byrne?

6

7 DR. KEVIN O'BYRNE: First of all, I'd like to say that Dr. 

8 Timms and Jerde are not going to participate as associate 

9 discussants of this question because there is no obvious 

10 link between LH secretion and prostatitis. So they are 

11 going to convey their deliberations in charge question 

12 number 7. 

13

14 So we can see here that four days exposure to low levels 

15 of atrazine declines LH secretion. But if we consider 

16 what we heard yesterday concerning a paper that‟s 

17 published in 2001, by Goldman et al. that showed that 

18 absent at 100 milligrams per kilo per day, which is 

19 certainly not a low dose by any stretch of the 

20 imagination to ovariectomized estrogen-primed Sprague21 Dawley rats attenuated the LH surge by a mere 54 percent, 

22 and that was AUC. Actually the peak levels of LH were 

23 not significantly suppressed in that paradigm. 

24

25 I'll remind you that two-day treatment with the same 

26 dosage had absolutely no effect on the LH surge. So the 

27 question is, might the surge reduction cause these 

28 disturbances to cyclicity and the onset of puberty. 

29 Well, we know from this historical data of laws from 2000 

30 that 50 or more milligrams per kilograms per day, 

31 starting on postnatal day 22, which is when rats are 
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1 usually weaned, did delay puberty. However, 12.5 and 25 

2 milligrams per kilogram per day had no effect on puberty. 

3

4 And when you consider that they were being treated with 

5 25 milligrams per kilogram per day for almost two weeks 

6 and it had no impact on puberty or cyclicity, then it's 

7 difficult to image that a 4-day treatment at 100 

8 milligrams per kilograms for just four days would impact 

9 on those systems. That would be unlikely, in my view. 

10

11 Now we know from the work way back in the early 70s by 

12 Everett that there is a huge variation in the levels of 

13 LH in the spontaneous surge, and I think this was touched 

14 on in the September meeting. And we are talking about a 

15 range here of 200 to 1000 nanograms per mil. And despite 

16 that huge range, the rate of ovulation -- in other words, 

17 the number of ova that were expelled from the ovary was 

18 not different. 

19

20 So there is a huge redundancy within this system, and 

21 that‟s perhaps just as well because without reproduction, 

22 we wouldn't be sitting around this table discussing 

23 atrazine. Now if you think about a hypothalamic level, 

24 Fred Carr (phonetic) showed some years ago that you only 

25 need 10 percent of the GnRH surge to drive a full-blown 

26 LH surge in the U; so again, huge redundancy. 

27

28 So perhaps this 50, 54, 55 percent reduction in LH that 

29 is seen with 4-day treatment with a hundred milligrams 

30 per kilograms per day is unlikely, again, to -- in my 

31 opinion -- to disturb cyclicity and other aspects of 

32 reproduction. And there is a sort of slight digression. 
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1

2 Allan Herbison published a paper in 2008 where he used a 

3 transgenetic model to selectively knock down GnRH 

4 neurons, and he showed quite convincingly that you only 

5 need 12 percent of GnRH neurons in a mouse brain to go 

6 through a normal puberty and have your first estrous 

7 cycle. So again, that‟s another example of a huge 

8 redundancy and robustness within this control system. 

9

10 But perhaps, what's more important and troubles me is the 

11 consideration, which I mentioned 16 months ago, that the 

12 toxicological doses that we are discussing are so far 

13 removed from what one would be exposed to in the normal 

14 environment unless, of course, your little rat manage to 

15 nibble it's way into the subnet atrazine in the farmer's 

16 shed. I mean, I think this is something that we, on this 

17 side of the table anyway, are quite concerned about and I 

18 don‟t know how that‟s really going to be addressed. 

19

20 I am delighted to hear that people are beginning to lower 

21 the dosage that they're exposing their animal models to, 

22 but there is a real need to think outside of the 

23 toxicological mindset. So the other point that I would 

24 like to make is that we saw some rather nice data from 

25 Syngenta yesterday that we simply cannot ignore. They 

26 showed that 50 milligrams per kilogram for four days 

27 attenuated the spontaneous LH surge by only 50 percent, 

28 again, in Sprague-Dawley rats. 

29

30 So we've heard a lot of discussion about Long Evans 

31 versus Sprague Dawley. Well here there are two studies 

32 in Sprague-Dawleys from different sources and I think 
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1 it's speaking volumes to how much of a reduction is 

2 taking place in this surge-generating mechanism, which we 

3 know has a huge built-in margin of safety in terms of 

4 reproductive outcome in terms of ovulation. 

5

6 They also showed that 10 milligrams per kilogram for four 

7 days had absolutely no effect on the surge at all. I'll 

8 just remind you that 10 milligrams per kilogram for four 

9 days is a whopping great dose of atrazine. So, I also 

10 feel that the spontaneous LH surge may be much more 

11 fragile and vulnerable to perturbations compared with the 

12 steroid-induced, because in the spontaneous circumstance 

13 you‟re relying on the ovary to release a certain amount 

14 of estrogen, which is driven by what one can only assume 

15 as a normal functioning pulse generator; in other words, 

16 pulsatile release of LH, and obviously FSH. We can‟t 

17 ignore FSH as well. 

18

19 If you contrast that with pouring in buckets of estrogen 

20 or estrogen plus progesterone in your ovariectomized 

21 model, then I think we should focus in on those data that 

22 we saw yesterday in the gonadol intact animals. 

23

24 My conclusion is that four days exposure at the doses 

25 that we are considering is unlikely to have effects on 

26 onset of puberty and the ovarian cyclicity. I am also 

27 quite pleased to see that there is a move to use other 

28 models, apart from the rat, and I think Syngenta need to 

29 be highly commended for putting some money into sort of 

30 primate research. I do wonder why that has taken so long 

31 to be put into effect, because I think we do need primate 

32 data. So I think that‟s all I have to say.
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1

2 DR. DANIEL SCHLENK: Thanks. Dr. Akana?

3

4 DR. SUSAN AKANA: I'd like to add only two personal points of 

5 view. One is, I totally concur with Dr. O'Byrne. 

6

7 What I do want to add is that it is highly unlikely that 

8 the doses we're using giving for four days in adult rat 

9 are going to have adverse reproductive outcomes. 

10 However, what we don‟t know is, if you go back in, say 10 

11 days later, and give them a second exposure, if they'll 

12 have an additive or interactive effect. 

13

14 So in my person view, it's important to recognize that 

15 when these animals have received such a dose and you see 

16 no apparent adverse outcome, that doesn't mean they're 

17 necessarily a normal animal from that point on. The 

18 second personal point of view I have is, I'm very mindful 

19 from the datasets and the docket and that we've seen 

20 yesterday that very frequently animals administered the 

21 atrazine have a drop in food-take immediately, and a drop 

22 in body weight. 

23

24 In my world of physiology, this is not a normal behavior 

25 in an animal. It is something to be very mindful of how 

26 these animals are going into negative energy balance; 

27 their metabolism is shifting. And what's more important 

28 in this case of atrazine is they are choosing not to eat. 

29 And the results on their physiology can look similar to 

30 animals, a different cohort, where you artificially food31 restrict them. 

32
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1

2 So we know from some studies in the literature and some 

3 nice studies done by Susan Moz (phonetic) here with food 

4 restriction that, yes, if you decrease food intake, you 

5 get a different metabolic shift, and they look similar to 

6 the anorexia that you might see with atrazine 

7 administration. But there are differences in the brain 

8 neurocircuitry and that‟s something to be mindful of as 

9 you pursue the atrazine studies.

10

11 DR. DANIEL SCHLENK: Okay. Dr. Jerde, do you have any 

12 comments? You‟re going to delay those to seven, right?

13

14 DR. TRAVIS JERDE: Yes.

15

16 DR. DANIEL SCHLENK: Do you have anything on the 

17 nonprostatitis endpoints? No? Yes?

18

19 DR. TRAVIS JERDE: Well, the only thing that I would add is, 

20 I've sort of alluded to it a little bit before. The 

21 mechanisms of action that we haven't really defined yet 

22 could turn out to be very important. I would encourage 

23 more research in this area looking at sibling mechanism, 

24 genetic imprinting and things like that that may occur. 

25 Because these hormonal changes, systemic changes, are 

26 oftentimes associated with those sorts of more subtle 

27 effects that probably ought to be addressed.

28

29 DR. DANIEL SCHLENK: Thanks. Dr. Roby, next?

30

31 DR. KATHERINE ROBY: I have no more to add.

32
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1 DR. DANIEL SCHLENK: Okay. And Dr. Timms, do you have 

2 anything to add?

3

4 DR. BARRY TIMMS: At this point, no. We'll refer to our 

5 charge question.

6

7 DR. DANIEL SCHLENK: Okay. Other panel members, anything to 

8 add? Yes, Dr. Horseman?

9

10 DR. NELSON HORSEMAN: I want to go in the charge question 

11 here. It says that these perturbations are being 

12 considered as the basis for atrazine risk-assessment. So 

13 I hear a lot of skepticism, and this may be just stating 

14 something that Dr. Jerde just asked in a different way, 

15 but my question is does the use of this LH surge, as a 

16 sentinel effect, plausibly capture an apparent 

17 hypothalamic mode of action that is, as yet, poorly 

18 understood at a site or molecular level, but is important 

19 to understand. 

20

21 In other words, it seems to me like we're being asked to 

22 consider whether this effect that is hard to understand 

23 its particular physiological meaning, is telling us 

24 something else. And if that‟s what we're being asked to 

25 consider, we need to see if we can give you better advice 

26 as to how to go about finding that something else, it 

27 seems to me. And maybe that‟s a poorly worded question 

28 but maybe not.

29

30 DR. DANIEL SCHLENK: Other comments? Dr. O'Byrne?

31

32
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1

2 DR. KEVIN O'BYRNE: I mean, the surge generating mechanism in 

3 the rodent is a little bit odd. But nevertheless, it is 

4 a central mechanism and it's reasonably well understood. 

5 And if you do perturb it, then you are going to impact on 

6 the reproductive cycle because it's part of that control 

7 system. So if you don‟t have a surge then you‟re not 

8 going to have a normal estrous cycle, and that would be 

9 true in other species as well, so it is quite important.

10

11 The problem is the dose that is used to completely wipe 

12 this out is a hundred milligrams per kilo. So I

13 appreciate what you‟re saying. It is important. And 

14 these guys have already shown the effects of atrazine on 

15 LH pulses, and we heard a lot yesterday about LH pulses 

16 are so important. Well of course they are, but they have 

17 to give a hundred milligrams per kilo and they still 

18 don‟t see any significant effect. I'm not quite sure 

19 when that was published and Ralph will remind us. So in 

20 the context of the pulse generator, pulsatile LH 

21 secretion, you need even higher doses.

22

23 DR. DANIEL SCHLENK: Dr. Roby?

24

25 DR. KATHERINE ROBY: I think your point is a good one though. 

26 I think the measure of LH is really just a measure of 

27 what's happening upstream. I think everyone agrees to 

28 that and there are a lot of studies, although they would 

29 be difficult studies to do to look at what's changing in 

30 the hypothalamus. But I think it's also interesting to 

31 note the studies where during the LH-dependent portion of 

32 gestation, resorption occurs, and I wonder if those point 
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1 to some alternative or additional points of input of 

2 atrazine in that maybe the regulation of LH at that point 

3 is different than the regulation occurring during the 

4 surge. But I think the point of understanding the 

5 molecular mechanism is important.

6

7 DR. DANIEL SCHLENK: Dr. Horseman?

8

9 DR. NELSON HORSEMAN: Okay. To come around to a different 

10 part of this, so we've talked a lot about roots of 

11 administration; and may mean gavage or in the food or in 

12 the water or whatever. But if the EPA is asking us to 

13 help them understand whether this apparent hypothalamic 

14 mode of action is relevant for the basis of atrazine 

15 risk-assessment, I wonder if we shouldn't see data where 

16 atrazine is applied to the hypothalamus. 

17

18 I don‟t know if there is any literature out there. This 

19 is a fairly straight-forward type of ICV infusions, 

20 because -- I'm uncomfortable with the notion, well 

21 there's a mode of action that we are hanging a general 

22 physiological toxicological affect on, but then everybody 

23 knows for sure that that particular mode of action isn't 

24 directly coupled to an adverse outcome in terms of 

25 ovulation, and I keep saying that.

26

27 DR. KEVIN O'BYRNE: I think if you knock the LH surge 

28 sufficiently then you do impact on the other aspects of 

29 the reproductive cycle. So from that point of view, I 

30 mean, I think it is a good marker.

31

32 DR. DANIEL SCHLENK: Dr. Akana?
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1

2 DR. SUSAN AKANA: I had a conversation with Dr. Handa 

3 yesterday and actually almost the same conversation in 

4 the April SAP. 

5

6 In the April SAP he presented some c-fos information in 

7 the brain and I asked him specifically, "Did you look in 

8 the paraventricular nucleus," that being my favorite part 

9 of the hypothalamus. In conversation yesterday he said, 

10 "They looked extensively through the hypothalamus. 

11 They're looking at c-fos an hour after atrazine injection 

12 -- and I'm sorry I can't remember the dose -- but the 

13 hypothalamus was totally quite. 

14

15 I did ask him in April, "Check the distributed CRF 

16 system; look at amygdala." And yesterday he told me, 

17 yes, they saw some c-fos in parts of the amygdala. So 

18 that‟s what I know of the hypothalamus c-fos studies. My 

19 comment about, for instance, ICV injection into the brain 

20 is remember the solubility of this compound is pretty 

21 poor. It almost has to be a crystalant implant.

22

23 DR. DANIEL SCHLENK: Okay. Any other input? Yes, Dr. 

24 Griffith?

25

26 DR. DANIEL GRIFFITH: A question that I think is relevant to 

27 answers 1 through 3 questions; what would be some good 

28 dose levels to explore then if you think 100 milligrams 

29 per kilogram is too high? What would be some good dose 

30 levels, lower dose levels to explore?

31

32
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1

2 DR. KEVIN O'BYRNE: Well, we rely on you guys to tell us what 

3 make it in the water. And my understanding is its so 

4 low, and that‟s what we should be considering, in my 

5 opinion. We should be administering the doses that 

6 replicate the maximum levels that are found in the water 

7 and it should be given chronically or intermittently. It 

8 doesn't really matter. Those would be just part of the 

9 experimental strategies. This hopefully will be what 

10 will be done more carefully in the primates, because when 

11 you start working with primates you control and design 

12 your experiments exquisitely. The rat guys -- and I'm 

13 now a rat guy -- tend to be a little complacent.

14

15 DR. DANIEL SCHLENK: Yes, Dr. Timms?

16

17 DR. BARRY TIMMS: Actually, I think the answer to that 

18 question is more relevant if you consider the levels that 

19 may be predicted or found in humans because those are the 

20 levels at which we are exposed and those would be 

21 relevant to the dose exposure that Dr. O'Byrne was 

22 talking about.

23

24 DR. DANIEL SCHLENK: Yes, Dr. Roby?

25

26 DR. KATHERINE ROBY: Is also though seems to me that the 

27 experimental approach breaks down two different but 

28 important questions, and one is the biology and what 

29 happens with an exposure to atrazine, and secondly is 

30 what is the real risk factor? And one is assessing 

31 effects at realistic exposure levels, and one is doing 

32 more of the kind of pharmacologic type of studies.
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1

2 DR. DANIEL SCHLENK: Dr. Horseman?

3

4 DR. NELSON HORSEMAN: To bring in a concept from a different 

5 SAP we had recently -- and maybe that‟s the source of 

6 this question I'm asking -- moving toward the notion of 

7 an adverse outcome pathway requires understanding the 

8 biological substrates of that adverse outcome. And from 

9 the diagrams we've seen here, you know, we give to the 

10 organism and something happens at these lower levels of 

11 organizations. We have no information about those other 

12 levels of organizations. 

13

14 I think the question about these relevant doses and such, 

15 also can break down. If you consider the fact that we're 

16 going to understand this toxicology or ovarian physiology 

17 from studies of a small number of animals, relatively 

18 small number of animals, but millions of people are going 

19 to be expose to this. 

20

21 So I don‟t think it's as simple as saying nothing happens 

22 as three parts per billion and only one and a half 

23 million people were exposed to more than three parts per 

24 billion. You know, that‟s you guy's problem there, 

25 essentially, but understanding the biology and from this 

26 toxicology in the 21st century notion of adverse outcome 

27 pathways and that sort of thing, I think you‟re a long 

28 ways away from that for this hypothalamic mechanism that 

29 seems to be proposed. That‟s my only comment.

30

31 DR. DANIEL SCHLENK: Okay. Dr. Chambers?

32
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1

2 DR. JANICE CHAMBERS: I just want to pick up the same thought 

3 that Dr. Horseman was having. Again, the last SAP we 

4 were talking about the mechanisms involved at the adverse 

5 outcome pathways. When so little is known about the 

6 mechanism, then it's really hard to tell whether this is 

7 a biologically plausible phenomenon in the rat compared 

8 with the human or not anyway. So it just really seems 

9 like it's very important to try to identify some of the 

10 real mechanisms going on and find out whether those are 

11 relevant in humans.

12

13 DR. DANIEL SCHLENK: Yes, Dr. O'Byrne?

14

15 DR. KEVIN O'BYRNE: Well, I think the comments made by Tony 

16 Plant yesterday that there should be a focus on the pulse 

17 generator might be something that should be explored a 

18 little bit more. But I'll remind you that Ralph has 

19 already shown that LH pulses are not affected with 100 

20 milligrams per kilo. 

21

22 DR. DANIEL SCHLENK: No further comment from the panel at this 

23 point? Dr. McManaman?

24

25 DR. JAMES MCMANAMAN: It looks like it's 50 mgs per kg does 

26 reduce the LH surge. This is from Syngenta data. So I 

27 think that we're in the ball-park of -- and that

28 corresponds to about 500 PPMs -- so we're not too far 

29 off. We're in the toxicological area, but we're not so 

30 far. We're getting close to the physiological area, I 

31 think.

32
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1

2 DR. DANIEL SCHLENK: Okay, alright. So we'll throw it back to 

3 the EPA folks. Do you have comments requiring 

4 clarification?

5

6 DR. RALPH COOPER: I just have a point of clarification, I 

7 think, for dose selection and what seems to this panel to 

8 be a disparity between environmental levels and the doses 

9 that are used in laboratory studies. 

10

11 I think it's a good thing that we're seeing this 

12 disconnect between what's needed in order to identify a 

13 potential adverse effect in the test species and what's 

14 in the environment and what's potentially exposure to the 

15 humans. And this didn't just happen by chance with a 

16 chemical that‟s been around for 50 years, there have been 

17 programs to maintain low concentrations. 

18

19 I look at it a little bit differently. I think what we 

20 are looking for is, as we felt that, as we look at the 

21 mechanisms or the potential mechanisms involved in the 

22 test species, we want to make sure we're using the right 

23 set of tools, measuring the right parameters so that we 

24 can be sure we're making a good guess as to what's going 

25 on. 

26

27 And what we're doing essentially is reinforcing the 

28 safety factors, if you will. I mean, look at what we're 

29 talking about. We're talking about very slow or very 

30 small differences in previous LOAELs, NOAELs, points of 

31 departure that have been used for the risk assessment. 

32 We may be tweaking the timing of those events and things 
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1 like that but we're pretty much still at the level we 

2 were back in 2000 when we did the six-month evaluation 

3 and you had the 1.6 NOAEL and 3 point something LOAEL. 

4

5 What's changed maybe -- and I don‟t think it's 

6 earthshaking. It's not an order of magnitude -- is the 

7 acute exposure, if you will, for four days. Previously, 

8 it's my understanding it was 10. And we're identifying 

9 something in an intact animal, and I certainly agree with 

10 O'Byrne. He made a very good point when he said, "The 

11 less contrive that you have your experimental model 

12 perhaps the better information you'll get. Study an 

13 intact animal. Study that animal at the times when you 

14 anticipate the effect to be taking place that you‟re 

15 looking for. In the intact animal you'll see lower 

16 LOAELs and NOAELs. 

17

18 When you give estradiol to an animal, you've taken its 

19 ovary out, you give estradiol to it; the rules change. 

20 You can still use it as a model and get information, but 

21 this dose response information falls apart. Secondly, 

22 when you give estradiol plus progesterone, which is what 

23 most of the registrant studies are, I've seen that 

24 totally blow out in effect that you may have seen 

25 elsewhere. 

26

27 So these are not all comparable studies. When you talk 

28 about the ovexed animal, I mean, they're just as 

29 different as the monkey and the rat. They're different 

30 tools and they are different approaches that you use to 

31 get at things. But the two main points is that the 

32 LOAELs that we see in an intact animal -- I totally 
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1 concur with. It's really difficult to say that we see 

2 changes. I don‟t even recall the percent change in the 

3 amplitude of the surge of the area under the curve in our 

4 data but it certainly wasn't going to be one that John 

5 Everett or many of the people who later on looked at the 

6 threshold level of LH for ovulation. 

7

8 The question that always remained -- and I worked with 

9 John Everett for years -- and the question was, well what

10 is the rest of that LH doing then? Is it just there as a 

11 frill or is there some other important function that that 

12 may have? If you look at the literature on reproductive 

13 physiology, there are a lot of other things that we 

14 haven't explored.

15

16 DR. DANIEL SCHLENK: Yes, Dr. Fowle or Steve, did you want to 

17 say something?

18

19 DR. STEVEN BRADBURY: Yes. Along the lines of what Ralph was 

20 just saying, but also what Dr. Horseman was saying, I 

21 guess one of the things is we're at the point now is from 

22 a regulatory perspective we want our decision to be 

23 informed by the best available science. I mean our 

24 decisions must be informed by the best available science. 

25

26 However, we don‟t have the luxury of waiting until all 

27 the T's and crossed and I's are dotted. So at various 

28 periods of time we have to pull together the science and 

29 take advantage of that best available science and make a 

30 decision at that particular point. And we have our re31 registration view process that‟s periodically come back 

32 and we keep looking at these chemicals in repeated 
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1 fashion in the future so we can add additional science as 

2 we proceed. 

3

4 So what Dr. Horseman is saying, at one point in time, 

5 we're kind of faced with a dilemma. It's not likely 

6 we're going to be able to have a lot of new data along 

7 these lines in the next couple of years, so to the extent 

8 that we don‟t have data that would get at these issues, 

9 what would really help us is what kind of guidance can 

10 you give us in terms of stitching together the best 

11 assessment we possibly can, given the science we have 

12 right now. 

13

14 So one of the things we may be hearing, and I'm not 

15 exactly sure of the clarification, how, for instance, Dr. 

16 O'Byrne, you were saying that at the environmental levels 

17 that we would encounter with respect to atrazine, would 

18 not likely have an impact on reducing the LH surge. 

19

20 We could interpret that a couple of ways. One would be 

21 that we would need to basically sort of eliminate that 

22 surge to be able to -- well, I might be saying this 

23 wrong. If we're not seeing the effect of the LH surge 

24 disease, environmental rapid exposures are those higher 

25 level doses where LH surge events intact might serve as a 

26 node, which would lead to a variety of adverse impact. 

27

28 Is there any advice or guidance you'd give us for how we 

29 might use that data in the context for all the other data 

30 we have available to try to stitch together as best we 

31 can right now? What effect, if anything, atrazine might 

32 be causing?
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1

2 DR. KEVIN O'BYRNE: I think the data we saw yesterday, for 

3 example, from Syngenta where they showed that 50 

4 milligrams caused a reduction of about 50 percent of the 

5 surge is very clear and unambiguous. And with 10 

6 milligrams there is no effect. I mean, that‟s hard 

7 evidence and I think you can work with that. 

8

9 What I was trying to explain to you is that a 50 percent 

10 reduction in the LH surge, in the spontaneous LH surge, 

11 may have absolutely no impact on the ovulation and the 

12 cyclicity that is driven by that. So I think you've got 

13 some very hard data there. And so, I think that‟s 

14 important. 

15

16 The other thing is, the discussion yesterday about the 

17 problems associated with the surge generating mechanism 

18 in the rat as opposed to other species, and particularly 

19 human, is something that you ideally would like to have 

20 that data. But if you don‟t have it then you have to go, 

21 as you say, with the best that you have. 

22

23 So perhaps in the timeframe that you‟re talking about, 

24 you are not going to get any further data but you've got 

25 hard evidence now. And when some primate data does come 

26 out on terms of pulsatility, it would be just interesting 

27 to see how that fits because there's no doubt about it, 

28 what Tony Plant said yesterday in terms of the LH pulse 

29 generator, you don‟t have a surge unless you've got a 

30 normal functioning pulse generator. So you know you've 

31 got a normal functioning pulse generator at 10 milligrams 

32 per kilo. Otherwise, these rats would not be having 
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1 spontaneous normal surges. So I think you've got some 

2 good data. 

3

4 DR. DANIEL SCHLENK: Any other comments? Okay. Back to the 

5 agency, is this the answer that you need?

6

7 DR. ELIZABETH MENDEZ: We've heard a lot to think about, so we 

8 appreciate the input. So if you'll allow, I'll keep 

9 going then to charge question number 6. And charge 

10 question number 6 has to do with some advice or some 

11 comments that we've heard from the panel back in 

12 September about the significance of 1-day versus 4-day 

13 exposure. And in response to those comments, our 

14 scientist went back to the lab and conducted some 

15 studies. And we've sort of started alluding to them in 

16 the previous question, but we are going to ask the 

17 question anyway. Please comment on the potential 

18 relevance of 1-day exposure to elicit an adverse outcome 

19 and the significance of an increase versus a decrease in 

20 LH.

21

22 DR. DANIEL SCHLENK: Before we move on, I just want to remind 

23 the lead discussants of each of these questions, it's 

24 your responsibility to summarize all of the panel's 

25 discussions for that. And I know that, based on the last 

26 question, it was somewhat widespread and unclear. So 

27 again, that‟s sort of the responsibility of the lead 

28 discussants to do that, so I just want to make sure 

29 you‟re aware of that as we go forward. So with that, Dr. 

30 Roby?

31

32
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1

2 DR. KATHERINE ROBY: Okay. This question does, I think, 

3 segway from our discussion that we've been having. And I 

4 think to specifically address the absolute question on a 

5 1-day exposure, we have to go to the new data that was 

6 presented with the ongoing study, as I understand it, 

7 where the model system of ovariectomy and estradiol 

8 replacement was used in looking at a single versus 

9 multiple-day exposure. 

10

11 A single day exposure elicited an augmentation of the LH 

12 surge, so that‟s 1-day exposure. So what would be the 

13 significance or outcome of that, probably very little to 

14 nothing. An augmented LH surge, we've already talked 

15 about the excess in the LH surge in what we considered to 

16 be the redundancy in the mechanisms downstream of the LH 

17 surge. 

18

19 So probably ovulation would have occurred just as it 

20 would have if the surge had been at those "normal 

21 levels." Now, the significance of a decrease; the new 

22 experiment shed some additional light and I think 

23 highlights the complexity of the system. And I think the 

24 model system was appropriate to address the question. 

25 The results, again, are interesting in the augmentation 

26 of the surge and it's relation to the progesterone that 

27 was probably secreted by the adrenal gland with the 

28 atrazine exposure. 

29

30 Then, with the subsequent administrations and either the 

31 down regulation or desensitization of the continued 

32 exposure to progesterone each time, and then you see the 
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1 attenuation of the LH surge. I know this gets into 

2 multiple exposures, but if there were a single exposure 

3 that resulted in a decrease, to directly answer the 

4 question again, the overall outcome again would be 

5 probably pretty minimum. If the LH surge was inhibited 

6 enough, it would result in an anovulatory cycle. From a 

7 practical standpoint, there is no ovulation, and the 

8 cycle would probably be extended slightly in a rodent and 

9 a couple of days maybe in a woman. But subsequent cycles 

10 would then be normal. This is just single exposure. 

11

12 So, I guess, bottom line is a single exposure would 

13 really have minimal effect downstream of the effect on 

14 the LH surge. I think something that the study presented 

15 that goes back to the pretext to the question is; 1) is 

16 the effect on the LH surge due to the peak exposure to 

17 the atrazine or is it due to this accumulation and this 

18 potential pseudo steady state? Or is it really just that 

19 multiple exposures eliciting those multiple increases in 

20 progesterone are -- you need to have so many exposures to 

21 have the down regulation in response to progesterone, 

22 which is maybe different than reaching a steady state or 

23 a pseudo steady state level. So multiple, short 

24 exposures may still be relevant to effect. And I think 

25 that this experiment leads to more questions, based on 

26 the result. 

27

28 The other point that is in the discussion, but is not 

29 addressed at least in the data that‟s been presented 

30 recently -- and I think it's an important question -- is 

31 when during the cycle is the exposure relevant? So these 
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1 exposures were done at 1300, I believe, which is really 

2 basically the onset of the LH surge. 

3

4 If exposure occurred at 0900 or the previous day, would 

5 there be any effect on the surge? I don‟t know. I guess 

6 that‟s to be shown. We certainly know of other compounds 

7 that, when administered, based on time of day, will 

8 either shut down the surge or have no effect on the 

9 surge. And atrazine could be functioning through similar 

10 mechanics. We don‟t know. 

11

12 So, I think time of exposure is still a question that‟s a 

13 little bit open. I think when we want to translate that 

14 then to the human, obviously the amount of time within 

15 any menstrual cycle further away from the LH surge is 

16 significant relative to the time near to the LH surge. 

17

18 I think the other question then related to pulsatile 

19 exposure versus what might be a pseudo steady state 

20 relates to the amount of time that the drug might need to 

21 be at a certain level to elicit a change in a woman 

22 compared to in a rodent, given the different dynamics of 

23 the LH surge in women versus rodents. 

24

25 I think those are still some questions that are 

26 uncertain. Okay. So in summary, directly for the 

27 question, 1-day exposure, I think, really will have 

28 little effect on ultimate outcomes in fertility. 

29

30 DR. DANIEL SCHLENK: Okay. Dr. Akana?

31

32 DR. SUSAN AKANA: I have nothing to add. 
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1

2 DR. DANIEL SCHLENK: Okay. Dr. O'Byrne?

3

4 DR. KEVIN O'BYRNE: I concur with what's been said. But 

5 actually, the 2-day exposure, I just realized, 2-day 

6 exposure had absolutely no effect, so that‟s quite 

7 relevant. It's giving us a little bit more information 

8 about that window that you are sort of asking about, so 

9 you've already got part of the answer.

10

11 DR. DANIEL SCHLENK: Dr. Roby?

12

13 DR. KATHERINE ROBY: If I could comment though; I think what 

14 we don‟t know is if that isn't due to now two exposures 

15 to progesterone and down-regulating that system and the 

16 sensitivity or adjusting the sensitivity, so the duration 

17 of exposure to progesterone in combination with exposure 

18 to the drug atrazine.

19

20 DR. KEVIN O'BYRNE: But I think one of the questions these 

21 guys were asking was if they started the treatment at a 

22 different phase of the cycle -- you've only got four 

23 days, so I think that is relevant to the question that 

24 was being posed.

25

26 DR. DANIEL SCHLENK: Sure.

27

28 DR. KATHERINE ROBY: Can I also add --

29

30 DR. DANIEL SCHLENK: Sure.

31

32
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1

2 DR. KATHERINE ROBY: I did want to say that the rodent 

3 obviously is probably not the optimum model system to 

4 really look at time during the cycle. The estrous cycle 

5 is so short. It's not a true luteal phase, for example. 

6 Probably other model systems, obviously, like the 

7 nonhuman primate would be excellent to look at what 

8 effect exposure across different times of the cycle would 

9 actually have. 

10

11 DR. DANIEL SCHLENK: Yes, Dr. Cooper?

12

13 DR. RALPH COOPER: I understand your comments and they are 

14 right on target with the question that was asked in the 

15 September SAP. We didn't have all that time to address 

16 them, but the one question that we didn't get to, and I 

17 think is still possible, is let's say for example you 

18 dosed on estrous only: there is a literature that says, 

19 and again goes back to Dr. Everett's lab where if you 

20 give one dose of progesterone, on the estrous early 

21 diestrous-1, I think it is, it would delay ovulation one 

22 day, so there is another example of timing, and timing is 

23 critical. 

24

25 One other point that I'd like to make is that we've done 

26 studies with other chemicals that show that if we dose 

27 the animal between the hours of two and four in the 

28 afternoon, a vaginal pro-estrous, we can get a blockade 

29 total 100, which I guess you'd consider relevant, with 7 

30 milligrams per kilogram of the chemical. But yet, if we 

31 drift back in time and give it earlier and earlier, so 

32 now the peak, the area under the curve, is earlier that 
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1 day -- so this just emphases the critical nature of the 

2 timing -- that there would be no effect at all. 

3

4 I mean, you know, it's tox, you like to have effects. 

5 They are just so robust they last forever, but the real 

6 nature of this cyclical beast, the circadian rhythm, 

7 makes it such that timing is important. And that takes 

8 me to the question of why does one guy got 50 milligrams, 

9 another guy has got 10 and another guy has got 5 

10 milligrams, that‟s a LOAEL? Well, if you go back and 

11 look at those studies you'll see, in those cases, timing 

12 wasn't always the same, and I think that could be 

13 contributing to the part of this. That cycle, there are 

14 certain things that are sensitive at different portions 

15 of the cycle. 

16

17 DR. DANIEL SCHLENK: Okay. The rest of the panel? Yes, Dr. 

18 Lee?

19

20 DR. HERBERT LEE: I want to tie this back to the hydrology a 

21 little bit and ask a different question. So I hear you 

22 saying that one exposure may have a few short-term 

23 effects, but really no long-term effects. But if you‟re 

24 living in a small watershed community water system area 

25 you may get a high dose once a year because there is a 

26 one peak and then it comes back down. Is there any 

27 consideration as to what happens if you have a once 

28 annual 1-day exposure or, in a rat, maybe you want to 

29 translate this back on the length of the cycle, it'll be 

30 a lot faster than annual for a rat, but that sort of slow 

31 repeated exposure.

32
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1

2 DR. DANIEL SCHLENK: Yes. I think we're going to kind of get 

3 to that in some of the later questions, actually. Yes. 

4 Feel free to answer, if you like. 

5

6 DR. KATHERINE ROBY: Well, I think that even a once annual 

7 exposure that, say in worse-case scenario completely 

8 inhibit the LH surge, would still have very minimal 

9 effect on your overall reproductive capacity. You would 

10 have one anovulatory cycle. In reality, women very often 

11 have anovulatory cycles. It's not an uncommon event.

12

13 DR. DANIEL SCHLENK: Okay. Any other comments from the panel 

14 on this particular question? Okay. Let's go ahead and 

15 break for our afternoon break here. Let's try to be back 

16 at 2:35.

17

18 DR. DANIEL SCHLENK: Okay. Let's go ahead and get back at it 

19 if we can. Let me just remind the panel members if you 

20 do have discussions with agency folks you need to state 

21 that for the record kind of what you were talking about, 

22 in terms of your outcomes there. Yeah, only if it's 

23 relevant to the charge questions, of course. Don‟t get 

24 too personal there. So let's go ahead and get started 

25 on number 7 and we're going to read that into the record.

26

27 DR. ELIZABETH MENDEZ: This one has to do with the prostatitis 

28 findings that we're seeing in the rat and that we're 

29 seeing after exposure from PND-1 to PND-4. And the 

30 question that we have is, given the biological processes 

31 involved in atrazine-mediated prostatitis in rats, please 
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1 comment on the human relevance of these findings in rats, 

2 for the overall hazard characterization of atrazine.

3

4 DR. DANIEL SCHLENK: Our lead discussant on that is Dr. Timms.

5

6 DR. BARRY TIMMS: This morning we were exposed to a very 

7 sophisticated, statistical model. This afternoon I'd 

8 like to expose you to some sophisticated anatomical 

9 models. If I could have the slide up I'll be referring 

10 to that during our presentation. 

11

12 I'll present some background information and then address 

13 our responses to the agency, but I think it's important 

14 just briefly to summarize some of the information 

15 regarding prostatitis and it's relevance to the human 

16 condition. In men, younger than 50 years of age, it's a 

17 very common neurological diagnosis. In men over the age 

18 of 50, it's the third most common neurological diagnosis. 

19

20 What that translates into is that, in this room, 

21 approximately half the men will experience prostatitis 

22 sometime during their lifetime, so it's not an 

23 insignificant health-related disease. The National 

24 Institutes of Health has re-categorized prostatitis into 

25 four types; the bacterial, acute and chronic, and the 

26 nonbacterial chronic pelvic pain syndrome and an 

27 asystematic histologically evident inflammation. 

28

29 So there are different types of prostatitis that‟s mostly 

30 divided into the bacterial and non-bacterial. When we 

31 use animal models such as the Lewis, Copenhagen and 

32 Wistar rats, they are animal models that develop a 
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1 spontaneous non-bacterial prostatitis with advancing age. 

2 And so they'd been used as animal models for 

3 investigation of the disease with the premise that if you 

4 increase or exacerbate that incidence or severity, it can 

5 be a reflection of a particular treatment.

6

7 I should point out that you can also increase the 

8 incidence and severity of prostatitis by treating male 

9 rats with estradiol. And a number of studies that have 

10 used these models have also shown that the spontaneous 

11 non-bacterial prostatitis observed in rats is very 

12 similar to the histological profile to that observed in 

13 humans. So it, again, is a good model. 

14

15 Come back to estrogen, and estrogen-inducted prostatitis 

16 is partly related to the inhibition of dopamine secretion 

17 at the hypothalamus and that can result in the production 

18 and secretion of prolactin that eventually is associated 

19 with inflammation in the prostate that‟s been reported in 

20 1993 by Tandin Lucal's (phonetic) group in which they 

21 showed E2-induced prostatitis was correlated with 

22 increase serum prolactin, elevated pituitary weight and 

23 that the administration of bromocriptine, which is a 

24 dopamine D2 agonist, was effective in suppressing the 

25 pituitary weight in hypoprolactinemia and it mitigated 

26 the prostate inflammation. 

27

28 So the studies that we've been asked to review have 

29 implicated that exposure to atrazine during the late 

30 gestation period, that‟s days 15 to 19 in the rat, just 

31 prior to birth, or the early postnatal period, just after 

32 birth, days 1 through 4, in male rats can lead to 
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1 inflammation of the prostate. And in these studies they 

2 looked at ventral and lateral regions at later stages of 

3 postpubertal growth. It was considered that the 

4 inflammation was a result of elevated prolactin levels 

5 and these elevated levels were through to play a 

6 significant role. 

7

8 The underlying mechanisms for that cause of prostatitis 

9 are not yet defined, but may be related to hormonal 

10 changes during what we call critical periods of 

11 development that may have subsequent adverse affect 

12 during agent. It's also important to note that all these 

13 studies have used short-term exposure with a range of 

14 doses and different rodent species.

15

16 So our response then with regard to these studies and the 

17 human relevance are the following: We believe it's 

18 unlikely that exposure in these animal model systems 

19 follow the same pattern of exposure that humans are 

20 exposed to in terms of atrazine and degradates which are 

21 more likely to occur over a lifetime and at much lower 

22 levels than those used in the animal studies. This 

23 refers to the comments that Dr. O'Byrne made earlier on 

24 about the relevance of the 100 milligrams per kilogram 

25 dose to actual human exposure levels. 

26

27 In vitro and in vivo studies which have looked at 

28 cellular and molecular expression changes in response to 

29 human exposure levels should therefore be conducted. I'm 

30 going to refer to this diagram here. The prostate of the 

31 rodent consists of distinct lobes, which is not the case 

32 in the adult human gland. But the prostate of the rodent 
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1 has been the animal of choice for reproductive biology 

2 for many, many years, the mouse and the rat. One of the 

3 values of this model is that we can examine the effects 

4 of treatments, such as endocrine disruptors and observe 

5 the region or regions which have a specific sensitivity 

6 to the compound under investigation. 

7

8 So based upon such studies, it became clear that the 

9 region of the rodent prostate exhibiting the most 

10 sensitive response to the effects of estrogen and 

11 endocrine disruptors, for example, was the dorsal lateral 

12 lobe. What I show here on the left-hand side is a 

13 schematic diagram of a rodent prostate showing the early 

14 developmental stages. On the left, it is showing the 

15 individual budding outgrowth and in the middle is a 

16 reconstruction of a late gestation day of birth 

17 approximately rodent prostate where all the regions of 

18 the prostate have been color-coded. 

19

20 So you can see green, yellow and bluish grey indicating 

21 respectively the dorsal lobe, the lateral lobe and the 

22 ventral lobe of the rodent prostate. These are the lobes 

23 that we typically see described in these studies. 

24

25 On the right-hand side is a 13-week human fetal prostate 

26 showing -- and we have shown and others have shown --

27 that the green area and the yellow area are homologous to 

28 the dorsal lateral lobe in the rodent model. And this 

29 region in the human is the one which is important 

30 because, if you notice, in the human, the human does not 

31 have the equivalent ventral lobe, and I want to make that 

32 point very significantly. The human prostate in that 
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1 region has particularly stromal component. So it's 

2 important for us to understand these homologies and their 

3 relationship to any studies that are done in the animal 

4 model. I'll come back to that. 

5

6 While this wasn't the primary objective of this atrazine 

7 study in the issue paper, there is some evidence that the 

8 atrazine effects may be mediated through alterations of 

9 steroid agenesis, including estrogens. It's important to 

10 emphasis that the dorsal lateral prostate, which I said 

11 is homologous to the equivalent peripheral zone in the 

12 human, which is the zone which develops a preponderance 

13 for cancer, is an important correlation. 

14

15 Most of the studies that we reviewed have actually looked 

16 at prostatitis in the lateral lobe, that‟s the yellow 

17 region up there. The dorsal lobe, or combined 

18 dorsal/lateral lobe have typically not been examined. Of 

19 interest in an earlier study was, as I said, that this 

20 region, the dorsal/lateral region is actually homologous 

21 to what we call the peripheral zone in the human prostate 

22 where cancer develops. 

23

24 An endpoint in the atrazine rodent studies is the use of 

25 tissue weights to determine growth and/or adverse 

26 effects. In a recent national toxicology program 

27 scientific review of an endocrine disruptor, a specific 

28 concern was raised regarding the reliability and 

29 usefulness of this parameter as a measurement of 

30 physiological effects. As stated in that report, and I 

31 quote, "Perhaps the most important confounding factor in 

32 all of the prostate studies is that prostatic wet weight 
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1 is an extremely poor measure of prostatic growth, which 

2 substantially diminishes the strength of data advanced 

3 both for and against an effect of whatever substance on 

4 prostatic growth. 

5

6 So in the light of that statement, you'll notice that 

7 many of these studies report on ventral prostate weight 

8 and, as I pointed out, ventral prostate really doesn't 

9 have relevance to the human, and that prostatic wet 

10 weight is a poor measure of growth parameter. 

11

12 So in the light of this, the inflammatory response should 

13 be characterized in this model, we feel, using 

14 contemporary approaches such as BrdU-labeling, 

15 immuncytochemistry to determine proliferation rates as a 

16 consequence of treatment, and more importantly, 

17 characterization and histological quantification of the 

18 hyperplastic or inflammatory response. 

19

20 Furthermore, there are very significant modern 

21 contemporary approaches such as tissue microarray 

22 analysis that can be performed using laser-capture micro23 dissection, and these might better define the cellular 

24 mechanisms responsible for the region-specific 

25 inflammatory responses. 

26

27 Based on several animal models, these inflammatory 

28 mediator induction responses would likely occur prior to 

29 and at much lower doses than histologically-evidenced 

30 cellular inflammation. What that means is that, when you 

31 see the inflammation, what precedes that initiation might 
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1 have much more consequence in terms of an effective 

2 mechanism of the action of atrazine. 

3

4 Cytokines induction, for example, can induce a number of 

5 effects in the tissue, micro environment, which can 

6 result in hyperplasia, dysmaplasia and dysplasia. In 

7 addition, cytokines are known to induce developmental 

8 growth regulators, including IGF, TGF and FGF, which are 

9 very important growth regulators during development and 

10 growth of the gland. 

11

12 We feel that levels of these factors should be evaluated 

13 as part of this process of understanding the mechanisms 

14 behind the initiation of prostatitis. And finally, 

15 inflammation is associated not only with that, but also 

16 with DNA damage and loss of imprinting of certain genes 

17 and that should also be evaluated. 

18

19 One interesting aspect of the gland in the rodent, and 

20 also in the human, not discussed in any of the relevant 

21 publications is the fact that the prostate, and 

22 especially the lateral lobe of the prostate in the rodent 

23 model, has very high levels of endogenous zinc. For 

24 example, tissue zinc levels are reported to be actually 

25 lower in prostate pathology, benign prostatic hyperplasia 

26 and cancer. While we don‟t know and fully understand the 

27 mechanisms and the reasons for the high levels of zinc, 

28 we know it's important in fertility. It might be 

29 relevant to an understanding of why inflammation 

30 specifically develops in that region. 

31

32
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1

2 So a question might be, are the dorsal lateral levels of 

3 zinc reduced in earlier prostate development by atrazine 

4 exposure and does this play a later role in the 

5 development of prostatitis? And we feel that the 

6 relevance of this to the incidence may be important with 

7 regard to mediators of inflammation. Zinc concentrations 

8 and their correlations with inflammatory mediator 

9 expression and cellular inflammation should be evaluated. 

10

11 To date, there's been no causal link between atrazine 

12 exposure and prostate cancer from the studies that we've 

13 heard about. However, several important questions remain 

14 unanswered and unresolved from the present literature, 

15 such as the effects of repeated life-long exposure to low 

16 doses on both prostate cancer incidence in grade and any 

17 effects on progression to advance disease. The causes of 

18 prostate cancer are unclear but several studies have 

19 indicated that chronic prostatic inflammation may proceed 

20 benign prostatic hyperplasia and/or cancer in humans 

21 depending, again, on the region and the zone. In fact, 

22 inflammation is the most tightly correlated histological 

23 anomaly to prostate cancer development. 

24

25 If the link between prostatic inflammation and atrazine 

26 exposure is confirmed, this ascends to added importance 

27 and implores us to consider the related consequences. An 

28 additional component of these studies is that treatment 

29 of Wistar dams with daily doses of atrazine on postnatal 

30 days one to four resulted in suppression of what's called 

31 the suckling induced prolactin release in offspring. 

32
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1

2 Taking the same animals and looking at them at 120 days 

3 of age, the male offspring showed increased incidence and 

4 severity of prostate inflammation in the ventral and 

5 lateral lobes. There was significant increase in ventral 

6 prostate tissue weight at the low does 6.25 mgs per 

7 kilogram, but that was calculated as non-significant when 

8 body weight was taken into consideration. No other 

9 weight changes were observed. 

10

11 The lateral lobes were examined using mylar peroxidase

12 assay for looking at inflammation responses and also 

13 histology. The type of inflammation was characterized as 

14 a focal neutrophill infiltrate, and in the lumen of the 

15 glands and focal mononuclear cells in the stroma. Though 

16 in these studies, the reaction was described as a chronic 

17 inflammatory response, the authors did not make a 

18 comparison of the inflammation with a classification of 

19 human prostatitis types that have been described by NIH. 

20

21 A major point in considering the relevance in this type 

22 of study to the human health hazard is with regard to the 

23 modus operandi for the exposure. So in the rat model, 

24 offspring suckled from atrazine-treated dams, so we have 

25 breastfeed pups and at PND, postnatal day 120, the 

26 lateral prostate, the right lobe, was taken for 

27 histological examination; the left lobe was taken for 

28 mylar peroxidase. 

29

30 What I find interesting is that a recent report from the 

31 CDC indicates that only three out of four mothers 

32 initiate a breastfeeding regimen, and by three to six 
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1 months, this rate is not maintained. So the study that 

2 was reported by Stoker et al. in 1999 suggests that early 

3 lactational exposure to prolactin is important for the 

4 normal development of the tuberoinfundibular neurons. 

5 However, according to the CDC data, approximately 25 

6 percent of US babies are not exposed to breast milk 

7 prolactin. This may pose the question as to whether 

8 these individuals are at the same risk for later 

9 development of prostatistic if they're not exposed to the 

10 mother's breast milk prolactin.

11

12 And a question again from us is do we know or do we have 

13 an estimate for the measured levels of atrazine and its 

14 metabolites in human breast milk. 

15

16 One other consideration is that, if you belong to the 

17 baby-boomer generation, you‟re the ones that are going to 

18 be the most likely to be affected and subsequent 

19 generations by any health hazards from atrazine exposure, 

20 given that the chemical has only been in use since the 

21 1950s. 

22

23 Let me summarize our responses. We believe there are 

24 several unresolved questions which limit the conclusions 

25 that can be drawn regarding the human relevance of 

26 atrazine's affect on the prostate. The results in rodent 

27 models are of limited applicability due to 

28 inconsistencies in atrazine exposure levels and 

29 methodology. 

30

31 Secondly, the inflammation atrazine causes in rodents has 

32 not been sufficiently characterized regarding molecular 
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1 and cellular events that may indicate critical changes to 

2 the tissue microenvironment leaving open the possibility 

3 that lower doses of atrazine could produce subtle but 

4 very biologically significant events. 

5

6 The cellular signing mechanisms involved have not been 

7 elucidated and molecular events such as DNA damage and 

8 imprinting changes may be possible at low levels, and 

9 this has been shown in other studies of low level 

10 exposure to environmental endocrine disruptors. Such

11 changes may accumulate during the aging process of men. 

12

13 And finally, it is unclear what effects atrazine may have 

14 on the truly relevant measures of prostate cancer 

15 effects, including grade, progression and aggressiveness. 

16 We believe further research is needed to confirm or 

17 refute a possible role for atrazine in human prostate 

18 disease. 

19

20 DR. DANIEL SCHLENK: Thank you, Dr. Timms. Dr. Jerde?

21

22 DR. TRAVIS JERDE: Thank you. I agree with the sentiments 

23 that Dr. Timms has just presented. I would like to 

24 reiterate that the data so far in both the 

25 epidemiological association of prostate cancer to 

26 atrazine exposure, as well as the animal studies, do 

27 remain inconclusive with regard to any role hat atrazine 

28 may play in disease. 

29

30 A little bit about prostate cancer; to my view, prostate 

31 cancer is not a disease. It's probably five or six or 

32 ten different diseases. And when you look at 
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1 epidemiological evidence of a disease that‟s going to 

2 affect half the population, a one or two percent change 

3 in incidence that may or may not occur is really not 

4 going to show up. But if there is an increase in the 

5 clinically significant, highly aggressive forms that some 

6 men get -- and those would be the very small percentage 

7 of the men that have the disease that will actually end 

8 up dying from this disease -- that would be a more 

9 applicable measure. 

10

11 So when Dr. Timms talks in his report here about the more 

12 relevant measures, what we could be looking at are those 

13 cancers that are a Gleason 4 plus 3 or higher, those 

14 cancers that occur earlier in men, those cancers that are 

15 faster or achieve in androgen-independent state. Okay, 

16 those are the prostate cancers that kill people. 

17

18 So that‟s something that I hope that the data can be 

19 extracted from in what's been published so far. And I 

20 agree with the dosing regimens, repeated dosing 

21 throughout one's life, because that is what one sees with 

22 prostate cancer, it's a disease of aging. 

23

24 Now if it is true that this compound could cause 

25 prostatic inflammation, particularly as a life-long 

26 exposure, the results of this on human health actually 

27 could be quite profound. As Dr. Timms pointed out, 

28 inflammation is the most associated histological feature 

29 associated with prostate cancer. And we have another 

30 very common disease in the prostate, the second most 

31 common neurological condition diagnosed, and that‟s 
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1 called benign prostatic hyperplasia or LUTS, lower 

2 urinary tract symptoms in men. 

3

4 I chaired a session as the SBUR this spring on new 

5 advances in BPH research and, among other things, one of 

6 the themes that came out of this meeting was that 

7 inflammation is the most tightly correlated histological 

8 feature to symptoms of BPH, moreso than is prostate size, 

9 in fact. And so, one thing we haven't addressed so far 

10 is the presence of BPH LUTS symptoms in exposed 

11 individuals. 

12

13 We have no idea what causes prostatic inflammation in 

14 humans. It is not likely to be bacterial infection. 

15 Those prostates have been cultured. We've looked for 16S 

16 ribosomal RNA; inconclusive results in those studies. 

17 And so this gives rise to all sorts of different 

18 hypotheses. And there are hormonal changes, the presence 

19 of the metabolic syndrome, type 2 diabetes seems to be 

20 correlated a little bit, lifestyle, diet, and

21 environmental exposures are one of those things, 

22 including some of the endocrine disruptors that Dr. Timms 

23 is well-known for investigating. 

24

25 But I'll just leave you with a little bit of epidemiology 

26 that I think is quite striking and is why we care about 

27 these issues in our field. In the United States, 50 

28 percent of men will get prostate cancer. That‟s well 

29 established. In Asia it's less than 10 percent. In 

30 China it's about one and a half percent. If you look at 

31 full-blooded Chinese-Americans who have lived in the 

32 United States for greater than two generations, their 
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1 lifetime incidence is 40 percent, suggesting that a 

2 lifestyle change of whatever is a very important 

3 progression of this disease. That is also associated 

4 with an increase in the clinically significant deadly 

5 forms of prostate cancer. So these are the reasons why 

6 we care about this and why -- there's a lot of 

7 recommendations for you guys to go back to your lab and 

8 say, now do these studies. But over the course of the 

9 next few years, these things continue to -- it's like a 

10 continually evaluation process, but those are some of the 

11 important questions that need to be addressed from our 

12 end as prostate biologists. 

13

14 DR. DANIEL SCHLENK: Okay. Any other comments from panel 

15 members? Okay. Let me go back to EPA. Do you guys have 

16 what you need for that particular question?

17

18 DR. ELIZABETH MENDEZ: Yes. We have a lot to think about, but 

19 we wanted to also circle back, if we may, to a question 

20 that we have as we were sitting here during break 

21 thinking about what we've heard this afternoon. 

22

23 One of the things that, if you remember when I first 

24 started my first presentation yesterday, the 

25 introduction, and I was going through the adverse outcome 

26 pathway. There was a bifurcation in the road of when do 

27 we see something that is just a mere perturbation, that 

28 it's not going to lead us to a biological adverse effect, 

29 and when do we trip or go over that line where 

30 perturbation does become clinically significant. 

31
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1

2 The BND that we're working on right now that was brought 

3 to the table, it's based on a one standard deviation. 

4 It's not a BNDL10. It's not a BNDL15. When we do the 

5 BND at one standard deviation, we're talking about a 33 

6 percent decrease in LH surge or attenuation of LH surge. 

7 And I guess, what I would like to get a little bit of 

8 clarity is, during the September meeting, we heard some 

9 comments about 80 percent being necessary for the ovarian 

10 cyclosity disruption, but there was also some comments 

11 about, well there might be something else happening at 

12 lower levels of LH attenuation and you may just not have 

13 the data and we can‟t negate that.

14

15 I would like to get a little bit of feedback from the 

16 panel in terms of do I hear that you‟re a little bit 

17 closer to giving us a range at least between 33 or 80, 

18 somewhere in there, that you feel is where we trip that 

19 censor that we go from a mere perturbation to something 

20 that leads to an adverse effect, given that we have at 

21 about 12 and a half megs or 25 megs, we start seeing 

22 things in terms of apical endpoints. So I guess that‟s 

23 sort of a question that I would like to get a little bit 

24 of clarity on.

25

26 DR. DANIEL SCHLENK: This would probably be a follow-up to 

27 question five, right? Yes.

28

29 DR. ELIZABETH MENDEZ: Correct.

30

31 DR. KATHERINE ROBY: I'll try to get the discussion started. 

32 I think, from last September to now, we have no greater 
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1 insight in the significance of depression of the LH surge 

2 to subsequent outcomes downstream. I think, still, 80 

3 plus percent needs to be reduced before you see a shift 

4 in ovulation, for example. 

5

6 I think what was mentioned last September is what is an 

7 unknown is if there were a 30 or 50 or 60 percent 

8 decrease over time, it's completely unknown -- I know of 

9 no literature that has addressed what might be the 

10 overall effect in your lifetime fertility. So I think we 

11 still don‟t have a great idea of where you go from a 

12 biological modifier that you‟re measuring LH and a 

13 concrete downstream negative effect or negative outcome 

14 in regard to fertility in the female. I'm not sure if 

15 that was very clear.

16

17 DR. DANIEL SCHLENK: Any other comments from the panel? Yes, 

18 Dr. McManaman?

19

20 DR. JAMES MCMANAMAN: Yes. I think that you‟re asking for a 

21 tertiary affect when we don‟t even know what the primary 

22 affect is. And so I guess I would echo the sentiments 

23 that have been express by Dr. Horseman and others that we 

24 really begin to look at what's going on at the level of 

25 the hypothalamus because that‟s the most likely affect, 

26 because you‟re affecting not only LH but you‟re affecting 

27 prolactin. And unless somebody can explain to me how LH 

28 is causing a decrease in prolactin then I think that it 

29 looks like, I'd say, probably a global affect or a 

30 potentially global affect on the hypothalamus and not 

31 specifically on an LH affect.

32
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1

2 DR. DANIEL SCHLENK: Yes, Dr. Mendez?

3

4 DR. ELIZABETH MENDEZ: Just a little bit of a follow-up then 

5 just for my own personal understanding. The way that 

6 we've been thinking about the LH surge is sort of a node 

7 from whereas some other effects may make happen that LH 

8 diminished decrease. So I understand the 80 percent for 

9 ovarian cyclicity disruption but I'd like to hear a 

10 little bit more discussion about other endpoints like the 

11 puberty onset or something along those lines as well. 

12

13 DR. DANIEL SCHLENK: Dr. Horseman? 

14

15 DR. NELSON HORSEMAN: This gets to the point that we were 

16 discussing earlier. And I think the problem and the 

17 reason Dr. McManaman is suggesting other hypothalamic 

18 effects is there is no coherent way that any of us, I 

19 thin, can figure out that this suppression of the LH 

20 surge and these other reproductive physiological affects 

21 ranging from delayed vaginal opening to estrous cyclicity 

22 to prostatitis in the male to whatever, and then add on 

23 top of that affects on appetite that are presumably 

24 hypothalamic. 

25

26 As far as we can tell, or at least me, there is no way to 

27 consolidate those under one umbrella at this point. That 

28 doesn't mean that there isn't a final common pathway that 

29 explains all these things, but it's just that I don‟t 

30 hear any of us saying, "Oh, yeah, we can consolidate 

31 those things under one mode of action." Thank you.

32
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1

2 DR. DANIEL SCHLENK: Yes, Dr. Mendez?

3

4 DR. ELIZABETH MENDEZ: One last follow-up. I just want to 

5 make sure that I'm hearing the panel correctly. So as 

6 I'm sitting here, am I hearing you say that you‟re not 

7 entirely certain that this is a node from whence 

8 everything else comes down? Is that what I'm hearing 

9 panel say?

10

11 DR. DANIEL SCHLENK: Anybody want to respond to that? 

12

13 DR. KATHERINE ROBY: I will.

14

15 DR. DANIEL SCHLENK: Dr. Roby?

16

17 DR. KATHERINE ROBY: It might be a node, but I'm not a 100 

18 percent convinced it's only the surge that is a node. 

19 And you asked a question about other downstream effects. 

20 We measure vaginal opening as an indication of onset of 

21 puberty in a rodent. And that occurs because of changes 

22 in LH, IA, GnRH, pulse secretion around the time of 

23 puberty stimulating the ovary, increasing estradiol 

24 production, which ultimately affects the vaginal tissues 

25 and allows for vaginal opening through some mechanisms 

26 that are well-known now. 

27

28 So that ties back to LH. It doesn't tie back to a change 

29 in what we call the LH surge, the ovulatory surge, but 

30 probably a change in the pulse or the increase in pulse 

31 and amplitude that occurs around the onset of puberty. 
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1 So in the sense, that LH is still that node, but again, 

2 the mechanisms are not explored.

3

4 DR. DANIEL SCHLENK: Yes, Dr. McManaman?

5

6 DR. JAMES MCMANAMAN: Yes. So to follow-up on what Dr. Roby 

7 said, it potentially could be a node, but if it is, it's 

8 not the only node. Because the atrazine causes a 

9 decrease in LH, that was suggest a decrease in GnRH. 

10 Atrazine also causes a decrease in prolactin, so 

11 prolactin is regulated in a different way, so if it was 

12 affecting the hypothalamus to affect dopamine release, 

13 decreased dopamine release would actually increase 

14 prolactin and not decrease prolactin. 

15

16 So I suggest that there's something more complicated 

17 going on at the level of the hypothalamus and not at the 

18 level of -- I mean, LH is the secondary effect to the 

19 GnRH -- it's just hard to measure GnRH, that‟s why 

20 everybody measures LH -- but it suggests that it's at the 

21 level of the hypothalamus and not at the level of the LH, 

22 per se.

23

24 DR. DANIEL SCHLENK: Yes, Dr. Akana?

25

26 DR. SUSAN AKANA: Yes. I think LH is a node in the net, so 

27 it's just one of many, and it's the one that‟s probably 

28 most manipulable and measurable for you. But on a 

29 different tact, when I study male stressed rats, the 

30 first general rule of thumb we look for an unhappy rat is 

31 a drop in body weight. Like, a 10 percent drop in body 

32 weight is an automatic flag, regardless of whatever the 
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1 provocation is. Now you‟re working with a lot of female 

2 rats, which have a much slower growth rat. So I think it 

3 would be even tighter, maybe a five percent drop in body 

4 weight. 

5

6 DR. DANIEL SCHLENK: Okay. Does that clarify your question 

7 there, Dr. Mendez?

8

9 DR. ELIZABETH MENDEZ: I think I'm starting to get an idea 

10 that we're talking about, as Dr. Akana said, a net. We 

11 may have a part of the net. We don‟t have the whole net. 

12 And I guess, at this point in time, as Dr. Fowle 

13 mentioned earlier today, we have to, from a regulatory 

14 standpoint, go with what we have in front of us but 

15 remain vigilant to what may come further down the line. 

16

17 So I guess that‟s the reality of the situation were in 

18 the regulatory arena, but we'll certainly keep our eyes 

19 open. Thank you.

20

21 DR. DANIEL SCHLENK: Okay. At this point, I believe -- no? 

22 She answered it; okay. I guess we've got that one done; 

23 awesome. All right. Well there's no further comments on 

24 5, 6, 7; let's go to question number 8 then.

25

26 DR. ELIZABETH MENDEZ: Number 8 question, the genesis of that 

27 was we had some conflicting data about rat mammary gland 

28 development that was presented during the September SAP. 

29 And some other feedback that we got from the panel was, 

30 well, we have these two studies; one uses subjective 

31 measures for mammary gland development, the other one is 

32 using objective measures, namely morphometrics, but we've 
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1 never seen the two methodologies compared side-by-side. 

2 Based on the feedback that we heard during the September 

3 SAP, our colleagues in the Office of Research and 

4 Development tried to address that question. 

5

6 The question is, please comment on the agency's findings 

7 in addressing the issues raised by the SAP during the 

8 September 2010 meeting. Please comment on whether this 

9 study, along with the negative studies by CODAR adds to 

10 the weight of evidence that it is unlikely that atrazine 

11 impacts mammary gland development.

12

13 DR. DANIEL SCHLENK: Thanks. Our lead discussant on that is 

14 Dr. Horseman.

15

16 DR. NELSON HORSEMAN: So a simplifying answer to this question 

17 would be a simple yes. But as you might expect from the 

18 panel, you‟re not allowed to give just that. Let me read 

19 from my answer that Dr. McManaman and I have discussed 

20 somewhat. 

21

22 The new data presented from Dr. Cooper's study address 

23 the concern that the studies from Fenton's group and 

24 those from Hovy, contracted with Syngenta, used quite 

25 different approaches for capturing mammary gland 

26 morphology. In the former, a ranking system was used; in 

27 the latter, a set of measured morphometric variables was 

28 applied. 

29

30 While the ranking system has been referred to a 

31 subjective and qualitative, it is in fact neither. The 

32 method, when done in a blinded fashion with trained 
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1 application and morphological criteria is clearly 

2 objective and no less so than a morphometric approach. 

3 It is also quantitative because the established 

4 morphological criteria converted to quantities, that is 

5 ranks that can then be compared with standard statistical 

6 methods. So it is, in effect, objective and 

7 quantitative, ultimately. Morphometric measurements may 

8 be objective, but the only objective is if the measured 

9 variables are not chosen subjectively. So it's nothing 

10 magic about these two approaches, I think. And the fact 

11 that the data come out the same then isn't too 

12 surprising. 

13

14 There's a 2009 workshop that‟s been referenced in the 

15 white paper that did a good job of summarizing best 

16 practices for using morphological variables to 

17 characterize rodent mammary glands. So based on using 

18 both approaches, the ranking approach and morphometry in 

19 a careful manner with Sprague-Dawley rats -- and again, 

20 rats trained is an issue that runs through a lot of these 

21 questions -- the Cooper study presented in the white 

22 paper demonstrates, number one, that both approaches 

23 produced similar conclusions, and number two, that any 

24 effects of prenatal atrazine exposure on mammary gland 

25 development early in life -- and I think the measurements 

26 were done at day 45, which is just after puberty is 

27 finished -- are very subtle and are not measurable by any 

28 of these techniques. 

29

30 So while Long Evans rats might be a different case, good 

31 arguments have been made that the Sprague-Dawley model is 
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1 appropriate and adequate, and this study is definitive in 

2 that regard. 

3

4 So to the larger questions; first the use of "mammary 

5 gland development" as an index of adverse environmental 

6 chemical effects, has been advocated based on a number of 

7 features of mammary gland growth, morphology, 

8 pharmacology and physiology. There are several papers 

9 from the number of groups advocating this as a model 

10 system. 

11

12 These features that are used in this advocacy include the 

13 exquisite hormone responsiveness of the mammary glands 

14 and a distinct developmental sequences of events, most of 

15 which occur after birth and are therefore accessible in 

16 ways that some other developmental events might not be. 

17 These are compelling notions, but I would say, thus far, 

18 implementing this practically has been difficult and not 

19 finally proven to be that helpful. 

20

21 Given the centrality of lactation, though, in the life 

22 history of mammals, continued concern about the affects 

23 of environmental chemicals on mammary gland biology is 

24 extremely important. Therefore, inadequacies in the 

25 literature relating to atrazine effects on mammary gland 

26 development should not deter future studies. 

27

28 So the direct answer to the charge question is that the 

29 new evidence does not provide any support for an 

30 effective atrazine on mammary gland morphology. In the 

31 new studies, SD rats were treated in utero with a wide 

32 range of doses. Tissues were taken for analysis on 
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1 postnatal day 45. Mammary gland morphology was measured 

2 by both the arbitrary ranking system and by morphometric 

3 quantification using image analysis. And because certain 

4 morphological characters occur in a predictable manner, 

5 measurement of this presented here are taken to signify 

6 development. It's always important to remember though 

7 that development refers to processes that underlie these 

8 morphological changes, not the morphology, per se. 

9

10 So while the charge question is focused on resolving 

11 differences of experimental design, data gathering 

12 interpretation between studies primarily from the Fenton 

13 lab and those from Cooper's lab and the Davis 2011 paper 

14 and the Hovy lab, also published this year, these 

15 ambiguous findings need to be considered in large 

16 context. So stepping back, earlier concerns about breast 

17 carcinogenesis are mammary gland cancer in atrazine18 treated rats were resolved satisfactorily by discovering 

19 that mammary tumors came about by a process of disorder 

20 postnatal development. 

21

22 It's driven by accelerated reproductive senescence and 

23 appropriate secretion of gonadotropin steroids and 

24 prolactin and that was reviewed by Cooper et al. most 

25 recently as 2007. 

26

27 So these well-accepted conclusions lead to the simple 

28 deduction that atrazine does have effects on mammary 

29 gland development, even if those effects did not appear 

30 unambiguously in the results from the early life studies 

31 from Fentons or the other labs cited here. 

32
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1

2 So the more relevant question seems to be whether 

3 development in the rodent mammary glands early in life 

4 provides any adequately robust model in which to observe 

5 subtle adverse effects of potential environmental 

6 toxicants such as atrazine. For a variety of reasons, it 

7 seems unlikely that mammary gland morphology, standing as 

8 a surrogate for underlying developmental processes, is 

9 adequately sensitive to fulfill this role. 

10

11 One limitation of rodent mammary gland morphology is it 

12 is subject to wide variations among rodent strains, 

13 depending on differences in hormone secretion patterns, 

14 the presence of endogenous retrovirus, particularly, 

15 MMTV, and on their nutrition. In addition, there are 

16 internal differences between morphological 

17 characteristics of the glands within an individual and 

18 even within regions of a particular gland. So robustness 

19 is difficult to come by there. 

20

21 The second limitation is one's ability to define 

22 differences in morphology or development as being 

23 adverse. Given that the function of the glands is to 

24 produce adequate milk for the offspring, for any change 

25 in morphology to be defined as adverse it would need to 

26 be connected in some objective way to a deficiency in 

27 milk supply. And given that the glands are controlled by 

28 a host of intrinsic and extrinsic homeostatic mechanisms 

29 that are focused on ultimately regulating milk 

30 production, it's not surprising that subtle effects of 

31 environmental chemicals on morphology may not, by 
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1 themselves, perturb function sufficiently to be 

2 definitely adverse. 

3

4 Concerns remain, however, as to whether an environmental 

5 toxicant such as atrazine which affects reproductive 

6 hormones or other mammary-related physiological variables 

7 might interact in important ways with other environmental 

8 factors that predispose individuals to poor mammary gland 

9 function and ultimately to inadequate lactation and poor 

10 breastfeeding outcomes. 

11

12 In particular, obesity is a known risk factor for poor 

13 mammary gland function in humans as well as in rodent 

14 models, and is the number one contributor to failure of 

15 breastfeeding and failure of women to implement their 

16 breastfeeding goals. It's certainly conceivable, maybe 

17 likely, that subtle affects of an environmental chemical 

18 will have important consequences in overweight 

19 individuals. 

20

21 In conclusions, it is true that the current data "adds" 

22 to the weight of evidence that it's unlikely that 

23 atrazine impacts mammary gland development, which is the 

24 statement in the question. However, the evidence off 

25 effects in some studies, combined with the known affects 

26 of atrazine on reproductive hormones provides an 

27 important basis for continued concern in efforts to 

28 design better studies that would determine whether these 

29 hormonal effects could contribute to poor lactation, a 

30 clearly adverse outcome, in susceptible individuals. 

31
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1 DR. DANIEL SCHLENK: Okay. Dr. McManaman, anything to add on 

2 that?

3

4 DR. JAMES MCMANAMAN: As Dr. Horseman said, I concur. 

5

6 DR. DANIEL SCHLENK: Great. Thanks for working together on 

7 that. Any other comments from the panel? Okay. I think 

8 that‟s pretty clear; yes? 

9

10 DR. ELIZABETH MENDEZ: Yes. Thank you.

11

12 DR. DANIEL SCHLENK: Okay. All right. So we're moving right 

13 along. Let's go ahead and read in charge question 9 and 

14 hopefully call it a day after that, maybe.

15

16 DR. ELIZABETH MENDEZ: All right. Charge question 9, it 

17 speaks to the sensitivity between the adults and infants 

18 and children in the analyses that has been conducted by 

19 the agency regarding the studies that we have in front of 

20 us and the evidence or lack thereof of an enhanced 

21 sensitivity of the young. 

22

23 So the question is please comment on the weight of 

24 evidence analyses conducted by the agency and the extent 

25 to which the uncertainties related to the potential for 

26 differential sensitivity of the young are addressed with 

27 the additional data. Let me clarify that by additional 

28 data, what we mean is, all of the life-stage data that we 

29 have that is typically not available to us. 

30

31 DR. DANIEL SCHLENK: Okay. Our lead discussant is Dr. Fenner32 Crisp.
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1

2 DR. PENELOPE FENNER-CRISP: Okay. We've crafted three 

3 questions that I think re-characterized your request for 

4 comment, and they are as follows. 

5

6 Does the existing body of data exploring the potential 

7 for adverse consequences following exposure of either 

8 direct or indirect at relevant life stages, in fact, 

9 encompass all of the life-stages of interest prenatal, 

10 perinatal, pre- and peripubertal and adult. 

11

12 Secondly, do the study design employed in this existing 

13 body of data allow for an adequate assessment of the 

14 potential for differential sensitivities in light of the 

15 fact that these studies do not necessarily include 

16 measurement of the same endpoint or phenomenon that 

17 currently serves as the basis for the quantitative 

18 characterization of hazard, in other words, the 

19 suppression of the LH surge. 

20

21 In reading the issue paper, we've concluded that chapter 

22 5 is supposed to serve as the weight of evidence and the 

23 uncertainty analysis. So the question becomes, does 

24 chapter 5 adequately present the weight of evidence and 

25 the uncertainty analyses on the question of potential 

26 age-related differences in sensitivity? Let's answer 

27 this question first. 

28

29 Chapter 5 is not a weight of evidence analysis of the 

30 datasets and it does not present the uncertainties of the 

31 database as it currently exists. The truly robust weight 

32 of evidence discussion would include a recapitulation of 
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1 the previously submitted and reviewed studies, along with 

2 the newer ones, so that one can understand where and how 

3 each contribute to the determination of whether or not 

4 sufficient information exist to answer the question of 

5 whether or not we know enough about differential 

6 sensitivities. 

7

8 Secondly, there seems to be little or no discussion of 

9 the extent to which the uncertainties related to the 

10 potential for differential sensitivity of the young are 

11 addressed. 

12

13 With regard to the first question, it would've been 

14 helpful or enlightening to have had available either a

15 table or a figure which summarizes all of the relevant 

16 studies bearing on this issue. Dr. Mendez included 

17 several tables in her third presentation yesterday, which 

18 could've served as a starting point for a composite table 

19 of such studies. And we had, among the slides presented 

20 by Syngenta, a figure that could've been useful for 

21 assembling the dataset and had visual display of the 

22 available data to help answer the question. 

23

24 Nonetheless, the panel believes that there is sufficient 

25 information available to reach the conclusion that the 

26 issue of differential sensitivity has been adequately 

27 studied if one accepts the premise that the data on the 

28 LH surge is the appropriate one for making the 

29 comparisons. 

30

31 The panel continues to agree with the agency's conclusion 

32 that exposure during the earlier life-stages does not 
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1 lead to greater sensitivity, again, when comparing with 

2 that, the BMDL-based dataset. 

3

4 Speaking to the second question, which was the issue of 

5 whether or not the parameters evaluated in the studies 

6 put forth to assess the potential for differential 

7 sensitivity are adequate or appropriate for that purpose. 

8 There are a wide variety of studies and a wife variety of 

9 endpoints that have been evaluated in these other 

10 studies, and I guess, in general, we feel that they do 

11 provide a nice variety of things against which to 

12 compare. 

13

14 I'm going to follow with what I have characterized the 

15 subgroup as mission creek. In other words, we're going 

16 to answer a question that you haven't asked. That has to 

17 do with the FQPA safety factor. Selection of it is a 

18 combination of what one does or doesn't know about the 

19 science and what one applies in terms of policies. So 

20 we're sticking the toe on the water on the hook of the 

21 science side. 

22

23 As summarized in the agency's policy guidance entitled 

24 Determination of the Appropriate FQPA Safety Factors and 

25 Tolerance Assessment, Section 408(b)(2)(c) that Liz 

26 mentioned yesterday instructs the agency in making its 

27 reasonable certainty of non-harm finding that it apply an 

28 additional 10-fold margin of safety. I won't read the 

29 rest of that. The section for the administrator may use 

30 a different margin of safety if she wishes. 

31

32
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1

2 It should be noted that the law does not impose any 

3 directional constraints on the choice for a different 

4 margin of safety. The different margin of safety could 

5 be greater than 10x or less than 10x. While this 

6 flexibility exists in the law, there is little precedent 

7 explicitly or implicitly for application of an FQPA 

8 safety factor greater than 10. I'm not going to tell you 

9 what the example is. There is substantial precedent for 

10 the reduction of the FQPA safety factor to either 3x or 

11 1x, but there is no precedent for application of an FQPA 

12 safety factor less than 1. Should note, cases where the 

13 FQPA safety factor has been removed equates to a safety 

14 factor of 1x. 

15

16 As articulated in the 2003 IRED, EPA retained the FQPA 

17 safety factor of 10 for atrazine and its metabolites to 

18 protect the safety of infants and children in assessing 

19 risk from dietary, that is in food and drinking water 

20 exposure, and they offer the rationale as to why. I'm 

21 not going to repeat that here. And for residential 

22 exposures, they applied an FQPA safety factor of 3x, was 

23 reduced by roughly half. 

24

25 In summary, the 10x factor was applied in the dietary 

26 risk assessment reflecting concerns both with regard to 

27 the neuroendocrine MoA and the uncertainties regarding 

28 exposures in drinking water. And the 3x factor for 

29 residential exposure reflects concerns only with regard 

30 to the neuroendocrine MoA. 

31

32
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1

2 The July version of the issue paper, we have available 

3 for this meeting, summarizes the results of a series of 

4 studies, some predating and some postdating last 

5 September's SAP meeting and concludes, although 

6 additional experimental toxicology studies are still 

7 ongoing to better characterize the potential adverse 

8 health outcomes resulting from atrazine exposure, 

9 including the duration of exposure that may lead to an 

10 adverse health outcome, available data do not indicate 

11 that pre- and/or postnatal exposure leads to increased 

12 sensitivity in the young relative to the attenuation of 

13 the LH surge and serves as a basis for the atrazine risk14 assessment. The panel did agree with that last fall. 

15

16 If the panel continues to agree with the agency's 

17 conclusion with regard to the lack of early age-related 

18 sensitivity to the neuroendocrine effects that are 

19 driving the hazard assessment, at least two options with 

20 regard to the appropriate magnitude of a revised FQPA 

21 safety factor could be considered. In the current issue 

22 paper, the agency proposes to replace the old NOAEL from 

23 the Morseth study with the new BMDL to .56 to serve as a 

24 point of departure. Going on to note three additional 

25 studies evaluating the effect of atrazine exposure across 

26 life-stages have become available within the last few 

27 months. 

28

29 These studies reinforce the conclusions reached during 

30 the September 2010 meetings since all of the affects 

31 observed in the young in these set of studies occurred at 
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1 doses roughly 25 times higher than the dose EPA is 

2 proposing to use as the point of departure. 

3

4 So we see two options that one might elect to use for 

5 reevaluating the safety factor in the new upcoming risk6 assessment. The 10x safety factor currently applied in 

7 the dietary risk-assessment could be reduced to 3x. 

8 Removing the 3x or reducing to 1, that portion of the 

9 safety factor addressing concerns regarding the hazard 

10 potential. The other half, which is currently applied, 

11 because of the exposure issues, would be revisited when 

12 they are resolved. The 3x safety factor currently 

13 applied in the residential risk-assessment also could be 

14 removed or reduced to one. That‟s the more conservative 

15 approach. 

16

17 The second option is, given that one could conclude, from 

18 the agency's statement above about the 25-fold difference 

19 thing, is that not only is there no differential increase 

20 in sensitivity in the young as a consequence of pre21 and/or early-postnatal exposure; there is, in fact, the 

22 decreased sensitivity when compared with the adult 

23 female. On the basis of this finding, one might argue 

24 that the 3x FQPA safety factor currently applied to 

25 account for the uncertainties are concerned around the 

26 neuroendocrine effects could be reduced to less than one. 

27

28 For example, on page 29 of the issue paper, the agency 

29 states that all of the effects and sexual maturation and 

30 altered androgen status reported after about 30 days of 

31 exposure occur at dose levels 5 or more fold higher than 

32 those leading to the LH surge. Further, they say the
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1 dose level eliciting the increase in the incident of 

2 prostatitis in the offspring is greater than 10-fold 

3 higher than the dose leading to the LH surge attenuation 

4 used as the basis for the risk-assessment. This argues 

5 then, that the FQPA safety factor component addressing 

6 the hazard potential could be reduced not just to 1x, but 

7 further by at least 5-fold. 

8

9 DR. DANIEL SCHLENK: Okay. Thank you, Dr. Fenner-Crisp. Dr. 

10 Akana?

11

12 DR. SUSAN AKANA: I have nothing more to add. Thank you.

13

14 DR. DANIEL SCHLENK: How about subtract? Dr. Chambers?

15

16 DR. JANICE CHAMBERS: Penny is a hard act to follow. I don‟t 

17 have much to add either. I do agree that there is 

18 nothing in the evidence you provided to us that indicates 

19 that the young are more sensitive. I also agree with 

20 Penny that there wasn't a good compilation of the studies 

21 for us to try to sort that out very easily, but the issue 

22 paper does indicate that additional studies are ongoing, 

23 so I assume that when those data are derived, they'll be 

24 looked at. 

25

26 Some of the studies that you did quote in there did show 

27 functional endpoints, in terms of looking at vaginal 

28 opening and behavior and that sort of thing, and I tend 

29 to think that those sorts of functional endpoints are 

30 more important to look at than something that may not be 

31 a true adverse effect.

32
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1

2 DR. DANIEL SCHLENK: Dr. Meek?

3

4 DR. BETTE MEEK: Right. Well, I agree with most of what's 

5 been said and maybe all of it if I understood all of it, 

6 so just a couple of additional points. 

7

8 I really didn't see a weight of evidence analysis, so 

9 that was really to underscore the point. I think a 

10 weight of evidence analysis of being kind of a simulation 

11 of the data looking at the consistency, the dose response 

12 concordance, et cetera. So I didn't really see that in 

13 the issue paper. Of course, consideration of FQPA really 

14 should be based on transparent and systematic 

15 consideration of the most important qualitative and 

16 quantitative uncertainties associated with both exposure 

17 and the effect, and we've restricted this discussion 

18 principally to hazard; our relevant to susceptible life19 stages, and again, I really didn't see that discussion in 

20 the paper. 

21

22 A couple of points, though I probably stated them in a 

23 slightly different manner but underscore some of the 

24 previous points. I think that we need to take into 

25 account the interplay between uncertainty and safety 

26 factors, and the point of departure. And we need to 

27 recognize that the point of departure in this case is 

28 likely very protected, having been based on the lower 95 

29 percent confidence interval for the benchmark response 

30 for an early precursor event rather than an adverse 

31 effect. 

32
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1

2 So I agree that, based on the data that we have been 

3 presented, there appears to be no basis for application 

4 on the factor on the basis of hazard. So in relation to 

5 exposure, I'm just going to leave one point with you, 

6 although we haven't discussed that at this point. I 

7 wondered if any thought had been given to estimating the 

8 internal dose metric for younger age groups of the human 

9 populations. Human population for consideration of the 

10 context of FQPA recognizing that the chemical specific 

11 adjustment factor for interspecies or animal to kinetic 

12 differences are likely to be less than default. So that 

13 is just something to keep in mind for future. 

14

15 One other point, given the interplay between the point of 

16 departure and applied uncertainty or safety factor, I 

17 think that the question gives me -- I'm taking license 

18 anyways to raise a recommendation that I made at the 

19 September SAP meeting. In the interest of transparency, 

20 I think it would be helpful to consider an array of 

21 points of departure for various endpoints and their 

22 biological significance with a view to bounding 

23 potentially the degree of conservatism associated with 

24 the ultimate choice. 

25

26 This would include but not be limited to the benchmark 

27 dose for the impact on the LH surge, but including also 

28 those for more traditional endpoints generally considered 

29 to be adverse. This seems rather critical as a basis to 

30 interpret the derived benchmark dose in the context not

31 only of its biology significance, but also its degree of 

32 conservatism in the risk-assessment construct which we 
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1 use traditionally to address more severe endpoints. So 

2 again, that really builds on some of the earlier 

3 conversations that we've heard today as well, and I think 

4 I'll leave it at that.

5

6 DR. DANIEL SCHLENK: Thank you, Dr. Meeks. Any other input 

7 from the panel? Dr. Portier?

8

9 DR. KENNETH PORTIER: I got a question of Dr. Meek. If I 

10 understood what you were saying, when we talk about the 

11 LH surge we're talking about something really, really, 

12 really early in a process to an adverse event. When you 

13 think of FQPA factors, there's like a 10-fold multiplier 

14 from the normal population that‟s susceptible. 

15

16 Is that something that‟s on the table when you‟re talking 

17 about now setting an endpoint that‟s very early in some 

18 kind of process? Is that one way of addressing that 

19 susceptible individual in the population or are you 

20 thinking about something else? It just kind of came up 

21 in my mind as you were saying this, and I was thinking, 

22 "Is that what she thinks?"

23

24 DR. BETTE MEEK: To me, you can‟t separate the discussion of 

25 the point of departure, its degree of conservatism and 

26 how health protective it is from the discussion of the 

27 uncertainty factors. Ultimately, those uncertainty 

28 factors have to be informed by the degree of adversity of 

29 the critical endpoint, the extent to which you think 

30 sensitive populations are protected, the interspecies 

31 differences in kinetics and dynamics. 

32
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1

2 So I find it difficult to discuss a single factor outside 

3 of all of those considerations for which there is 

4 interplay. I also am not particularly fond of collapsing 

5 an entire database to one point of departure, which is 

6 why I think it's important as well to consider several 

7 points of departure in terms of the increasing degree of 

8 adversity of the effects for which you‟re trying to 

9 protect and bound those points of departure with 

10 considerations related to their adversity.

11

12 DR. DANIEL SCHLENK: Okay. Any other panel input? Okay. 

13 Back to the agency. Are you content with that answer, I 

14 guess?

15

16 DR. ELIZABETH MENDEZ: Well, I have to say an FQPA factor of 

17 less than one is -- it's one of those wow moments, but I 

18 certainly recognize what I am hearing from the panel and 

19 we'll do a better job of compiling the data for the 

20 weight of evidence. We were trying in the interest 

21 because we had presented a lot of that data in September, 

22 we were trying not to repeat everything again, but it 

23 appears that it would've been helpful at this point in 

24 time to do so, so we'll certainly take that under 

25 consideration. 

26

27 Other than that, no; I think you've answer our question. 

28 I am going to, for question 10, pass it on to Dr. 

29 Christensen because I think I'm starting to lose my voice 

30 a little bit here.

31

32 DR. DANIEL SCHLENK: We're not going to go to question 10.
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1

2 DR. ELIZABETH MENDEZ: You‟re not going to go on to--

3

4 DR. DANIEL SCHLENK: No.

5

6 DR. ELIZABETH MENDEZ: You‟re going to call it a day?

7

8 DR. DANIEL SCHLENK: We're going to call it a day; yes.

9

10 DR. ELIZABETH MENDEZ: All right. In that case, any other 

11 questions? 

12

13 DR. DANIEL SCHLENK: Yes. Dr. Fenner-Crisp, you have one more 

14 comment?

15

16 DR. PENELOPE FENNER-CRISP: One of your last points, Liz, 

17 about a document; I see the issue papers at any one time 

18 as a living document there, and how many of them -- at 

19 least three that gets bigger and bigger and bigger. But 

20 this one should have built on the last one and should 

21 have acknowledged the work that was done in the earlier 

22 ones, particularly on this point. 

23

24 So it should've been brought forward into that chapter, 

25 because ultimately you‟re going to have a last one and 

26 it's going to become the background document for the 

27 risk-assessment and you‟re going to want to have all of 

28 that there. So grow it with time.

29

30 DR. ELIZABETH MENDEZ: Yes. We'll certainly do that.

31

32 DR. DANIEL SCHLENK: Okay. Have a question, Dr. Griffith?
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1

2 DR. DANIEL GRIFFITH: Listening to the responses to questions 

3 5 through 9 and linking that back to the water 

4 monitoring, my impression is, there is some need to also 

5 look for the repeatability, the cyclability that‟s in the 

6 water monitoring data from year-to-year. Most of what 

7 I've seen has been concentrated on what's going on in a 

8 given year. But if there is something such as atrazine 

9 damage accumulates through time, then it would be useful 

10 to see how those time series are replicating themselves 

11 from one summer to the next. And I didn't see any of 

12 that in the background material, but it seems to me that 

13 that might be something that need to be inspected.

14

15 DR. ELIZABETH MENDEZ: I don‟t know if our water-monitoring 

16 colleagues are still in the room. 

17

18 DR. DANIEL SCHLENK: Yes. That may be something we'd want to 

19 do on the last question, I believe; yes. Okay? With 

20 that, I'll turn it over t Joe Bailey and he can close 

21 this out.

22

23 JOSEPH BAILEY: Thanks to everyone. I don‟t have any closing 

24 comments except thanks for your input today. 

25

26 July 28, 2011 - 8:30 a.m. Day 3

27

28 JOSEPH BAILEY: Let's get started here. My name is Joe 

29 Bailey, and I'm with the FIFRA Scientific Advisory Panel 

30 staff, serving as designated Federal Official. This 

31 FIFRA for Scientific Advisory Panel meeting, reevaluation 

32 of the human health effects of atrazine, review of non-
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1 cancer effects, drinking water and monitoring frequency, 

2 and cancer epidemiology.

3

4 I don't have any announcements to make this morning with 

5 regard to the docket or anything. The EPA presentations 

6 are there, available, and the public comments are not 

7 there yet, but they will be, hopefully by the end of the 

8 week. And that's it. I'll turn it over now to Dr. 

9 Schlenk, our Chair. 

10

11 DR. DANIEL SCHLENK: Thanks, Joe. Good morning, everyone. I 

12 think we'll skip our normal round-robin introductions 

13 this morning since I think everybody kind of knows 

14 everyone at this point. So we're going to begin with 

15 Question 10. And, Dr. Christensen, you're going to read 

16 that into the record? That would be great.

17

18 DR. CAROL CHRISTENSEN: Thank you. Good morning; Carol 

19 Christensen with EPA. So we are moving into cancer 

20 epidemiology portion of the meeting. So Questions 10 and 

21 11 relate to Chapter 3 and Appendix B of the draft Issue 

22 Paper from EPA. So at this time we're looking for 

23 feedback on our evaluation of the individual studies, our 

24 synthesis across the epidemiology database, as well as 

25 the integration with the experimental toxicology 

26 database. As was mentioned yesterday, we're sort of 

27 stopping short from making a request as to what the 

28 cancer classification should be, per se, but more looking 

29 for feedback on the process at which we came to our 

30 preliminary conclusions and the extent to what the data 

31 support those conclusions at this time.

32
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1 So having said that, Question 10, Part A reads: "Please 

2 comment on the sufficiency of the Agency's cancer 

3 epidemiology reviews with respect to identifying the 

4 major strengths and limitations of each study in the 

5 overall synthesis of results by cancer type."

6

7 DR. DANIEL SCHLENK: Thank. And our lead discussion on that 

8 is Dr. Bove.

9

10 DR. FRANK BOVE: Good morning, everyone. Having sat on the 

11 2000 and 2003 science advisory panel meetings for 

12 atrazine and registered my frustration in each of these 

13 meetings that the EPA had not done a systematic and 

14 comprehensive evaluation of all the cancer at the work 

15 that had been done up to those times, I'm pleased that 

16 EPA has finally done so. And I think that they've done a 

17 pretty good job. Although we have some differences with 

18 EPA on some of the evaluations, for the most cancer sites 

19 we're in agreement with EPA's assessment. We think it‟s 

20 pretty comprehensive. 

21

22 Focusing first on the methodology of EPA's literature 

23 search, we find that the methods were sufficient, 

24 thorough and transparent. EPA's method of evaluating 

25 studies was, in general, sufficient and, as I said, 

26 comprehensive. Important aspects of the studies were 

27 considering, including accurately measuring the cancers 

28 and exposures, issues of bias, sample size and 

29 statistical power. 

30

31 Major strengths and weaknesses were identified, and in 

32 particular, whether exposures were assessed 
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1 quantitatively; the ranges of exposure, whether critical 

2 windows, time windows of exposure were evaluated. And 

3 they used the usual etiological criteria, such as 

4 temporality, magnitude of the measure of association, 

5 which would be the relative risk or the odds ratio, for 

6 example, exposure-response trends, consistency of 

7 findings across studies and biological plausibility. 

8 However, there are some issues with EPA's methods of 

9 assessment, at least I have. 

10

11 First, the focus of the assessment should be on the 

12 individual level studies. Ecological studies should be 

13 evaluated only if there are compelling reasons to do so. 

14 For example, if there is no other individual level 

15 studies to evaluate. 

16

17 Secondly, the focus should not be on whether a finding is 

18 statistically significant, especially given the low 

19 statistical power of most of these studies. Emphasizing 

20 statistical significance when power is low will likely 

21 result in Type II errors. I did notice a couple of times 

22 in the text the notion of borderline significance was 

23 mentioned. There really is no such thing; a finding is 

24 either statistically significant or it isn't. And there 

25 was also a statement in the text that the findings that 

26 are borderline are more likely to be chance findings and 

27 that's not accurate either. We can get into that if we 

28 want, but that's --

29

30 A third issue is concerning biases. It's important to 

31 provide some evidence, not just charge that the bias 

32 might be there, but actually provide some evidence as to 
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1 why a particular bias is likely to be present in this 

2 study; the likely magnitude of the bias and the likely 

3 direction of the bias. In particular, the likely impacts 

4 of non-differential exposure misclassification be taken 

5 seriously. That is, bias towards the null for 

6 dichotomous exposure variables and distortion of 

7 exposure-response relationships, in particular, 

8 attenuation at the high end so that monotonic trends are 

9 not observed. 

10

11 This is also the case with the healthy worker survivor 

12 affect biases and since we're studying occupational 

13 cohorts most of the time here, that's also an issue. 

14 Again, these kinds of biases tend to distort exposure15 response relationship so they're not monotonic. 

16

17 Also, the issue of confounding: It‟s important to keep 

18 in mind it's not just that a factor is correlated with, 

19 for example, pesticides correlated with another 

20 pesticide. There really needs to be a strong risk factor 

21 involved. An example, with asbestos and lung cancer and 

22 smoking as a possible confounder; even though the work 

23 forces that are studied have a high prevalence of 

24 smoking, the confounding effects of smoking in these 

25 studies are usually no more than 20 to 30 percent. And 

26 smoking is an extremely strong risk factor for lung 

27 cancer and is also very highly correlated with asbestos 

28 exposure and yet that's about as much as the confounding 

29 that exists. So when we're talking about confounding by 

30 other risk factors that are weak risk factors -- even 

31 though they may be correlated with pesticide exposure --
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1 you do not see much confounding. I'll talk about that a 

2 little bit later and I'll give you an example.

3

4 Fourth, it's problematic to make a general statement 

5 about all cancers. That's true whether you've studied 

6 all cancers combined, as sometimes the Agriculture Health 

7 Study does, or whether you make a blanket statement -- as 

8 in page 71 of the text -- that atrazine is not likely to 

9 be carcinogenic in humans. 

10

11 The evidence across cancer sites is considerably mixed, 

12 with some sites having evidence of no association and 

13 other sites having at least suggestive evidence. So 

14 lumping, making these kind of blanket statements -- and 

15 also lumping all cancers together to analyze them -- is 

16 not very helpful.

17

18 Fifth, the appendix: The text, actually, was very good 

19 in describing each cancer site and the evidence for that 

20 in the epi literature. The appendix, on the other hand, 

21 had studies of different sites all mixed in together. It 

22 would be helpful if the appendix was better organized, 

23 like the text was. And that also includes the tables 

24 that are in the text. And actually, Dr. Gold has put 

25 together a table that we'll put in our report that is an 

26 example of what might be done.

27

28 Okay. The EPA discussed the strengths and weaknesses of 

29 the Agricultural Health Study and we thought it was 

30 comprehensive. One strength that the Agricultural Health 

31 Study has is that it is a longitudinal follow-up and 

32 exposures and risk factor are updated every five years. 
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1 Information is updated every five years; however, this 

2 information has not been used -- at least analyzed yet --

3 in these studies we evaluated. The information is still 

4 from Phase I.

5

6 Another issue with the Agricultural Health Study cohort 

7 is that it is predominately white and may therefore not 

8 include more susceptible populations; for example, 

9 prostate cancer if more prevalent among African10 Americans. And another limitation is there is a small 

11 number of female applicators making it difficult to study 

12 cancers in females occupationally exposed.

13

14 One other point that EPA makes in the text is they state 

15 that most of the agricultural health studies were 

16 hypothesis generating. And actually, all of them were 

17 hypothesis generating and that's true of most scientific 

18 research, but they were also all hypothesis-testing 

19 studies. They all had an interest in particular cancers 

20 as well as other cancers, but there were particular 

21 cancers of interest and hypotheses were tested. 

22

23 Okay. Now let‟s get to the particular cancer sites. EPA 

24 pointed out correctly that most of the studies focused on 

25 cancers of the lympohematopoetic system, the reproductive 

26 and the endocrine system, and that's what we‟ll be 

27 focusing on. First, prostate cancer; it was evaluated by 

28 the 2003 Science Advisory Panel. Back then, the Science 

29 Advisory Panel's position was that the database was 

30 insufficient to support a conclusion regarding the 

31 potential of atrazine to cause prostate cancer. 

32
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1 This conclusion remains valid today even though the 

2 agricultural health study cohort provides evidence 

3 against an association. And the reason it‟s still valid 

4 is that there are still lingering questions about the 

5 Saint Gabriel triazine manufacturing studies. These 

6 studies were initiated because of an excess of prostate 

7 cancer -- five observed and two expected -- that occurred 

8 prior to the start of the prostate screening program at 

9 the plant. Now, the screening program can explain most 

10 of the excess cases at the plant and we said that back in 

11 2003 -- but not the excess represented by these five 

12 cases. The exposure experience of these five cases has 

13 never been presented. The follow-up case control study 

14 could've compared the exposure experience of these five 

15 cases with controls that were employed prior to the 

16 screening program, but that wasn't done.

17

18 At the previous 2003 SAP panel, it was concluded that 

19 "Lack of association among farmers does not preclude the 

20 existence of a positive association with triazine 

21 manufacturing plant workers," and this is because the 

22 exposure experience is different. Farmers had more 

23 intermittent exposures; workers would have more chronic 

24 exposures. 

25

26 Moving onto breast cancer, a study of the agricultural 

27 health cohort observed relative risk hovering around 1.0. 

28 So this is negative evidence. On the other hand, there 

29 is a drinking water study in Wisconsin where the mean 

30 atrazine level in the high use area, high pesticide use 

31 area, was very low: less than .5 parts per billion. And 

32 only a handful of cases were exposed to well water with 
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1 equal to or greater than three parts per billion 

2 atrazine. But the odds ratios range from 1.2 to 1.4, 

3 again, based on small numbers of cases. 

4

5 The evidence for association is extremely weak in this 

6 study, it shouldn't be dismissed. EPA correctly 

7 identified the limitations in these studies, including 

8 the agricultural health survey and that was small 

9 numbers, low power exposure, misclassification, inability 

10 to evaluate critical time windows of exposure and 

11 inability to evaluate exposure-response relationships; 

12 however, EPA states in the text that there is a lack of 

13 evidence, and that's not correct either. There is some 

14 evidence. It‟s very weak. So it may be better to 

15 characterize breast cancer as having inadequate 

16 information to assess whether atrazine can cause breast 

17 cancer. 

18

19 Ovarian cancer: EPA focused on four studies, three of 

20 which were individual level studies; a study in Italy, a 

21 study in Central Valley, California, and the Agricultural 

22 Health cohort studies. Both the Italian case control 

23 Study and the Agricultural Health study observed positive 

24 associations and the Italian study observed higher odds 

25 ratios with longer duration of exposure to triazines. It 

26 did not look at atrazines specifically. And when they 

27 more precisely defined the exposed group, they also found 

28 higher odds ratios. Although it didn‟t evaluate atrazine 

29 specifically, the Italian study reported that the sales 

30 of atrazine in the area where these pesticides were used, 

31 the sales of atrazine were 10 times higher than the sales 
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1 of other triazines. So it looks like atrazine was 

2 predominately used in that area.

3

4 The Central Valley, California case control study used 

5 pesticide usage reporting data and questionnaire 

6 information to construct the job exposure matrix to 

7 assess occupational exposure to atrazine. They also 

8 evaluated residential proximity to areas where the 

9 pesticide was applied. Based on two exposed cases, the 

10 odds ratio forever occupationally exposed to atrazine was 

11 .76, and for residential proximity it was .88, based on 

12 eight exposed cases. However, the study excluded cases 

13 that died, they were too ill to participate, which EPA 

14 pointed out may have introduced a selection bias. Given 

15 the positive findings in two relatively well-conducted 

16 studies, EPA should consider the evidence as suggestive 

17 for an association between atrazine and ovarian cancer. 

18

19 Moving on to lympohematopoetic cancers, the evidence is 

20 negative for leukemias, except for hairy cell leukemia --

21 I'll talk about that in a second -- and multiple myeloma. 

22 There are two studies, both of which are hospital-based 

23 case control studies -- I think both are French studies -

24 - that evaluate hairy cell leukemia. Both were positive 

25 for triazines. Atrazine wasn‟t looked at separately. 

26 Both of the Italian studies used similar methods for 

27 control selection. EPA had some problems with their 

28 control selection, but in reviewing these studies, I 

29 don‟t find any problem whatsoever. They also had similar 

30 exposure assessment methods. 

31
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1 In the earlier Italian study, an odds ratio of 2.4 was 

2 observed for definitely exposed to triazines, based on 20 

3 cases. The analysis was restricted to cases and controls 

4 unexposed to organophosphates, the odds ratios were 

5 reduced to between 1.5 and 2, depending on what variables 

6 were included in the models. No exposure-response 

7 relationship was reported, but they did not present any 

8 data in this study.

9

10 A more recent Italian study observed an odds ratio of 5.1 

11 for hairy cell leukemia, based on four triazine-exposed 

12 cases. This study evaluated several exposure lag periods 

13 and didn‟t find any differences when they did that, but 

14 they did not report any of their exposure-response 

15 analysis. However, given that there are two positive 

16 studies here, there is, I would think, suggestive 

17 evidence for an association between triazines and hairy 

18 cell leukemia that should be followed up. 

19

20 As for non-Hodgkin's lymphoma -- and this was discussed 

21 briefly in the 2000 Science Advisory Panel, but again, 

22 not in a comprehensive fashion -- there were several 

23 positive individual level studies of atrazine and non24 Hodgkin's lymphoma, including a pooled analysis of 

25 Midwestern studies that used hierarchical methods to take 

26 into account a large number of pesticides simultaneously. 

27 I think it was greater than 40 pesticides. 

28

29 Interestingly, the study concluded: "Adjustment for 

30 multiple pesticides suggested that there were few 

31 instances of substantial confounding of pesticide effects 

32 by other pesticides. Again, confounding, it's very 
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1 important to remember, it has to be strong risk factor as 

2 well as being associated with the other exposure of 

3 interest. This study of hierarchical pooled analysis 

4 observed an odds ratio of 1.5 for atrazine and non5 Hodgkin's lymphoma. 

6

7 Another study found an association between atrazine and a 

8 specific sub-type of non-Hodgkin's lymphoma, the 

9 chromosomal translocation T-1418, and the odds ratio was 

10 1.7, based on 15 exposed cases. On the other hand, for 

11 the negative T-1418, the odds ratio was 1. There were 

12 some issues here; they could not get tissue samples for 

13 most of the cases. They had to do a missing value 

14 algorithm to impute values. So there are some issues 

15 there. But in any case, they did see a positive 

16 association for that specific sub-type. 

17

18 A French study that examined hairy cell leukemia also 

19 observed associations between triazine and the non20 Hodgkin's lymphoma subgroups diffuse large cell, odds 

21 ratio 2.1 based on eight cases and follicular lymphoma 

22 odds ratio 2.3, based on four cases. On the other hand, 

23 the Agricultural Health Study recent update was negative 

24 for atrazine and non-Hodgkin's lymphoma, including the 

25 subgroups. But given the positive studies, there is 

26 suggestive evidence of an association between atrazine 

27 and non-Hodgkin's lymphoma and I think that should 

28 continue to be evaluated. 

29

30 Thyroid cancer: Here we have just the recent update of 

31 the Agricultural Health Study which observed elevated 

32 risks within the highest three quartiles of exposure 
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1 lifetime days. But the trend was not monotonic, again, 

2 probably due to exposure misclassification bias. The 

3 categorization was, as I think it was pointed out a day 

4 or two ago, was kind of funny. The categorization used 

5 in the analysis was based on all cancer cases and was not 

6 really appropriate for this cancer. Unfortunately, 

7 researchers could've used some smoothing methods to 

8 evaluate how the curve looked and maybe done the 

9 categorization based on that but they did not. In any 

10 case, this study provides suggestive evidence and should 

11 be followed up. 

12

13 A number of other cancers that have been studies, usually 

14 there's only one study for them, except for gliomas, and 

15 they've been negative. And those include lung, pancreas, 

16 melanoma, colorectal, and as I said, gliomas. In the 

17 recent Agricultural Health Study update, non-monotonic 

18 exposure-response trends were found for liver and 

19 esophageal cancers. So you may want to say the evidence 

20 is inadequate and requires follow-up for those two. 

21

22 Finally, two studies evaluate childhood cancers. The 

23 Agricultural Health Study evaluated paternal use of 

24 atrazine in all childhood cancers combined and observed 

25 1.27. They only had small numbers of particular cancers, 

26 so they had to lump them all together. I think that is 

27 problematic. The second study in California evaluated 

28 residential proximity to areas where triazines were 

29 applied and acute lymphocytic leukemia and observed a 

30 non-monotonic exposure-response trend. When lifetime 

31 exposure -- lifetime of a child -- was evaluated, but 

32 didn‟t observe any elevations whatsoever. They just 
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1 looked at the first year of life proximity. They 

2 reported limitations in both, which EPA pointed out in 

3 its evaluation. So this again, inadequate evidence of an 

4 association here as well and requires follow-up.

5

6 So in summary, the cancers for which one can consider the 

7 suggestive evidence of carcinogenic potential -- I'm 

8 using these categories that EPA uses -- would include 

9 ovarian cancer, non-Hodgkin's lymphoma, hairy cell 

10 leukemia and thyroid cancer. Cancers for which there is 

11 inadequate evidence would include prostate, breast, 

12 childhood cancers, liver cancer and esophageal cancer. 

13 Both categories I would say the cancer sites require 

14 follow-up studies. And then cancers not likely to be 

15 caused by atrazine include oral, lung, colorectal, 

16 pancreas, bladder; leukemia, except for hairy cell, 

17 multiple myeloma, melanoma, kidney, larynx, brain, 

18 gliomas. That's it. 

19

20 DR. DANIEL SCHLENK: Thank you, Dr. Bove. Dr. Gold?

21

22 DR. ELLEN GOLD: Thank you. Good morning. I also want to 

23 commend the staff on a really much more thorough review 

24 and a very thoughtful one. We thought it was very good. 

25 We've consulted with each other, so I don't have a whole 

26 lot to add, but I did want to underscore just a couple of 

27 things. So the point about adjusting for the multiple 

28 other pesticides, I think is really important because the 

29 statement is made in multiple places that adjustment was 

30 made if these pesticides were used together a lot, if 

31 they were highly correlated. But if they're not related 

32 to the outcome, then I think what you're seeing, 
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1 potentially, you're over-adjusting and potentially seeing 

2 an attenuation, an adjustment toward the null in general. 

3 So I think that needs to be considered when interpreting 

4 the results. 

5

6 Also, this is a relatively minor point, but I also saw in 

7 several places that the comment was made that the 

8 agricultural health study had minimal selection bias 

9 because they had only two percent lost to follow up. 

10 Well, that's not the only source of selection bias. So 

11 they enrolled 82 percent, which is a really good 

12 enrollment, but the other 18 percent could be completely 

13 different and so could introduce selection bias that way. 

14 So the lost of follow-up is not the only source of bias.

15

16 The third point, which Dr. Bove touched on and I just 

17 want to highlight a little bit more, is this is a pretty 

18 typical occupational prospective study in that exposure 

19 assessment was made at the beginning and then long 

20 intervals passed before it was ever assessed again, and 

21 then people were followed up for outcomes. The reason I 

22 asked the question when I did the first day about the 

23 data collection is because while that's typical of some 

24 occupational studies, it's not typical of well-done 

25 cohort studies that at regular intervals reassess the 

26 exposure and the covariates. So this could potentially 

27 relate in either under-ascertainment or over-assessment 

28 of exposure, number one, and inadequate assessment of 

29 covariates. So that's why I think that‟s a limitation 

30 that could be added to the design features. 

31
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1 And then finally, the issue of the non-representativeness 

2 of the AHS cohort and the small numbers of women. So 

3 it's impossible to know if the most highly susceptible 

4 group, namely black males, have a different -- if there's 

5 an effect modification by race in terms of the effect, 

6 say, on prostate because they have a much higher risk of 

7 prostate cancer. 

8

9 And in terms of women, we know, as it was said about 

10 prostate cancer yesterday that there are probably 10 

11 different types, the same is true with breast cancer. So 

12 it was not possible to look at whether there were 

13 particular subtypes that -- in other words, whether the 

14 exposure might predispose to certain subtypes; you simply 

15 couldn‟t do that. So that's a limitation as well. 

16 That's all I have.

17

18 DR. DANIEL SCHLENK: Thank you. Dr. Young.

19

20 DR. HEATHER YOUNG: I mostly concur with what's already been 

21 said because we've consulted on the answer. I just want 

22 to make one other point, is that when you're doing a 

23 literature review, I also think it‟s important to note 

24 the gaps in the literature. And for the most part all of 

25 these studies are looking at occupationally exposed 

26 populations and there's a real dearth in the literature 

27 of drinking water exposure and residential exposures. 

28 And that is what the primary concern is when we're 

29 thinking about the population as a whole. So I think 

30 it's worth noting and acknowledging in the literature 

31 review that there's a real gap in the literature, looking 

32 at exposures other than occupational exposures.
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1

2 DR. DANIEL SCHLENK: Thank you. Yes, Dr. Gold?

3

4 DR. ELLEN GOLD: Because Dr. Bove mentioned this table that I 

5 drafted yesterday, I'm going to as my colleagues to 

6 double-check it because it was done kind of quickly. But 

7 also, all I did was take the papers since 2003, and that 

8 were individually based. So we excluded the ecologic 

9 analysis because we felt they should be down weighted. 

10 If -- and we would kind of encourage EPA to consider this 

11 if they use such a table -- the idea is to use the weight 

12 of the evidence, then the papers that came before 2003 

13 that are individually based, case control or cohort, 

14 should be included and I have not done that.

15

16 DR. DANIEL SCHLENK: Any other panel comments? Dr. Portier.

17

18 DR. KENNETH PORTIER: One of the reasons I kind of asked about 

19 thyroid cancer the other day was of all the cancers they 

20 looked at, that's the one that seems to be not going down 

21 and possibly going up. So, you know, a lot of the other 

22 cancers, it's kind of hard to get excited because they're 

23 all kind of declining and you would expect it would at 

24 least be staying level if atrazine and the long duration 

25 of cancer was occurring. But with thyroid cancer, we 

26 really do suspect something is going on. And with the 

27 odds ratio of 4 in two of those four categories, our 

28 flags went up and said this is one we should be looking 

29 at. 

30

31 DR. ELLEN GOLD: Can I just point out that thyroid cancer is 

32 another good example of where the high-risk group is 
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1 really not looked at and yet you still see an elevation; 

2 it's much more frequent in women, and they are under3 represented here.

4

5 DR. DANIEL SCHLENK: Any other comments from the panel? 

6 Question 10? Okay. Let me go to the EPA. Do you have 

7 any questions or clarification at all?

8

9 DR. CAROL CHRISTENSEN: No, not at this time. Thank you very 

10 much for the time and effort you put into that question.

11

12 DR. DANIEL SCHLENK: Okay. Would you want to read in Question 

13 11 into the record?

14

15 DR. CAROL CHRISTENSEN: Yes. Question 11, subparts A and B: 

16 "Please comment on the extent to which the scientific 

17 information supports the integrative analysis contained 

18 in Section 3.3 of EPA's draft Issue Paper with respect to 

19 the similarities, differences of the experimental 

20 toxicology and epidemiology findings. Please comment on 

21 any significant uncertainties in the epidemiologic 

22 findings."

23

24 And Part B: "Please comment on whether the epidemiology 

25 literature published since the last SAP review, including 

26 the findings from the Agricultural Health Study is 

27 sufficient to justify changing the Agency's conclusion 

28 that atrazine is not likely to be carcinogenic to 

29 humans."

30

31 DR. DANIEL SCHLENK: Thank you. Our lead discussion on that 

32 is Dr. Gold.
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1

2 DR. ELLEN GOLD: Thank you. So we've consulted on this as 

3 well and we felt that there was a lot of overlap between 

4 this question and the prior one, especially Part A of 

5 this one. So if it‟s okay, I mean, we do go cancer-by6 cancer, but Dr. Bove did a really good job of that so I'm 

7 not going to repeat that for Part A if that's okay with 

8 you, unless you want something specific.

9

10 DR. CAROL CHRISTENSEN: Yeah. Certainly no need to repeat 

11 concerning the epidemiology finding. On Part A we were 

12 looking for sort of cancer-by-cancer, considering both 

13 the epi and the experimental database; is that correct?

14

15 DR. ELLEN GOLD: I don‟t have anything to add to what Dr. Bove 

16 said. I can ask my colleagues if they do.

17

18 DR. DANIEL SCHLENK: Well, is that consistent with Dr. Bove 

19 and Dr. Young? You have no further comments on A?

20

21 DR. HEATHER YOUNG: No. No further comments on A.

22

23 DR. DANIEL SCHLENK: Okay. Let me go back to the EPA just to 

24 make sure. Do you have some questions, clarification 

25 you‟d like to ask about A?

26

27 DR. CAROL CHRISTENSEN: Right. Yeah. So in Part A, we're 

28 specifically thinking about that Section 3.3 again of the 

29 draft Issue Paper, pulling together what's known from the 

30 experimental toxicology, the animal bioassays, the in 

31 vitro studies for each specific cancer site. You know, 

32 for example, thyroid cancer that was just mentioned, you 
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1 know, the animal model is particularly sensitive to 

2 thyroid tumors, kind weighing that, providing some 

3 feedback and opinion, kind of weighing the fact that we 

4 have a very sensitive animal species in which we're not 

5 seeing. 

6

7 In fact, we have a high quality epi study in which we are 

8 seeing something, but only one, you know, that kind of 

9 thing - pulling that kind of thing together. So any 

10 feedback that any member has on that kind of thing by 

11 anatomical cancer site, that was ideally what we were 

12 getting at, and if we were not clear, hopefully that 

13 clarifies a little bit.

14

15 DR. DANIEL SCHLENK: Yes, Dr. Young?

16

17 DR. HEATHER YOUNG: I think I'm correct in stating for all of 

18 us that the recommendations that Dr. Bove went through 

19 for each of those cancers individually, we really did it 

20 considering both pieces of the evidence. For Question 

21 10, we weren't really just looking at the epi studies in 

22 a vacuum, but we looked at them looking at also, what 

23 were the experimental toxicology findings, mode of 

24 action, those types of things. And so the 

25 recommendations we made in Question 10 would really be 

26 consistent with what we would we say for Part 11(a). We 

27 didn‟t look at the epi studies in a vacuum when we were 

28 doing that. Am I speaking for everyone correctly?

29

30 DR. ELLEN GOLD: And also I will say that some of this will 

31 come up in response to Part B. 

32
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1 DR. DANIEL SCHLENK: Let‟s go ahead with B. Yes, Dr. Fowle.

2

3 DR. JACK FOWLE: I don't know. Perhaps not sufficient amount 

4 is known, but I was just wondering is enough known about 

5 the cancer of mechanisms leading to thyroid cancer in 

6 experimental animals versus humans to say that they're 

7 some different mechanisms that would make the humans more 

8 sensitive? Any information that could shed light on 

9 possibly evaluating that because rats tend to be 

10 exquisitely sensitive to thyroid cancer and we didn't see 

11 it at all. So we were really contemplating that fairly 

12 deeply.

13

14 DR. DANIEL SCHLENK: Yes, Dr. Mendez.

15

16 DR. ELIZABETH MENDEZ: Also, another little bit of information 

17 that I got this morning from our colleagues in ORD is 

18 that there are some data in frogs who are also very 

19 sensitive to thyroid hormone perturbation and that does 

20 not seem to be affected either. So we're struggling as 

21 to what appears to be a little bit of a disconnect there. 

22

23 DR. DANIEL SCHLENK: So if I understand you correctly, you're 

24 asking the panel to comment on other modes of action 

25 related to thyroid hormone impacts? Is that kind of the 

26 question?

27

28 DR. ELIZABETH MENDEZ: I guess we're trying to understand what 

29 the etiology might be that would lead us to a different 

30 path in a human.

31
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1 DR. DANIEL SCHLENK: Okay. Understand the susceptibility 

2 issues between species then for those particular effects. 

3 Does anyone have any input with regard to that? Yes, Dr. 

4 Meek?

5

6 DR. BETTE MEEK: I just wanted to make the point that when we 

7 first discussed the framework for integration of 

8 epidemiological and toxicology data at one of the earlier 

9 meeting that in fact it seemed relevant to walk through 

10 the weight of evidence for causality for the 

11 epidemiological data, initially, and then to integrate 

12 with the toxicological data, including weight of evidence 

13 for mode of action. So I think this would explicitly 

14 call out, addressing for each of those cancers, 

15 biological plausibility. So again, I'm not sure the 

16 extent that you did that, and certainly from the thyroid 

17 cancer perspective, it‟s a bit difficult to understand 

18 the human findings based on what we know about modes of 

19 induction of thyroid cancer in animals.

20

21 DR. FRANK BOVE: I think what we're saying is this, that first 

22 of all, let‟s evaluate the epi evidence separately. And 

23 when we do that, we see that for most of the sites 

24 there's no evidence. For some of the sites, there's some 

25 suggestive evidence in the epi literature, which need to 

26 be followed up. I don‟t think we're ready to talk about 

27 mechanism at all. When we say suggestive evidence, we 

28 mean that yeah, there are a few studies out there that 

29 seem to be positive, but, you know, we're not ready yet 

30 to merge the tox with the epi. If you do that, what you 

31 end up doing most of the time is ignoring the epi 

32 findings and letting the tox findings trump it. So what 
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1 I would prefer we do is to take the epi evidence 

2 seriously, for a change, and see where the gaps are, 

3 where the research needs to be done and do it. And then 

4 when we get to a point where we feel that the epi 

5 evidence is pretty good, then start merging it with the 

6 tox; otherwise, you'll ignore the epi evidence.

7

8 DR. ELLEN GOLD: I think also we have to be a little bit 

9 careful. I mean, I understand, looking at the weight of 

10 the evidence and looking at mechanisms and so forth, but 

11 in the early days of cancer epidemiology, we didn‟t know 

12 mechanisms. We still don‟t. For many, I would contend, 

13 maybe for thyroid cancer we don't. We know relatively 

14 few risk factors, probably three that I can think of. 

15 And so sometimes the epidemiology will spur people on to 

16 do the mechanistic studies. And so I think in the case 

17 of -- well, let me back up. 

18

19 For many of the cancers, the approach was well there are 

20 some neuroendocrine mechanism and that justified looking 

21 a prostate over and breast, for example, maybe thyroid, I 

22 don‟t know. You know, if it's having an effect on 

23 pituitary, maybe. But I think for some of these others -

24 - and I think we do have it in our comments -- that we 

25 perhaps, don‟t know enough or the animal literature might 

26 inconsistent, but I think as epidemiologists, we worry 

27 about extrapolating farm animals in the fact that the 

28 mechanisms may not be the same. So I would put some 

29 cautionary notes like that. And I think we did try and 

30 integrate in what we wrote, sort of consideration of 

31 that.

32
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1 DR. KATHERINE ROBY: I just want to comment with respect --

2 I'm not an expert on thyroid cancer, but I know ovarian 

3 cancer and I think it was well added into the position 

4 paper that actually, there's a real dichotomy there 

5 because what we do know about ovarian cancer is that an 

6 inhibition of LH should actually be protective. So there 

7 is really a separation between what I think, based on the 

8 epidemiology you say is suggestive, but the evidence that 

9 we know, mechanistically, would indicate really the 

10 opposite should be the case, which may point to some 

11 other mechanism if the suggested comes to be correct. So 

12 it could be pointing to some different mechanism. 

13

14 The other point that I wanted to make is that, again, 

15 just specifically with regard to ovarian cancer, there 

16 really are no animal model systems for the initiation or 

17 causal factors of ovarian cancer. So whatever is in the 

18 literature that might indicate there's a model looking at 

19 this compound or this toxicant causes ovarian cancer, 

20 there are no model systems to address that issue.

21

22 DR. ELLEN GOLD: I would just point out that it was also 

23 mentioned in the issue paper that that mechanism that was 

24 originally based on the animal studies for breast cancer 

25 doesn‟t apply in humans. And so I think we have to be 

26 really careful on two scores; one is can you extrapolate 

27 from one species to the other in terms of mechanisms? 

28 And secondly, the fact that you don‟t have an animal 

29 model doesn‟t mean that you shouldn't pay attention to 

30 the epidemiology. 

31
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1 DR. DANIEL SCHLENK: Any other comments? So back to Dr. 

2 Christensen and Dr. Mendez, is this something you find 

3 useful, I guess, that you need?

4

5 DR. CAROL CHRISTENSEN: Yeah. Maybe we can hear the responses 

6 to B and we'll kind of try to sum up what we‟ve heard at 

7 the end.

8

9 DR. DANIEL SCHLENK: Yeah. I think that'll probably be a good 

10 strategy. So let‟s go ahead and move onto B. Dr. Gold?

11

12 DR. ELLEN GOLD: Okay. By the way, in the introduction to 

13 Question 11, it seemed like you were asking whether there 

14 was a basis, sort of cancer-by-cancer, to change your 

15 opinion from the 2003 SAP decision. So that's sort of 

16 the orientation of the comments for B. And I have sort a 

17 bullet point for each sort of category, if you will, and 

18 mostly grouped by cancers. 

19

20 So the epidemiologic evidence compiled since the last SAP 

21 review in 2003 regarding the carcinogenicity of atrazine 

22 does not justify changing the Agency‟s conclusions 

23 regarding prostate cancer, breast cancer, adult gliomas, 

24 oral, esophageal, pancreatic, melanoma, renal, laryngeal, 

25 lung, bladder, colorectal and liver cancer, or leukemia -

26 - with the exception of the hairy cell leukemia perhaps -

27 - chronic lymphocytic leukemia or multiple myeloma. 

28

29 The epidemiologic evidence regarding a potential 

30 association of atrazine exposure with ovarian cancer is 

31 suggestive of an association, but still inconclusive and 

32 requires more rigorous investigation with larger sample 
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1 sizes, which is difficult for this rare cancer that is 

2 likely to have a long latent period, and which also 

3 greatly complicates the exposure assessment. 

4

5 For thyroid cancer we only have one study, the recent AHS 

6 cohort analysis, but it suggests a strong relationship --

7 fourfold increased odds ratio -- that is unlikely to be 

8 due to residual confounding. We've already mentioned 

9 some of the concerns about how the cut-offs were made for 

10 exposure. So those might be reconsidered, but that might 

11 explain why there's a non-significant exposure-response 

12 relationship, you know, like cutting it off at the median 

13 might have worked better. 

14

15 So this is very suggestive finding from a single study 

16 and is not sufficient to be certain of a causal relation 

17 between atrazine and thyroid cancer and thus requires 

18 replication in a larger study and more experimental 

19 investigation with regard to potential biologic 

20 mechanisms. 

21

22 The epidemiologic findings regarding an association of 

23 NHL and hairy cell leukemia with triazine use after 

24 adjusting for other pesticide exposures, although having 

25 small numbers of exposed cases in most of these studies, 

26 suggest about a one and a half to two-fold increase. 

27 Some of these estimates were statistically significant, 

28 and some findings had non-significant exposure-to29 response relationships, although the numbers of cases for 

30 each of these malignancies were fairly small. 

31
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1 However, these findings were not duplicated in the most 

2 recent cohort analyses from the AHS, which had twice as 

3 many cancer cases. Thus, while early studies suggested 

4 possible relationships of atrazine use with NHL and HCL, 

5 the more recent better designed and controlled studies 

6 with larger sample sizes did not replicate these 

7 findings, indicating, as mentioned in the Issue Paper, 

8 that sufficient evidence for associations of atrazine 

9 with NHL and HCL is lacking in humans or animal 

10 experimental studies. Although, the limitations of the 

11 AHS that we noted in response to Question 10 should not 

12 be ignored in considering these results.

13

14 And then studies of pediatric cancers -- by the way, I 

15 would just agree with the comment about not lumping all 

16 cancers together, and so with the pediatric ones, this is 

17 a little bit problematic in some that have teased out 

18 acute lymphocytic leukemia and found an increased risk, 

19 although a monotonic exposure-response relationship was 

20 not observed. This is also an extremely rare cancer, but 

21 the most frequent one in children. 

22

23 So the Issue Paper correctly concludes that the evidence 

24 is currently insufficient to determine if atrazine 

25 exposure increases the risk of pediatric cancers, 

26 particularly leukemia. That is all I have. 

27

28 DR. DANIEL SCHLENK: Dr. Bove, anything to add?

29

30 DR. FRANK BOVE: No. I have nothing to add.

31

32 DR. DANIEL SCHLENK: Dr. Young, anything to add?
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1

2 DR. HEATHER YOUNG: No. Nothing to add.

3

4 DR. DANIEL SCHLENK: Okay. So we'll go back to the -- oh, let 

5 me open it up. Any other comments from further panel 

6 members? Yes, Dr. Akana?

7

8 DR. SUSAN AKANA: Just a mild observation. I might've missed 

9 it, but we know in the rodent data that the atrazines can 

10 activate the adrenal's downstream in certain situations. 

11 So notice here that adrenal cancers are not on the list. 

12

13 DR. DANIEL SCHLENK: Okay. Any other comments from the panel? 

14 Okay. Dr. Christensen, any questions or clarification 

15 for you?

16

17 DR. CAROL CHRISTENSEN: Not specifically. Again, thank you 

18 very much for the time and attention to addressing this 

19 part of the question. In an attempt to sort of recap, 

20 maybe let me do so -- and you can correct me if I'm in 

21 error -- but what I heard you say, you know, in your 

22 evaluation of the cancer of the epidemiological evidence 

23 and the cancer-specific sites, you sort of automatically 

24 and inherently considered both the observational and 

25 experimental data within those evaluations and your 

26 comments regarding insufficient evidence or suggestive 

27 evidence or considering that information implicitly. I 

28 also heard a caution concerning moving, perhaps, too 

29 quickly to integrate the tox and the epi when there are 

30 some suggestive findings out there with some limitations 

31 and uncertainties as to how to interpret how far you can 

32 take that inference within the observational data at this 
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1 time. But still, your conclusions regarding specific 

2 cancer sites, again, inadequate or sufficient, in your 

3 opinion is informed by both toxicology and epidemiology.

4

5 DR. DANIEL SCHLENK: Anyone want to take that one on? Dr. 

6 Gold?

7

8 DR. ELLEN GOLD: I think that's a fair statement because we 

9 were also impressed by the fact that the Bradford Hill 

10 criteria were used, and one of those is biologic 

11 plausibility. And that sort of implies that you 

12 consider information about mechanism or evidence from 

13 toxicologic experiments to see if it‟s consistent. 

14

15 So I think that it's fair to say that the emphasis of our 

16 comments was on the epidemiologic studies, but I think it 

17 was also in consideration of the animal experiments and 

18 toxicologic data as well, when it existed. You know, I 

19 think it's really important to remember that a lot of 

20 public health policy is based on sort of imperfect 

21 science and sometimes you don‟t have the animal 

22 experiments, but you still can take preventive action and 

23 influence the incidence of disease. I think smoking and 

24 lung cancer is a great example. We didn‟t understand the 

25 path of physiology, but it didn‟t prevent public health 

26 from acting. 

27

28 DR. HEATHER YOUNG: I just want to emphasize again that just 

29 because we aren‟t sure about the biological plausibility, 

30 doesn‟t mean that it's implausible. And so because you 

31 don't know about the mode of action, it doesn‟t mean that 

32 there is no mode of action. So I think that‟s what we're 
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1 trying to say is that although it may not be known what 

2 the mode of action for some of these effects that we're 

3 seeing in humans, it doesn‟t mean that one doesn‟t exist 

4 or that may not appear in experimental evidence with 

5 animals at some point, or maybe it's not going to show up 

6 in experimental animals because we're not using the right 

7 models. And so I think you need to not throw away 

8 epidemiologic evidence that has really strong risks 

9 appearing just because we're unsure about mode of action.

10

11 DR. DANIEL SCHLENK: Okay. So do you guys have any other 

12 further questions? Oh, Dr. Bradbury, do you want to make 

13 a statement?

14

15 DR. STEPHEN BRADBURY: Question 10 and 11 really get to the 

16 crux of our February 2010 SAP where we're trying to bring 

17 together this framework concept of how to integrate 

18 experimental toxicology information with epidemiology 

19 information. And our goal is to more fully and 

20 completely and hopefully adequately bring epidemiological 

21 information into our risk assessment and evaluations. So 

22 to the extent you were thinking or the panel's thinking 

23 were not wanting to use epidemiology data on it, I want 

24 to make sure that's very clear, quite the opposite. 

25

26 What we're trying to work through is when you get to a 

27 very specific case, like let‟s say the thyroid 

28 information, getting back to what Bette Meek was saying 

29 is with a very specific set of information before us and 

30 trying to exercise that framework is thinking through. 

31 So how do we try to reconcile and understand the 

32 uncertainties and try to articulate even qualitatively? 
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1 Here's what we know about these experimental models. How 

2 they react to what we do know in say, the rat or the 

3 mouse in terms of how cancers of the thyroid play out --

4 blah, blah, blah -- and here's this epidemiology 

5 information which is suggestive. 

6

7 And given that at a certain point in time you have to 

8 make a decision about what's the likelihood of risk 

9 associated with exposures to different amounts of 

10 atrazine. How do you try to pull this together, 

11 qualitatively, you know in many cases? And so that's 

12 where you are hearing some of us trying to probe a little 

13 bit, not so much in the generic, but when you have a very 

14 specific example before us. For example, the thyroid 

15 cancer, any advice you have on sort of how to integrate 

16 the information. It's advice, you know, how do you pull 

17 this information together and try to reconcile some 

18 things.

19

20 DR. HEATHER YOUNG: So as a public health professional, I 

21 would say you proceed with caution because you don‟t 

22 have a lot of evidence, until you do have more evidence, 

23 to make a decision either way. I think that's why we are 

24 sort of in the suggestive camps. We‟re not saying that 

25 there really is an association. We have a population26 based study that's highly suggestive. We're not sure 

27 about mode of action. So we're not saying there's a 

28 causal association, but we're also not prepared to say 

29 that it‟s unlikely. And so I think what we're saying is 

30 proceed with caution until we have more evidence.

31
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1 DR. ELLEN GOLD: I would just reiterate once again that

2 biologic plausibility is only one component of the 

3 Bradford Hill. And so I would agree with what Dr. Young 

4 said, but I think, you know, what you're doing is 

5 building a case and all the pieces may not fit perfectly, 

6 but if the case is strong for the other components, the 

7 fact that you don‟t know the mechanism, or that the 

8 mechanism appears to be different in a different species, 

9 would add a note of caution, but it doesn‟t down weight 

10 the rest of the strength of the evidence.

11

12 DR. STEPHEN BRADBURY: The word "caution" is an interesting 

13 word. In different context it means different things. 

14 So could I ask if you could discuss how to articulate 

15 this kind of information in the context of uncertainty, 

16 as opposed to caution? If I can indulge the panel to 

17 think about ways to talk about uncertainties as you try 

18 to bring different information together. 

19

20 DR. DANIEL SCHLENK: Anybody want to take that on? Bette?

21

22 DR. BETTE MEEK: I'm going to push the agenda a bit more on 

23 this to pose the question, given the uncertainty that we 

24 have about the observed association, is there any way 

25 that we can use the information, quantitatively, in any 

26 context, to give us a comfort level, perhaps, or not, 

27 about the focus of any kind of dose response relationship 

28 modeling that we do? Because that would at least enable 

29 us to take the information into account. This really 

30 goes back to an issue that came up, I think, early on 

31 when we were talking about a framework to integrate, and 

32 in terms of the epidemiological data, was really to do a 
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1 problem formulation to consider where will the epi data 

2 play out in this risk assessment and how can we most 

3 meaningfully us it. Because I'm sensitive to the issue 

4 faced by the Agency to say well, we should follow this 

5 up, but on the other hand, they have to make decisions 

6 now. So how could we use that information, even in some 

7 kind of semi-quantitative sense to give us at least a 

8 comfort level or not? I think that's kind of the issue 

9 that I see. 

10

11 DR. ELLEN GOLD: I think it‟s hard to do this generically. 

12 You kind of need to do it cancer-by-cancer as we tried to 

13 do it. So let's take the example of thyroid cancer. 

14 They had four categories of exposure, and in a couple of 

15 them the risk ratio is four-fold or more. But when I 

16 looked at the numbers, they had, I think in the four 

17 categories, 3, 12, 3, and 11. Something like that. I 

18 think we would agree that there's such variability around 

19 those estimates in the categories where you only have 

20 three people that trying to figure out -- you could have 

21 a lot of misclassification, a lot of variability. And 

22 the fact that you don‟t have a monotonic dose trend 

23 doesn‟t really say much. So I made the comment if it 

24 were my data, I think I would've looked at the 

25 distribution and maybe tried to make sure that I have 

26 enough numbers in maybe two categories, like above and 

27 below the median, for example, which is not as satisfying 

28 as having four categories, but if reflects the reality of 

29 the situation. 

30 So that said, I mean, as the Agency has pointed out, you 

31 have the temporality of the association. You have a 

32 four-fold association -- even if you divided it on a 
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1 median, my guess is that would go down a little bit in 

2 each category, but let's say it's three-fold, which I 

3 think it would be because the two larger categories were 

4 the ones where it was more closer to four so that's going 

5 to heavily weight your estimate. So it might come out 3, 

6 3.5. That's a risk estimate that's unlikely to go away 

7 with adequate control for confounding and stuff like 

8 that.

9

10 So my point being, temporality, strength of the 

11 association - if you looked at it like that, you might 

12 actually have a dose response. We know very little about 

13 the etiology of thyroid cancer. And we're sort of 

14 missing, maybe biologic plausibility. We also don‟t have 

15 other studies, so we don‟t have consistency. That's why 

16 we use language like suggestive, as opposed to unlikely. 

17 So when talk about caution, I think it's reflecting those 

18 kinds of words, "suggestive" rather than "unlikely." 

19

20 DR. FRANK BOVE: Yes. And also "inadequate" as opposed to 

21 "not likely." Not likely is a very statement. 

22 Basically, you would have to ignore the epi evidence for 

23 many of these cancers to say not likely. So that's one 

24 of the things we're trying to caution you against doing. 

25

26 As for risk assessment, it would be nice if there was 

27 animal data and human data. For example, for 

28 tricoethylene, there is some animal data for kidney 

29 cancer and there is some human data. You can check and 

30 see if you extrapolate from epi studies and animal 

31 studies, you can get some kind of bounding that is great. 

32 That's not what we have here.
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1

2 On the other hand, suppose we have a situation where 

3 there's a birth defect cluster that's happening in a 

4 skyrocketing number of cases and there's no animal model, 

5 or the animal model is negative like flutamide for 

6 example, what do you do then? You obviously work on the 

7 epi data. So I'm just saying, you know, for risk 

8 assessment purposes, you have information in front of 

9 you. You have some evidence from tox; you have some 

10 evidence from epi. You may have less evidence in one or 

11 the other. You're just going to have to make some 

12 judgments. Okay. If there is evidence in both, that's 

13 makes it easy, but in most of these cases, that's not 

14 going to happen, especially in this case I don‟t see it 

15 happening. 

16

17 So what do you do? The tendency has been -- and as I 

18 said, I've been on these panels since 2000 -- the 

19 tendency has been to really give sure shift to the 

20 epidemiological evidence. And this is a plea to not do 

21 that, even if it doesn‟t jive with the tox information 

22 because the tox information may be wrong. The animal 

23 models may be wrong. We may learn something down the 

24 pike. The epi information could also be wrong that's why 

25 we're asking for follow-up work, especially on thyroid 

26 because there's only one study. But even on non27 Hodgkin's lymphoma where there are several studies and 

28 you can pool those studies, and that's been done, and get 

29 some kind of overall odds ratio somewhere in the range of 

30 1.5 and 2 and you can use that if you want, but there's 

31 no animal information to bound that with. So what do you 

32 do then?
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1

2 DR. DANIEL SCHLENK: Any comments that we have? Yeah, Dr. 

3 Meek?

4

5 DR. BETTE MEEK: Just a point of clarification, I wasn't think 

6 that we necessarily have to have the animal evidence to 

7 bound, but rather whatever approach is taken in the 

8 ultimate dose response characterization that we could say 

9 something, semi-quantitatively, at least, about the risk 

10 that we've seen or we suspect in the epi studies. So it 

11 would be bounding it for another end point.

12

13 DR. DANIEL GRIFFITH: You might think of it in terms of given 

14 the small numbers, your probability is somewhere between 

15 zero and one, and it might've only shrunk to somewhere 

16 between .1 and .9 because the numbers are just so small.

17

18 DR. DANIEL SCHLENK: Any other comments. Again, let me remind 

19 the panel, please send your comments to Dr. Gold as she 

20 has to basically put this together in a manner that 

21 reflects the panel's input there. So just be sure to 

22 send your comments there.

23 Okay. Are we ready to move on? All right. Question 12. 

24 I think you're going to switch out to readers here; is 

25 that right?

26

27 DR. ELIZABETH MENDEZ: I'm actually going to be reading. 

28 Dr. Rodriguez is going to come up to address any 

29 questions the panel may have. So good morning; Elizabeth 

30 Mendez, EPA. So we're shifting again and now we're going 

31 from epidemiology data to the pharmacokinetic information 

32 that we've been evaluating and considering in this 
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1 process. And I want to preface this by saying that one 

2 of the questions that you will see within Question 12 is 

3 about the PBPK model that we have no fully reviewed at 

4 this time. We felt that since we had you all in the 

5 room, it would be wise of us to avail ourselves of your 

6 expertise as we move forward towards the reg review 

7 process in 2013. So with that in mind, I'm just going to 

8 go forward and read the questions. Do you want me to 

9 read all four parts or --

10

11 DR. DANIEL SCHLENK: I think just A through D would probably be 

12 appropriate, yeah.

13

14 DR. ELIZABETH MENDEZ: Question 12, subpart A: "Please 

15 comment on the strengths and limitations associated with 

16 a simplified pharmacokinetic modeling approach for human 

17 extrapolation." And that is in regard to the one we've 

18 been proposing. 

19

20 Subpart B: "Compare and contrast the strengths and 

21 weaknesses of using total radioactivity for 

22 pharmacokinetic analyses, as presented in Agency's Issue 

23 Paper, as opposed to using available pharmacokinetic data 

24 for the parent and the chloro-s-triazine metabolites that 

25 have similar toxicological properties to the parent."

26

27 Subsection C: "As pointed out in the Agency Issue Paper, 

28 we are still reviewing a PBPK model submitted by 

29 Syngenta. As we complete our review of the Syngenta 

30 model, please comment on key aspects that EPA should be 

31 considering, concerning a PBPK model, including model 

32 credibility and a structure parameter values and 
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1 documentation, model reliability. How well does the 

2 model simulate the dose metric relevant to the mode of 

3 action, and model applicability? Does the model have 

4 essential features for intended application?"

5

6 Finally, Subpart D: "Please comment on the extent to 

7 which the one-compartment linear model of total plasma 

radioactivity derived from 14 8 C labeled atrazine, may 

9 account for interspecies differences in 

10 pharmacokinetics."

11

12 DR. DANIEL SCHLENK: Okay. Our lead discussion on that is 

13 Dr. Greenwood. Let's go through them, A, and then break 

14 and then B and then break.

15

16 DR. RICHARD GREENWOOD: Okay. There is some overlap in some 

17 of these, but we'll be able to refer back to where we've 

18 covered it in earlier sections. We spent a little time 

19 discussing between ourselves and the people who were in 

20 discussions on this, some of the data and approach. What 

21 I'm going to say is sort of a compilation of inputs from 

22 other people, but then will also have some other comments 

23 to make. So I'll make a start with an overview. 

24

25 I think the approach taken by the Agency assumes the area 

26 under the plasma concentration curve reflects the 

27 opportunity for exposure of the site of action to 

28 atrazine. That‟s one of the assumptions. And it also 

29 assumes that the toxicities of the metabolites are very 

30 similar. And on the evidence presented by the Agency, 

31 it's a reasonable assumption, particularly when you 
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1 consider that one metabolite, the deoxycholated atrazine 

2 dominates the profile.

3

4 However, when we look at some of the data presented by 

5 Syngenta, which compare dosing by oral gavage and the 

6 dietary route, this suggests that actually, it might be 

7 worth revisiting some of the assumptions that underlie 

8 the approach using total radiolabel, and I'll explain 

9 why. If you're wanting to use area under the curve as an 

10 appropriate measure of exposure of whatever the site of 

11 action or sites of actions are involved in the 

12 suppression of the LH surge.

13

14 Now, the Syngenta data show that when atrazine was 

15 administered by oral gavage, the area under the curve is 

16 larger than that found by dietary dosing, particularly 

17 for the parent compound and the mono-deoxycholated 

18 metabolite. But the major difference is, really, were a 

19 much smoother plasma concentration curve found with only 

20 a few fluctuations when it's given with the feed. When 

21 you look at what happens with gavage, you get these huge 

22 transient peaks. And this is something to bear in mind 

23 when you go back to looking at the radiolabel data. 

24

25 Now, if we look at the relatively modest differences in 

26 the overall area under the curve for DACT, then it's 

27 surprising that the suppression of the LH surge was 

28 produced by gavage administration, but not by dietary 

29 administration. So there's still a reasonable area under 

30 the concentration curve when it‟s dietary.

31
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1 Now, several explanations offer themselves -- I've put a 

2 little thought into this with colleagues that might 

3 explain this -- it could be that for atrazine, total area 

4 under the curve might not be the appropriate measure for 

5 exposure. It could be -- there's only one explanation, 

6 there's no evidence -- it may be the area under the curve 

7 above a critical threshold concentration. So this is 

8 just a hypothesis. 

9

10 Another hypothesis that might explain this is that a 

11 sustained constant low concentration may not be 

12 sufficient to cause the effect. So the gentle pressure, 

13 it may require pulses, intermittent pulses of high 

14 concentrations that you get with oral gavage. Again, no 

15 evidence at the moment, it is just potential 

16 correlations. 

17

18 The other difference between dietary and gavage is that 

19 the dietary route takes about 24 hours to reach the high 

20 plateau of concentration but then is maintained. And 

21 because feeding goes on longer -- after the oral gavage 

22 finishes the last dose, they're still feeding -- then, of 

23 course, the peak is maintained for longer in dietary. So 

24 a lot depends, I think, on where that might fall within 

25 that critical four-day period because the effect on the 

26 LH surge. 

27

28 It does open up some questions, and I think because of 

29 the approach taken by the Agency sort of depends on these 

30 assumptions being made, these really need to be checked 

31 out and looked at very careful. Given the importance of 

32 identifying an appropriate dose metric, I think it's 
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1 important that some effort is put in to just looking at 

2 that. I think a few suggestions for the Agency of how 

3 this might be tackled - you don‟t have to be hung up on 

4 giving this stuff by gavage or in the diet, there are 

5 lots of methods available now for giving, achieving 

6 constant plasma levels by subdermal implantation of slow7 release formulations. And you can get very high pulses 

8 in a very short time just by intravenous injection. It's 

9 an old trick, but it's been used lots of times. 

10

11 So some of these things could be tested with those sorts 

12 of experiments. But in the absence of information to 

13 where the interpretation of the Syngenta data of this 

14 link between pharmacokinetic behavior and pharmacodynamic 

15 activity -- this is for the LH surge suppression -- which 

16 is just one of the secondary lesions, resulting from some 

17 unidentified primary lesions. This is where we're stuck 

18 all the time. We don‟t know what the primary lesion is. 

19 But it's reasonable to examine all the available 

20 pharmacokinetic data in the way that the Agency has 

21 proposed. You've proposed to look at all the 

22 pharmacokinetic data, and that's the only way you can go 

23 forward. It's sensible.

24

25 The area under the curve is the dose metric that 

26 represents the exposure of all tissue, target and non27 target, to the toxicant. You can‟t get away from that, 

28 so that area under the curve approach does give you a 

29 measure of the potential exposure of all tissues, not 

30 just target tissues, non-target tissues. Everything that 

31 gets a blood supply gets exposed. So it really is a 

32 sensible way forward, from the pharmacokinetic point of 
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1 view, to consider area under the curve as a reasonable 

2 dose metric. The problems come when you then try to 

3 relate to the pharmacodynamic activity. But I think the 

4 advantage of the use of the total radiolabel is that you 

5 can get quite a lot of reassurance from a mass balance 

6 check. It gives you some confidence that all the 

7 administered dose is accounted for. And I'll refer back 

8 to this when we look later at the physiologically based 

9 pharmacokinetic data because that is something that the 

10 registrant is going to look at, I think in trying to get 

11 a mass balance. It needs to be done. It's easy with 

12 radiolabel.

13

14 It‟s still unique to apply some caution when you're using 

the old 14 15 C label atrazine studies because I've now got 

16 access to these and I've looked at them. It‟s quite 

17 reasonable because it‟s the usual thing to assume first 

18 order kinetics, which is what you've done for the overall 

19 elimination process. But there's good evidence when you 

20 look at the data you've presented, the Agency presented 

21 and Syngenta presented, that that's what it is. 

22

23 But the other thing that seems to hit me when I look at 

24 all of this is that there's evidence that there are two 

25 first order elimination processes going on 

26 simultaneously. And that in fact, if you had enough 

27 data, you'd get a pretty good fit to a double exponential 

28 model. I'm pretty certain if you fitted a double 

29 exponential model you'd get it because there seem to be 

30 two fractions of material in the plasma that have been 

31 operated on by a fast-rate constant, one by a slow-rate 

32 constant. The first one we will probably be 
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1 representing, elimination of that, in fact, the free 

2 material. And the slow elimination compartment, it would 

3 be probably that bound to proteins where the turnover is 

4 very much -- the proteins are slower. 

5

6 So there is some evidence that that's from the non-human 

7 primate data for double exponential nature of the 

8 elimination process, but there's also evidence in the 

9 rodent radiolabel data which you use to estimate the 

10 fraction elimination rate constant. Because if you look 

11 at those lower concentrations linear time plots and you 

12 look at the pattern of residuals from the fitted straight 

13 lines, given that you've only got four points for these 

14 things, it's always difficult. You've got three degrees 

15 of freedom, so it's tough to try and get anything, but if 

16 you look at the pattern of residuals, it's actually 

17 consistent: high, low - low, high. And it's consistent 

18 across the different doses, across the different studies. 

19 And in fact, I've looked at the mouse study of Ross and 

20 co-workers, 2009, and you get a similar picture there. 

21 So there is quite a lot of evidence that there are two 

22 compartments.

23

24 Well, fortunately, I think the fraction which is operated 

25 on by the slow process is probably very small, 

26 negligible, compared with the fast process, the free 

27 process. So although there is some bias introduced into 

28 the estimates for the first order rate constant, it's not 

29 going to be -- when you look at the variability in the 

30 whole system, it's not going to be really too important, 

31 I don't think. Though you went on to look at quite a few 

32 studies and you get very similar values for the first 
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1 order elimination rate constant across the study, which 

2 is, again, given the small number of points in each, it's 

3 heartening. I always get a bit more confident when I see 

4 it's repeatable rather than just statistically 

5 significant.

6

7 So I think the approach is sound. And the only 

8 deviations that I find from this consistency, which you 

9 pointed out, are the high elimination constants observed 

10 for the 50 and 100 milligrams per kilogram doses in the 

11 feed data. If you remember, there were factor of three 

12 probably out -- well, in the grand scheme of things, 

13 that's again, not exactly a problem. But you do need to 

14 be careful about the interpretation of the radiolabel 

15 studies because all of them use a similar experimental 

16 design. That is, they use either single-dose or equally 

17 spaced constant doses. And they take samples 

18 infrequently, usually every 24 hours. 

19

20 So this gives a really lousy definition of the 

21 pharmacokinetic profile. And you can't get anything else 

22 but a smooth plateau out of it because when you join two 

23 lines together it‟s a straight line. And if they're 

24 taken at the same point each day, they're going to be 

25 roughly the same height. So you end up with what appears 

26 to be an nice plateau, but actually, if you look at the 

27 Syngenta data or where some of the others were, they've 

28 got a better definition, you see these huge spike 

29 superimposed on the top of it. If it‟s by gavage and you 

30 still see wobble about it in the dietary dosing. 

31 So again, that needs to be bore in mind in an 

32 interpretation. But despite all of this, the studies can 
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1 still be useful because the area under this apparent 

2 plateau is still consistent proportion of the total area 

3 under the curve. So if you're trying to use that and 

4 correlate it with pharmacodynamic activity, you're still 

5 in with a fighting chance. But the other thing that you 

6 need to be very careful about is this concept of the 

7 pseudo steady state being achieved after four days 

8 dosing. And there's no evidence of this in the Syngenta 

9 data or any of the others where they look at the 

10 individual components rather than the total radiolabel. 

11

12 In fact, if you look those, you get a pretty steady state 

13 in DACT, even with gavage, after Day 1. So after 24 

14 hours, it's up there and it's pretty well maintained. So 

15 there is very limited evidence for this. And the feed 

16 study is very difficult to explain. Now, there's one 

17 possible explanation -- well, there are several 

18 explanations, again, and I give them for what their 

19 worth. It's possible that some bindings occur in over 

20 four days and once that's all saturated, then you do get 

21 this pseudo steady state. I don't see what some of those 

22 binding sites would be, but you may have other ideas. 

23 But if the pseudo steady state did involve binding, then 

24 the area under the curve would not be the freely 

25 available, and that is the pharmacodynamically relevant 

26 fraction, but it would be material which is bound, which 

27 is not available for interaction with the site of action, 

28 wherever it is. So again, you've got to be careful about 

29 how you interpret it. 

30

31 But if the rise in plasma concentration is -- if we say 

32 that the plasma is in equilibrium with the tissues, then 
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1 the implication is that there is a compartment within one 

2 or more tissues which needs to be saturated before you 

3 can get the final rising plasma concentration. And in 

4 that tissue or tissues -- it could be any tissue -- it 

5 could be that there is a slower distribution process 

6 taking place over four days so that you get this rise 

7 that feed observed in the total radiolabel in the plasma. 

8 And it would be interesting to see whether there was also 

9 a slow elimination process from this compartment.

10

11 Well, if you look at some of the various studies, if you 

12 look at the poll dataset, there is some indication that 

13 following the single-dose by oral gavage, there is a very 

14 slow elimination for liver and kidney, but an even slower 

15 elimination from red blood cells, and we know that that's 

16 due to binding, covalent binding, to the red blood cells, 

17 but also in muscle. The rate of elimination from muscle, 

18 if you look at the poll data, is very similar to that 

19 from the erythrocytes. That's following the cessation of 

20 dosing and they did seven-day daily dosing in that study. 

21

22 So I think that the whole topic has become rather 

23 confused in people's mind because the time to the pseudo 

24 steady state happens to be four days, which just by 

25 coincidence happens to be the critical exposure of which 

26 you got to hit in the rat estrous cycle. But the two are 

27 independent of each other; one is pharmacokinetic and the 

28 other is pharmacodynamic. So it wouldn‟t matter if you 

29 had a study state when it is not at the critical period, 

30 it has no effect whatsoever. 

31
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1 Actually, if you say that you need a steady state in 

2 order to suppress the LH surge, then logically, that 

3 isn't achieved until after four days dosing. So you 

4 would never suppress the LH surge if you started dosing 

5 on the first day because it takes four days to achieve 

6 steady state. You'd always have to start dosing, if you 

7 apply that logic, four days before the start of the 

8 critical period in the estrous cycle. Because it's only 

9 then that you'd get this pseudo steady state. So I think 

10 it's actually been a bit of a red herring, this idea that 

11 you need a pseudo steady state and it's come out of the 

12 fact that there are limitations in some of the 

13 radiolabeled studies. I'm afraid I've spilled over into 

14 some of the others, but I think this is probably the 

15 place where I felt my comments would best fit. 

16

17 So really, as far as I can see, there really aren't any 

18 grounds -- or there are grounds, certainly, for 

19 examining, reexamining this idea of what‟s required, in 

20 term of the nature of the exposure and the level of 

21 exposure to suppress the LH surge over that critical 

22 period. Whether you need spikes, whether a constant 

23 pressure ain't going to do, or whether you just need to 

24 get the concentration high enough that some critical 

25 period over those four days. I'll leave it there and 

26 hand over to colleagues.

27

28 DR. DANIEL SCHLENK: Okay. Just so I'm clear, Dr. Greenwood, 

29 it sounds like you kind of hit all A through D in some of 

30 your comments.

31
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1 DR. RICHARD GREENWOOD: I've got some other comments which 

2 are specific to the others, but I will relay it back to 

3 this. It has been sort of a long explanation, but I 

4 thought it was the easiest way of doing it.

5

6 DR. DANIEL SCHLENK: Sure. No worries. I was just wondering 

7 how it was going to split up with everybody else. Okay. 

8 Thank you. Dr. Hayton?

9

10 DR. WILLIAM HAYTON: Well, I agree with my colleague's 

11 comments and I would add a few thoughts, in terms of Part 

12 A question to comment on strengths and limitations of the 

13 simplified pharmacokinetic modeling, using the one 

14 compartment model. I think one strength we could mention 

15 is that it is simple, in the sense that it has a minimal 

16 numbers of parameters to estimate. And because of that 

17 you need a fairly limited number of data points. So a 

18 lot of the radioactivity concentration time profiles 

19 really wouldn't support more complicated modeling. So I 

20 found that a strength that could be mentioned. 

21

22 I also found another strength is that the one compartment 

23 approach does have utility in that the basic idea here is 

24 to estimate exposure to total triazines and because the 

25 data seemed to conform to the model that the purpose for 

26 which we want to use the model is satisfied. And then 

27 finally, we have data available from three species: from 

28 rat, monkey and human. I thought that was a strength. 

29

30 In terms of limitations, you know, I think the point that 

31 Dr. Greenwood brought up that total radioactivity seems 

32 to include some fraction that is albumin adduct or plasma 
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1 protein adduct, which doesn‟t have toxicologic activity. 

2 So it would be nice to be able to get rid of that. From 

3 the data that I saw, particularly the CODAR 2011, study. 

4 It seemed that if you look at atrazine and its three 

5 chloro-s-triazine metabolites, the DEA, DIA, and DACT, 

6 the total radioactivity, if we could rid of the bound 

7 radioactivity, covalently bound, that that would track 

8 the sum of those four metabolites fairly closely. I 

9 think that's one weakness or limitation of using just 

10 total radioactivity and because it's data from the 

11 literature -- old data -- there's probably no way to 

12 subtract that out. 

13

14 Another limitation, I thought of the one compartment 

15 system, its simplicity is a virtue, but also, some of the 

16 finer points of the pharmacokinetics tend to be obscured. 

17 So we don‟t know much, using that model, about saturable 

18 binding, transport of metabolism that could give some 

19 kind of a non-linear relationship between the 

20 administered dose and the exposure of the site of action. 

21

22 I found some comfort from the fact the half-life of total 

23 radioactivity is dose independent over a broad range of 

24 doses. I thought that give some comfort that there are 

25 non-linearities that could confound the analysis. 

26 Thanks.

27

28 DR. DANIEL SCHLENK: Okay. Dr. Meek?

29

30 DR. BETTE MEEK: Yeah. I have very little of substance to 

31 add. I'm really encouraged that the Agency is moving 

32 along to estimate the internal dose metric. And I think 
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1 this is as step along the way. I think that you've also 

2 indicated that the ideal approach would be a 

3 physiologically based pharmacokinetic model for all of 

4 the reasons kind of mentioned by previous commenters, in 

5 terms on the limitations of the approach.

6

7 DR. DANIEL SCHLENK: Okay. Any other panel member input on 

8 the PK stuff here? Okay. Are you guys going to address 

9 B separately then? Okay. We have a question from Dr. 

10 Rodriguez - question/clarification. 

11

12 DR. CHESTER RODRIGUEZ: Just a comment, actually. One of the 

13 issues that we found, especially with the human study 

14 that were presented is that it was a significant mass 

15 balance issue. When atrazine DEA, DIA, and DACT were 

16 monitored, those four species only accounted for 14.5 

17 percent of the dose. So 85 percent of the dose, they 

18 don‟t know where it went. So in terms of us using 

19 caution, we feel at this point that radiolabel studies 

20 may actually safeguard against that. But until we have a 

21 better understanding of mass balance, I think this 

22 represents a reasonable approach at this time.

23

24 DR. DANIEL SCHLENK: So I'm assuming that‟s a question that 

25 you're asking. Is that what you're asking the panel to 

26 correspond on that?

27

28 DR. CHESTER RODRIGUEZ: No. It‟s just a comment. 

29

30 DR. DANIEL SCHLENK: Oh, okay. I think we got that. Do you 

31 guys have anything to say about that?

32
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1 DR. RICHARD GREENWOOD: I agree that you need to have mass 

2 balance, and it's one of the things I think that is going 

3 to be addressed if we look at the contribution by 

4 Syngenta in their paper they presented. It's one of the 

5 things they need to look at. It‟s true, it does need 

6 looking at, but the problem is it's not just mass balance 

7 with the total radiolabel. You know where it is, but you 

8 don't know how it's divided within compartments within a 

9 tissue. You just combust it and you get the total. 

10

11 You often need to go on for a long time, the study, to 

12 make sure you can see what the real turnover rate of it 

13 is - get enough points to be able to do the proper 

14 analysis of the elimination to see whether it's single, 

15 double, or triple exponential, for instance, whether it's 

16 saturable and so on.

17

18 DR. DANIEL SCHLENK: Sure. The comment -- it‟s just my own 

19 personal comment; it seems you would also need to know 

20 what those glutathione adducts are as well because it 

21 seems that a fairly large chunk of the metabolism seems 

22 to be glutathione conjugated. So consequently, maybe 

23 that 76 percent that‟s there, a large amount of that 

24 could be some of these unknown conjugates that are 

25 present. So that needs to be characterized as well 

26 because that would not necessarily be toxic, per se, it 

27 would be a nontoxic metabolite at that point. 

28

29 Any other comments on A? Okay. Let's go ahead and go 

30 through B and then we'll take a break after B. Dr. 

31 Greenwood, again, do you want to lead off?

32
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1 DR. RICHARD GREENWOOD: Yeah. Thank you. Well, I think the 

2 question, it's the sort of thing that you might set for 

3 the undergraduates actually, compare and contrast is the 

4 wording. But it's a problem. This is not the 

5 straightforward business, choosing what sort of model to 

6 use because all of these methods have their own strengths 

7 and weaknesses, and I'll try to look at some of these in 

8 light of what we've seen with atrazine. 

9

10 I think all people doing studies in pharmacokinetics have 

11 to ask themselves to start with, what do I want to use 

12 the data for because there are two extreme approaches; 

13 one you use a single compartment model, use total 

14 radiolabel, and it's got advantages, it's very simple to 

15 carry out. You've got a very, very good sensitivity and 

16 the modeling is easy. On the other extreme, you've got a 

17 big physiologically-based pharmacokinetic study, where 

18 you have lots of compartments, and when you do the 

19 modeling, lots of boxes connected by differential 

20 equations and you've got to parameterize all of those, 

21 you can end up with more than 40 parameters. 

22

23 If you look at the Syngenta model, I think there are 40-

24 odd parameters that I looked at. And it‟s a lot of hard 

25 work to get sufficient data to estimate all of those, 

26 some available in the literature. So often people opt 

27 for a sort of middle path, which is a compromise between 

28 the two, in terms of the amount of work and what you get 

29 out of it. Because what you get out of a radiolabeled 

30 study is difficult to interpret. And the big advantage 

31 of the physiologically based pharmacokinetic models is 

32 that they are very easy to interpret, in terms of the 
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1 physiology and the lesions that happen, a disruption of 

2 physiology when you put in toxicant. And you can 

3 actually look to see whether the toxicant is affecting 

4 the pharmacokinetics, which can happen. In some of the 

5 studies I've done, it certainly does happen. So you can 

6 either go for the complex and the arguably more realistic 

7 physiologically based model, but you need to get the 

8 information to parameterize them. 

9

10 These days, life has been made a little bit easier 

11 because you can use mass spec, and so the limits of 

12 quantification really have gone down. The problem is, in 

13 order to get it to the mass spec, the LC mass spec, 

14 you've actually got to do sample preparation, which all 

15 needs validation, involves dissection of individual body 

16 component, quantitative extraction, preliminary clean up, 

17 and often -- well, usually you have to, in order to get 

18 reliable results, you need labeled unalikes to correct 

19 for matrix effects in the mass spec analysis. And it is 

20 very difficult, as been pointed out, to achieve a mass 

21 balance. Even though mass spectrometry detectors can get 

22 down to lower levels of quantification, they still cannot 

23 achieve the lower levels of quantification that you can 

24 get with radiolabeled compounds with combustion and 

25 scintillation counting. 

26

27 I guess the important thing is, though, that what the 

28 methods used for extraction of tissues do is make sure 

29 that you are actually extracting the free. Normally you 

30 do not extract if it's bound -- if it's covalently bound, 

31 you don't extract it -- but you extract the free 
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1 material, which is the material that's physiologically, 

2 toxicologically relevant. 

3

4 Assuming a sort of mammillary model with the circularly 

5 system providing rapid mass transport around to every 

6 tissue. Then when steady state is achieved the levels in 

7 all the tissue will change at matching -- not necessarily 

8 at the same rate -- but matching rates and you get a sort 

9 of steady state achieved. That's with one dose and then 

10 elimination. The area under the curve does represent the 

11 overall opportunity for exposure of the site of action 

12 and it doesn‟t matter whether it's one or more tissues. 

13 It does not matter where it is located. 

14

15 So these physiologically based pharmacokinetic models do 

16 provide information which is readily interpretable and it 

17 supports interpretation of modes of action. It doesn‟t 

18 assume that the parent compound and metabolites are equi19 toxent, you don't have to make that decision. 

20

21 But it also can help to identify where you get deviations 

22 from the expected behavior -- which do happen -- where 

23 you change the physiology by the poison in the animal. 

24 For instance, if you modify cardiac output or you modify 

25 hepatic function. Then you are going to change 

26 distribution and you are going to change elimination 

27 rates. That will happen in time, you get time-dependent 

28 parameters. I'm sure it makes life very interesting for 

29 the modelers.

30

31 Now, simple models which are based on far fewer samples 

32 to be analyzed, a lot less preparation required and 
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1 total radiolabel, it's easy to measure and it‟s sensitive 

2 and it could be automated so you can get through a lot of 

3 samples. As I said before, the mass balance is readily 

4 checked and that is important. However, I think as Dr. 

5 Hayton said, the problem is if you've got bound material 

6 mixed in that total fraction, then that's not available 

7 for distribution to the site of action. 

8

9 So the modeling is simpler, but the interpretation, in 

10 terms of toxicology and mode of action is far more 

11 difficult and it could be misleading, particularly if 

12 there were big differences between the toxicities of 

13 parent compound and metabolites. If there is significant 

14 binding, then depending on the method of preparation, of 

15 course, the area under the curve might not provide a good 

16 measure of exposure of the site of action. 

17

18 I mean, one way of getting around this is if you think 

19 that there is significant binding, you could always do a 

20 radiolabel study, ultra filtrate the plasma, count the 

21 filter and count the filtrate and you'll then see what 

22 proportion of it is actually bound and you can get a 

23 handle on it, but it is one more step. So without doing 

24 that, you can actually overestimate, if you like, the 

25 exposure of the site of action. So I'll leave it there. 

26

27 DR. DANIEL SCHLENK: Dr. Hayton?

28

29 DR. WILLIAM HAYTON: Yeah. Let me just quickly summarize what 

30 I thought strengths of using radioactivity were, compared 

31 with specific assays for the chloro-s-triazines. I think 

32 with total radioactivity, you get some comfort that you 
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1 haven't missed any toxicologically active metabolites and 

2 you get good sensitivity. Of course that depends on 

3 specific activity, but that can usually be made very 

4 high. I thought that's the strength of the total 

5 radioactivity approach. 

6

7 A weakness is that the label is distributed among 

8 multiple chemical species and each one of those species 

9 has its own pharmacokinetic behavior, so total 

10 radioactivity tends to hide much of the underlying 

11 kinetic behaviors. From what we know of the chloro-s12 triazines, they seem to be equally equipotent, 

13 toxicologically. So to the extent that they represent 

14 total radioactivity, you know, that's going to work out 

15 okay. I guess the uncertainty that's already been 

16 mentioned several times is that there seems to be quite a 

17 bit of radioactivity in plasma that may not be the 

18 chloro-s-triazines and some of it has quite a bit longer 

19 half-life. So what‟s going on there introduces some 

20 uncertainly into the overall consideration?

21

22 DR. DANIEL SCHLENK: Okay. Dr. Meek?

23

24 DR. BETTE MEEK: Yeah. I have nothing much to add.

25

26 DR. DANIEL SCHLENK: Any other panel comments for that? Dr. 

27 McManaman? 

28

29 DR. JAMES MCMANAMAN: You know, I think these were good 

30 comments from the group that explored this issue, but I 

31 want to add a couple of cautionary notes. If you look at 

32 your Slide 22 from the Agency, there is a difference in 
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1 the elimination rates for the various compounds of DIA, 

2 it is much faster than the DACT. If you look at the data 

3 presented by Syngenta, they have DACT as being the 

4 primary compound under which protein adducts can occur or 

5 glutathionylation.

6

7 So depending on differences -- and there be may be 

8 differences between species and the rates of these 

9 adduction processes or the elimination processes, so by 

10 using the radioactivity, I think you don‟t get at that. 

11 And using the single compartment model, I don‟t think you 

12 get at that. So I would be a little careful since we 

13 really don‟t know what‟s going on with the human as much 

14 as we do with the rat, I don‟t think you can extrapolate 

15 because the rates, you know, if the rate of adduction is 

16 different from humans to rats, then you may have a 

17 different toxicity and that has to be considered. So 

18 that's just a cautionary note.

19

20 DR. DANIEL SCHLENK: Thank you. Any other comments before we 

21 break? We'll just wait until we finish the complete 

22 question before we come back to you guys to finish up, if 

23 that‟s okay. Let's go ahead and move on to Question 

24 12(c). Dr. Greenwood?

25

26 DR. RICHARD GREENWOOD: I'm not going to say a great deal on 

27 this, but one of my colleagues will, I think. I think 

28 we've already gone over a lot of this, but one of the big 

29 advantages, I think, of this model of mice worth pursuing 

30 and validating and so on is it does hold out the prospect 

31 of really reliable, scientifically based extrapolation 

32 between species. It's one of the advantages, I think. 
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1 So I think it is worth pursuing this because 

2 physiological models for humans there have been because 

3 of the pharmaceutical industry.

4

5 There are some problems with this model, as submitted by 

6 Syngenta, and one of the weaknesses is that there was 

7 some in vivo parameterization from in vitro metabolic 

8 studies. So that's always a problem. But the curves are 

9 well defined by frequent measurements in time. The 

10 predictions, though, if the tissue concentration depend 

11 heavily or will depend heavily on selected tissue plasma 

12 partition coefficient. These are really critical and you 

13 get this with lots of methods, both in environmental 

14 analysis and in pharmacokinetic analysis. Those values 

15 are critical and can introduce real bias. And I'm really 

16 glad to see that Syngenta intend to verify these in vivo. 

17 I think that‟s essential and it's one of the things that 

18 they said they would intend doing.

19

20 Amongst other things, it may actually help to identify 

21 binding within tissues in multiple compartments within 

22 tissues and might provide a check on what appears to be a 

23 slow distribution compartment as we mentioned earlier. I 

24 think the limitation of this, and again the Agency needs 

25 to think about this, is that because of the limits of 

26 quantification, it‟s going to be very difficult to use 

27 this to validate for low, probably human-relevant doses. 

28 I see that as something that needs looking at carefully 

29 by the Agency -- well, would need to be looked at before 

30 they can go ahead and adopt it. Thank you.

31

32 DR. DANIEL SCHLENK: Dr. Hayton?
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1

2 DR. WILLIAM HAYTON: I'd like to pass to Dr. Meek because I 

3 think she is going to enumerate all of the information 

4 requested in the question. And if she doesn't I do have 

5 a laundry list, but I think she's got the better one.

6

7 DR. DANIEL SCHLENK: Okay. Dr. Meek, you've been tapped.

8

9 DR. BETTE MEEK: Well, the question is fairly broad, as you'll 

10 note. So we're trying to determine how best to divide 

11 this up. I wanted to say that I I'm suitably impressed 

12 with, first of all, the considerable progress on the 

13 development of the PBPK model and its review for all the 

14 reasons and the value of the model for all the reasons 

15 that we've heard here. It avoids a number of 

16 generalizations and the value of the sensitivity analysis 

17 associated with the model for testing hypothesis is 

18 considerable as well. So I would strongly encourage the 

19 Agency to work with the proponent to ensure that the 

20 model is sufficiently robust to meet their needs. I 

21 mean, given its considerable potential to more accurately 

22 predict interspecies and intraspecies differences in 

23 kinetics and to test a wide range of hypothesis regarding 

24 critical determinates.

25

26 So in relation to key aspects that should be considered 

27 in review of the PBPK model submitted by Syngenta by the 

28 Agency, I referenced the recently released WHO guidance

29 on the Characterization and Application of

30 Physiologically Based Pharmacokinetic Models and Risk 

31 Assessment. It was referenced by one of folks presenting 

32 to the meeting of the other day. 
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1

2 Development of the guidance drew broadly on expertise 

3 internationally in both PBPK modeling and risk assessment 

4 and involved protracted input from a drafting group in a 

5 series of related workshops. This group developed a 

6 comprehensive list of questions for consideration 

7 relevant to evaluation of the biological bases, model 

8 simulations, reliability and applicability of specific 

9 PBPK models for application in risk assessment. 

10

11 I‟d note also that a subset of these questions is equally 

12 applicable to other types of pharmacokinetic models and 

13 it would be helpful to step through them, then, in 

14 relation to the modeling approach currently proposed by 

15 the Agency. 

16

17 I‟m always concerned when we hold, often, more data 

18 informed approaches such as PBPK modeling to higher 

19 standards of verification than approaches which are based 

20 on less inference. So it's important to consider 

21 stepping through these questions for all modeling based 

22 approaches.

23

24 The document also makes recommendations concerning 

25 process for consideration of PBPK models in regulatory 

26 risk assessment. This includes early and iterative 

27 involvement of regulatory risk assessors in model 

28 development, access to both internal and independent 

29 expertise, documentation by model developers in standard 

30 format risk assessment applications and independent 

31 review.

32
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1 So I'm not sure whether I want to go through the list of 

2 considerations for considering PBPK models in risk 

3 assessment. I'll briefly try to summarize what‟s here 

4 and submit, for the record, the more detailed listing. 

5 But for the biological basis: 

6 Are the major sites and processes of absorption, 

7 storage, transformation and clearance included in 

8 the model?

9 Are the mathematical equations of ADME based on a 

10 sound theoretical biological basis?

11 Are the input parameters related to the 

12 characteristics of the host, chemical or 

13 environment?

14 Is the sum total of the tissue blood flow rates 

15 equal to the cardiac output?

16 Is the ventilation perfusion ratio specified in the 

17 model within physiological limits?

18 Are the volumes of compartments within known 

19 physiological limits?

20 Is the approach used to establish partition 

21 coefficients within the domain of valid application?

22 Is the method used for estimating biochemical 

23 parameters adequate?

24 Is the allometric scaling of parameters, if 

25 applicable, done appropriately?

26 Is the integration algorithm proven for solving 

27 differential equations in similar models?

28 And has the computer model code been verified for 

29 syntax errors and the accuracy of units?

30 And then the model simulation of data:
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1 Has the model been evaluated for its ability to 

2 predict kinetics under various conditions, 

3 consistent with its intended application?

4 Does the model consistently reproduce the general 

5 trend of the data, the peaks, bumps and valleys, 

6 saturation of metabolism, or only portions of one or 

7 more data sets?

8 Are the model predictions within an acceptable level 

9 of correspondence with the experimental data that 

10 was considered to be within a factor of 2? 

11 And the reliability for model testing, uncertainty and 

12 sensitivity.

13 Is the model capable of providing predictions of the 

14 concentration time course of the candidate dose 

15 metrics in the target organ or a suitable surrogate 

16 compartment?

17 Has the uncertainty in model predictions of dose 

18 metric been assessed for the relevant exposure 

19 conditions?

20 What is the reliability of the data used for 

21 calibrating and/or evaluating the PBPK model?

22 And is the sensitivity of the dose metric to change 

23 in numerical values of input parameters 

24 characterized for relevant exposures?

25 And then, of course, Applicability:

26 Has the model been developed and evaluated in the 

27 species and life stage of relevance to the risk 

28 assessment?

29 Do the exposure routes in the model correspond to 

30 those of anticipated human exposures, as well as 
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1 those of the critical studies chosen for the 

2 assessment?

3 Has the model been tested for the exposure doses and 

4 durations of relevance to the intended 

5 extrapolations?

6 And does the model contain point estimates of 

7 parameters, consistent with the purpose of 

8 application?

9

10 So one of the difficulties that we came up against is 

11 really this kind of transparent presentation of the model 

12 content for risk assessment and I was encourage to hear 

13 that, certainly, Syngenta was aware of the requirements 

14 for documentation and are presenting it in that context. 

15 I was also pleased to hear that they had the model 

16 evaluated by external reviewers as well. And I'll leave 

17 it at that. 

18

19 DR. DANIEL SCHLENK: Anything to add, Dr. Hayton?

20

21 DR. WILLIAM HAYTON: No.

22

23 DR. DANIEL SCHLENK: Okay. All right. Open for any other 

24 panel members. Comments? Yes, Dr. Horseman?

25

26 DR. NELSON HORSEMAN: This is almost certainly a completely 

27 naïve question, but this last point here, model 

28 applicability, I wonder if I might hear some comments on 

29 how these models might be applicable, depending upon 

30 whether one is concerned about LH surge suppression 

31 versus thyroid tumorigenesis. It seems to me that model 

32 applicability implies a relationship to the 
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1 toxicological, physiological endpoints. That would be 

2 interesting for me to hear.

3

4 DR. DANIEL SCHLENK: Who wants to take that one? Dr. 

5 Greenwood?

6

7 DR. RICHARD GREENWOOD: I think that it‟s difficult. All the 

8 pharmacokinetic data will tell you is, what is the likely 

9 exposure of all tissues. And this is one of the problems 

10 we face here with atrazine. We don‟t know for sure where 

11 the site of people has suspicions. We don't know where 

12 the primary lesion occurs and we don‟t know how much 

13 actual exposure, if you like, of the primary site of 

14 action or sites of action to produce a sufficient lesion 

15 to cause the secondary lesions that we observed as the 

16 symptoms, including LH suppression and so on. 

17

18 So at the moment, we're really in the dark. And what 

19 we're trying to do with all of this sort of approach, 

20 what people try to do, is to just get a measure of the 

21 opportunity for a particular compound or a group of 

22 compounds to interact with a site of action. If you know 

23 what that is, and with the pyrethroids, with OP's, then 

24 it's really easy, relatively. But when you don‟t know 

25 where it is and what it is, this is the best that you can 

26 do in order to try and decess overall exposure of the 

27 site of action because the area under curve, the other 

28 plasma, really, because all tissues get exposed to that. 

29 It really is the best measure of overall opportunity for 

30 a compound to interact with any particular site of 

31 action. 

32
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1 One of the problems is that you don‟t know that when 

2 you're looking at individual tissue distributions, for 

3 instance. You don‟t know whether that‟s just a sync 

4 which keeps it away from the site of action or whether 

5 it's a good thing, if you like, from the atrazine, if 

6 it's getting there to the site of action. So it is 

7 difficult, but I think at the moment, this is probably 

8 the best that we can do and you can use these such 

9 models, even the simple models to try and get an estimate 

10 of exposure over various time scales. And, of course, 

11 some things can sometimes take longer exposure, maybe, 

12 than for some of these things like interaction of an OP 

13 with an enzyme, where you get an instant effect.

14

15 DR. BETTE MEEK: Really, we're trying to get a little closer 

16 to the internal does even though we don't necessarily 

17 know what the target is to consider much more accurately, 

18 interspecies differences and human variability. So 

19 ultimately, you're trying to move from the external dose 

20 to at least a closer surrogate for the internal dose to 

21 be able to replace the kind of default uncertainty 

22 factors that we use for that purpose, but it relates 

23 solely to exposure when you don‟t necessarily understand 

24 the adverse outcome pathway.

25

26 DR. PENELOPE FENNER-CRISP: One of the areas that iterated in 

27 the WHO guidance, with respect to applicability, had to 

28 do with developing models appropriate to the 

29 subpopulation of interest that you were focusing on. And 

30 of course, with respect to atrazine, there's a whole lot 

31 of data and interest in defining the toxicological 

32 consequences of exposures at various life stages. 
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1 There's a lot data generated on that point, but as far as 

2 I can tell, from this point in time, the simple PK model 

3 only models for an adult; an adult which isn‟t even an 

4 average adult. 

5

6 And to this point in time, seems not to be modeling for 

7 any of the younger life stages of concern that's really 

8 the focus of this risk assessment. So it's just a point 

9 to reemphasize in conducting and developing these models, 

10 whether it continues to be the simple one or the most

11 sophisticated PBPK model, one has to consider having 

12 variations in the application of the model that are 

13 consistent with the life stages of concern.

14

15 DR. DANIEL SCHLENK: Any other comments on Letter C? Yes, Dr. 

16 Portier?

17

18 DR. KENNETH PORTIER: You know, in that long laundry list that 

19 you did, is there something in there that says 

20 implementing this on a platform that's transparent for 

21 others to look at?

22

23 You know, I was sitting there thinking okay, five years 

24 from now we'll be sitting and you will have implemented 

25 it in something that we can‟t run on our computers. So 

26 I'd like add that, way at the bottom of the list, that I 

27 think it‟s consistent with other things we've heard like 

28 with the dietary programs, you implemented in SAS, I 

29 don‟t have SAS, so why didn't you implement it in 

30 something I can run it in.

31

32 DR. DANIEL SCHLENK: Dr. Meek?
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1

2 DR. BETTE MEEK: Just to underscore the point, there was a lot 

3 of discussion in this project on exactly that issue and 

4 the need for transparent modeling platforms that are 

5 available to all. And some of that is evolving. First 

6 of all, for the purposes of increasing understanding and 

7 uptake.

8

9 DR. DANIEL SCHLENK: Okay. Any other comments on Letter C? 

10 Okay. Let‟s go on to D. Dr. Greenwood, do you want to 

11 start off on that?

12

13 DR. RICHARD GREENWOOD: Yeah. It‟s asking us to comment on 

14 the extent to which this one compartment model can 

15 account for intraspecies differences. You can do it, but 

16 there's a lot of uncertainty associated with it because 

17 it‟s difficult to introduce into one compartment model 

18 differences in metabolic capabilities, for instance, 

19 binding properties of various tissues. But you can 

20 extrapolate using empirical allometric factors. You can 

21 do that, empirically. But what you can‟t do is carry out 

22 the extrapolation on the basis of any sort of good 

23 scientific physiological and metabolic information. So 

24 again, they'll be a large uncertainty associated with any 

25 extrapolation between species, using the single 

26 compartment model.

27

28 Another problem with it, really, it's difficult to 

29 compare the exposure time needed for infecting rats with 

30 that in humans. But a lot of the reason is the 

31 information we've got in rats is based on the same dose 

32 being given on each day. It‟s simply because people are 
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1 looking at the suppression of the LH surge. So all of 

2 the experiments are being carried out in the same way 

3 because that's what they're interested in. So it may be 

4 more difficult to try and extrapolate this sort of 

5 information across to human exposure because that‟s not 

6 the usual mode of exposure. 

7

8 One worry for me is that the rat did not scale to monkey 

9 when they used the standard allometric scaling factors. 

10 In fact, he went the wrong way. Part of the problem, I 

11 think, is in different species, you need to consider 

12 binding sites as well as metabolism because, -- as I've 

13 said earlier, without going on about it any longer --

14 it‟s actually the concentration of free material that 

15 counts. And one area where I think these physiologically 

16 based pharmacokinetic models are going to have the 

17 advantage in terms of extrapolation over these simple 

18 models is because you can get the physiological 

19 parameters. You can actually parameterize a lot of these 

20 from the literature. 

21

22 Well, it's got that advantage. It‟s based on something

23 you can measure as well as cardiac output and so on, 

24 blood flow to various organs, organ weights. And there 

25 have been a couple of studies lately, one by Boudoir (ph) 

26 and one by Buoy last year, where they've actually taken 

27 human physiologically based pharmacokinetic models and 

28 then played games. They've used stochastic modeling. 

29 And what they've done is to say, okay, if you look at 

30 young children, you look at old people, then things like 

31 cardiac output, renal function, hepatic function all 

32 change, and we've got measures of how they change. 
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1

2 So you can also say, okay, what happens if you get 

3 somebody who weighs 120 kilograms instead of 60 

4 kilograms? And you can play the games by altering the 

5 parameters of the model and then doing a stochastic 

6 approach and trying to generate a population based on the 

7 sort of population variability that you've got. Okay, 

8 it‟s in early stages, but what it's trying to do is to 

9 try to look at individual variability using the model. 

10 You can‟t use those sorts of games. You can‟t play those 

11 sorts of games with a single compartment model. 

12

13 So again, going back to what Dr. Meek said, actually, 

14 you've got to decide what do you want to use this model 

15 for and if you want to extrapolate between life stages 

16 and if you want to extrapolate between species, probably 

17 it‟s not the best way of doing it. You may get more joy 

18 out of the more complex model, given all the drawbacks to 

19 those that we've already outlined.

20

21 DR. DANIEL SCHLENK: Dr. Hayton?

22

23 DR. WILLIAM HAYTON: I agree with my colleague and I guess 

24 I'll just emphasize, based on total radioactivity -- I 

25 don't know what to make of it, but I don‟t know whether 

26 it rises to the level of being disconcerting, but why the 

27 half-life in monkey actually came out to be shorter than 

28 rat when you'd expect it to go the other way. We have 

29 only have three species here. I mean, you know, rat, 

30 monkey, and a very limited amount of data in human for 

31 the scaling part. But other than that anomaly, it seemed 
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1 like the volume of distribution for total radioactivity 

2 is relatively constant across the species. 

3

4 Certainly, the rat elimination rate constant seemed to 

5 scale to the human value, even though that's based on 

6 limited observation, it seemed to scale according to 

7 expected allometric scaling relationships, you know, 

8 three-quarter our body weight relationship. I'd say 

9 overall, from what we have now, it‟s seems to work 

10 satisfactorily. Well, let me hasten that it will be 

11 interesting to see what's going on with the monkey and it 

12 seems like those studies are in the works, right, that 

13 Syngenta is doing that?

14

15 DR. DANIEL SCHLENK: Dr. Meek?

16

17 DR. BETTE MEEK: Yeah. I have nothing to add. Thanks.

18

19 DR. DANIEL SCHLENK: Any other panel input on Letter D, 

20 Question 12? Okay. Go back to the Agency and determine 

21 whether or not you guys have what you need. Any 

22 questions or clarification? Okay. Let‟s go ahead and 

23 move onto 13. And before we do, I think Agency has some 

24 further questions or clarification for some of the 

25 previous questions we were given.

26

27 DR. ELIZABETH MENDEZ: Yeah. Before we proceed to Question 

28 13, Dr. Dellarco would like to ask a question for 

29 clarification. 

30

31 DR. VICKI DELLARCO: I'm Dr. Vicki Dellarco. I'm in the 

32 Office of Pesticide Programs. I'm the science advisor in 
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1 the Office of the Director. I want to come back to the 

2 question of how we integrate different lines of evidence 

3 that we're seeking your advice on. There's been logic in 

4 how we've proceeded with all these SAP reviews. 

5

6 In the first review that we had in February, introduced a 

7 framework that we would use to pull together information 

8 to inform an opinion about what the compound might do in 

9 humans. And so, really this question that we're asking 

10 you about how to integrate the experimental, both 

11 mechanistic, empirical, and the epidemiology comes back 

12 to that framework. 

13

14 What we're seeking advice from you on is this: if you 

15 remember that framework, there were certain attributes of 

16 it. It was a hypothesis-based framework, taking all the 

17 evidence and trying to understand what the compound does, 

18 kinetically, dynamically along a pathway. It was an 

19 evidence-based framework. And it was a framework that 

20 integrates different data streams and being able to 

21 characterize a conclusion and the confidence in that 

22 conclusion and the uncertainty around that conclusion.

23

24 So if we go back to the thyroid example -- because it was 

25 discussed a lot -- to kind of use it as an example of the 

26 guidance that we're seeking on. As we look at the 

27 different tumor sites that are suggested in the 

28 epidemiology and how we bring all information to bear on 

29 interpreting that is -- it's hypothesis-based, so you 

30 would ask, okay, what do we understand about thyroid 

31 cancer in humans because we're interested in humans? 

32
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1 Rather than saying we don‟t know. There could be any 

2 mechanism to try to lay down some reasonable hypothesis, 

3 based on the experimental, medical, epidemiologic 

4 literature about what could be some key events involved 

5 in that. So as an example, we know radiation is a 

6 factor, so one would want to look at urogenesys. One 

7 would want to go to thyroid human disease models and see 

8 what the association is in those models with thyroid 

9 cancer, and perhaps, perturbation of that thyroid axis 

10 and elevation of THS could be a factor. 

11

12 But again, laying down that hypothesis, drawing on all 

13 knowledge. And then to start looking at the experimental 

14 evidence to see what it tells us about how it may evoke 

15 those key events. You know, is the compound a mutagen? 

16 Do you have a sufficient basis to draw that conclusion? 

17 Do we have studies that have looked at perturbation of 

18 the hypokalemic pituitary thyroid axis? What do we 

19 understand about that? What is the epidemiology telling 

20 us? 

21

22 So where we could use your help is how can we structure 

23 that analysis in a scientifically rigorous way? How can 

24 we structure it in a way that is transparent in how we 

25 reach conclusions so that it's understood how we're 

26 weighing different line of evidence? In some cases, the 

27 epidemiology may be given weight. In other cases, 

28 experimental and epidemiology may be given equal weight. 

29 It depends on the tumor site that we're looking at and 

30 the information that we have. So this is basically what 

31 we're asking you to do so that when we go into the 

32 experiment, what are the things that we should think 
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1 about in characterizing the strength of that evidence and 

2 the limitations in that evidence. 

3

4 When we look at the epidemiology, you've given us some 

5 very good advice there on how we should look at the 

6 epidemiology. Now we need your advice in how we bring 

7 this all together, again, in a structured, rigorous, 

8 transparent way so that in the end, when we reach 

9 conclusions, it‟s very transparent to everybody what the 

10 uncertainties were; how much weight you put on those 

11 uncertainties or how much confidence in the conclusion. 

12 And so it's a multi-discipline process. So this is 

13 probably a question that requires a multi-disciplined 

14 input. It would really be helpful if the panel could 

15 come together; the epidemiologist, the biologist, and the 

16 risk assessor to give us guidance on this.

17

18 DR. DANIEL SCHLENK: Does anybody want to sort of tackle 

19 that? We have Dr. Portier. 

20

21 DR. KENNETH PORTIER: It's really good. I know what you're 

22 trying to get at. And I was sitting here looking back at 

23 the notes from the February meeting. You broke it out 

24 into the exposure modeling, the PBPK modeling, the PD 

25 modeling, and then you're trying to bring it all 

26 together. And nowhere in there did we really link the 

27 epidemiology quantitatively. It's kind of qualitatively. 

28 And that the framework starts with the exposure modeling. 

29 So we've been doing a lot of discussion about exposure 

30 modeling. And I think in this case, we have that 

31 compartment pretty well discussed. I see that going on. 

32
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1 The issue I was going to bring with the PK modeling is 

2 that in your framework, you kind of start with the 

3 typical adult, but I didn‟t see that PK model here. What 

4 I saw was an atrazine-specific model that's much simpler 

5 than your typical adult model that you kind of started 

6 with in your framework. And I felt like, in this case, 

7 you kind of did what I'd call "top down" modeling rather 

8 than a "conceptual up" modeling, which I thought what the 

9 framework was all about. Dealing with here's a typical 

10 adult, here's some typical processes; atrazine is going 

11 to impact these processes, these pathways and produce 

12 this kind of signal in the body that's going to result in 

13 this kind of -- and I'm not sure the PK model we're 

14 looking at here is quite -- and I'm looking at Dr. 

15 Greenwood because he was sitting there as well. I hope 

16 he knows what I'm talking about. I don't think the PK 

17 modeling in the atrazine cases is as good as what you 

18 would conceptually add in the framework. I have nothing 

19 to say on the PD modeling. 

20

21 I've been sitting here thinking, though, about how we get 

22 the epidemiology end, and I understand their point. When 

23 we start looking at the PK and the PD modeling and 

24 there's no information to inform the typical adult model, 

25 then we have to fall back on the epidemiology and say,

26 what are the associations? What's the strength of the 

27 association? Are these associations helping me to better 

28 understand whether something is really happening in the 

29 body that I should look at?

30

31 I don't think we really discussed that very well, even in 

32 the last meeting. We make the qualitative link, but then 
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1 we go looking -- and I think that's the epidemiologist 

2 point -- we go looking for excuses to throw the 

3 epidemiology out rather than think of excuses for why the 

4 epidemiology should force us back into the lab to look 

5 more carefully at mechanisms. I don‟t know how -- you're 

6 asking how we put those together. And I think I'll throw 

7 that back to the panel and say do we have any paradigms, 

8 any structures to help us think that way? And I haven‟t 

9 seen any. 

10

11 I know what you‟re asking for, but I don‟t know how to 

12 take that loose epidemiology information and kind of link 

13 it in a qualitative way or as a driver for mechanism 

14 research, even if it's to push the mechanism research 

15 out, or push more epidemiology out. 

16

17 Unfortunately, pushing the epidemiology out is typically 

18 too expensive, in terms of time and effort and timeframe 

19 - for EPA's timeframe. You know, they have to make a 

20 decision in a year and a half; they're not going to do a 

21 full-blown repeat epidemiology study in a year and a 

22 half. So I'll turn it over to Dr. Bove. Maybe that's 

23 kind of got them thinking, trying to translate some of 

24 this.

25

26 DR. FRANK BOVE: I don‟t know if this is going to help at all, 

27 but in the February meeting and in this meeting, again, 

28 it's important, first of all, to get the evaluation of 

29 the epi evidence right. There were problems with the 

30 2010 Issue Paper. There are problems with this one. We 

31 see an advance, but we still see weaknesses in just 

32 evaluating the epi data. So that's the first thing. 
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1 Before we talk about integration, let's actually evaluate 

2 the epi data in the best way we can and maybe -- I mean, 

3 sure, you can‟t do an epi study right off the bat, but 

4 you can pool data. For example, for the prostate cancer 

5 situation with the Saint Gabriel plant, a simple thing 

6 could be done/should've been done to answer that 

7 question. What about these five cases? What were their 

8 exposure experiences? What happens when you compare 

9 those five cases with the controls that also were there 

10 before the plant started the screening program?

11

12 This is nothing to do. And you can actually then answer 

13 the question: Is there something there at that plant or 

14 not? If there isn‟t, then the case is closed on prostate 

15 cancer, for example. 

16

17 So these are things that can be done if you evaluate the 

18 epi evidence properly and appropriately. Again, go over 

19 the years, there's a history to this, in 2000, there was 

20 hardly any. We called it a brevity and superficial 

21 evaluation of the epi evidence. In 2003, it was a little 

22 bit better, but there was a lot of evidence that wasn't 

23 discussed and it was focused on prostate cancer and there 

24 were problems with that. 

25

26 In 2010, we had ecologic studies, mostly to evaluate, and 

27 the evaluations were problematic. That's the best I can 

28 say. This is better, but you still have a ways to go. 

29 So my feeling is let's get the evaluation of the epi data 

30 right. There are things that could answer some of these 

31 questions. Thyroid cancer - there's no way to answer 

32 that question without further study. Most of the cancers 
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1 where we talked about suggestive evidence or inadequate 

2 evidence, they require further study. There's no way 

3 around it. You could do some pooled analysis, though, in 

4 the meantime, but you have an ongoing agricultural health 

5 study. You could encourage states -- we were just 

6 talking about this -- you can encourage states to use 

7 their municipal drinking water data and their cancer and 

8 birth defect registry to start answering some of these 

9 questions too.

10

11 So that's on the epi side. First things first; before 

12 you talk about integration, let's see what evidence we 

13 have, evaluate it appropriately, and see what things we 

14 can do in the short term to enhance that information. 

15 Then when we‟ve got that together, then we can start 

16 integrating. It‟s done all the time. If you look at the 

17 risk assessments for trichloroethylene PCE, so on, that 

18 are just recently being done, they're integrating to tox 

19 and epi information. In the case of trichloroethylene, 

20 it's the human data that's taking precedence. In PCE, 

21 it‟s not. And they're categorizing it for carcinogenicity 

22 based differently because of that. I think that those 

23 are examples that EPA can look at its own risk assessment 

24 process to see how these things are getting integrated.

25

26 DR. DANIEL SCHLENK: Dr. Meek?

27

28 DR. BETTE MEEK: Thanks. I think sometimes what complicates 

29 true integration of available data is that we really are 

30 reacting to chemical-specific data. So we're not drawing 

31 more broadly on, for example, what we know about human 

32 disease. And I also think that the way the questions are 
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1 posed here, looking at pieces of the -- we haven't been 

2 asked to look at the totality of the data. We've been 

3 asked to look at the epidemiological data or what's the 

4 critical effect. That kind of thing. 

5

6 I think that there needs to be some broader thought about 

7 how we bring what we know about diseases models into 

8 account in interpreting both the epidemiological and 

9 toxicological data. When I read through the section 

10 here, which related to integration, I kind of walked away 

11 saying I don‟t feel there's been an integration yet. 

12 Again, I don't think we're drawing broadly enough on the 

13 available information. 

14

15 I think the other issue that complicates this is we don‟t 

16 really have a hypothesized adverse outcome pathway yet. 

17 So it's as a basis for trying to integrate available 

18 information that's complicating the issue as well. 

19

20 DR. DANIEL SCHLENK: Dr. Chambers and then Dr. Horseman.

21

22 DR. JANICE CHAMBERS: Well, I appreciate that you're going to 

23 have to deal with atrazine in 2013, and so it's on the 

24 table right now. It seems like for answering this broad 

25 question, which is a very good question, you need a case 

26 that has a little bit more solid information here and 

27 there. The epidemiology evidence seems to be suggestive, 

28 at best, at this point. The mechanisms we're looking at 

29 for the point of departure and everything is an entirely 

30 different type of thing. The mechanism isn‟t known 

31 there. The exposure data from environmental and to a 

32 certain extent, from the occupational are pretty fuzzy. 
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1 So you really don‟t have good solid information and 

2 enough mechanistic information and enough pharmacokinetic 

3 information right now to do that.

4

5 If you're going to look at integration, I think you need 

6 a more solid case where you have a mechanism solid 

7 epidemiology data and more information that all can be 

8 integrated as the first case to try to do that, and 

9 probably atrazine doesn‟t have all of those elements. I 

10 know it's on the table and all, but I don‟t think that's 

11 really feasible right now.

12

13 DR. DANIEL SCHLENK: Dr. Horseman?

14

15 DR. NELSON HORSEMAN: My point is very similar to Dr. 

16 Chambers' but maybe worded in a different way. I don‟t 

17 know whether you can call it pharmacodynamic, 

18 toxicologic, physiologic - the fact that we've been asked 

19 to consider as a point of departure, which is the LH 

20 surge suppression, I had a sense that that was related to 

21 epidemiological findings, if you will, related to 

22 menstrual cycle irregularities and reproductive 

23 senescence, and some other animal things -- so on and so 

24 forth -- which we heard nothing about in this meeting. 

25

26 The epidemiology that we heard about is cancer 

27 epidemiology. We haven‟t heard anything about cancer 

28 pharmacodynamics or toxicology or anything else in this 

29 meeting or in the white paper. I get the sense that 

30 we're being asked to integrate two things that have 

31 nothing to do with one another, except atrazine - to go 

32 back to Dr. Chambers' point. Maybe that's a wrong 
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1 interpretation. If it is, I'd like to hear somebody 

2 correct me.

3

4 DR. DANIEL SCHLENK: Dr. Greenwood?

5

6 DR. RICHARD GREENWOOD: Again, I was at the earlier meeting 

7 where we started to look at epidemiology, and I think 

8 there, the message that came across to me was that one of 

9 the main weaknesses in many of the studies was poor 

10 exposure data. I think the Agency is trying to pool 

11 together the exposure data, but they‟re not there yet, 

12 but they're getting close now. I think once that's 

13 together, then it may be possible to do some stronger 

14 studies. 

15

16 DR. KENNETH PORTIER: I wanted to follow-up on something Dr. 

17 Greenwood was talking about at the end of his discussion 

18 on Part D, which was that they're starting to use these 

19 PKPD models in a play mode, right, a "what if" mode, a 

20 hypothesis generation mode. So again, when I thought 

21 about the framework, I was thinking, oh, great, they'll 

22 have kind of a generic model and then they can look at 

23 the epi and say well, thyroid. What would have to be 

24 happening in this mode for us to see something happening 

25 with thyroid?

26

27 So in a sense, you're generating that hypothesis. You're 

28 looking at the epi and saying okay, the epi people are 

29 saying it‟s kind of possible. Can we do something in our 

30 generic model to produce -- you know, we know this is an 

31 endocrine disrupter. We know from the ecotox that it has 

32 affects on certain animals. Amphibian effects maybe are 
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1 not directly translatable to humans, but we know 

2 something about what's going on. 

3

4 Can we just play with this model and see if something 

5 happens? That was, to me, one way of generating a 

6 hypothesis. It‟s not testing it, but it kind of 

7 feasibility. Does the model say it's feasible? If you 

8 can‟t tweak the model enough to get that effect, then 

9 you're at least in a position where you're saying, well, 

10 you know, we've looked at our current big knowledge, our 

11 current understanding of PKPD processes, and we can‟t 

12 make it happen. Then you can turn back to the community 

13 and say if you still think there's really something 

14 happening here, propose something different, but we've 

15 kind of exhausted the obvious avenues. To me, I thought 

16 that was part of the framework. What I'm calling "that" 

17 is kind bottom up from basic concepts and trying to make 

18 something happen in this conceptual model.

19

20 DR. DANIEL SCHLENK: Dr. Meek?

21

22 DR. BETTE MEEK: Just a brief response to that, Ken. I think 

23 what the modeling is telling us currently is that we 

24 handle the chemical very similarly to rats. In fact, 

25 that the chemical is rather evenly distributed. So it 

26 doesn't address the PD component because we don‟t have a 

27 hypothesized AOP here. So the PBPK model doesn‟t really 

28 help us in that context.

29

30 DR. DANIEL SCHLENK: Dr. O'Byrne and then Dr. Jerde.

31
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1 DR. KEVIN O'BYRNE: I share Nelson's concerns about the 

2 fragmentation. One of the things that frustrates me is 

3 the lack of epi data on those reproductive impact of 

4 atrazine. Apparently in the September meeting there were 

5 two papers brought forward. I mean I looked at those. 

6 One wasn't worth looking at and the other one was just so 

7 weak. And I just wonder, why has that been neglected, in 

8 terms of -- I don‟t mean neglected from your perspective, 

9 but in terms of the epidemiologists tackling this issue. 

10 Is there a reason why it's being left, given that the 

11 gonadatropines seem to be so central to the mode of 

12 action, et cetera, with atrazine?

13

14 So that's one point that I'd like to make. The other is 

15 -- I think Richard Greenwood made some very good comments 

16 earlier on about the diet versus the gavage dynamics of 

17 atrazine and its metabolites in the plasma. And he's 

18 absolutely right. Those sorts of peaks following gavage 

19 versus their absence in diet could have major affects on 

20 how the brain is perceiving and processing those signals. 

21

22 I think very recently, Stavert Lightman, in Bristol in 

23 the UK, has been looking at the brain's response to 

24 pulsatile release of corticotropins, up to the 

25 glucocorticoids. And it's astonishing, the sensitivity 

26 of the brain to the pulsatile reception of these hormones 

27 and how you get translocation and pulsatile 

28 transcription. It's absolutely fantastic what he's 

29 demonstrated the sensitivity of the brain to pulse modes. 

30

31 When they gavage these animals and you get these huge 

32 peaks, goodness knows what's going on inside the brain. 
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1 I think the move towards the -- in my point of view -- as 

2 a sort of simple physiologist, moving towards feeding 

3 animals, if you can't get it into the water, is much more 

4 relevant to looking at the mechanisms of action of 

5 atrazine on the reproductive system, and I suspect other 

6 systems as well. I don't know if that‟s helpful at all.

7

8 DR. DANIEL SCHLENK: Dr. Jerde?

9

10 DR. TRAVIS JERDE: I wanted to second what Dr. Horseman said 

11 and offer this: so you read through a lot of this and 

12 what's available to the public and what‟s available to 

13 the scientific community we‟ve heard from industry 

14 representatives, something along the lines of there's no 

15 compelling evidence available that atrazine may be 

16 carcinogenic to humans. And that sounds an awful lot 

17 like atrazine is not carcinogenic to humans, to a lot of 

18 people.

19

20 I guess I'd like to support the epidemiologic side of 

21 this because it seems like the conclusions that they're 

22 making, based on a very limited literature in most cases 

23 -- separate types of cancers, separate diseases, likely 

24 separate mechanisms of actions -- the conclusions they're 

25 drawing are, wait a minute, step on the brakes. We can't 

26 really say something so definitive that sounds an awful 

27 lot like this isn‟t carcinogenic. 

28

29 Furthermore, a lot of these studies are on high exposures 

30 in the plant. As a public health issue, we could even 

31 deal with that personal protective equipment, help the 

32 workers and that sort of thing. And yet the water, to a 
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1 larger prospective, the exposure in water might be the 

2 bigger problem. From what I understand, from what you 

3 guys have been saying, there's almost nothing on it. And 

4 so this gets back to Dr. Horseman's point, integrating a 

5 model - we're losing what I think is a fairly strong 

6 statement from them, which is we need to understand this 

7 a lot more than what we currently do. That's becoming 

8 obscured in trying to get the right model and we really 

9 don‟t know what the right model is because we don‟t have 

10 any clue, from limited epidemiologic studies what that 

11 might be. 

12

13 And once we have better data from say, the thyroid, and 

14 now we may look at the thyroid and say okay, in these 

15 patients that are exposed, if there is an increase in 

16 thyroid, we looked at the thyroids, this changes. Now we 

17 can go back to the model system and say, okay, we're 

18 going to change this and see what happens and now we've 

19 got our model.

20

21 So I'm just concerned as a scientist who looks somewhat 

22 translational, but I do consider myself a molecular 

23 biologist who uses models. I'm just concerned that some 

24 of those epidemiologic findings might be getting obscured 

25 by so much talk about modeling and integration. 

26

27 DR. DANIEL SCHLENK: Okay. Dr. Young?

28

29 DR. HEATHER YOUNG: I think I want to come back to the point 

30 that I made earlier, too, is the huge gap in the 

31 literature is that it's looking almost exclusively at 

32 occupational exposures, which is why we have very few 
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1 studies looking at reproductive outcomes because the 

2 occupation that we're looking at is predominately male3 dominated. And there are a few studies looking at female 

4 gynecologic cancers, but for the most part, again, we're 

5 focusing on occupational exposures, not on community 

6 exposures where you would expect to see, if there are 

7 any, the reproductive effects. Because then we would 

8 have a much broader population that we're looking at, a 

9 much better representation of female. So again, I think 

10 it's hard to ask us to integrate when we don't have all 

11 the pieces of the information. 

12

13 DR. DANIEL SCHLENK: Dr. Roby?

14

15 DR. KATHERINE ROBY: And to take that to the next step, also, 

16 we need to continue to explore the mechanism of action. 

17 As we learn more about the mechanisms of cancer, and we 

18 learn about the mechanism of atrazine action, along with 

19 epidemiology, we can begin to make the ties, but there's 

20 not only gap in the epi data, but there's gap in

21 understanding mechanism of action of atrazine and 

22 mechanism of cancer development. So all of these data 

23 need to grow together, but there needs to be continued 

24 exploration of atrazine's mechanism of action. 

25

26 DR. DANIEL SCHLENK: Dr. Gold?

27

28 DR. ELLEN GOLD: I wanted to make a couple of points. First, 

29 I want to agree -- I think Mr. Horseman made the point --

30 in the Issue Paper, the mode of action that's emphasized 

31 is largely the neuroendocrine LH surge one. And it is 

32 true that that was discussed pretty thoroughly in the 
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1 September meeting to look at reproductive effects, but 

2 I'm not sure that it applies, necessarily. It applies, 

3 perhaps, to some of the cancers we're looking at, but not 

4 all of them. So point Number one is there was a little 

5 bit of a disconnect there, but it probably reflects what 

6 we know at the moment.

7

8 Secondly, the question was raised why we're not doing a 

9 better job of looking at reproductive, and part of it is 

10 because we're not looking at the community. But part of 

11 it is because the epidemiologic study that we're 

12 depending on the most was a cancer institute study that 

13 was focused on cancer outcomes. So when they made an 

14 attempt to look at reproductive outcomes, I think it was 

15 done in a less than optimal way, even in that cohort.

16

17 The other point I want to make is it's occurred to me, 

18 sitting and listening to this and being part of the SAP 

19 that talked about the framework for using epidemiologic 

20 evidence that I think the Agency is undergoing a little 

21 bit of a culture change in trying to figure out how to 

22 incorporate epidemiologic data; that it comes from a 

23 place of using mostly toxicologic data. And this is 

24 like, you know, a new piece. How do we deal with this? 

25 And culture change takes time. 

26

27 So I think we've been trying to inform you about our 

28 feelings about if you have epidemiologic data but you're 

29 lacking toxicologic or mechanistic data, you shouldn't 

30 ignore the epidemiologic data. And finally, that leads 

31 to having the epidemiologic data drive -- or provide the 

32 impetus for doing future mechanistic -- but it shouldn't 
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1 -- just because you don‟t know, it doesn‟t mean you 

2 should ignore it and say there's unlikely cancer risk.

3

4 DR. DANIEL SCHLENK: Okay. Nice discussion. I'm not sure 

5 where it's going to go in the report. Perhaps, yeah. 

6 All right. Yes, Vicki?

7

8 DR. VICKI DELLARCO: So I think we've gotten some nuggets of 

9 advice. If it could be pulled together in a logical way, 

10 it would be very helpful. And if I can summarize what 

11 I've heard, you guys don‟t have to integrate, we have to 

12 integrate. So we want your advice in things that we 

13 should be thinking about. And what I've heard is some 

14 advice.

15 1) Before you can integrate, you have to ensure that 

16 you've done a thorough analysis of the individual lines 

17 of evidence. We've been given some advice on the 

18 epidemiology and things that we should go back and look 

19 at more closely, in terms the analysis. And that 

20 includes the experimental data too. 

21 2) Secondly, I heard that if there is an empirical 

22 finding, whether it comes from an animal study or it 

23 comes from epidemiology, you start to look to see do you 

24 have an understanding around that. So simply, you don't 

25 have the mode of action and the animal model, as Dr. Meek 

26 pointed out, start to look in the middle literature to 

27 see if you can begin to look at plausibility and lay some 

28 testable hypothesis down. 

29 3) I also heard that we do have an understanding of 

30 some perturbations occurring that could lead to some 

31 outcomes. And as you look at that understanding, you 
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1 should look in the epidemiology to see how well the 

2 population of interest has been characterized. 

3 So again, that helps you as you integrate, be able to 

4 begin characterizing strength, limitations and 

5 uncertainties. 

6

7 I don't know if I've pulled together everything that 

8 you've said, but what's useful is the things that we 

9 should be thinking of, where we should be focusing as we 

10 bring these different lines of evidence together. 

11 Because we are going to have to do that at some point 

12 before more research or more epidemiology is done. With 

13 any pesticide, we continue to look at it because we have 

14 reevaluation schedules. Thank you.

15

16 DR. DANIEL SCHLENK: Thanks. I think what we're going to do 

17 -- Dr. Portier is taking furious notes here -- I think 

18 what we'll do is maybe put this on the end of Question 

19 11. If you can do that, Dr. Gold, that would be great. 

20 So we'll tack it on to Question 11.

21

22 We have some travel issues that the panel needs to deal 

23 with, so think what we're going to do is take an early 

24 lunch and if the panel would meet in the coffee room, we 

25 need to very rapidly decide a few things because 12:00 is 

26 check out time. So we need to make a decision very 

27 quickly. So we're going to take an hour and a half 

28 lunch. We'll be back at 1:00 and finish the last two 

29 questions.

30

31
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1 DR. DANIEL SCHLENK: Good afternoon, everyone. Let's go ahead 

2 and get started on Question 13. Dr. Mendez, are you 

3 going to read that?

4

5 DR. ELIZABETH MENDEZ: Good afternoon. Charge Question Number 

6 13 has to do with the temporal relationships between 

7 exposure and tox endpoint. 

8

9 Question A: "Please comment on the rationale used by the 

10 Agency for selecting these exposure duration options" 

11 that I mentioned in the preamble of the question. 

12 "Please discuss the rationale for other alternative 

13 durations of concern, if any."

14

15 Question B: "Please comment on which exposure duration 

16 in humans most closely corresponds to the exposure 

17 duration found to cause adverse effects in rats."

18

19 Question C: "Please comment on the approach used by the 

20 Agency, i.e. the one compartment linear model to relate 

21 atrazine levels from the water chemographs to predict 

22 corresponding human plasma triazine levels for the 

23 proposed durations of concern. In particular, please 

24 comment on the Agency's proposed approach to use water 

25 AUC estimates to calculate a time-weighted daily average 

26 of atrazine exposure for a given duration of concern. 

27 Please suggest alternative approaches as appropriate."

28

29 DR. DANIEL SCHLENK: Our lead discussant on this question is 

30 Dr. Bill Hayton.

31

32
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1 DR. WILLIAM HAYTON: Our group did meet to discuss this and 

2 tried to come to some meeting of our minds on the 

3 response. So I'll read my response, and if the other 

4 members want to add to that, that is what we'll do. 

5

6 So my response to Part A is a time to reach steady state 

7 and time to effect are not necessarily closely related. 

8 It could simply be a coincidence that they both take 

9 about the same amount of time. The time to accumulate 

10 radioactivity to steady state and the route with the oral 

11 gavage dosing takes about four days, and it also takes 

12 four days of exposure before you start to see LH surge 

13 suppression. 

14

15 So we didn‟t really see any evidence for a cause-effect 

16 relationship there at all. It seemed like if one looked 

17 at the CODAR, 2011 study -- I better get back to my text 

18 or I'm going to get all balled up here. Where he 

19 measured atrazine and then the three DEA, DIA, and DACT 

20 metabolites, the exposure produced by the pseudo steady 

21 state level for those for compounds, in four days, was 

22 about the same as produced during the first day. In 

23 other words, the accumulation in the steady state for 

24 those substances seem to happen fairly quickly. 

25

26 The CODAR study of four daily doses of atrazine by oral 

27 gavage, followed by a four-day washout period with plasma 

28 concentrations measured intensively during both the 

29 treatment and washout periods, showed very similar Cmax, 

30 Cmin and AUC for treatment days two through four, for 

31 atrazine and the toxicologically active metabolic. 

32 Again, DEA, DIA, and DACT.
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1

2 Treatment Day 1 exposures were only slightly smaller than 

3 those observed for Days 2-4. So in other words, there 

4 was little accumulation of the chloro-s-triazines with 

5 daily multiple dosing regimen, which is very consistent 

6 with the relatively short half-lives of the triazines, 

7 compared with the 24-hour dosing interval. 

8

9 The one dose I looked at fairly intensively was the 50 

10 milligram per kilogram per day oral gavage treatment. 

11 The longest half-life of the four triazines was that for 

12 DACT, which was about seven hours. It should be noted it 

13 was a much longer half-life, starting around 36 hours 

14 after the fourth dose, and that half-life was about 17 

15 hours. But that half-life controls just an insignificant 

16 fraction of the overall accumulation of the systemic 

17 exposure to DACT. 

18

19 So the seven-hour half-life and 24-hour dosing interval 

20 indicate that accumulation would be negligible, and 

21 therefore, that exposure after the first dose is pretty 

22 much the same as you would see at steady state on Day 4. 

23

24 So since the accumulation of nacreous triazines is 

25 negligible when atrazine is dosed daily by oral gavage, 

26 the time to effect is apparently not controlled by the 

27 time required for the systemic concentrations to reach a 

28 minimum critical level associated with the onset of 

29 effect, as the triazine exposure, after the first daily 

30 dose, is similar to that after the fourth dose. 

31
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1 So the logic here is we're not looking at a 

2 pharmacokinetically-controlled accumulation to some 

3 threshold level because you must hit that threshold level 

4 right after the first dose, so it doesn't take four days 

5 of dosing to get there. 

6

7 So we concluded that it is therefore more probable that 

8 the time to onset of effect is controlled by the 

9 pharmacodynamics; in other words, the kinetics of events 

10 downstream from the chemical initiating event are in 

11 control of the onset of effect. And the kinetics of 

12 downstream adverse outcome pathway events for LH 

13 attenuation in human versus rat are not well 

14 characterized and it is therefore, not apparent what the 

15 appropriate duration of human exposure is to use in 

16 conjunction with setting maximum level of exposure to 

17 prevent LH attenuation in humans. Without the relative 

18 rat versus human effect kinetics, the conservative 

19 approach would appear to be to use the four-day duration 

20 identified in the studies with rats.

21

22 And so that's based on the individual chloro-s-triazines. 

23 For total radioactivity, plasma concentration, the 

24 elimination half-life is longer and the expected 

25 accumulation profile has a considerably longer time to 

26 steady state. In this case, the daily exposure would 

27 increase day-by-day, with three to four days of exposure, 

28 required to achieve 90 percent of the steady state plasma 

29 concentration. 

30

31 An accumulation to a threshold concentration could define 

32 the time to onset of the LH surge suppression. As long 
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1 as the long half-life of total radioactivity likely 

2 reflects the half-life of albumin adducts, which are not 

3 active in LH surge suppression, this explanation of the 

4 four-day exposure being defined by the time to reach 

5 steady state is unlikely. 

6

7 DR. DANIEL SCHLENK: Thank you. Dr. Chambers?

8

9 DR. JANICE CHAMBERS: I was actually not part of that earlier 

10 discussion group. I don‟t know what that means, but the 

11 only thought that I had that I think is worth reiterating

12 is something you mentioned also, that it‟s probably just 

13 a coincidence that the four-day and the 28-day 

14 extrapolation are just a coincidence, nothing to do with 

15 a common mechanism and an amount leading to an effect.

16

17 DR. DANIEL SCHLENK: Okay. Dr. Greenwood?

18

19 DR. RICHARD GREENWOOD: I think I've already said pretty much 

20 what I needed to say earlier. I think I just, again, I 

21 support what Dr. Chambers' said. It's just really very 

22 difficult in the absence of any scientific knowledge to 

23 be able to extrapolate to human exposure that's 

24 equivalent to this exposure that's necessary to suppress

25 the LH surge in rats.

26

27 DR. DANIEL SCHLENK: Okay. Dr. Fenner-Crisp?

28

29 DR. PENELOPE FENNER-CRISP: I don‟t have anything to add. I 

30 had my input yesterday.

31

32 DR. DANIEL SCHLENK: Okay. Dr. Meek?
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1

2 DR. BETTE MEEK: I don‟t have very much to add. I think it's 

3 important to recognize that if you use the four-day kind 

4 of period, that‟s really a science policy choice to be 

5 conservative because essentially what we were saying is 

6 that there's this really limited information on which to 

7 base that period. 

8

9 Just one other point I'd like to make is the allometric 

10 scaling that was done for the 21 to 30 days in humans, 

11 you normally wouldn't use allometric scaling where you're 

12 expecting the effect to be mediated by metabolites. So 

13 it's probably something to think about.

14

15 DR. DANIEL SCHLENK: Other panel members? No? Okay. Let‟s 

16 move onto B. Dr. Hayton?

17

18 DR. WILLIAM HAYTON: For this question we did consult, Dr. 

19 Greenwood and I, briefly with Dr. Rodriguez about what 

20 they were really looking for there. We had difficulty 

21 really responding to their question because the molecular 

22 initiating event and the adverse outcome pathway are not 

23 well enough understood at this point, we felt, to fully 

24 address this question. It seems possible that the 

25 kinetics of events downstream from the chemical 

26 initiating event control the time to onset of LH 

27 attenuation. 

28

29 Another factor to consider is the minimum duration of LH 

30 attenuation that must occur before adverse toxicological 

31 effects ensue. Is a brief transient suppression for LH 

32 to be avoided or suppression of longer duration? How 
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1 large a suppression of the LH surge must be avoided? And 

2 we felt there just isn‟t the quantitative information 

3 available to answer those questions. So without answers, 

4 you know, I guess this would be science policy kind of 

5 consideration, but a conservative approach would be to 

6 avoid even a brief transient suppression of LH, but 

7 really no evidence to conclude that.

8

9 DR. DANIEL SCHLENK: Dr. Chambers?

10

11 DR. JANICE CHAMBERS: The only thing I wanted to add here is a 

12 couple of things that have already been said; 1) is I 

13 don't think that it is really understood whether a steady 

14 state concentration is what's causing the effect or 

15 whether it's a high-dose pulse. So it's hard to 

16 interpret this question in not knowing that.

17

18 And the other thing that I think that is worth 

19 reiterating is that it's been mentioned several times 

20 that the suppression in the LH surge may not really be a 

21 truly adverse effect at this point. So we don‟t know.

22

23 DR. DANIEL SCHLENK: Dr. Greenwood.

24

25 DR. RICHARD GREENWOOD: I don‟t think I have anything to add.

26

27 DR. DANIEL SCHLENK: Dr. Fenner-Crisp?

28

29 DR. PENELOPE FENNER-CRISP: I'd probably ask a different 

30 question here. I'd probably ask a question about what 

31 exposure duration in humans not most clearly corresponds 

32 to the exposure duration found to cause adverse effects 
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1 in rats, but rather, what exposure duration in humans 

2 would be needed to induce, in effect, representative or a 

3 correlate to those observed in the rat studies. 

4

5 We've talked about the menstrual cycle correlations in 

6 terms of time. We haven‟t talked about expected 

7 durations of exposure for any of the plethora of other 

8 adverse effects seen in the animal studies that may well 

9 have human correlates like the delaying in puberty and 

10 all those kinds of things. I think that would be the 

11 appropriate question to ask. It may well be something 

12 other than either the 4 or the 14 of the 28, depending 

13 upon which adverse correlate you're talking about.

14

15 DR. DANIEL SCHLENK: Okay. Dr. Meek?

16

17 DR. BETTE MEEK: I think I would echo. I would certainly 

18 like to see the discussion broaden to consider other than 

19 simply the effect on LH attenuation. So again, it would 

20 be drawing on more of the data to consider what the 

21 appropriate kind of timeframe might be.

22

23 DR. DANIEL SCHLENK: Other panel members? Dr. O'Byrne?

24

25 DR. KEVIN O'BYRNE: I suppose the frustration here is the 

26 huge difference between primates, whether it's rhesus 

27 monkeys or humans and the rat model. The whole panel is 

28 mindful of this because it was discussed apparently in 

29 September when Tony Plant was here and gave a lengthy 

30 presentation. He touched on this just the other day on 

31 certain salient aspects of the difference. And that's 

32 just the surge generating mechanism. The different time 
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1 scale, you know, days rather than hours of the surge -

2 the estrogen dependence. 

3

4 I mean, you got to have 30, 36 hours of continuous 

5 estrogen stimulation to get a surge in a monkey or a 

6 woman, or a man if you remove his testicles. But in 

7 terms of moving to puberty, I mean, the time scales are 

8 even greater between primates and rats. So that‟s an 

9 even greater mind field, as far as I can see.

10

11 DR. DANIEL SCHLENK: Dr. Roby?

12

13 DR. KATHERINE ROBY: I think the other complicating factor is 

14 if you're talking about a one-time event where you were 

15 causing a one-time transient inhibition of LH, and if 

16 we're talking about onset of puberty. A one-time event 

17 really is not going to have a significant effect on the 

18 onset of puberty. If you're talking about eliminating LH 

19 for an extended amount of time, then you'll have a 

20 negative effect downstream. But again, the one-time very 

21 transient inhibition in itself is going to have minimal 

22 effect, even on something like the onset of puberty.

23

24 DR. PENELOPE FENNER-CRISP: My comment is, in the four-day 

25 window of exposure would be of no use in answering that 

26 question.

27

28 DR. KATHERINE ROBY: Correct.

29

30 DR. DANIEL SCHLENK: Any other comments from the panel on B? 

31 Okay. Let's move on to Letter C. Dr. Hayton?

32
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1 DR. WILLIAM HAYTON: Yeah. Just to briefly review, I guess 

2 it‟s on the screen, isn't it? That they're asking for 

3 comment -- the Agency is asking for comment on using 

4 water; the area under the curve estimates that calculated

5 time-weighted daily average of atrazine exposure for a 

6 given duration of concern. 

7

8 And the response is that the approach is theoretically 

9 sound. The integral of the water chemograph, divided by 

10 the time span of the chemograph provides an estimate of 

11 the time-weighted average concentration of triazines in 

12 the water during the time span. And multiplication of 

13 the average concentration by the daily water ingestion 

14 rate quantifies the daily triazines dose. And the daily 

15 dose divided by the human triazine clearance, which is 

16 estimated allometrically, provides an estimate of the 

17 steady state average plasma concentration of 

18 radioactivity. And when that is multiplied by 24 hours, 

19 then the AUC for the human over a 24-hour period is 

20 obtained. I think we all agreed that if you make the 

21 assumption that it's behaving as a one compartment system 

22 that will work. 

23

24 And then I had one little additional piece here. The use 

25 of water AUC to calculate a time-weighted daily average 

26 is theoretically sound. An alternative to the water AUC 

27 is simply to average the measured water concentrations, 

28 but this would be inferior. You wouldn't get a time29 weighted average; it would just give you a simple 

30 arithmetic average. So we didn‟t see a better way 

31 forward there.

32
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1 DR. DANIEL SCHLENK: Okay. Dr. Chambers?

2

3 DR. JANICE CHAMBERS: Nothing to add.

4

5 DR. DANIEL SCHLENK: Dr. Greenwood?

6

7 DR. RICHARD GREENWOOD: I think that the approach is sound. I 

8 agree with that, in terms of theoretically sound. I 

9 think it just is necessary to check some of the 

10 underlying assumptions about that pharmacokinetic curve.

11

12 DR. DANIEL SCHLENK: Dr. Fenner-Crisp?

13

14 DR. PENELOPE FENNER-CRISP: I don‟t have anything to add.

15

16 DR. DANIEL SCHLENK: And Dr. Meek?

17

18 DR. BETTE MEEK: Nothing to add.

19

20 DR. DANIEL SCHLENK: Other panel members? Okay. That 

21 completes 13. Let me go back to the EPA and ask if they 

22 have any questions or clarification.

23

24 DR. ELIZABETH MENDEZ: I'm just looking at the team, and I 

25 don‟t see anybody that is jumping out with a question. 

26 So I guess we could move onto Question 14.

27

28 DR. DANIEL SCHLENK: Okay. Sounds good. Hold on. Dr. 

29 Portier has a question.

30

31 DR. KENNETH PORTIER: And it‟s to Dr. Roby. You know, I was 

32 listening to what you were saying and I thought to myself 
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1 well, under a scenario, suppose that just slightly above 

2 background atrazine were enough to completely suppress LH 

3 in the human female. In a typical year it would be maybe 

4 three months, right? So what do you think the impact 

5 would be if three months out of every year you had that 

6 suppressed? And I was sitting there thinking, I don‟t 

7 know if we know the answer to that, although women take 

8 birth control pills. I guess if you're really forgetful, 

9 you could be doing it three times out of a year, but do 

10 you have any -- I'm thinking worst-case scenario here. 

11 I'm just trying to think --

12

13 DR. KATHERINE ROBY: Let's put that scenario in a mature 

14 reproductive woman. In that case, probably little 

15 because we have the example of oral contraceptives that 

16 are doing the same thing. And now there are oral 

17 contraceptives that basically inhibit the cycle for 

18 months at a time, in a row. So the effect, again, 

19 probably very little because we know that those oral 

20 contraceptives are safe, and when you stop using them, 

21 you reinitiate your cyclicity, which is the important 

22 endpoint. 

23

24 Now, if that were to happen at maybe a different life 

25 stage, maybe the impact could be a little bit more, but 

26 if we take the mature situation where we assume the 

27 cyclicity in the brain, it‟s reinitiated when you stop 

28 the inhibitory effect. It would, again, just push 

29 puberty a little bit further and it might hasten 

30 senescence or menopause, or the transition through 

31 menopause. So whether those are significant adverse 

32 events, I guess remains to be decided.
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1

2 DR. KENNETH PORTIER: This is Dr. Portier. I think that 

3 thinking would be good for Section B if you kind of added 

4 that in because you guys were focusing on the very short5 term exposure and I was sitting there saying well, but 

6 the worst-case scenario is we use it during the season 

7 and it really impacts the woman. And what I'm hearing 

8 you say is well, even if it did that, you know, our 

9 current knowledge might seem to indicate, at least in a 

10 normal adult woman, if it were operating like an oral 

11 contraceptive, the long-term impacts could be mild.

12

13 Now, what we don‟t know is whether atrazine maybe is 

14 different than direct estrogen and progesterone in the 

15 sense of what it does to the brain and whether it short16 circuit something else, and I think I got that 

17 implication. 

18

19 DR. DANIEL SCHLENK: Just to clarify, what question do you 

20 think that should be added to?

21

22 DR. DANIEL SCHLENK: Oh, B -- on 13(b)

23

24 DR. KENNETH PORTIER: B, talking about exposure duration in 

25 humans.

26

27 DR. DANIEL SCHLENK: Oh, okay. Dr. O'Byrne?

28

29 DR. KEVIN O'BYRNE: I think there's a slight difference 

30 because when you're on oral contraceptive pill, then 

31 you're not hypoestrogenic. It's quite possible that if 

32 atrazine is switching off your pulse generator and that 
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1 may be common, maybe similar to functional hypothalamic 

2 amenorrhea, where you would be hypoestrogenic. So there 

3 you could end up with potential for osteoporosis, et 

4 cetera, et cetera. 

5

6 But I'm just mindful of all the seasonal animals that 

7 switch off for weeks or months or even half-a-year, and 

8 there's no adverse effect there. I mean, they just come 

9 back and breed and switch off again, year in/year out. I 

10 still feel that even a brief loss of reproductive 

11 function would not have any great impact.

12

13 DR. DANIEL SCHLENK: Okay. Any other comment? Dr. Horseman?

14

15 DR. NELSON HORSEMAN: I think I'd like to speak for the males 

16 in the Midwest. Well, this discussion about integrating 

17 this into the female reproductive cycle and we haven‟t 

18 talked at all about suppressing LH pulse generation in 

19 males, which I don‟t mind too much seasonal suppression 

20 of reproductive function in ground squirrels. I don't 

21 think in men that would be such a great thing. There has 

22 been no consideration of how you might view this LH surge 

23 suppression in males.

24

25 DR. DANIEL SCHLENK: Okay. Dr. Roby first.

26

27 DR. KATHERINE ROBY: So I guess my comments are assuming that 

28 the only effect is at the level of the ovulatory surge, I 

29 think that if there's a level of effect at the pulse 

30 throughout the rest of the cycle, then it's a different 

31 story. Absolutely.

32
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1 DR. DANIEL SCHLENK: And Dr. O'Byrne?

2

3 DR. KEVIN O'BYRNE: I can see a male contraceptive coming on 

4 line here, but I think the thing to appreciate here is 

5 that a reduction of post generator frequency in the

6 female is serious business because the whole menstrual 

7 cycle is exquisitely sensitive to changes in post 

8 generator frequency. Us men, our pulse generator can be 

9 knocked down quite considerably, and we still produce 

10 testosterone to protect our bones and maintain our 

11 libido, et cetera, et cetera, and our spermatogenesis. 

12 So we're much more robust than women, in that respect.

13

14 DR. DANIEL SCHLENK: Nice addition there. Way to pull the 

15 foot out of the mouth on that one. Very good. Very, 

16 very diplomatic there. Any other comments, with that? 

17 Okay. Let's go ahead then and read in Question 14.

18

19 DR. ELIZABETH MENDEZ: Question 14 relates to the case study 

20 that was at the end of the Issue Paper and the Agency's 

21 use of the 95th and 5th percentile of conditional

22 simulations of daily concentration. "Please comment on 

23 the use of a 95th percentile of the conditional 

24 simulations for providing an upper bound on rolling 

25 average concentrations in the case study."

26

27 DR. DANIEL SCHLENK: Our lead discussion for that is Dr. 

28 Portier.

29

30 DR. KENNETH PORTIER: Thank you. This is Ken Portier. While 

31 they're bringing up my slides, I just have some slides 

32 for illustration. I want to read this into the record.
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1

2 EPA asked this panel to discuss its epidemiology and PK 

3 findings in light of the framework discussed before the 

4 SAP in February 2010, the consultation with the panel. I 

5 want to say for the record that as I write up this 

6 discussion that we had right before lunch, I'll be 

7 including in this report a copy of Figure 1 from the 

8 February 2010 SAP report, and that Figure 1 is of the 

9 framework diagram. So I can refer back to it and kind of 

10 put the discussion in context. 

11

12 In addition, it's very likely that a second figure may be 

13 included in the report to allow better illustration of 

14 the issues and discussion on this topic. And the second 

15 figure is likely to be a slight enhancement or 

16 modification to the framework diagram. Of course, I 

17 don't have it at this point. So I can‟t show it to you, 

18 for the record. So just watch my hands, and we're going 

19 to do this. But I just wanted it for the record. 

20

21 The panel likes to make sure that nothing shows up in our 

22 report that we haven't talked about in the room. And so 

23 I wanted to make sure that you were warned that when you 

24 see these diagrams, you're not surprised because I was 

25 trying to figure out how to write up the discussion 

26 without the diagram. I don‟t think that I could do it. 

27

28 A lot of what we're going to be talking about here really 

29 follows on Questions 1 through 4. If we don‟t get what 

30 we talked about in one to four correct, we're not going 

31 to be able to do this correctly as well. 

32
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1 So the AUC water value is a time series that's computed 

2 from an input water concentration time series by 

3 successfully integrating, numerically, using this 

4 trapezoin method, over the period of concern, say four 

5 days, and then it uses this as input into computing the 

6 AUC plasma value. 

7

8 The time step for the underlying concentration time 

9 series is daily, and the resulting time step for the AUC 

10 time series is also daily. The goal is the estimation of 

11 human average daily concentration of atrazine over the 

12 period of concern. For the four-day duration of concern, 

13 concentrations from four sequential days -- in this case, 

14 using five actual time points -- are input into the 

15 trapezoid method to produce an average AUC value. 

16

17 For the 14-day duration concern, we used 15 data points. 

18 And for the 28, we're going to use 29 data points. An 

19 example of this, you can see Figure 21 in the white 

20 paper. Again, this is just trying to make sure everybody 

21 understands what's going on here because the write-up in 

22 the white paper was pretty tight. It wasn't a lot of 

23 illustration there. 

24

25 The first period of concern, for example, uses 

26 concentrations from Days 1 to 5, and the second period 

27 using Days 2 to 6. So it's really a rolling average kind 

28 of thing that's going on. So we have a total of T days 

29 of data. We're going to have T-minus four data points in 

30 the subsequent AUC time series. 

31
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1 So consider first the daily sampling concentration time 

2 series. In Figure 29(a), it shows the actual daily time 

3 series curve in a simulated 95th percentile curve and a 

4 simulated 5-percentile curve. Passing the actual daily 

5 sampling time series through the numerical integration 

6 function produces the actual AUC water times. 

7

8 So what I've done here is I tried to recreate what EPA 

9 did in that Figure 29(a), but I don‟t have all the 

10 kriging tools and everything else. So what I did is I 

11 took the data from the 1995 Maui River data and I kind of 

12 smoothed it out to produce a daily time series. I 

13 apologize for those of you way in the back, but right in 

14 the middle of all that green is a solid black line and 

15 that represents the actual smoothed time series for a 

16 concentration. And the green around that is the 1,000 

17 simulated time series with uncertainty or noise. 

18

19 Just as we did with the geospatial model where you added 

20 some variability for those points between the sample 

21 points, you get kind of noise. You get 1,000 

22 realizations of what this time series might have really 

23 been. And the black line becomes the average. And what 

24 EPA did -- and again, I apologize for those of you in the 

25 back -- I've just drawn a dotted line across the top of 

26 all those greens and a dotted line along the bottom of 

27 the greens that represents the 95th -- or the maximum, if 

28 you like -- upper bound and the minimum lower bound. I 

29 could've done it on the 95th percentile. I just wasn't 

30 thinking at 11:30 last night as I was doing it. But that 

31 top curve represents -- you can think of the top curve as 
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1 representing the 95th percentile of all those simulations 

2 and the bottom one representing the bottom percentile.

3

4 So you have these kind of three curves. You have the top 

5 curve, the 95 max, and the bottom curve, the 95 min in 

6 the black line, and then this is past -- so here are 

7 these curves and we pass the concentration curve through 

8 this trapezoidal integrator, which computes and average, 

9 and you end up with these three time series. Kind of one 

10 in the middle which represents the mean or the median; a 

11 95th percentile upper and a 95th percentile lower. So 

12 all I did was take that dotted line from the previous 

13 graph, pass it though the AUC formula and here's what I 

14 get back again. This is actually AUC times 24 hours. I 

15 didn‟t divide it by 24; it just would change the scale.

16

17 If I did that same AUC process and did it for every one 

18 of the thousand simulations, I get something that looks 

19 like this. And the point I was trying to make is that 

20 you can't take the percentile function from the 

21 concentration and just kind of directly pass it through 

22 the integrator. You have to integrate every one of the 

23 realizations and then compute the upper 95th percentile 

24 and the lower 95th percentile because that would be the 

25 top of the green line. The top of the green area is a 

26 better estimate of that. 

27

28 So you can see what EPA did. My understanding from 

29 reading the document and then talking personally with 

30 Nelson, what they did was take the formula and pass it 

31 through and I think that produces a very conservative 

32 upper concentration AUC estimate. And a better way to do 
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1 it would be to pass your simulations through and then 

2 compute your statistics on the simulated numbers. I 

3 think that's the whole point of what I wanted to make in 

4 my comments, and these graphs will all be in the 

5 document.

6

7 So what I was saying is that the way EPA did it is not 

8 the way a statistician would have done it. We would have 

9 gone back to the original, individual simulations, run 

10 those through the AUC average and then compute the 

11 percentiles and use those as our estimates of upper and 

12 lower bounds. And I think they'd be more realistic, 

13 assuming everything that we discussed in Questions 1 

14 through 4, we get the model fitting right. We get the 

15 infill correct and all this other stuff. 

16

17 I want to make two other points that kind of came to me 

18 last night. So part of the discussion we have is where 

19 we're doing just averaging. You're saying well, what if 

20 I have the daily time series? What if I average and look 

21 at four-day averages or seven-day averages or 14-day 

22 averages or 28-day averages? Again, these don't show up 

23 really well in the back, but the point we were trying to 

24 make the other day is that as you average the variability

25 and the process gets decreased, and that as you actually 

26 expand the average amount, you could lose some of the 

27 patterning. The point I was making, you lose some of the 

28 duration of exposure information, so that you can see for 

29 four and seven-day averaging it's not bad, but once you 

30 get to say, 28-day averaging, you've lost two of the 

31 major peaks. They've been averaged into one peak. And 

32 that one peak is much broader than the individual two 
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1 peaks that we average then. And so you get the wrong 

2 picture. So you don‟t see a lot of interest on our part

3 when you're dealing with kind of an average time series. 

4 We'd rather you deal with the daily time series and 

5 infill. 

6

7 And then the second point is that when simple averaging -

8 - all I did here was take four days and averaged. You 

9 know, make a window of four days, and get an average. 

10 Move it one, get another average. That‟s what that red 

11 graph is. But for the AUC, when we use that trapezoid 

12 function, we're doing exactly the same thing, it‟s just a 

13 slightly different averaging. So on the previous slide I 

14 took four points and averaged it. On this slide you're 

15 really taking five points and you're giving half the 

16 weight to the first point and the fifth point, and full 

17 weight to the middle three. It‟s a slightly different 

18 average. It's going to be slightly smoother, but I think 

19 if I had taken the red line here and the red line on the 

20 previous graph, they would've been almost identical.

21

22 So what you're really doing when you're computing the AUC 

23 is very similar to what you're doing when you're 

24 averaging these series. I mean, they're the same thing, 

25 except that the AUC is scaled to an hourly value, at 

26 least using your equation. So to me, the bottom line 

27 here is I wouldn't want to be computing AUCs on five or 

28 four-day averages because I basically would be averaging 

29 averages. And you‟re going to end up with the equivalent 

30 of a 16-day average instead of what you think you're 

31 doing is four-day averaging. It's kind of hard to see 

32 that. I think that was the statistical way of looking at 
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1 this issue of the 95th percentile and providing an upper 

2 bound on rolling average concentration. So with that, 

3 I'll rest to the panel.

4

5 DR. DANIEL SCHLENK: Thanks, Dr. Portier. Dr. Fenner-Crisp is 

6 our first associate on that.

7

8 DR. PENELOPE FENNER-CRISP: I couldn't possibly add anything 

9 to that. 

10

11 DR. DANIEL SCHLENK: Dr. Greenwood.

12

13 DR. RICHARD GREENWOOD: Me too.

14

15 DR. DANIEL SCHLENK: Ditto, I guess, huh? Dr. Griffith.

16

17 DR. DANIEL GRIFFITH: Sorry. I have something to add.

18

19 DR. DANIEL SCHLENK: I know. Fabulous. 

20

21 DR. DANIEL GRIFFITH: I agree. It reflects back on some of 

22 the discussion from Questions 1 through 4. And again, 

23 I'll read this. I made a comment yesterday, which I 

24 think links to Questions 1 through 4 as well about 

25 sources of error and some of that will come out in here. 

26 I do have one table which I will describe. 

27

28 Identified sources of error noted in the reports include 

29 sample size, to which sampling error links, spatial and 

30 temporal proximity of samples which alludes to coverage 

31 and hence, quality of samples. But I note that spatial 

32 proximity does not appear to be used, and the nature of a 
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1 given phenomenon, in other words, its inherent 

2 variability. Model misspecification should be added to 

3 this list, as should measurement error. This latter 

4 source of error could be linked to the substitution of 

5 kriged or deterministic model-generated imputations into 

6 a daily time series, as well as the handling of below 

7 detection limit values. 

8

9 Although it furnishes a tool to ascertain uncertainty and 

10 risk, conditional simulation, which utilizes Monte Carlo 

11 techniques, does not embrace all of these sources of 

12 error. One weakness is that conditional simulation is 

13 sensitive to the data upon which conditioning is made. A 

14 simulation replicates its conditioning values on average. 

15 Frequently, the normal or log-normal distribution is the 

16 probability model of choice that is attached to the 

17 conditioning values. In other words, the conditioning 

18 values may be the means of a collection of normal 

19 distributions, one for each day in a time series. 

20

21 For the specimen atrazine data, a log-normal distribution 

22 assumption fails to adequately track the serial 

23 correlation in the data and furnishes a poorer 

24 statistical description from the monitoring data than 

25 selected alternatives statistical distributions. 

26

27 When considering a 90 percent confidence interval, which 

28 is what the 5th to 95th percentiles are referring to, a 

29 balance should be maintained between claiming an 

30 excessive atrazine concentration when one does not exist, 

31 and failing to detect an excessive atrazine concentration 

32 when one does exist.
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1

2 The latter is the riskier of these two situations. For 

3 the purpose of atrazine impact analysis, this confidence 

4 interval focuses attention on the 95th percentile for 

5 conditional simulation. Because rolling average 

6 concentration are means, by definition they result from 

7 smoothing data so that peaks disappear. Consequently, 

8 actual peak concentrations are underestimated. These 

9 averages are easier to predict, specifically because they 

10 are means. 

11

12 Figures 27 and 29 illustrate that 14 and 28-day rolling 

13 averages may be of little value for decision-making and 

14 monitoring purposes, even though they have relatively 

15 tight confidence intervals. Although four-day rolling 

16 averages are better, the crucial peak missed by them is a 

17 substantial peak. This point may be of less importance 

18 if the rolling averages represent duration of exposure. 

19

20 The situation may well improve what the change in the 

21 variogram model as well as the change in the probability 

22 model employed for the stochastic simulation. A 

23 variogram model that better captures autocorrelation 

24 effects will better differentiate between the conditional 

25 and stochastic components. A probability model that 

26 allows more variability has potential to better capture 

27 the peaks. And those go back to some of the comments I 

28 made earlier.

29

30 A standard conditional simulation fails to capture all 

31 sources of variation. It assumes that the conditioning, 

32 the kriged values, are fixed and true. We saw a 
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1 reference to this before. What happens then is that the 

2 confidence band shrink to -- and in the classical case --

3 they shrink to zero at the observe data as though those 

4 data values are true values. And then simulate sampling 

5 error about these values. It fails to incorporate 

6 parameter estimation error. The log-transformed atrazine 

7 example time series data reveals the following additional 

8 sources of error, and this is the table. 

9

10 So we see the spherical models being used quite 

11 extensively in the paper, and the spherical model has a 

12 relatively poor fit compared to other models. It's 

13 weighted some of the squared error, is 28, whereas, for 

14 the Gaussian, it's 13 and the Bessel function is 12.9. 

15 It gives a good nugget estimate because it can't estimate 

16 the nugget effect, so it defaults to zero. The Gaussian 

17 gives a slightly higher nugget effect. The Bessel 

18 function gives something very close to zero, but it's not 

19 statistically significant from zero. And the advantage 

20 of those two -- which is why they probably capture the 

21 pattern better -- is that the Gaussian and the Bessel 

22 have a cusp near the origin, and that cusp means that 

23 your autocorrelations structure actually goes out a bit 

24 stronger than what the spherical is capturing. 

25

26 The scale parameters between the spherical and the Bessel 

27 are about identical. The Gaussian is slightly lower. 

28 And then when you look at range, well the spherical has a

29 rang, by construction, the Gaussian and Bessel are 

30 asymptotic functions, so you can only get an effective 

31 range. The spherical suggests that there is a range of 

32 about 22 days. The Gaussian about 14 days, which is a 
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1 substantial difference, and then the Bessel actually 

2 suggests something more like 37 days. And if you look at 

3 the time series, just like the ones we saw, 37 actually 

4 seems more reasonable. 

5

6 The conditional simulation fails to incorporate 

7 measurement error. The substitution of selected 

8 quantifies for below detection limit values, should have 

9 minimal impact upon these results. I saw in the reports 

10 where .05 were used in some cases, in the Syngenta six 

11 supplemental water system monitoring data, it looked to 

12 me like they used .03. I don‟t think that that's going 

13 to make much difference. 

14

15 The variability in an assay to detect and quantify 

16 atrazine in water samples -- and my understanding from 

17 what I've reads on EPA websites is that the RaPID Assay 

18 Kit, analytical precision standards, apply and they're 

19 plus or minus 30 percent. So all of these measures can 

20 be off by as much as 30 percent above or below the 

21 reported values, which is substantial. And none of that 

22 measurement error is actually being captured in what has 

23 been done.

24

25 The cumulative effect of these errors, at least some of 

26 them may compound, which propagate through an analysis, 

27 may well invalidate the claim of a 95th percentile. 

28 Without tracing the cumulative effects of these different 

29 sources of error, perhaps a more representative approach 

30 would be to use a 97.5 percentile; in other words, switch 

31 to a 95 percent confidence interval to try and adjust for 

32 these, but as I said before, I think it would be much 
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1 better to see what is compounding, in terms of the error 

2 and how it's propagating through the system.

3

4 DR. DANIEL SCHLENK: Thank you, Dr. Griffith. Dr. Hayton?

5

6 DR. WILLIAM HAYTON: I can‟t add to that.

7

8 DR. DANIEL SCHLENK: Dr. Meek?

9

10 DR. BETTE MEEK: I have nothing to add.

11

12 DR. DANIEL SCHLENK: Okay. General panel comments? Yes, Dr. 

13 Gilliom?

14

15 DR. ROBERT GILLIOM: I guess I just want to add the 

16 perspective that I think all the statistical issues with 

17 the exposure estimates are manageable. And the exposure 

18 part of the equation is maybe two or three orders of 

19 magnitude easier to come to resolution on than the 

20 duration of exposure in organism that's of concern. 

21

22 It's kind of perspective on the answer rather than adding 

23 to the statistics, but I think all the exposure 

24 statistical issues -- and there were a lot of great 

25 comments made to consider and work in -- but it's 

26 relatively manageable. 

27

28 DR. DANIEL SCHLENK: Anyone else? Okay. Dr. Mendez, do you 

29 have any questions of clarification? Or Mr. Thurman?

30
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1 DR. NELSON THURMAN: I just want to say I agree with Bob 

2 Gilliom that as I was listening to this, I was thinking 

3 those are things that we can account for, we can do it. 

4

5 DR. DANIEL SCHLENK: Sounds promising. Okay. At this point, 

6 what I think we'll do is we'll just go around the table 

7 once. This is your chance to provide your final 

8 comments, anything that you would like to add to the 

9 record. Then we'll go back to the Agency and have some 

10 closing comments from them as well. Dr. Hayton, do you 

11 want to start us off?

12

13 DR. WILLIAM HAYTON: Nothing too profound. I guess the 

14 frustration is after so many years of looking at 

15 atrazine, we still don't really understand its chemical 

16 initiating event and all of that toxicodynamics. And if 

17 we knew that story, I think we would be quite a bit 

18 further down the road to solving a number of the 

19 questions.

20

21 DR. DANIEL SCHLENK: Dr. Greenwood?

22

23 DR. RICHARD GREENWOOD: I think we've seen a lot of progress 

24 over the last couple of years on the pharmacokinetic 

25 side. That's for sure. I feel quite confident with the 

26 way that's going forward, that we're going to have tools 

27 that are going to be able to help us with the internal 

28 exposure, just as we're getting the external exposure 

29 better defined. But I just echo what Dr. Hayton said, I 

30 really feel that until we can get hold of this primary 

31 lesion, at least one of them and maybe more, then we're 

32 still in the dark.
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1

2 DR. DANIEL SCHLENK: Dr. Meek?

3

4 DR. BETTE MEEK: Yeah. I'm really encouraged by the progress

5 on, for example, development of the physiologically based 

6 pharmacokinetic model. I think integration of information 

7 at the moment is a challenge, again, for the reasons that 

8 others have mentioned, but perhaps not insurmountable, 

9 given how much progress was made on the PBPK side in a 

10 very short period of time.

11

12 DR. DANIEL SCHLENK: Dr. Fenner-Crisp?

13

14 DR. PENELOPE FENNER-CRISP: I've been mulling over again the 

15 issue of determination of whether or not there is life 

16 state differences in sensitivities. And thinking again 

17 about the methodology that was used to determine whether 

18 or not that exists. And it‟s coming out in my mind to be 

19 an apples and oranges kind of thing. 

20

21 On the one hand, one's using an apparently not adverse 

22 effect precursor event to an apical event in the adult, 

23 the four-day LH surge suppression, comparing it against 

24 NOAEL's and LOAEL's for apical effects, generated in data 

25 for other life stages than the adult. And I'm wondering 

26 if it might not be appropriate to also do an analysis 

27 where you select an apical event in an adult that's a 

28 consequence of the LH surge suppression and compare it 

29 with the set of NOAEL's and LOAEL's that were used in the 

30 first instance; and then ask the question again, "Do you

31 see a differential sensitivity", and compare it with the 

32 original analysis. It may well turn out to be you would 
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1 conclude the same thing. Obviously, in that amount of 

2 time, I haven't done that analysis, but in might be an 

3 interesting exercise to see if you could, in fact, come 

4 to the same conclusion.

5

6 DR. DANIEL SCHLENK: Dr. Jerde?

7

8 DR. TRAVIS JERDE: Yeah, I agree. It‟s great to see progress 

9 on pharmacokinetics because I think what those of us on 

10 the cellular and molecular side need is to be able to 

11 conduct mechanistic studies on how atrazine and its 

12 metabolites may affect cells, tissues, and organisms as a 

13 whole; using concentrations that are reasonable, in terms 

14 of exposure and ground water exposure to effected 

15 populations. And that's kind of what I think this side 

16 of the field has been waiting for. And I also think 

17 maybe that‟s what epidemiology has been waiting for as 

18 well because they need to -- it‟s clear that we're not 

19 ready to make a conclusion, definitely yet, particularly 

20 in terms of cancer or non-cancer effects until we know 

21 what the exposure are and we can effectively study it, 

22 epidemiologically in the human and molecularly in models.

23

24 DR. DANIEL SCHLENK: Dr. Timms?

25

26 DR. BARRY TIMMS: Yes. I concur with Dr. Jerde that I think 

27 it‟s important to look at molecular mechanisms of action 

28 and with regard to that, it‟s also going to be important 

29 to determine the actual level of human exposure so we can 

30 be working in a framework of exposure that we can mimic 

31 in model systems.

32
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1 DR. DANIEL SCHLENK: Dr. Roby?

2

3 DR. KATHERINE ROBY: I really just agree with everything 

4 that's just been said down the table, understanding the 

5 mechanism, the actual exposure and the kinetics of the 

6 relevance to those two endpoints. That's all I have. 

7 Thank you.

8

9 DR. DANIEL SCHLENK: Dr. O'Byrne?

10

11 DR. KEVIN O'BYRNE: Well, I remain optimistic because the 

12 levels of atrazine that are needed to effect elements of 

13 the reproductive system are just extremely high. And in 

14 addition, my thoughts that the reproductive system has 

15 got such a huge margin of robustness that I have very 

16 little disquiet about atrazine. 

17

18 DR. DANIEL SCHLENK: Dr. Akana?

19

20 DR. SUSAN AKANA: Much to my amazement, I concur with Dr. 

21 O'Byrne. When I first was invited to the panels and 

22 started reading the literature, every alarm in my head 

23 was going off, in terms of stress and energy imbalance. 

24 And through the long process, I am really persuaded that 

25 the margins of safety -- or at least the effective doses 

26 that are affecting animals are not a major concern for my 

27 favorite endocrine axis.

28

29 DR. DANIEL SCHLENK: I'm glad you're relaxed. Bob Gilliom.

30

31 DR. ROBERT GILLIOM: I guess one of the main gaps in our 

32 knowledge that‟s frustrating to me -- and this has come 
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1 in a few side discussions -- is that there's so much more 

2 we could do with available data to look at the 

3 relationships between the population served by specific 

4 drinking water supplies and what's actually in the 

5 supplies. So I don‟t know enough about whether I'm 

6 calling it right, but to me, it‟s a category of an 

7 epidemiological study that can now be done fairly 

8 comprehensively and it‟s one of the main missing pieces, 

9 from what I hear, of linking all these theories and 

10 possibilities to increased incidence of adverse outcomes 

11 in people.

12

13 So the real life experiment is out there. We haven't 

14 sampled it or analyzed it. I would say we probably have 

15 sampled it, but we haven't organized and analyzed it yet. 

16 So whoever is to do it, I think there's a lot that could 

17 be done by mining the data and evaluating the 

18 relationships between these patterns of outcomes and the 

19 actual exposure that's happened over the last couple of 

20 decades. So that's my main point. 

21

22 The secondary point, I would add that has just kind of 

23 come up in thinking about the future is that we're using

24 past records of observations to reconstruct a lot of 

25 assumptions and understandings about future exposure and 

26 how to monitor it and so forth. And I think what we've 

27 seen with other chemicals -- and atrazine kind of stands 

28 out as an exception -- is that usually these things are 

29 going through changes over time that span over several to 

30 a few years and systematic downtrends or uptrends. 

31
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1 So I think I just want to put in the marker that we have 

2 to be aware that when we design the monitoring 

3 approaches, we have to anticipate that there are likely 

4 going to be long-term changes and shifts in regionality, 

5 perhaps, that just happen because of changing crop 

6 patterns - maybe genetically-modified crop come in and 

7 market forces, things like that, replacement compounds. 

8 So that we just have to have that built into the thinking 

9 of how we track future exposure and not assume it's a 

10 steady state. Thank you. 

11

12 DR. DANIEL SCHLENK: Dr. Griffith?

13

14 DR. DANIEL GRIFFITH: Well, I guess I'd like to echo two 

15 things that were raised yesterday, and one has to do with 

16 when we look at the impact on drinking water, what are 

17 the actual dosages for exposure and will they really have 

18 a consequence when humans are exposed to them?

19

20 And a second point that was raised that I think needs to 

21 be thought about is sort of void in the analysis so far, 

22 of the water data, is the whole notion of repetition. If 

23 you have repeated exposure, even at small levels, but it 

24 has cumulative damage, then you need to be looking at 

25 long-time series of water levels - atrazine levels in 

26 water, not just one year, and then the confounding factor 

27 that's going to impact on that is people move all the 

28 time and so what happens as people move in and out of 

29 these water systems. 

30

31 DR. DANIEL SCHLENK: Okay. Dr. McManaman?

32
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1 DR. JAMES MCMANAMAN: I was struck last time and continue to 

2 be struck this time by the difference between what the 

3 epidemiology data is telling us and what the toxicology 

4 data is telling us. And I'm concerned that we really 

5 don‟t have a way of integrating those two and we may not 

6 be using the correct animal models by focusing a lot on 

7 the rat. It‟s a convenient system. We understand its 

8 biology pretty well, but I think we might be getting 

9 mixed signals about mechanisms. So it makes it really 

10 difficult for those of us who are interested in 

11 mechanisms to say very much about it. 

12

13 DR. DANIEL SCHLENK: Dr. Horseman?

14

15 DR. NELSON HORSEMAN: The only thing I would add and I would 

16 echo the thoughts of some of the other panel members that 

17 it seems obvious that the EPA, and for that matter, the 

18 participation of the registrants and everyone else is 

19 doing a good job of regulating this in a safe way. 

20

21 But I do think that one of the issues I don't think we 

22 have come to grips with is that the mode of action and 

23 maybe in both the cancer side and this other reproductive 

24 toxicology side, but maybe not, there's an implication 

25 that this has to do with effects that are happening in 

26 the brain, particularly in the hypothalamus. But we 

27 really don‟t have any studies, at least good studies, 

28 focused on that organ. And I would think that if that's 

29 the mode of action everyone feels like is relevant, then 

30 direct studies on the brain as an organ -- and the 

31 hypothalamus as an organ -- need really to be 

32 prioritized. 
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1

2 DR. DANIEL SCHLENK: Dr. Young?

3

4 DR. HEATHER YOUNG: I just want to say I was encouraged by the 

5 fact that the Agency undertook a comprehensive review of 

6 the cancer epidemiology literature. I think it was a 

7 major step in the right direction. So I hope that they 

8 continue to do that. Also to echo Robert Gilliom's 

9 comment in that there are big gaps in the literature with 

10 regard to community exposures and the data there, the 

11 water monitoring data is there. There are cancer 

12 registries, there are birth defect registries. And so 

13 it‟s a matter of really combining those and getting some 

14 information on what some of the reproductive effects may 

15 be, and also looking at the community effects for cancer.

16

17 DR. DANIEL SCHLENK: Dr. Bove?

18

19 DR. FRANK BOVE: Ditto. I don‟t think it would take that much 

20 effort either, to do these types of studies. It could be 

21 done rather quickly. The states have the data. Simple 

22 data linkage would be important. Moving away from the 

23 studies we evaluated last time, the ecologic study and 

24 doing individual level studies using the cancer 

25 registries and birth defect registries.

26

27 DR. DANIEL SCHLENK: Dr. Gold?

28

29 DR. ELLEN GOLD: Well, being toward the end here, I'm not sure 

30 I have a lot to add. I concur with just about everything 

31 that's been said. I think the importance of looking at 

32 community water supply exposure is important because it's 
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1 chronic low-dose, as opposed to the high-dose that you 

2 see either in the applicators or the manufacturing 

3 workers or in the animals for that matter. So it might be 

4 more relevant. 

5

6 I just wanted to make one comment about the potential 

7 effect on menopause, age at menopause, which seems like 

8 it‟s not all that important, but there actually is a huge 

9 literature on age at menopause being an indicator for 

10 lots of long-term disease risk and life expectancy. 

11

12 But I would also note that oral contraceptive use in high 

13 parity usually delay with the age of menopause, which is 

14 usually a good indicator of long-term survival, life 

15 expectancy, early age at menopause associated with 

16 smoking is usually a bad thing. 

17

18 DR. DANIEL SCHLENK: Dr. Klaine?

19

20 DR. STEPHEN KLAINE: I wanted to comment on challenges with 

21 analyzing episodic exposure data and make the comment 

22 that, or suggest that in addition to looking at durations 

23 of exposure, you might also want to look at the duration 

24 of the periods between exposures. And they might be just 

25 as important. 

26

27 I think that as you get to the point of understanding the 

28 atrazine receptor better and the reversibility of that 

29 particular process, it'll probably better drive how you 

30 analyze your exposure data. So it's just something to 

31 keep in mind.

32
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1 DR. DANIEL SCHLENK: Dr. Chambers?

2

3 DR. JANICE CHAMBERS: I'll just kind of go back to the initial 

4 comments that were made at the first part of the table 

5 here; encouraged by the pharmacokinetics progress; kind 

6 of discouraged about not knowing what the mechanism is 

7 and do hope that a little bit more about that is known so 

8 that a good human relevant risk assessment can be done 

9 when you get to that in a couple of years.

10

11 DR. DANIEL SCHLENK: Dr. Portier?

12

13 DR. KENNETH PORTIER: I just wanted to point out, this is 

14 probably the first time in about 60 years that every 

15 member on the panel has had closing remarks to make. I 

16 think it speaks to the fact that everybody is engaged 

17 with EPA on this. The panel that has been here a number 

18 of times, those of us on the permanent panel, really want 

19 to kind of see this work. If we haven't answered all of 

20 your questions, it's probably because those questions 

21 can't be answered at this point in time and that there's 

22 a lot of research, epi and mechanism research, that needs 

23 to be done that we just kind of keep pointing at.

24

25 Unfortunately, we don‟t know who's going to do it and who

26 is going to pay for it, but if you're going to answer the 

27 questions, someone is going to have to do it and someone 

28 is going to have to pay for it.

29

30 DR. DANIEL SCHLENK: Yeah, I'll do it. Just closing comments: 

31 I also applaud the Agency's framework for this approach 

32 as we've had several panels, recently even, that have 
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1 pursued the adverse outcome pathway approach. I think 

2 it‟s the way to go. 

3

4 I would like to encourage the Agency, even though things 

5 may not be going as quickly as possible, but in my 

6 opinion, I think you're on the right track. It's just a 

7 matter of time to get those linkages that are out there 

8 that we've seen with other compounds, such as some of the 

9 cholinesterase inhibitors and thing of that nature, where 

10 there's been some success in that capacity. I think it's 

11 just a matter of time, I think, before the top down and 

12 bottom up actually meet in the middle, perhaps, in terms 

13 of the effects that are present. So I'm encouraged. 

14

15 As usual, when we come to these things, the amount of 

16 information we learn is amazing. So I‟d like to applaud 

17 the Agency for the presentations and the questions that 

18 you are asking. I think that's very encouraging. With 

19 that, I'll turn it over to Dr. Mendez, if you have any 

20 closing comments for yourself and the Agency. 

21

22 DR. ELIZABETH MENDEZ: On behalf of the team and the Agency, I 

23 want to express our deepest gratitude to the panel for 

24 their thoughtful considerations and deliberations during 

25 this process. 

26

27 As Dr. Portier alluded to, there are a lot of questions. 

28 There are some questions that you couldn‟t answer because 

29 the data is just not available. I'm personally a little 

30 bit encouraged by the fact that I think we're wrestling 

31 with the right issues. That, to me, is encouraging that 

32 we are looking where we need to be looking at. 
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1

2 As you may have noticed, during the past few days I have 

3 been furiously taking notes. You have given us a lot to 

4 think about, and as we move forward with this process, we 

5 will take your advice under consideration. 

6

7 As always, we remain committed to keep vigilant to any 

8 new developments, and as those become available, we will 

9 try to integrate it into our evaluations and we 

10 appreciate all the hard work. I mean, we understand that 

11 600 pages plus of documentation to go through is a lot 

12 and the fact that you've gone through it with such care 

13 and such precision, we truly appreciate that. 

14

15 DR. DANIEL SCHLENK: Thanks. I'd also like to express my 

16 appreciation to the panel for working together as well as 

17 you have. You know, on the permanent panel, we see a lot 

18 of these things and often times there's not the congruity 

19 or collaboration that takes place. And it's obvious you 

20 guys have gone the extra mile in working together to get 

21 us through a fairly lengthy process about a day ahead of 

22 schedule, and I really appreciate that and I appreciate 

23 you working together for that. That doesn't always 

24 happen and my thanks, at least personally, for that. 

25

26 I'd also like to appreciate or give thanks to the EPA, 

27 again, for their staff for the presentations, the PIs for 

28 the research that they are doing, and the FIFRA staff, 

29 Laura Bailey and the staff for making it as easy as 

30 possible to get here and to get away and making our stay 

31 as comfortable as possible. They‟ve done a great job for 

32 that. With that, I'll turn it over to Joe Bailey.
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1

2 JOSEPH BAILEY: Dan certainly covered all of the thanks that 

3 I wanted to make, but first and foremost, I do want to 

4 thank the panel for their commitment to come here and 

5 take the time out of their schedules and prepare as well 

6 as they did for the meeting; the time at the meeting as 

7 well as the follow-up work we'll be working on to get the 

8 report finished. Thanks to EPA, OPP, and ORD for working 

9 with us to coordinate all of this information. And the 

10 public commenters for bringing their thoughts forward. 

11 The final report, we will have it done within 90 days 

12 after the meeting. So thank you all very much.

13

14 DR. DANIEL SCHLENK: Meeting is adjourned.

15

16 (Whereupon, at 2:15 p.m., the meeting was adjourned.)

17 * * * * *