FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

 OPEN MEETING

 REEVALUATION OF THE HUMAN HEALTH EFFECTS

 OF ATRAZINE:

 REVIEW OF NON-CANCER EFFECTS AND

 DRINKING WATER MONITORING FREQUENCY

 DOCKET NUMBER: EPA-HQ-OPP-2010-0481

 UNITED STATES

 ENVIRONMENTAL PROTECTION AGENCY

 CONFERENCE CENTER LOBBY LEVEL

 ONE POTOMAC YARD (SOUTH BUILDING)

 2777 SOUTH CRYSTAL DRIVE

 ARLINGTON, VIRGINIA 22202

 SEPTEMBER 16, 2010

 8:30 A.M.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 2

1 FIFRA SCIENTIFIC ADVISORY PANEL

2 MEETING

3 SEPTEMBER 16, 2010

4 DR. STEVEN HEERINGA: Good morning

5 everyone. Welcome back to the third day of the FIFRA

6 Science Advisory Panel Meeting on the topic of the

7 Reevaluation of the Human Health Effects of Atrazine:

8 Review of Non-cancer Effects and Drinking Water

9 Monitoring Frequency.

10 I'm Steven Heeringa, I'm the Chair of

11 the Panel for today. This afternoon, I have an

12 obligation at NIH and Dr. Portier is going to step in

13 this chair and then I'll be back tomorrow morning.

14 I think at this time, if we could just

15 quickly go around the table one more time just to have

16 the Panel give their name and affiliation and we'll get

17 underway.

18 DR. KENNETH PORTIER: I'm Ken Portier,

19 Director of Statistics at the American Cancer Society

20 in Atlanta.

21 DR. JANICE CHAMBERS: I'm Jan Chambers,

22 I'm a professor at the College of Veterinary Medicine

23 at Mississippi State University.

24 DR. CAREY POPE: Carey Pope, Oklahoma

25 State University.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 3

1 DR. JOHN BUCHER: John Bucher, National

2 Toxicology Program.

3 DR. DANIEL SCHLENK: Dan Schlenk,

4 University of California, Riverside.

5 DR. RICHARD GREENWOOD: Richard

6 Greenwood, University of Portsmouth, UK.

7 DR. KANNAN KRISHNAN: Kannan Krishnan,

8 University of Montreal, Canada.

9 DR. MOIZ MUMTAZ: Moiz Mumtaz, ATSDR

10 CDC.

11 DR. NELSON HORSEMAN: Nelson Horseman,

12 University of Cincinnati.

13 DR. JAMES MCMANAMAN: And Jim McManaman,

14 University of Colorado.

15 DR. KATHERINE ROBY: Kathy Roby,

16 University of Kansas Medical Center.

17 DR. BARRY DELCLOS: Barry Delclos, FDA,

18 NCTR.

19 DR. SANDRA LEGAN: Sandy Legan,

20 University of Kentucky.

21 MR. WESLEY STONE: Wes Stone, United

22 States Geological Survey.

23 DR. RICHARD COUPE: Richard Coupe, US

24 Geological Survey.

25 DR. HERBERT LEE: Herbert Lee,
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 4

1 University of California, Santa Cruz.

2 DR. BETTE MEEK: Bette Meek, University

3 of Ottawa, Canada.

4 DR. SUSAN AKANA: Susan Akana,

5 University of California, San Francisco.

6 DR. PENELOPE FENNER-CRISP: Penny

7 Fenner-Crisp, Private Consultant, Charlottesville,

8 Virginia.

9 DR. ELLEN GOLD: Ellen Gold, University

10 of California, Davis.

11 DR. SHELLEY HARRIS: Shelley Harris,

12 University of Toronto and Cancer Care Ontario.

13 DR. GERALD LEBLANC: Jerry LeBlanc,

14 North Carolina State University.

15 DR. STEVEN HEERINGA: Thank you very

16 much, Panel members and before we begin, may we return

17 to our Designated Federal Official for the meetings,

18 Mr. Joe Bailey.

19 MR. JOSEPH BAILEY: Thank you, Dr.

20 Heeringa. Good morning, everyone, welcome back. I'm

21 Joe Bailey, Designated Federal Official for the

22 meeting. Just a couple of reminders that first of all,

23 this is a Federal Advisory Committee Act Meeting, which

24 means that the Panel's recommendations that are

25 presented to EPA are advice and recommendations only.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 5

1 And that any policy and regulatory decisions further

2 down the road are strictly the Agency's decisions after

3 taking into consideration advice the Panel gives. And

4 the other thing is all the presentations from

5 yesterday, I was hoping I would be able to get them in

6 the docket so they're available to everybody, but there

7 are some technical glitches, so they're not there yet.

8 But hopefully we'll get them there today and everybody

9 will have access to them. So again, welcome and thanks

10 for being here this morning.

11 DR. STEVEN HEERINGA: Thank you, Joe.

12 Just to remind us we are in the period of public

13 comment for this SAP meeting and we had concluded

14 yesterday with presentations by a number of public

15 commenters, but I agreed to leave the period of public

16 comment open through this morning in the event that the

17 Panel members had additional questions for any of the

18 public presenters yesterday. Have any additional

19 questions, again of clarification that would bear on

20 the charge questions or our proceedings, Dr. Akana, who

21 would you like to ?

22 DR. SUSAN AKANA: I believe it would be

23 Dr. Breckenridge from Syngenta.

24 DR. STEVEN HEERINGA: Possibly Dr.

25 Breckenridge and your colleagues who are here this
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 6

1 morning, if you would just like to come forward 'cause

2 I anticipate that...

3 DR. CHARLES BRECKENRIDGE: Mr. Chairman,

4 before I proceed with answering some question of Dr.

5 Akana, could I correct a statement that we made

6 yesterday in relationship to the life cycle for the

7 breast development?

8 DR. STEVEN HEERINGA: Yes, please do.

9 DR. CHARLES BRECKENRIDGE: Okay, so the

10 question was what duration of life cycle may be used in

11 that study, and I think the statement was that we used

12 1410. 1410 is correct for all the ALIC serves that we

13 did. The breast development study was unique in that

14 we were trying to replicate the conditions of Dr.

15 Fenton's work.

16 And so we obtained animals from Charles

17 River labs in Raleigh, just as she had done, and we

18 learned that Charles River labs maintained their

19 animals on a 12/12 schedule. And because we were

20 bringing those animals into the study on gestation days

21 four, five, six and seven, this was necessary for the

22 staggered nature of the design of the study.

23 We elected to keep those animals on a

24 12/12 schedule rather than switching a schedule to a

25 14/10. So as practice in the lab, we have a regular
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 7

1 14/10 schedule.

2 But for this particular instance, we

3 didn't want to disturb the animal in the sense of a

4 period where in fact we would be applying test material

5 within a very short time of their receipt at the

6 laboratory. So we were incorrectly stating the life

7 cycle in the breast development study yesterday.

8 DR. STEVEN HEERINGA: Thank you very

9 much, Dr. Breckenridge. Dr. Akana.

10 DR. SUSAN AKANA: Concerning the life

11 cycle, what time of the day in life cycle were the

12 gavages administered?

13 DR. CHARLES BRECKENRIDGE: Okay, so

14 there are many studies here and I will I guess I'll

15 refer to the latest ones. That is to say the studies

16 where we investigated the dose response function for

17 the intact animal model. And under those conditions,

18 the lights were turned on at 5:00 AM in the morning,

19 and the dose was administered just after lights were

20 turned on.

21 DR. SUSAN AKANA: And the question I'm

22 somewhat concerned about is the atrazine diets. The

23 diets that have atrazine in it. As you know, rats eat

24 normally in a circadian fashion and they eat about

25 maybe 20% of their food in the light cycle and the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 8

1 remainder in the dark, and generally in about two

2 manners.

3 Now the atrazine diets are they actually

4 palatable, do you have any sense of that? And do you

5 have evidence to believe animals are eating in a normal

6 meal pattern?

7 DR. CHARLES BRECKENRIDGE: Okay, thank

8 you very much. Yes, we were mindful of the fact that

9 the animals at high doses tend to consume less food

10 than controls, and that might in fact be part of

11 response to the body weight effect that we're getting

12 in the early stages of the dosing by diet.

13 We obviously used other data sets to try

14 to match the PPM concentration in diet so that we could

15 achieve the targeted dose as you remember in the high

16 risk group, for instance, we were looking to achieve 50

17 milligram per kg equivalent over the entire 24-hour

18 food consumption period. We fell somewhat short of

19 that in the toxicity study. It was 40 milligrams per

20 kg and we were slightly better in the PK component

21 which is about 43, 44 mgs per kg.

22 In addition, we also were aware of the

23 diurnal pattern of food intake, so what we in fact did

24 was we had a 14-10 schedule on this study and we did

25 food intake for each block of time. And when we
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 9

1 finally submit that report, you'll see what the dose

2 consumption was in the dark versus in the light.

3 And in regard to the dietary study,

4 especially as it relates to the kinetics component, we

5 started the compound administration the night before

6 the first day of blood collection, so that effectively

7 we had an entire night of dosing when we started to

8 collect the first blood samples for the PK component.

9 So in summary then, we basically

10 calculated individually daily doses. But we do know

11 the dose in half-day segments with 14 hours light, 10

12 hours dark. We would have had preferred it to be a

13 12/12 cycle.

14 But again, since that was the standard

15 model, we always use for the LH surge, we didn't want

16 to change that variable just to get a better or equal

17 representation. And I guess I should say that we

18 didn't quite see as strong a pattern as a 20/80 percent

19 shift between the light and dark but there clearly was

20 an impact.

21 DR. SUSAN AKANA: So if there was an

22 approximate diurnal rhythm in eating, there's probably

23 a diurnal rhythm in corticos drug?

24 DR. CHARLES BRECKENRIDGE: Yes.

25 DR. SUSAN AKANA: Thank you.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 10

1 DR. CHARLES BRECKENRIDGE: I guess I

2 could just add one really interesting fact that we

3 noted in the data as we were looking at food intake,

4 because we standard every animal to a four-day cycle

5 and every panel entered the study out on constant day

6 on a day of estrus it imparted scalloping the food

7 intake pattern because as I understand it, estrogen

8 actually modifies food intake and behavior in the

9 animal.

10 And that scalloping food intake

11 persisted through the entire 28th day where we had the

12 immuno tox phase, it was delightful to actually see

13 what happens when you standardize animals on a

14 physiologic parameter that impacts some other variable.

15 DR. STEVEN HEERINGA: Dr. McManaman.

16 DR. JAMES MCMANAMAN: Yeah, I also have

17 a followup question regarding the diet. I noticed that

18 the day of vaginal opening in your study where they

19 were dosed during gestation was significantly longer

20 than that of Dr. Fenton and I was wondering, do you

21 have any comments about possible explanations for that,

22 is there that much variability in this particular

23 strain of rats and day of vaginal opening? Do you know

24 what the variability is?

25 DR. CHARLES BRECKENRIDGE: We have only
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 11

1 investigated the Long Evans rat at one time in the

2 Coder study, and we did that explicitly matching with

3 Dr. Fenton's work. We have done and not only us, but

4 it's been reported on vaginal opening delays in various

5 case studies with the Wistar. We had a couple strains

6 of Sprague-Dawley. It is typically recorded that there

7 is a day or two shift in the vaginal opening on high

8 dose but I don't know if I can answer your question

9 with variability.

10 DR. JAMES MCMANAMAN: Well, it was

11 noteworthy that the atrazine dose did not affect

12 vaginal opening in your study as it did in her studies.

13 So that's my point.

14 DR. CHARLES BRECKENRIDGE: Okay. We

15 misunderstood. I guess most of those studies are

16 typically focusing on the non-gestational post delivery

17 and so the bulk of the evidence is actually in animals

18 treated from birth onward and that is clearly the way

19 you're saying it affects this, but this was the first

20 time that gestational effect was recorded.

21 DR. JAMES MCMANAMAN: So another

22 possible difference between the two studies might be

23 the way the animals were housed. They were all housed

24 singly. In your study, were they housed in plastic or

25 wire mesh cages?
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 12

1 DR. CHARLES BRECKENRIDGE: They normally

2 do these studies in plastic to create an environment

3 specific behavior, so yes, plastic.

4 DR. STEVEN HEERINGA: Dr. Chambers.

5 DR. JANICE CHAMBERS: I have a couple of

6 follow-up questions on the kinetic study also. Was

7 there any residual solvent in the food that was used in

8 the dietary aspect of this study?

9 DR. CHARLES BRECKENRIDGE: No, we did

10 not use solvent variants just a mixture I believe. I

11 might have to verify that.

12 DR. JANICE CHAMBERS: Also, do you, for

13 the gavage study, those were not fluid fed animals, is

14 that right?

15 DR. CHARLES BRECKENRIDGE: That's

16 correct

17 DR. JANICE CHAMBERS: And do you have

18 any idea what the amount or proportion of absorption of

19 the atrazine in either the gavage or the dietary

20 studies was?

21 DR. CHARLES BRECKENRIDGE: I think

22 someone else can better address that question.

23 DR. DAVID KIM: So you're asking, after

24 X amount of atrazine mixture given to the animals via

25 gavage, how much of it hasn't absorbed? We'll be able
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 13

1 to quantify that when we get into the PK model and when

2 we start summarizing the data but what we do know is

3 that within five minutes we're seeing atrazine enter

4 the systemic circulation.

5 So there's rapid intake into the

6 systemic circulation. We did some pharmacy mass

7 balance studies and have conducted in the past. But 1

8 percent after oral intake, 1% parent material is

9 measured in this. A lot of it is converted into

10 metabolites because of past effects. So we'll be able

11 to tease out all these factors that affect uptake when

12 we get the PK model.

13 DR. CHARLES BRECKENRIDGE: And just to

14 add further to that, in regards to the oral gavage, you

15 note that the when we're tracking the twelve 'o clock

16 thriving metabolite largely dose concentrations fall to

17 a non-detectable within a duration of time, and that is

18 presumed then to reflect the ending of the absorption

19 phase of that bolus dose. It's a much more difficult

20 question when you're dealing with dietary because that

21 dose has been distributed over time.

22 And so what you're getting to see is

23 just a snapshot of what is in at that time that the

24 animal's blood sample is taken. So it's a little bit

25 more hard to characterize is there a differential rate
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 14

1 or something of the diet versus the bolus dose. But I

2 think we can say from the bolus dose experiments that

3 absorption seemed to be complete within a short time

4 after the dose administration. We're not surprised

5 that they'll make that statement to the dietary level.

6 DR. JANICE CHAMBERS: And one last

7 question, do you know if there's any microbial

8 metabolism of the atrazine in either route?

9 DR. CHARLES BRECKENRIDGE: I think we've

10 heard some or read some reports that suggest that that

11 is true. We're interested explicitly in eco lab report

12 that this hydroxyatrazine found in the rodent plasma

13 and I believe some of the docket chilled EPA suggest

14 that that was the case. We're not aware of a

15 physiologic process within the animal that would permit

16 that reaction to occur.

17 And so we suspect that if it's being

18 detected in plasma or urine samples, its one of two

19 things, microbial or in fact it's a flaw in the

20 methodology because in fact the sometimes hydrolysis

21 reactions can happen and we have observed that even

22 with parent compound under storage conditions will

23 hydrolyze.

24 And so we are not yet resolving whether

25 in fact reports of hydroxy in plasma are in fact
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 15

1 reflecting true values or other processes that might

2 lead to that premise.

3 DR. STEVEN HEERINGA: Dr. McManaman

4 DR. JAMES MCMANAMAN: Yeah. I would

5 like to follow up on that. Did you measure the

6 concentration of atrazine or its metabolites in a peak

7 material?

8 DR. CHARLES BRECKENRIDGE: In these

9 specific studies we have not, but we have done chloro10 radiolabel studies in the past and it is typical that

11 we have 80% of total label in urine, 20% in the feces.

12 DR. STEVEN HEERINGA: Okay. At this

13 point, I think, I would like to thank the Panel for

14 their questions oh Dr. Greenwood.

15 DR. RICHARD GREENWOOD: Richard

16 Greenwood. Just one quick query. On your figure six,

17 you've provided plots of the atrazine metabolites

18 DR. STEVEN HEERINGA: Which presentation

19 was this, Richard?

20 DR. RICHARD GREENWOOD: So this was the

21 one of the Syngenta September 9th

22 DR. STEVEN HEERINGA: Oh, it was one of

23 the submissions.

24 DR. RICHARD GREENWOOD: Submissions,

25 yeah. A series of figures all those figures based on
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 16

1 joining the points together

2 DR. CHARLES BRECKENRIDGE: To clarify,

3 is this Dr. Kim's presentation?

4 DR. RICHARD GREENWOOD: This is one

5 which--it's a paper that you put on the docket from

6 you, Dr. Kim and Dr. Pasqual.

7 DR. STEVEN HEERINGA: Can you describe

8 the preliminary results from these studies on atrazine

9 update in the final report?

10 DR. DAVID KIM: Are these plasma

11 profiles or metabolite profiles?

12 DR. STEVEN HEERINGA: The 19 page...

13 DR. DAVID KIM: I could take you around.

14 And so those are, the reason why it

15 looks fairly smooth is because we collected plasma

16 samples with very intense sampling schedule. So those

17 are dots which represent sample points that are

18 connected. It's not a result from a model fitting

19 exercise.

20 DR. RICHARD GREENWOOD: The other

21 question, it's in the washout studies it looks almost

22 like for most of the time, there's a linear loss rather

23 than an exponential loss becoming exponential at later

24 times. Is that because of the--you've got less points

25 in that area to define it or is that what you observed?
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 17

1 DR. DAVID KIM: Okay. Yes. We have

2 quite a few samples during the washout period. We

3 collected, I believe okay, I don't have the numbers

4 off the top of my head but that would be in the report.

5 We have more than ten data points essentially to

6 characterize washout. It looks linear because of Y7 axis's lot scale.

8 So it's a lot in the near decay. It's

9 seems like there's a, it's a linear decay based on the

10 lot linear curve--at the high dose because the

11 concentrations are elevated. It may even look like

12 there is a two phase washout.

13 DR. RICHARD GREENWOOD: Thank you very

14 much.

15 DR. STEVEN HEERINGA: Dr. McManaman.

16 Okay, I'll allow this question, but I need to wrap it

17 up. Please go ahead.

18 DR. JAMES MCMANAMAN: So I wanted to get

19 a clarification. When you said that was a tracer study

20 that you find a 80:20 ratio, is that with gavage or is

21 that with dietary treatment? My question was about

22 dietary, how much goes in through the dietary?

23 DR. CHARLES BRECKENRIDGE: I'm sorry. I

24 didn't understand that. Yes, these are gavage studies.

25 We have not done that type of study in the dietary.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 18

1 DR. STEVEN HEERINGA: Dr. Krishnan, Dr.

2 Mumtaz?

3 DR. KANNAN KRISHNAN: In the same PK

4 report on February 8, I was also concerned that you

5 limit the LH suppression to a threshold level without

6 mentioning either C-max or AUC of the atrazine

7 equivalents. Was there any calculation based on any

8 comparison done based on the AUCs in these gavage

9 versus diet study?

10 DR. CHARLES BRECKENRIDGE: In the

11 relationship between what is observed in blood versus

12 what is observed with the LH Suppression, we went into

13 those experiments with the premise that in fact it

14 would be area at the curve above a threshold that would

15 actually drive the LH suppression.

16 We were somewhat surprised that in fact

17 it appeared not to be the area under the curve, and

18 that the differences between the diet and the gavage

19 treated animals appeared to be, have all mostly been

20 accounted for by c-max.

21 So there was about a tenfold difference

22 there as David had pointed out yesterday and yet only a

23 twofold when we're considering dimethyl chlorotriazine.

24 So I don't quite know yet how to get a resolution on

25 what is the component of the blood level that's driving
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 19

1 the GnRH effect.

2 But one would have to presume that it

3 has to last for some duration. That was my initial

4 assumption and that comes largely from the viewpoint

5 that high doses in young adult animals, you need

6 several days of dosing to accomplish the phenomenon and

7 if you apply that single dose that doesn't do it and

8 therefore somehow c-max isn't quite enough to, to

9 muster.

10 And so it's still experimentally

11 unresolved and I don't have any information yet how to

12 resolve that.

13 DR. KANNAN KRISHNAN: Another question

14 about the material that wasn't presented was that it

15 relates to the fraction of DCT residue, it was a slide

16 that was supposed to be given to us yesterday based on

17 the monitoring data?

18 DR. STEVEN HEERINGA: This was a single

19 loose slide. Dr. Hendley.

20 DR. PAUL HENDLEY: You're going to yes,

21 okay. So you want clarification on the content of the

22 slide?

23 DR. KANNAN KRISHNAN: Yeah. I looked at

24 the average atrazine level being 80 some percent and

25 the DACT, DIA, DEA, there is between 2% to 6% so the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 20

1 rest of it above 10 some percent would be the hydroxy

2 data?

3 DR. PAUL HENDLEY: No. This refers

4 specifically to the component of chloronitrotrizine

5 mixture. So these are samples that will run from the

6 three years when every sample, monitoring sample was

7 taken, was analyzed for all the chlorinating components

8 without a large pool of samples.

9 We're referring here to the high end of

10 a distribution, the dose exceeding 15 parts per billion

11 of total chlorotriazines. And in the case of raw

12 samples, that was 87 samples from the so called raw,

13 and then the relationship there the average atrazine

14 percentage in that total chlorotriazine was 87%, and

15 the remainder was made up of the chlorinated DACT, DIA

16 and DEA.

17 So this is referring purely to the

18 chlorinated triazines. And of course the key thing

19 here is the high TCT occurrence occurs typically in the

20 spring when there's a runoff event when atrazine is

21 freshly applied. Which is why in the higher

22 concentration residue pictures, you will always see the

23 high concentration of atrazine that makes up the vast

24 majority of the TCT mixture.

25 DR. KANNAN KRISHNAN: Thank you, because
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 21

1 I was looking for something that wasn't there.

2 DR. STEVEN HEERINGA: Dr. Mumtaz.

3 DR. MOIZ MUMTAZ: This question is for

4 Dr. David Kim. In the implications of PBPK modeling,

5 you say that this model can be used to reduce

6 uncertainty. So what is the basis of what you have in

7 terms of what we showed, a general statement of what

8 people can anticipate when using the model?

9 DR. DAVID KIM: Right. So it's a very

10 good question, that's a very broad question. So let me

11 try to gather my thoughts and I think that the

12 statement about the PBPK model and the reason why PBPK

13 model has not been used to do the animal rat to human

14 extrapolation currently is because the existing model

15 has a lot of uncertainties with it.

16 So one of the points that was made in

17 EPA's documents was that you model under predicts the

18 plasma profiles for atrazine and metabolites, and this

19 is based on Tami McMullins' 2007 publication, which was

20 an initial model based on in vivo data.

21 So there remains uncertainty with regard

22 to understanding the, predicting the plasma profiles

23 into that. What I meant by reducing uncertainty is

24 that based on the data that I have, both from in vitro

25 and in vivo experiments, I'll be able to address some
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 22

1 of those uncertainties with optimizing parameters in

2 that PBPK model and replacing it with better with lots

3 more data.

4 DR. MOIZ MUMTAZ: So you're going over

5 specific data that you have?

6 DR. DAVID KIM: Yes.

7 DR. MOIZ MUMTAZ: In that, in that lab

8 that would help you?

9 DR. DAVID KIM: Right. As well as

10 experiments, information that we have garnered in the

11 in vitro asset study as well as one of the issues with

12 regard to previous models was that the dosing was done

13 at a 150mg/kg, very high dose, but what Tami observed

14 in her plasma profile was a two phase plasma curve.

15 And at lower doses, we're not observing that.

16 So we will be able to address what the

17 kinetic profiles look like at lower doses which are

18 more realistic compared to what human, human scores.

19 DR. MOIZ MUMTAZ: All right, this sounds

20 good. Let me just follow up on this second.

21 DR. STEVEN HEERINGA: Dr. Breckenridge.

22 DR. CHARLES BRECKENRIDGE: Yes, Mr.

23 Chairman. Actually, anticipating questions relating to

24 the McMullin model versus our data, I prepared a one25 page summary of that and perhaps it's a good basis for
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 23

1 the discussions if you would like to hear that as to

2 how the model at this, at the moment will be modified

3 to...

4 DR. STEVEN HEERINGA: But in terms of

5 the Panel is that something--if we can do it fairly

6 quickly. Let's go ahead.

7 DR. DAVID KIM: So this is a handout

8 that was given to you this morning. I'll just walk you

9 through, there's four steps in this process and this is

10 something that Dr. Harvey Poole and I, as well as Dr.

11 Breckenridge, discussed yesterday.

12 Let me start off with the McMullin which

13 has uncertainties that EPA has pointed out and are

14 valid points as well as end points that we concurred

15 with. It has a lot of uncertainties primarily because

16 of the gut, the discretion for gut absorption.

17 So what was used to describe gut

18 absorption was a fast, slow and zero order gut uptake

19 and this is because of the two phase, two keys that was

20 observed in the plasma profile data. I'll be beginning

21 with that model. I'll be simplifying it.

22 Let's break down the gut model --

23 simplify the gut absorption model to have just a single

24 absorption rate because that's what we seem to be

25 observing with plasma profile data.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 24

1 The specific primers that we find PBPK

2 model which you see under circle number 2 step number

3 2, the partition coefficients as well as the clearance

4 values will be replaced with in vitro information that

5 I presented to you yesterday.

6 In addition to the fine model of

7 hypothalamus and pituitary compartments representing

8 those two tissues, and I did not show this data

9 yesterday, because they haven't been analyzed, but in

10 this PK experiment, at the end of blood collection for

11 each individual we collected the brain, separated the

12 hypothalamus and pituitary, and we are currently

13 quantifying levels of atrazine and some metabolites.

14 So what you will see in the final report

15 is a time course profile for those two compartments.

16 That refined model will be then--will review

17 predictions with that model with in vitro inputs to

18 assimilate the time course, in vivo time course data,

19 we'll do this iteratively until we have an optimum

20 model for your review.

21 DR. STEVEN HEERINGA: Thank you very

22 much, Dr. Kim. At this point I want to thank all of

23 the public commenters who contributed to the session

24 yesterday and again this morning. Dr. --I forgot.

25 I've got to cut off questions, I'm sorry.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 25

1 FEMALE: One quick comment to Dr. Kim is

2 if you're going to look at the concentration of

3 atrazine in tissues, don't forget the adrenal.

4 DR. STEVEN HEERINGA: It's a tough part.

5 As a statistician try to manage these scientists. We

6 deal with uncertainty all the time in here.

7 Okay, thank you very much everybody for

8 your contributions.

9 At this point in time, I'd like to bring

10 the period of public comment to a close and I'd like to

11 invite the scientific staff of the EPA back up to the

12 head of the table. I think as I indicated yesterday,

13 before we turn to the charge questions, I'll turn to

14 Dr. Lowit I believe for any wrap up that they would

15 have or potentially any rejoinder to proceedings of the

16 last half day. So please, you can move forward.

17 DR. ANNA LOWIT: Good morning. I

18 believe we have one quite a couple of things we wanted

19 to say, there was some clarification that Dr. Cooper

20 was going to make, and then I just have a couple of

21 comments I was going to make. So if someone could tee

22 up Dr. Cooper's slide, I'll start with that.

23 DR. RALPH COOPER: This is just to

24 address some of the things that took place yesterday.

25 I felt that some clarifications might be helpful in
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 26

1 dealing with some of what might appear to be

2 discrepancies but are not.

3 And one tick off the technical comment

4 about the control animals, we're still confused a

5 little bit about that. All the control animals are

6 included in there, and we're trying to figure out which

7 data set. I think if we're using a statistical

8 analysis Dr. Lin's comments might not even apply. But

9 still I think it's important that we share everything

10 we have and we'll try to make sure that we find out

11 what that issue is.

12 The other thing that Dr. Lin, on bullet

13 two up there, brought up was that the broad range of

14 dosing. That might appear to be broad but I think that

15 we generally aim to use young adult female animals that

16 are ninety to 120 days. That study included animals

17 that the outside range taken from the example used and

18 by the example that was given from Sielke & Associates.

19 The range and age of animals were 77 to

20 126 days. These are all, met the criteria of cycling

21 and I'm not 100% certain whether they were included in

22 the data analysis. But if it was in the 1800 data

23 point, then they would have been--point being that, of

24 those animals there were close to a hundred animals in

25 that study, in that particular time period, sorry. And
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 27

1 that 85% of them were from 84 to 105 days.

2 There were 6 animals in the control in

3 the high dose that were outside that range. On the

4 aging side and on the opposite. I think the point is,

5 is that even with this range of ages, which is within

6 the young adult female range, it was still

7 statistically difference statistical differences in the

8 LH and I think that may underscore the robustness of

9 that kind of response as opposed to introducing

10 variability.

11 To bullet number one up there, I think

12 it's important that we recognize that Dr. Handa's study

13 looking at the intact female animal dosed for four days

14 starting on the day of actual estrus versus pro estrus

15 with Sprague-Dawley rats.

16 And I just wanted to point out again

17 that-- let's go one quick slide down. You see the

18 Cooper, et al. study in 2000, we compared Sprague19 Dawleys with Long Evans. In the top bar you're

20 familiar with that figure. Long Evans on the left,

21 Spragues on the right, four days exposure.

22 These are very active mice and animals.

23 And the Spragues are certainly more sensitive, I'm

24 sorry, the Long Evans are certainly more sensitive than

25 the Long Evans, less sensitive than the Long Evans.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 28

1 And I think that one of the reasons why we I don't

2 think this is responding.

3 One of the reasons we wanted to look at

4 the intact animal was to see if we could, if that dose5 response was going to be different. This isn't working

6 up here.

7 So we used the Long Evans animals and I

8 think that may account for the differences in response

9 seen, this is the Gentner versus the Cooper et al.

10 study. Even in the Handa data, you saw he had a

11 suppression in LH in the intact animal, again arguing

12 that animal, the high dose anyway, that that animal,

13 the intact animal may be more sensitive, for lack of a

14 better term, model to use in this, and if you're going

15 after the most sensitive strain or sensitive species, I

16 think these are issues that we have to keep in mind.

17 And my last point is that as brought up

18 by Dr. Legan in the discussion, I just want to mention

19 that we too found, on the bottom bullet there, is dose

20 increased in the four day studies. The number of

21 females that achieved what we set out as pro-estrus

22 criteria was significant.

23 We feel we're saying that as we raise

24 the dose, we did see it affect within that one cycle on

25 the number of animals we went through. We excluded
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 29

1 those from our analysis because we felt we didn't want

2 to push the data in a direction that it was through the

3 LH suppression that we were getting an extension of the

4 cycle.

5 I thought it was just we were comparing

6 apples and oranges, so we try to keep all animals that

7 were deemed pro-estrus by our criteria and then look at

8 the hormones. So it was kind of funny in the sense

9 that we're making it difficult for ourselves to find an

10 effect. So again, I just want to point that out, and I

11 think Dr. Handa's had the same type of response.

12 And then the last thing was that dose

13 may not and we are 100%--I am. I am 100% agreement

14 that suppression changes in LH alone are not going to

15 be, within a single cycle, would not necessarily bode

16 an adverse outcome, but I think it clearly show that

17 these are precursor changes that might predict that

18 you're going to run into trouble either after a little

19 longer durations or a little more time. That's all.

20 DR. STEVEN HEERINGA: Thank you, Dr.

21 Cooper.

22 DR. LOWIT: I guess, are there any

23 questions on that for

24 DR. STEVEN HEERINGA: Quick questions on

25 Dr. Cooper's presentation. Dr. Legan.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 30

1 DR. SANDRA LEGAN: I have a question

2 because in the Cooper, et al. analysis, you said you

3 have the last point you made about the proportion of

4 animals that were actually--that met your pro-estrus

5 criteria decreased. So the ends, in table one are

6 these the correct references, this is what's recorded

7 in the Cooper study. So...

8 DR. STEVEN HEERINGA: We seem to have

9 too many pieces...

10 DR. SANDRA LEGAN: In some of the lower

11 dose groups, that's not the right data. In some of the

12 lower dose groups, you still have, I mean the ends are

13 two's and five's and in the higher, like in the 25 mg

14 group, they're all above 8 and a 75 mix. So it's hard

15 to see from here. Can you help out?

16 DR. STEVEN HEERINGA: Dr. Legan, could

17 you give the reference of the paper just for the record

18 here?

19 DR. SANDRA LEGAN: This is part of the

20 Cooper et al. data that were submitted, and there is a

21 section in there where there is a summary report. And

22 this is table one in that report.

23 DR. STEVEN HEERINGA: Thank you.

24 DR. RALPH COOPER: It's not the if you

25 look at that data, the way we put the study together.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 31

1 And again, I have to agree with Dr. Lamb's analysis.

2 This study was not set out to be an LH surge

3 sensitivity time study. We are actually, in that case,

4 looking at shifts in steroidogenesis over the day of

5 vaginal pro-estrus with response to this and we also

6 measured the LH, and if you look at the data, and this

7 is why we left two animals at 200 at whatever dose and

8 two animals at...

9 We started measuring LH baseline, and so

10 we looked at the 1200 to 1400 groups and I think those

11 are the ones where the concentrations are baseline.

12 And then as the surge started to appear, we wanted to

13 make sure that we had a sufficient number of animals in

14 there.

15 So we went, we called it loading up

16 those time points when we get the animal to come

17 through the cyclicity, we identified the animals, the

18 four day rat, we then had the tables where we assigned

19 them to the different groups. I told the, our

20 technical staff to please make sure that we cover the

21 peak, all right.

22 So we never, to be totally honest with

23 you, went back and filled in those baseline groups for

24 certain doses. Okay. So that explains it. And

25 frankly speaking, I don't think it would make much of a
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 32

1 difference. Unless we suspected there was a shift at

2 the peak height, in other words an earlier onset. And

3 that's possible.

4 We didn't explore that, the data

5 wouldn't suggest that. And then, of course, on the

6 other side, the delay, there would be a delay. And

7 again, from the 1800 dose responses, I didn't really

8 feel a delay would be something we could pursue, and we

9 had sufficient animals in those dose groups, at least

10 the higher dose groups, to say that, no, the hormones

11 in all groups are going down in 2000.

12 So that's why, and again, that's just I

13 don't see that as the ends aren't there, but I don't

14 think it was necessary for us to go back and fill them

15 in.

16 DR. SANDRA LEGAN: I see. Thank you,

17 but it was just that that, those data, it's hard to see

18 the point you made before from this table and that's

19 why I asked for the clarification. That clarifies it.

20 Thank you.

21 DR. STEVEN HEERINGA: Thank you very

22 much, Dr. Cooper. At this point, we have quite a

23 number of charge questions, okay. Dr. Roby, you

24 haven't had any questions.

25 DR. KATHERINE ROBY: Just a follow-up
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 33

1 then. You said that that study was originally designed

2 to look at steroidogenesis on pro-estrus? Do you have

3 any results from that study that you could share,

4 especially with regard to the current shifting in the

5 cycle from a four to a five day? I'm interested in

6 that.

7 DR. RALPH COOPER: This is again, a

8 historical memory of the committee. No.

9 The answer is this. We were looking at

10 the HPA and we've presented all that data on HPA in the

11 April meeting and we saw clear effects on the adrenal

12 axis that we're trying to link to changes, the

13 unraveling of the HPG over those four days and it was

14 agreed that the, that achieved exposure due to change

15 in the HPA, but more work was needed to make that

16 possible. So, what the ovaries steroids, you see

17 progesterone in the data set there, that one unusual

18 number there, but no. And estradiol really doesn't

19 change much.

20 DR. STEVEN HEERINGA: Dr. Lowit.

21 DR. ANNA LOWIT: Just a couple of

22 comments because there are a lot of questions in the

23 morning starting to hit your way. This is sort of

24 bringing us back to where we started a couple of days

25 ago. We're in the process, we're a significant amount
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 34

1 of the way through now of a re-evaluation of atrazine

2 building upon the 2003 risk assessment.

3 The goal of this is to complete science

4 evaluation, so the risk managers can begin to move

5 forward as to whether or not there needs to make

6 changes to the water monitoring scheme. And that, that

7 re-evaluation of the science is going to be complete in

8 2011.

9 So we will see you again one more time

10 for human health because I believe that the eco team is

11 a different group of people minus Nelson. We'll see

12 you again one more time next year, not clear when that

13 would be. The hope is spring time.

14 It's unclear that was looking to the

15 summer yet, but the goal is spring time and we'll just

16 pretend that spring is anything from March to June,

17 plus the months. I just want to sort of put a reality

18 check on some of the things we've heard for the last

19 few days.

20 Our review, we believe is up date as

21 July 15th. We've heard a lot of new data in the last

22 few days and that looks very encouraging. But

23 particularly as it relates to developing PPBK models,

24 as I count the months this morning. If we were to have

25 an April SAP at least five months to do experiments,
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 35

1 build models, write papers, and submit within the

2 agency in time for us to review about it.

3 So just a reality check, we're very

4 encouraged by the efforts by Syngenta to pursue PPBK

5 model. The science evaluation for atrazine is always a

6 living, breathing thing, but in 2011, we will have a

7 limited, we will say we're going to make some new check

8 decisions.

9 Depending--its very aggressive schedule

10 to do that kind of modeling in five to seven months

11 time frame. So as you think of after deliberations you

12 can keep in mind our regulatory needs. There are some

13 that are fairly immediate in the next--in the coming

14 months literally.

15 But the atrazine reviews always is

16 living and breathing, so continue to stay updated on

17 the literature over time. So even in 2011 we may stop

18 the current reevaluation, the science review never

19 stops truly.

20 So as you think about deliberating on

21 some of the things you've heard, if you can keep those

22 two temporal times in mind that there are immediate

23 needs and then there are also long term improvements to

24 the atrazine knowledge base.

25 DR. STEVEN HEERINGA: Thank you very
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 36

1 much, Dr. Lowit. At this point in time, I think I would

2 like to move on to the first of the charge questions

3 which relate to the epidemiologic literature on

4 Atrazine and I guess Dr. Christensen is going to read

5 this question into the record.

6 So we'll read the questions into the

7 record and then I'll call on the lead discussant and

8 then we'll turn to the associate discussants and then

9 open it up to the Panel.

10 DR. CAROL CHRISTENSEN: Hi, good

11 morning. The first charge question. Question No.1.1

12 reads, please comment on the sufficiency of the

13 agency's non-cancer epidemiology reviews with respect

14 to identifying the major strengths, limitations of each

15 study.

16 DR. STEVEN HEERINGA: That's the

17 shortest FEPA question I ever heard read into the

18 record, thank you. Doctor Gold, right to the point.

19 DR. ELLEN GOLD: Good morning. I have

20 rather lengthy comments so within the time, what I'm

21 going to do is make a few general introductory

22 comments, you asked for some comments on some specific

23 papers I'm going to keep those pretty--try to keep

24 those pretty brief in greater detail in my written

25 comments.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 37

1 And then I've work in this just the same

2 as section three so they are organized by--help out

3 from this basically. And so I'll see summarize my

4 comments for each of the print out in the epidemiology

5 and then I'll turn it over to my colleagues to indulge

6 further.

7 So to start out with I have a few

8 general comments. First of all the overall EPA critic

9 has been really thoughtful and comprehensive in

10 reviewing the epidemiology literature for non-cancer

11 outcomes in relationship to potential atrazine

12 exposure.

13 Second it is very easy to criticize

14 human studies which do I have the ability to control

15 exposures and outcomes and behaviors as well they are

16 holding on the studies.

17 But it is important to know that it is

18 not impossible especially the non-cancer outcome with

19 sufficient resources to measure the outcomes and

20 exposure well or at least better than some these

21 studies of man in human individuals.

22 Third I think we believe that the

23 ecologic study should be considered in this summit, in

24 the lower class of studies from cohort and case control

25 studies of individuals like exposures and outcomes
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 38

1 because they are largely using aggregate data on

2 exposure and outcome and normally we considered

3 suggestive for purposes of hypothesis or for purposes

4 of hypothesis generation.

5 And finally I would note that a number

6 the comments that what follows here that I'll make here

7 were also made in the February 2010 SAP comments and

8 some of them did not appear to have been heeded and

9 included in this issue paper so I'm going to make them

10 again and hope that and encourage you to incorporate

11 some of the advice from earlier this year regarding the

12 evaluation of the epidemiologic studies.

13 So without further ado I'll dive in to

14 the individual studies, so I guess I'm going to try and

15 keep it brief. So the first section dealt with female

16 reproductive and you ask for comments on the first few

17 studies, the first three there are from the

18 agricultural study.

19 And yeah I'm going to just extract and I

20 might going to read all on my comments, largely I agree

21 with the assessment, but I think there are some things

22 that should have been noted, for example the low

23 participation rate in the agricultural study for these

24 outcomes, which raises the potential for participation

25 bias, hypothetically women, hypothetically women with
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 39

1 more adverse reproductive outcomes might have been more

2 likely to participate or those that were more concern

3 about pesticide exposures. So you might have seen an

4 effect when there really wasn't one.

5 Also this was an issue we raised in

6 February that the representativeness of this study

7 population presents questions not only from the

8 participation rate point of view but it 97% White.

9 And fairly well educated so that

10 generalizability of the findings is limited in number

11 one and number two, it's not really possible to look at

12 the effect modification by race ethnicity or lifestyle

13 or their health factors which might be really

14 important. I think we're learning more and more that

15 those kinds of interactions are important.

16 So those comments I think apply to all

17 three of the studies, three analysis for two of our

18 papers and the Saltanga paper. There was one incorrect

19 so I mean also note a couple of minor things, the

20 single word changes but they're important ones.

21 So in the paper to date that dealt with

22 agent menopause on page 31, the hazard ratio of 0.79

23 for atrazine exposure does not constitute a greater

24 association which is the term used. Then for they're

25 not for hormonally active over toxic pesticides which
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 40

1 was 0.77 because the closer you are to one, the less

2 the association is okay, so that needs to be corrected.

3 Let's see, the EPA critique is correct

4 in pointing out that a non-standard question was used

5 to assess menopause and perimenopause in the pesticide

6 exposures assessment specifically atrazine exposure.

7 And the timing of exposure relative to the outcome has

8 not been really addressed.

9 The issue paper however also mention

10 that again that the participation rate was low

11 potential for bias--for participation bias and also the

12 generalizability and lack of ability to look at that

13 modification I mentioned before.

14 And then with regards to the Saldata

15 paper, the issue paper says that these results were

16 depicted only graphically. But I think you can tell

17 from the graph that the adjusted dot ratio is in excess

18 of one and then it appears that the confidence in this

19 does not improve. I think it's worth mentioning that

20 because on page 354 it's says these associations was

21 not statistically significant, it may not be exactly

22 right and then all the other one limitation.

23 So let me summarize then the female

24 reproductive section, so these were the three papers in

25 that section. I think that pointed out the limitations
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 41

1 already, so I don't need to repeat these and the ones

2 that I'd like to add about representativeness,

3 generalizability and that sort of thing.

4 So furthermore I'd say well the

5 agricultural health study included a study sample

6 likely pesticides exposure and that's make sense to

7 examine as a potentially high risk population. It

8 really was not designed to address these research

9 especially related to reproductive health.

10 And other epidemiologic studies have

11 been better designed to asses these outcomes, the

12 menstrual cycle outcomes, age of menopause. And some

13 of them have even infected biological samples that have

14 been stored and could be analyzed to asses the atrazine

15 exposure and might have better representativeness and

16 certainly have better diversity in the study sample in

17 the agricultural health study.

18 So I'm just encouraging the agency to

19 consider finding resources of value--. So you talked

20 about looking at future comments versus in the next few

21 months, finding resources to evaluate data and samples

22 from these other studies that would not only make the

23 findings more generalizable but the designs and the

24 data from these other studies would prove the

25 examination of potentially important modification by
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 42

1 the ethnicity, lifestyle, health factors etcetera and

2 which are important to consider.

3 Finally, the concern expressed in the

4 critic regarding the adequate control of physical

5 activity I think is overstated from age of menopause.

6 I don't know of any literature that suggests it's

7 important. But the key factors for that particular

8 outcome are smoking, parity, education level for all

9 kinds of symptoms.

10 Okay so that's it for female

11 reproductive and there is one setting on male

12 reproductive this long study, just one study which was

13 a test sort of cross section on case critical study.

14 And like the agricultural health study the study sample

15 which was derived from the multi-center study of future

16 families is almost entirely White.

17 That's limiting again generalizability

18 and the ability to examine interactions, the

19 participation was quite low which she acknowledges.

20 These are the common problems seen in characteristics

21 in human population, but it has the advantage of being

22 hypothesis driven and employed biomarkers both of the

23 exposures and outcomes, these are all strengths.

24 I thought that there was I mean this is

25 another one word correction on page 362, it says the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 43

1 study was of none persistent pesticides or anything she

2 was pretty clear with it was persistent. Sorry--in the

3 document it says persistent and she says non4 persistent.

5 So anyway this is the single study in

6 relation to atrazine exposure to reduce hormone quality

7 is very suggestive with the important strengths being

8 hypothesis driven and using biomarkers and exposures in

9 the outcomes, but have no really limitations that I've

10 just mention. So really this is something I really

11 needs to be addressed in future studies.

12 So the next section was on fetal and

13 perinatal outcomes then going to summarize my comments.

14 Again the EPA critic did a good job starting with the

15 Arbuncle paper which from the Ontario farm family

16 study.

17 So what I would add to what the EPA says

18 its unclear how they at least to me, maybe I missed it

19 how they handled most of the pregnancies from the same

20 woman which would represent a lack of independence

21 which violates the most statistical test and some I

22 mentioned how those were handled.

23 I also think that papers should

24 emphasize that the associations of preconception

25 atrazine exposure with early spontaneous abortions were
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 44

1 noticed in magnitude and most of them are statistically

2 significant.

3 And again this is a one word comment,

4 the term risks is used but they really will divide,

5 slightly different and we might have over estimated

6 that. There was also the Savitz paper which I thought

7 again you did a good job at evaluating that, I'm not

8 sure that I have much to add except that again this was

9 one that had a very modest participation rate.

10 So in summary the studies of the

11 relation of atrazine in fetal and perinatal outcomes

12 were suggestive with largely positive that's

13 statistically non-significant association which were

14 modest in magnitude and a number of limitations were

15 noted.

16 In addition I would add that there was

17 sort of lack clarity in how the independence of

18 multiple pregnancies from the same woman were handled,

19 the lab data on specific herbicide exposure and

20 potential for participation bias.

21 So the next section was on birth defects

22 and I'm really going to summarize these because three

23 of the five studies were ecologic and as I said we feel

24 like those ought to be in a separate category and

25 really can't. They really aren't looking at the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 45

1 individual maternal exposure and at best, they're

2 suggestive and I think that's the major thing about

3 those.

4 There were two studies two new study a

5 provocative study, so the first one looked at residence

6 near corn fields and said that atrazine is largely used

7 in corn fields and not on soy beans which was the other

8 comparison group.

9 I will just note that the sample study

10 used here was quite small for several types of the

11 birth defects and the likelihood the chance could have

12 resulted in findings are to be mentioned because there

13 were multiple statistical tests for each individual

14 defects and no adjustments was made for this multiple

15 comparison from the key diode that was considered.

16 And one other comment is that while the

17 others indicate that atrazine is largely used on corn

18 fields and not in soy beans. That they didn't really

19 address--well they didn't talk about other agriculture20 -agrichemicals like fertilizer nutrients that are

21 preferentially applied to corn fields. But they didn't

22 really say anything about other pesticides that might

23 be used in corn fields, so this is sort of an

24 uncontrolled uncommented on confounder.

25 Then there was the Waller case control
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 46

1 study, so this has the advantage of looking at

2 individual women. However they did kind of use an

3 ecologic measure of exposure which you noted in your

4 critique which was fine, but I would say in addition,

5 there was again certain minor things that the issue

6 paper lists variables that looked like they were

7 different, significantly different between cases in

8 controls that did control for them, but there some

9 other ones that were not noted in the issue paper like

10 marital status, race and income, so this is like those

11 ought to be mentioned.

12 When we summarized them by birth defects

13 studies three of them use considerably less than

14 optimal ecologic designs so that atrazine exposures and

15 outcomes were noticed that started individual level

16 which is the major limitation. The other two studies

17 can only be considered suggestive because one did not

18 examine the individual maternal exposures to atrazine

19 and the other used a largely ecologic measure rather

20 than an individual measure of atrazine exposure.

21 The additional limitation has included

22 multiple statistical testing that might have resulted

23 in statistical significance due to chance alone because

24 the key values were not adjusted for the multiple

25 testing. Not accounting for other exposures that might
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 47

1 explain observed effect in the Ochoa-Akuna study. And

2 not adequately describing or accounting for confounding

3 variables in the Waller study.

4 So next we have the adverse birth

5 outcome studies. Again I thought the EPA did a great

6 job in their critique. I'm just going to say

7 additional comments beyond what they've already said in

8 their paper.

9 I thought for example in the Ochoa10 Akuna, this is the second one that is out there. She

11 also--one of the strengths of this so this was a

12 historical cohort study and one of the strengths was it

13 had a relatively large sample size, but also they have

14 a limitation that again an individual maternal tap

15 water consumption was not obtained which might have

16 resulted in this classification of exposure.

17 Then there is the Villanueva Cross

18 Sectional Study, again EPA noted the appropriate key

19 strengths and limitations. I would say that the issue

20 paper should also note the findings are suggestive of

21 elevated odds of low birth weight which was not

22 statistically significant. And small for gestational

23 age which is found in pregnancy who's third trimester

24 overlap with high atrazine period. And similarly for

25 preterm delivery since first trimester overlap with
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 48

1 these periods not statistically significant though.

2 But probably the most important premise

3 in my hand is that none of the analysis in this paper

4 were adjusted for variables that are often influential

5 in these outcomes such as maternal smoking, educational

6 level of parent, you see that in the issue.

7 So there we have the ecologic study of

8 number--So here what I would add is that the analysis

9 assumed that the relative ranking of communities with

10 regard to drinking water contaminants remain the same

11 throughout the study period. And the study population

12 again was larger in White maybe the result is not very

13 generalized at all.

14 And again ecologically it did not look

15 at individual maternal drinking water exposures and

16 only control for some important in compounding

17 variables on a community level not in the individual

18 level.

19 I don't think that I have anything to

20 add about the Jabrowski paper and then the final AgCal

21 study paper on birth weight. The only thing I would

22 add there is that the number of low birth weight

23 infants who are preterm infants was too small to assess

24 the individual pesticide exposures and the technique

25 what difference as well as the adjustment for the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 49

1 finding.

2 So in summary the five studies that was

3 done, adverse pregnancy outcomes range from ecologic to

4 population base case control in historical cohort

5 designs. The ladder having strengths for a better

6 individual assessment to exposure to atrazine in large

7 sample sizes and adjustment for multiple compound

8 variables to asses the independent affect of atrazine

9 exposure although individual maternal water consumption

10 was not assessed to determine likely individual

11 exposures better.

12 So the findings are suggestive in

13 relation to atrazine exposure to small for gestational

14 age but two of the non ecologic designs focusing on

15 verbally only examine pesticides and not specific for

16 atrazine.

17 And then finally we have respiratory

18 effects papers. So first the paper by Hoppen and again

19 these studies in the agricultural health studies

20 suffers from the same limitations that I've mentioned a

21 couple of times before about participation in micro

22 generalizability, lack of ability to look at effect

23 modification.

24 And as noted in the issue paper, they

25 did a subsequent paper on this topic that they actually
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 50

1 stratify on the type of asthma and controls for

2 additional confounding on dose significant

3 relationship.

4 Then there was a cause in cross5 sectional study, I would only add that the sample size

6 here is really small and would not have provided

7 adequate statistical power to detect the equal

8 differences if specific chemicals were evaluated in

9 these associations were controlled or broken down and

10 similar criticisms of the Kimjinski study.

11 So in summary of the respiratory effects

12 finding studies, these three cross-sectional studies in

13 the relationship of pesticides to respiratory

14 impairment has--I think limitations including

15 inadequate sample sizes to provide adequate statistical

16 power to detect meaningful tendencies associated with

17 specific chemicals, adequate with controlling for

18 confounding and any observations were actually modest

19 in magnitude and that's it for that question.

20 DR. STEVEN HEERINGA: Thank you Dr.

21 Gold. I think we understand that you have more

22 detailed comments but you've covered all the high

23 points. Dr. Bailar is our first associate to discuss

24 it.

25 DR. JOHN BAILAR: I don't have a lot to
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 51

1 add except when I state otherwise. My comments

2 regarding these three closely related charge questions

3 refers--just to the epidemiologic part of the EPA

4 draft. I think EPA has done a good job of improving a

5 graph that was already pretty strong.

6 As always, revisions have brought out a

7 need for few more changes but I think they can be dealt

8 with rather handling. I have written elsewhere and I

9 think that I commented at the February meeting about

10 four problems common to all major public issues

11 regarding quantitative data.

12 The first is that the data are vast and

13 in the case of atrazine, they're getting vaster.

14 Second is that the data tend to be highly complex in

15 the number of disciplines involved in the problems

16 attacked and the methods used, the settings and so

17 forth.

18 Third, they're generally of poor

19 quality. And fourth, they're not what we want anyway.

20 For example, we may have high dose life time animals

21 ingestion studies, but we're really interested in low

22 dose human exposures by en large. All four of these

23 apply with some force the data relevance of atrazine

24 and EPA has struggled hard to resolve the problems.

25 It's not easy to do that.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 52

1 I've had the benefit of reading Dr.

2 Gold's more extensive written comments. I generally

3 agree with her about the individual studies and I'm not

4 going to repeat those remarks. At this point, I would

5 like to make a minor remark of Sir Austin Bradford Hill

6 used the first name Bradford and the last name Hill.

7 He was not to investigators named Bradford and Hill.

8 He was not one investigator with a hyphenated last

9 name. I've seen this misspelled at least two places in

10 the EPA draft and in at least one of the slides that we

11 have seen here not from EPA.

12 It appears that little is known about

13 human fertility related to atrazine exposure. If this

14 is in fact the serious data gap, if I haven't missed

15 anything, that gap should be noted in the EPA draft.

16 I'm not sure that such studies are needed, but if they

17 are undertaken, I believe that they should focus on

18 them with very high exposures.

19 In several of the reports, atrazine was

20 studied along with other pesticides and results for

21 atrazine had been presented in comparison with the

22 results for all pesticides together. This is

23 incorrect.

24 The result of such an analysis is to

25 reduce the apparent strength of the evidence to some
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 53

1 effect. The comparison should be between atrazine and

2 all other pesticides in that study. It may be that the

3 original authors often or always report the wrong

4 comparisons in a way that cannot be corrected but EPA

5 should at least be heard.

6 My last comment here is that SGA has

7 many causes and more than one cause if in fact there

8 are any might be affected by pesticide. Lack of

9 agreement amongst studies maybe a result of multiple

10 mechanisms.

11 For example, atrazine might have a

12 direct effect on fetus at some time or in some settings

13 and work through the mother's endocrine system or the

14 father's and the possibility of multiple mechanisms or

15 some of these specific outcomes. I think it needs a

16 little bit more attention. Thank you.

17 DR. STEVEN HEERINGA: Thank you Dr.

18 Bailar, Dr. Harris.

19 DR. SHELLEY HARRIS: Good morning.

20 First I'd like to congratulate the EPA for looking at

21 that immunologic literature and including it in this

22 process. It's really nice to see. Again, I'm not

23 going to duplicate Dr. Gold's comments, she's done

24 carefully through each study line by line. You can see

25 that in the report. But so I was planning to focus on
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 54

1 a little bit more on the methods that were used for

2 this review.

3 First of all, states that EPA in the

4 document states that the database is strong and I think

5 I would disagree with that. I think it need--the human

6 study should start with 29 papers and reduce that to 19

7 papers for various reasons. And I calculate there's

8 probably 38 papers of cancer code concept. Hopefully

9 we'll be seeing it on a later date. And because these

10 19 papers represent many different outcomes of interest

11 I'd say that actually the database is quite weak.

12 Imagine the cancer database would be

13 stronger and but also unfortunately more relevant for

14 men and picturing out women. As I said, Dr. Gold had

15 reviewed each paper and I may mention out one paper at

16 the end of my comments. I won't duplicate her comments

17 and I'll just focus a little bit on the methods.

18 So general comments in methodology on

19 the inclusion criteria for the review itself, I see on

20 a page--but first of all I think EPA should include a

21 list of references as an appendix. Includes all the

22 studies that were found in the literature review itself

23 and the reason for exclusion, so at that at a later

24 date, you can justify that you can handle a relevant

25 human literature and you've excluded various studies
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 55

1 for consideration for the various reasons.

2 On Page 324, on the Syngenta methods of

3 the 29 papers, there was a comment that to remove that

4 did not measure atrazine or triazines independently and

5 I thought this is a pretty interesting comment because

6 ecologic states were included and studies which

7 included measurements of triazines are proxies of

8 atrazine exposure, being a proximity to fields, the

9 residential proximity as measures.

10 And so I wondered how this was defined,

11 the measurement of atrazine, the exposure measurement

12 is defined and how that's applied to the different

13 studies so that they would be included in your review.

14 Is that something that needs to be justified for the

15 right way?

16 Another comment not related to

17 epidemiologic studies in cells, but to the process was

18 that of the studies that were obtained, 46 of those who

19 were exposure assessment or dose assessment studies I

20 would believe and for this, for the after review that

21 make sense to exclude those.

22 But I think in the future SAP meetings

23 especially the cancer meeting, we need to see those

24 studies. So at some point, there needs to be a review

25 of all the human exposure and absorb those studies and
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 56

1 it needs to be attached along for the epidemiologic

2 panelist as well as the tox, although we can't

3 ultimately know the risk assessment.

4 Also comment on the ecologic studies.

5 The inclusion of them into this process, Dr. Gold

6 mentioned again, they should perhaps be considered as

7 separate category and I would concur with that as well.

8 I would suggest that for the review of the individual

9 epidemiologic studies, that some type of scoring system

10 might be developed so that each paper can be scored as

11 to quality and so.

12 Once this is gone through the inclusion

13 or exclusion criteria, the methods can be reviewed as

14 you've done and shown in the appendix and it can be

15 scored for quality, so that at some point later, if

16 they're going to be influencing the risk assessment

17 process that would at least have a waiting system that

18 would be felt.

19 It may also these will have more than

20 one person score these types of papers and do that

21 independently. And as epidemiologists, we've built

22 numerous types of methods to do this and that analysis

23 and those types of things and it'd be nice to see it

24 applied in this process and it also be nice to see it

25 apply to some of the talks and studies as well. We'll
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 57

1 comment on that in Section 3.

2 Okay and just one final comment, Dr.

3 Gold went through and reviewed all the individual

4 studies. But a comment about the Arbuncle study and it

5 seems that EPA has largely ignored the study or

6 discounted it for being fairly old or published many

7 years ago and also for the exposure assessment and I

8 might argue that the exposure assessment was a little

9 more valid in that study for at least predicting the

10 occurrence of potentially more significant exposures in

11 women, and so the other studies that was sized specific

12 with the ecologic studies.

13 So I think that it may be that those

14 studies should be discussed appropriately and that wrap

15 my comments with that.

16 DR. STEVEN HEERINGA: Thank you Dr.

17 Harris. This point in time, I'd like to hopefully put

18 out to other members of the Panel who would like to

19 comment on Question 1.1. Okay. Question 1.1, I'd

20 suggest let's move on with Question 1.2 and we'll go

21 through the subparts and maybe see if that needs

22 clarification on both. So doctor Christensen if you

23 would read from the record. Dr. Gold just a moment, do

24 you want to consider the parts A and B separately or

25 together?
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 58

1 DR. ELLEN GOLD: I think we're doing it

2 separately.

3 DR. STEVEN HEERINGA: You're doing them

4 separately, okay. So read 1.2.

5 DR. CAROL CHRISTENSEN: Okay Question

6 1.2 Part A, please comment on the scientific

7 information that does and does not support the Agency's

8 conclusions as described in Section 3.0 with respect to

9 the characterization of quality and the limitations of

10 the non-cancer epidemiologic database and its utility

11 in hazard characterization, dose response analysis,

12 quantitative risk assessment.

13 DR. STEVEN HEERINGA: Dr. Bailar, lead

14 this one?

15 DR. JOHN BAILAR: I tend to agree with

16 EPA in its analysis and conclusions supplemented by Dr.

17 Gold's incisive comments regarding the individual

18 studies. So I'm not going to dwell on that. Instead

19 I'm going to offer some generic comments on three

20 broader issues that deal with interpretation of the

21 integrated hold the data.

22 This is not a place for a short course

23 and statistics but this matter stand out in the

24 discussions and analysis of the effects of atrazine.

25 I'll review them briefly because I think that you need
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 59

1 more explicit acknowledgment and discussion in the EPA

2 draft. These extend across the whole sets of studies

3 and are not limited to the interpretation of single

4 studies where EPA does often discuss these problems.

5 First the general failing in most

6 studies have toxic effect is the lack of sufficient

7 attention the matters of statistical power. Lack of

8 evidence, showing an effect is simply not the same as

9 evidence showing a lack of effect.

10 The EPA draft could be stronger if it

11 discussed the likelihood, that critical negative

12 results might be reversed by better, stronger and

13 especially larger studies whether in humans or animals.

14 The second broad problem is that there's

15 often insufficient appreciation of the multiple

16 comparison problem. This problem is widely recognized

17 as it applies to single studies but it applies with

18 equal force through any collection of paper through

19 other sets of independent of results.

20 There have been a lot of studies related

21 to health effect of atrazine, each with a lot of end

22 points and a lot of analysis. At the usual 5% level of

23 statistical significance and scientific analysis, an

24 average of 1 in 20 tests, where there is in fact no

25 effect will be labeled statistically significant.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 60

1 This 5% of approach or 95% in the case

2 of confident analysis has been very productive for many

3 decades across all science where important results can

4 be evaluated in a broad context with prior knowledge

5 and sometimes by the collection of new data.

6 The multiple comparisons problem is

7 pervasive and hard to deal with in reviews that cover

8 large numbers of reports. The question in such reviews

9 is not whether some result is statistically significant

10 but whether the overall pattern of results labeled

11 significantly lies outside the expected range.

12 Very roughly speaking, if the literature

13 reviewed reports 100 tests of data whether there is no

14 real effect, one should expect about 5 will be

15 statistically significant. You wouldn't be surprised

16 to find two or three, seven, maybe eight, but if there

17 are say ten or more of the reported key values that are

18 statistically significant, or if some of the ones even

19 with the smaller number have very little key values,

20 there maybe an effect present. Some effects may be

21 addressed.

22 I urge EPA to review that critical quasi

23 findings in its atrazine report and comment at greater

24 length on the plausibility that such findings are the

25 result of multiple comparison.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 61

1 One more point is that statistical

2 confidence balance and key values measure the effects

3 of random error and random error only bias always,

4 always increases the uncertainty. So the calculated

5 key values are too small, confidence balance are too

6 narrow and we don't know as much as we think these

7 measures tell us. Bias can be made rather small in

8 designed laboratory studies, but it's a major threat to

9 conclusions in other settings.

10 These three points can be summarized of

11 saying that negative findings do not always mean

12 there's no effect and positive findings could not

13 always mean that such an effect is operating. This is

14 as true for laboratory studies as it is for

15 epidemiology.

16 Educated scientific judgment cannot be

17 replaced by reliance on mechanical rules plays a real

18 effect. I understand the pressures to replace judgment

19 as much as possible with objective measures but it

20 would be helpful to have more explicit acknowledgment

21 of points where judgment and formed by statistical

22 analysis still has an important role.

23 In short, these three problems need more

24 attention in the EPA draft. A specific comment on page

25 37, line 18 of the EPA draft says that there is no
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 62

1 national birth defect registry in the U.S. I think

2 that CDC has in fact operated a very broad base

3 registry for many years. Are there reasons why it has

4 been used in geographic studies of atrazine?

5 The rationale for very frequent sampling

6 of water needs to be strengthened, but 1% peak in

7 concentration will occur in a random 1% of samples

8 whether they're taken hourly, daily, monthly or others.

9 If there is a need for frequent data at specific sites

10 rather than overall, that need should get a special

11 attention.

12 While exposures may be of short

13 duration, the effects might be expressed over much

14 longer periods. Now this is widely recognized or

15 answered, it might rule for hormonal disturbances in

16 the mother.

17 For example, I would not focus

18 exclusively on trimester of gestation. Last, it still

19 includes the data for gastric juices and of thalasemia,

20 both are abdominal wall defects but they're entirely

21 different conditions almost certainly brought about by

22 different attributes. Thank you.

23 DR. STEVEN HEERINGA: Thank you, Dr.

24 Bailar and our first associate discussant so far today,

25 Dr. Gold.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 63

1 DR. ELLEN GOLD: So again I think EPA

2 did a really good job in Section 3 and the Section is

3 very well considerate and carefully written. As the

4 synthesis Section 3.3 is very well done and highlights

5 the significant limitations of literature, so I'm not

6 going to repeat those. And I'm just going to summarize

7 what additions I think need to be included.

8 So the issue of paper should also add

9 the number of the studies including the agricultural

10 health study; A) a notable potential for a participant

11 biased given a little participation, B) lack of

12 diversity in the study sample which likely results in

13 lack of representative in some sample and has lack

14 generalizability of the findings; C) use of ecologic

15 designs that do not assess individual exposures in

16 outcomes, D) multiple statistical testing that might

17 result in statistical significance and the chance of a

18 if the key values are not adjusted for the multiple

19 testing; E) not accounting for other exposures that

20 might explain the observed effect and; F) lack in some

21 studies have use the standard measures of the outcomes

22 so that this classification are imprecision of the

23 outcome assessment have occurred.

24 So as noted, as I've said earlier, the

25 issue that we should encourage the use of other study
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 64

1 samples and other data sets and biological samples

2 which are evaluated menstrual cycle characteristics and

3 adverse male and female reproductive birth and

4 respiratory effects with better study designs and

5 outcome measures, the notes that were reviewed

6 including the agricultural health study.

7 DR. STEVEN HEERINGA: Dr. Harris.

8 DR. SHELLY HARRIS: Just a very short

9 comment on page 58 specifically the EPA's conclusion.

10 The EPA states that the epidemiology and toxicology

11 databases can be used to gather to better and inform

12 hazard identification and hazard characterization, but

13 for the purposes of quantitative risk assessment, the

14 experimental toxicology data particularly In-Vivo data

15 from Iraq will be used for endpoint selection and dose

16 response assessment.

17 And I think these are the conclusions

18 that you're referring to in the question. And I would

19 say instead of the epidemiology database, I fully agree

20 with that and I think that studies that they study do

21 not blend a tremendous amount of support for more

22 hypothesis generation and characterization.

23 DR. STEVEN HEERINGA: Thank you Dr.

24 Harris. And the third comments from members of the

25 Panel on this particular question. What I would like
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 65

1 to do is to wrap the question 1.2 up before our break,

2 next if you would read part B question.

3 DR. CAROL CHRISTENSEN: Sure. Okay,

4 question 1.2 part B, please comment on scientific

5 information that does and does not support the

6 integrative analysis and conclusions, contained in

7 Section 4.0 of the document, with respect to the

8 similarities, differences, and uncertainties of the

9 experimental toxicology in epidemiologic findings.

10 DR. STEVEN HEERINGA: Dr. Harris, please

11 answer this one.

12 DR. SHELLY HARRIS: Well I had a very

13 different answer on Monday compared to what I have

14 today after all the presentations yesterday. So this

15 is like a moving target. And when I reread the

16 question and looked at--that we've been asked to

17 characterize the uncertainties of the experimental

18 toxicology database and the epidemiologic database, so

19 I wonder if this better suit towards the end of the

20 meeting. But I will do my best as an epidemiologist to

21 discuss in the toxicology findings as well for the last

22 a couple of days.

23 Okay first of all, to be able to

24 incorporate the epidemiologic data into the assessment

25 process and integrated with the toxicology database, we
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 66

1 need well designed observational studies that test

2 hypotheses which are developed in part on the adverse

3 effects observed in animals and understanding of the

4 toxicological mode of action. And this is basically

5 what is stated in the document.

6 Today, we are assuming and sometime may

7 and I'm sure I'll be corrected later if I'm wrong, that

8 atrazine or a mixture of the relevant metabolites by

9 repeated exposures attenuates the LH surge in animals

10 and when this attenuation is significant and this can

11 result in some types of adverse reproductive effects in

12 animals and that's the assumption I'm going on now.

13 And also another assumption I'm making

14 and the hypothesis on Monday, I'm not sure now, is that

15 the mode of action that's relevant in humans. And

16 we've heard evidence to the contrary, I think the

17 Syngenta presentation by Dr. Tony Plant yesterday. So

18 the assumption I'm making again with the mode of action

19 is relevant. And I have a very good qualifier as well

20 as that I was not here at the April SAP meeting of the

21 review of the detox data.

22 And I'm not a toxicologist with

23 expertise to actually assess whether mode of action in

24 rats is irrelevant for humans and I really don't have

25 expertise to address the uncertainties in the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 67

1 toxicological database.

2 What I can say that it doesn't appear to

3 be a clear consensus regarding the end points and

4 effects on those databases from the presentations

5 yesterday and from the EPA document.

6 So having all those qualifiers, so

7 basically what EPA knows is to do this to integrate the

8 epidemiology and experimental toxicology, we need a

9 strong experimental toxicology database. We need this

10 toxicological mode of understanding and for the

11 purposes of this meeting; it's the attenuation of LH

12 surgical and multiple exposures. I'm looking for nods

13 of agreement here and an epidemiology database.

14 And so what we've observed is that we

15 have this well, I'm calling it very strong experimental

16 toxicology database, hundreds of studies. But clearly

17 there are some issues with say Coder 2010 study a

18 comment yesterday, animal selection and study design is

19 not well suited to risk assessment, best suited to

20 exploring mode of action pressures and this Dr. Lamb's

21 presentation.

22 So to compliment the toxicology

23 database, we need human epidemiologic studies which

24 have the specifically design to evaluate relevant

25 outcomes which can be biologically justified based on
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 68

1 mode of action. We need to see significant

2 improvements in the epidemiologic literature.

3 Some of the reproductive health effects

4 which have been discussed in the last couple of days

5 observed in animals are acute in nature and occur

6 relatively short periods of exposure including the life

7 menstrual cycle characteristics, longer in the cycles,

8 and maybe the precursors of the attenuation of LH

9 search and we can design perspective studies to

10 evaluate these outcomes or precursors in humans. And

11 they have been conducted at relatively low cost as

12 compared to some of these largest scale cohort studies

13 looking at long term cancer outcomes. So that is a

14 suggestion we'd like see those types of studies

15 conducted in humans.

16 To achieve the exposures and doses and

17 measurements that are relevant to our meeting exposures

18 are repeated over time and not a single occurrence, the

19 type of studies that we would propose may need to be

20 conducted neither occupational of cohorts or limit in

21 residing farms because we don't believe or I don't

22 believe that will be seeing the doses necessary to

23 serve the effect with humans unless they're

24 occupational exposures or exposures that occur greater

25 than general population exposures via drinking water
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 69

1 sources, that type of thing.

2 And then in these types of studies,

3 ideally we would obtain some kind of information on

4 current or strong exposures, the questionnaires,

5 records or diaries, at the same time collect

6 information on sort of dose estimates over time which

7 could include biological samples and analyzed for

8 atrazine parent compounds relevant in metabolites,

9 conjugates et cetera and this will help us merge these

10 two, the experimental toxicology in the human

11 databases. And this type of information is obviously

12 far more reasonable for risk assessment.

13 So we have none of that unfortunately,

14 what we do have is an experimental epidemiology

15 database are not experimental an observational

16 epidemiology database of 19 non-cancer studies that are

17 currently available. And as I said, this database is

18 small. So even though in the document, it states that

19 it's a fairly large database so I don't agree with

20 that.

21 So epidemiologic data relevant for

22 female reproductive health outcomes maybe obtained from

23 the existing cohorts which are designed for other

24 purposes and perhaps used in hypothesis generating

25 fashion, but really should the expected data will be of
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 70

1 sufficient quality to be useful for risk assessment of

2 outcomes that differ from those of the primary

3 hypothesis.

4 And so as an example of this, the

5 primary outcome of interesting agricultural health

6 study, I think you'll have historically have had

7 cancer. And so the methods of exposure assessments

8 that were designed to be relevant for those cancer

9 outcomes is actually long latent periods, some

10 consideration for frequency, seasonal and duration of

11 exposure over the years, other factors that might

12 affect those exposures such as protective clothing and

13 equipment use.

14 Those kinds of questions were designed

15 with the intent of looking at cancer outcome, I think

16 namely is the cohort setting of course if we're going

17 to look at other chronic and acute outcomes but the

18 primary focus is cancer.

19 So we really can't expect to have the

20 answer to very specific questions about reproductive

21 outcomes from studies that were designed for other

22 reasons or other purposes. And this is not opposed to

23 some of the agricultural health study, but this is just

24 a statement of how the data can be used.

25 The two studies published by far in 2004
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 71

1 and 2006, looking at menstrual cycle characteristics

2 and delayed menopause make use of this cohort but

3 aren't really severely limited by the availability of

4 the irrelevant exposure of the data.

5 I do not consider an ever, never

6 analysis and then corresponding results of all sorts of

7 odd ratios particularly useful other than for

8 hypothesis generation. They provide no information of

9 time frequency duration or magnitude of dose and all of

10 these measures are important for the incorporation of

11 any kind of epidemiologic data and various assessment

12 processes.

13 And based on what I've heard about

14 experimental toxicology, we need to be looking at lower

15 level of multiple exposures over time in humans and

16 this is important for epidemiologic studies when we

17 design duration of exposure is extremely important.

18 So in ever and never analysis, you just

19 need a comment on ever never analysis and if folks are

20 not used to those terms in epidemiology an ever never

21 analysis could be--if you smoke one cigarette of your

22 lifetime you have been exposed to a cigarette and is

23 that a relevant measure for a cancer outcome.

24 No, so the question might be, have you

25 smoke ten or more cigarettes a day for ten years, ten
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 72

1 years prior to your diagnosis, not so relevant exposure

2 measurement if it was classified as a dichotomous

3 variable ever and never.

4 If we're looking at reproductive

5 outcome, you might ask someone if he smoked ten

6 cigarettes a day over the past month and that's the

7 relevant question to be asking if you're going to

8 analyze it as an ever, never exposure.

9 And I would suggest that ever and never

10 being exposed to pesticides is just not useful

11 information for us to say to be incorporated into risk

12 assessment. Particularly when these ever and never

13 classifications due to all pesticides as a group or to

14 herbicides, insecticides, or fungicides separately,

15 again, I don't think they are particularly useful

16 information.

17 As we move towards categories of

18 classifications or groups of pesticides such as

19 triazines or phenoxies or whatever they maybe the

20 information becomes more important.

21 So as we move from individual exposures

22 in animals to multiple exposures over time and

23 mixtures, and humans who are trying to move from

24 mixtures, the exposures of the life stages down to

25 exposures to individual chemicals and isolating those
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 73

1 effects with appropriate data collection techniques, so

2 both of the two fields will merge at some point.

3 I have more comments. I'm just trying

4 to find them here. I mentioned this comment before but

5 on other cities that reporting including ecological

6 studies of group data, I don't believe should be given

7 much consideration in the analysis because of the

8 exposure assessment issues and compounding issues.

9 And also in the EPA report I think that

10 we need to make a distinction between ecological

11 studies and ecological approach and there's a lot of

12 confusion in the terminology that's used for exposure

13 assessment. When we talk about ecological studies, we

14 talk about looking at differences in rates over time or

15 occurrences at certain outcomes over time in group

16 level data.

17 We can take an ecological approach to

18 exposure assessment in observational studies but we

19 signing exposures to individuals regardless if they

20 represent the kind of needs. So if we are looking at

21 water contamination data or you asked yesterday about

22 drinking water, we assigned some kind of mean value

23 based on your residence, your location, your supply of

24 water, that's still scientific individuals.

25 So that's an observational study, not an
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 74

1 ecologic study even though it might be some ecological

2 measures to come up with assignments for exposures.

3 And again, I think we should give more weight to those

4 types of studies when we're looking at the individual.

5 Okay so I'm not going to include all

6 these comments today. I have created a bit of a table

7 that looks at the experimental studies--the

8 experimental toxicology and epidemiology, and some

9 issues about the role integration of the two. And just

10 a few comments and I'll include them in the report.

11 But basically I won't compare the two databases. We

12 have hundreds of toxicology studies and 19 epidemiology

13 studies.

14 Exposures in the toxicology are

15 ultimately were originally single exposures, multiple

16 exposures are of the same dose over short periods of

17 time, and now this is moving now over life stages,

18 whereas in the human data, we have exposures that occur

19 over lifetime, they're not consistent, between stages

20 or within stages and they change quite significantly.

21 We have issues about--when we look at

22 said quality; epidemiologists have a number of

23 procedures that is to go through a score study quality

24 and to come up with the cumulative test scores and we

25 can use this information to weigh these studies
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 75

1 appropriately in risk assessment.

2 I'm not sure if the same exists for

3 toxicology and how many folks go through and identify

4 which are the most important studies to base their

5 endpoint analysis and that type of thing.

6 There are a number of comments I have on

7 modes action whether the rodent is a good model or not,

8 I'm sure that it will be discussed further in this

9 meeting. And I will not go through the different areas

10 of female reproductive health and male reproductive

11 health, and the four areas that were identified

12 earlier.

13 But I will say that we have numerous

14 toxicology studies and generally only one reasonably

15 relevant epidemiology study in each category and they

16 all suffer from the form of exposure, measurement, and

17 lack of specificity of the chemicals being looked up.

18 So I'll include that in the report but

19 following just a couple of comments and I think it was

20 encouraging yesterday the presentations because what we

21 see that we really need is more information on

22 pharmacokinetics and pharmacodynamics in humans and a

23 very good review on the exposure in absorbed dose

24 assessment studies within this as well to be able to

25 connect the toxicology and epidemiology literature.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 76

1 And I think that those are coming certainly, so that's

2 good to say. And I think I'll keep it at that.

3 DR. STEVEN HEERINGA: Thank you Dr.

4 Harris. Dr. Bailar.

5 DR. JOHN BAILAR: Several additional

6 points. I'm concerned about the handling of studies

7 with substantial numbers of results below the limit of

8 detection. But can in fact be a lot of useful

9 information such as findings even if they account for

10 mostly observations.

11 And especially, when interest is focused

12 on the highest exposures anyway as they are with

13 atrazine. At the very least, one can do a rough test

14 by first assuming that all the below limit values are

15 in fact zero, then assuming that all of the upper end

16 of the no defect range.

17 If these two analyses are in rough

18 agreement about the most important conclusions, the

19 report can say so. There are of course much more

20 sophisticated ways to approach below limit of data but

21 again this is not the place for textbook.

22 I have two examples, the last paragraph

23 on page 32 and the last one on page 36. I thought also

24 in the docket in sections 3.3, the synthesis of that

25 epidemiologic data was particularly well done. And I
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 77

1 agree with the conclusion that this time the

2 epidemiologic database concerning the non-cancer health

3 effects of atrazine exposure is not sufficiently robust

4 or mature enough to support causal inference.

5 However and I'm not sure that the answer

6 is more research on either human epidemiology or animal

7 studies of apical effects. In the end, I think this

8 committee should make some generic recommendations to

9 EPA about the broadest research needs and directions.

10 A lot of time, effort, and money has been spent on a

11 lot of different approaches to epidemiologic study that

12 atrazine had helped.

13 And Dr. Lowit has usefully reviewed

14 where we stand. The available data did not settle the

15 issues. No smoking gun has been found. I do not see

16 any strong epidemiologic evidence of health effects of

17 concern at exposure levels of concern.

18 What I see at this time in the

19 epidemiology is about what I would expect to see from a

20 lot of studies in which investigators have tried hard

21 to find the effects where there maybe none. I would

22 not even describe the epic results as even strongly

23 suggested.

24 I recognize and share the concerns about

25 effects not yet established. Concerns based largely on
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 78

1 laboratory findings but I suspect that further records

2 in epidemiology will just produce conditions of

3 equivocal findings.

4 Also, I'm doubtful about the value of

5 additional water studies as support for the scientific

6 findings though they may have value in the context of

7 monitoring regulation. But none of those are more

8 imperative water studies if they're relevant to human

9 health effects, that this whole matter needs stronger

10 analysis in the EPA draft.

11 Is there a need for continuing research?

12 Definitely yes, and I believe that it should be focus

13 on mechanisms in humans with supporting laboratory

14 studies and with special attention to the exposure

15 levels of relevance to human populations.

16 I must stress this need for doses for

17 exposures that maybe ultimately relevant to human

18 apical events. I'm not against high dose studies in

19 light of the low dose effects and high dose studies

20 that are clearly negative are like would mean no effect

21 of lower doses either.

22 But keep in mind that statistical power

23 will be a major consideration in low dose research and

24 studies must be designed to have enough power to detect

25 outcomes of interest. Except for my skepticism about
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 79

1 new water studies, this seems to be pretty much for

2 EPA's account.

3 DR. STEVEN HEERINGA: Thank you Dr.

4 Bailar and Dr. Gold.

5 DR. ELLEN GOLD: I'm not sure if I have

6 a lot to add but I'll be brief or just try to be brief.

7 I think we've talked a lot about how well EPA did in

8 integrating the findings and noting certain

9 manifestations of the findings in the epidemiologic

10 studies.

11 I guess given some of the potential

12 modes of action that might be different in animals and

13 humans it's not unexpected that we might see different

14 findings that we can't try to explain at the moment.

15 And again I would encourage some other

16 existing databases on humans who try and look at some

17 of the outcomes and some of the exposures I've

18 mentioned before. And I wanted to say something about

19 the male reproductive study phase one.

20 I mean we've noted all these limitations

21 which were significant but I would also note that the

22 effect was pretty strong. Sample studies were kind of

23 small and so I have like confidence limit that did

24 include one. And so some of the findings are

25 consistent with what we heard yesterday in terms of the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 80

1 testimonies. So this seems like an adequately derived

2 design studies.

3 And because of the data, the human data

4 on small for gestational age and perhaps low birth rate

5 and prematurity are suggestive and not inconsistent

6 with the animal findings.

7 I wanted to address Dr. Harris' point

8 about ecologic study design versus ecologic data, and

9 well I agree largely with her, if you have to put

10 ecologic study designs in a separate category as

11 hypothesis generating and that it is useful to use

12 ecologic exposure assessment for individuals.

13 I think it's even more useful if you can

14 actually get some assessments of drinking water habits

15 and measuring drinking water of humans. And so I sort

16 of stratify to rank them let's say that way. So I

17 would encourage that as well.

18 I just have one or two more points. The

19 conclusion in- so I'd like to address the absurd uses

20 of the toxicology versus the epidemiologic data. So

21 the conclusion in the issue here first things

22 appropriate that the experimental toxicology data and

23 not the epidemiologic data are best suited for dose

24 response assessment of this at this time.

25 And both the epidemiologic and
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 81

1 toxicological databases can be use to inform hazard

2 rate and implication and characterization agree with

3 that.

4 But it would also seem contrary to what

5 is stated in the issue paper that the epidemiologic

6 findings can be used for endpoint selection when

7 studies are well designed. I mean it's not the case

8 right now but in future, I would want to exclude that

9 possibility even difficult if these studies are not

10 achieving the unattainable perfection. Because if you

11 don't have experimental data but you have a well

12 designed epidemiologic study, I think they can be used

13 in this way.

14 And so this has been the case

15 historically that response to outcomes for which

16 experimental data were not yet available but human

17 studies have suggested health outcomes associated with

18 the exposures that needed to be explored experimentally

19 to understand potential mechanisms and biologic

20 possibility, to also provide a sufficient information

21 to undertake preventive efforts including the absence

22 of experimental data, about physiologic mechanisms or

23 whatever. So I would disagree with that point.

24 So I think that the issue paper is

25 largely correct in its conclusions that the animal data
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 82

1 are likely to be relevant to humans although other

2 modes of action have play a role in humans, and the

3 human epidemiologic data are suggestive for adverse

4 reproductive and birth defects.

5 So again, I would encourage the EPA to

6 collaborate with other federal agencies that have

7 funded human studies of these types of outcomes, that

8 have assessed them better than in some of the studies

9 that we reviewed here, and then have biologic specimens

10 stored that could be evaluated for exposure assessment.

11 Thank you.

12 DR. STEVEN HEERINGA: Thank you Dr.

13 Gold. I thank the epidemiologic group for their well

14 organized and concise presentation. Comments from

15 other members of the Panel on this particular question.

16 Yes, Dr. Bucher.

17 DR. JOHN BUCHER: So I just wanted a

18 little clarification from that the group answer because

19 I think I heard two different perspectives on the value

20 of further epidemiology studies for atrazine. And I

21 would just state from my standpoint I agree more with

22 Dr. Gold's perspectives than Dr. Bailar had boasted.

23 As you put that question together that you can

24 reconcile those views or come up with single and

25 clearer message.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 83

1 DR. ELLEN GOLD: Thanks, I'll do my

2 best. But I also conclude that it's not bad if you

3 have a little bit of disagreement and some explanation

4 or a little disagreement.

5 DR. STEVEN HEERINGA: Alright absolutely

6 and I think just importance of views but the extent

7 could be rectified fine but if not if you want to sort

8 clearly explain why there are the differences. Dr.

9 Fenner-Crisp.

10 DR. PENELOPE FENNER-CRISP: The question

11 of the agency and know that there was a delay in the

12 presentation of the cancer epidemiology because there

13 was some unfinished work on egg health. Are there any

14 ongoing non-cancer studies within the egg health study

15 construct?

16 DR. STEVEN HEERINGA: Dr. Christensen.

17 DR. CAROL CHRISTENSEN: Yes this Dr.

18 Carol Christensen, within a non-cancer concept by

19 understanding it that much of that work if any by the

20 co-investigators with the NIEHS.

21 DR. PENELOPE FENNER-CRISP: I know.

22 DR. CAROL CHRISTENSEN: Yeah and so they

23 have sort of active research interest in both

24 respiratory health and female productive health.

25 However, I do know sort of eight priority hypotheses of
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 84

1 in the investigation about atrazine specifically, but

2 as they kind of go on and look at different test study

3 solutions and other exposures there maybe other

4 atrazine findings that we have them working closely

5 with those investigators and that's why there is

6 nothing outstanding, you know, sort of in the works.

7 DR. ELLEN GOLD: You're aware of

8 anything planned or ongoing sponsored by anybody else

9 on the planning or anything else?

10 DR. CAROL CHRISTENSEN: No.

11 DR. STEVEN HEERINGA: Dr. Christensen,

12 what about the National Children Study, EPA, do you

13 still have the reports on that particular study, will

14 atrazine be one because my understanding on the ongoing

15 other radical redesign and managerial changes on the

16 study that they were anticipating in or blood and other

17 samples, samples are essential?

18 DR. CAROL CHRISTENSEN: Yeah, at this

19 time, personally, it's really familiar working with the

20 design protocols. I do understand that they are taking

21 biological specimens overtime so there'll e banks and

22 certainly the potentially over time to put this little

23 piece, it may be with many different questions

24 including relevant questions here.

25 DR. STEVEN HEERINGA: Dr. Gold and Dr.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 85

1 Fenner-Crisp.

2 DR. ELLEN GOLD: Yes, so we do have

3 aside from the national children study in our place and

4 they are just beginning there.

5 DR. CAROL CHRISTENSEN: Yeah.

6 DR. ELLEN GOLD: And one thing because

7 of the way they designed it, they're trying to recruit

8 women who may become pregnant so that the actual return

9 of investments has been small so far so there may be a

10 need to reassess and redesign. They've just started

11 it.

12 DR. STEVEN HEERINGA: That's the longer

13 terms. Doctor Fenner-Crisp.

14 DR. PENELOPE FENNER-CRISP: Is atrazine

15 or the triazines and their aggregates still on the

16 NHANES monitoring list?

17 DR. ELLEN GOLD: Yes, atrazine is

18 included in the NHANES Program. I have to double check

19 on other triazines, but I know I just need to I know

20 there are measures of exposure obviously, potential

21 category and

22 DR. STEVEN HEERINGA: Thank you, that

23 thought also crossed my mind too.

24 DR. ELLEN GOLD: Included in the

25 database.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 86

1 DR. STEVEN HEERINGA: Dr. Lowit.

2 DR. ANNA LOWIT: I was just

3 anticipating, I was sensing that you were going to ask

4 for one more round and I've had a clarification. As

5 you before you close this question.

6 Dr. Harris said at the beginning of her

7 discussion of part B caveated her comments with her

8 expertise which makes sense. And I want to make sure

9 that as we move through the more animal based

10 toxicology studies, questions and comments related to

11 Cooper study and the animal relevance of the the human

12 relevance of the LH 20's and the animal that we circled

13 back, that to

14 DR. STEVEN HEERINGA: I have a similar

15 thought and in other words that, for exactly that

16 reason that Dr. Harris pointed out and as we go through

17 these other issues is in fact a response on the

18 epidemiologic cycle to try to add that to me, and

19 obviously in the report, we'll try to integrate it, but

20 as we go in sequence, we'll be sure to try to capture

21 and then presenting her additional thoughts that might

22 come from the epidemiologic team.

23 Well, at this point, I'd like to call a

24 break and again, thank three of our epidemiologists for

25 an excellent job at this point. Let's take 20 minutes
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 87

1 and we begin here at after 10 minutes.

2 (WHEREUPON, a Break was taken.)

3 DR. STEVEN HEERINGA: Okay let's find

4 our seats and we'll begin again. At this point, we're

5 ready I believe. I'd like to move to question 2.0,

6 review of studies on mammary gland development. Dr.

7 Lowit, will you read question 2.1.

8 DR. LOWIT: Okay question 2.1, please

9 comment on the quality, strengths, and limitations of

10 the mammary gland development study for Rayner et al

11 (2004, 2005) and Coder (2010) studies. Please discuss

12 in your comments factors that could lead to different

13 findings. Please comment on the Agency's conclusion

14 regarding studies on atrazine.

15 DR. STEVEN HEERINGA: Dr. McManaman,

16 start.

17 DR. JAMES MCMANAMAN: So I have this

18 broken out into the three components of the charge

19 questions. The first is to comment on the quality,

20 strengths, and limitations of the mammary gland

21 development study by Rayner and Coder.

22 So I will go through each one of the

23 studies beginning with Rayner 2004. In this study on

24 this is little review but they did this Long-Evans rats

25 that were dosed with 100 mg/kg of Atrazine once a day
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 88

1 by gavage vehicle else were housed on a 14/10 life-arc

2 cycle and at birth, half the dose from each treatment

3 were cross clustered with the other half of pups that

4 were originally in that letter to give four

5 experimental groups.

6 A group that was treated with vehicle

7 and main vehicle, a group that was treated with

8 atrazine during gestation and then with cross pups with

9 control pups with those clustered treated with control

10 for vehicle and then cross pups with atrazine pups in a

11 group that receive the atrazine during gestation and

12 cross pups with the atrazine pups.

13 So basically, they found that at the

14 specified times, each group has analyzed for body

15 weight, date of vaginal opening, weight at vaginal

16 opening as the cycle, mammary gland properties.

17 They've found significant effects of atrazine on the

18 age of vaginal opening and they found decreased body

19 weights of the pups set with day four in the atrazine

20 group.

21 The mammary gland properties, they

22 found--they used a developmental score to assess the

23 mammary gland development and they found significant

24 effects in the pups that were cross sponsored on the

25 atrazine moms at post natal day four and 22. And there
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 89

1 was also a decrease in the labeling index at these

2 times.

3 They also looked at expression of aroma,

4 taste and epidermal growth factor receptor and found

5 decrease to aroma, taste post natal day 33 and in the

6 pups from the atrazine that were cross-sponsored on the

7 atrazine moms. And increase EGFR and I think four pups

8 cross-sponsored them as well as the atrazine cross

9 pups.

10 The strengths of this paper is that

11 there were multiple time points, where the experimental

12 design which multiple time points were evaluated and

13 cross fostering approach. In addition, there was

14 technical expertise in the conduct of the experiments

15 and that they were able to cross foster which is

16 difficult for this strain of rats. And well I guess

17 that's the main point about that.

18 The Limitations for this one and for

19 this paper and for the subsequent paper of 2005 was the

20 subjective mammary gland scoring system. And also

21 there were limitations in the conclusions which covered

22 the age of a utero exposure.

23 They concluded that it was primarily

24 related to the neuro exposure to atrazine whereas cross

25 sponsored data set suggested there were significant
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 90

1 lactation effects on the other treatment and that there

2 was no discussion on mechanism. So those were the two

3 limitations on that.

4 Rayner 2005 was intended to find the

5 sensitivity, window of sensitivity during gestational

6 exposure. They dosed again, with like 100mg/ kg and

7 they did do a limited dose response curve here. Here,

8 they changed the protocols, so now they were giving

9 atrazine twice a day, 50mg/kg to give a total of one

10 day, 100mg and they evaluated the groups at days 13 to

11 15 gestation, 15 to 17, 17 to 19 and 13 to 19 to

12 identify the critical periods of atrazine exposure, and

13 both the F1 and F2 offspring.

14 Again, they examined maternal end feel

15 weight via vaginal opening, weight of vaginal opening

16 and use of mammary glands scoring system, in this day

17 however, they used a objective approach and that they

18 measured the length of the epithelial tree and they

19 measured the area of the epithelium and they looked at

20 postnatal days 4, 22 and 25, for the 13 and 19 group

21 and postnatal in 46 and 47 for using the objective

22 approach and they looked at postnatal day four through

23 67 for the subjective scoring system and they also

24 measured organ weights and there was no difference in

25 the groups. And significantly they found that the F2
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 91

1 pups there were differences at the postnatal day four

2 and postnatal day 11 in the weights of the atrazine

3 treated groups. This is not a cross foster study.

4 The strengths were the experimental

5 design to identify the specific periods of gestational

6 development, they were sensitive to atrazine and the

7 inclusion of experiments designed to test the effects

8 on the F2 generation.

9 Methodology was also a strength, they

10 used objective measurements of ductal growth and the

11 epithelial size and again used multiple time points.

12 Another strength is the reproducibility again they

13 found the same days of vaginal opening in their study

14 and they found the same effects of atrazine treatment.

15 The limitations were that they continue

16 to use the mammary gland scoring the subjective mammary

17 gland scoring system and there's concern among the

18 group that there might be that the methodology of the

19 scoring system may force identification differences

20 where differences really don't exist.

21 So there were some concern about that

22 but I think that Dr. Fenton addressed some of these

23 issues or try to address some of these issues in the

24 sense that the multiple investigators were using the

25 subjective scoring system and they get consistent
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 92

1 results.

2 And then finally, the last limitation

3 was that there was a lot of care feeding although they

4 recognized that atrazine was causing a decrease in gram

5 weight, they didn't put in the care feeding controls.

6 So now the next one is the Syngenta 2010

7 which is a combination of Dr. Coder and Hobie. Again

8 they used Long-Evans rats. In here there was a

9 variation between what the chem group used. They used

10 these animals that were on 12/12 cycle, my third cycle

11 which potentially is a compounding variable here.

12 Animals were dosed with atrazine, at right doses of 100

13 mg/kg by gavage, this is once a day compared to the

14 twice a day that the previous Rayner used and they were

15 dosed on gestational days 13 to 19.

16 They used an objective image analysis

17 approach to characterize mammary gland development

18 post-natally and significantly their planning crossed

19 fostered experiments where they started to do standard

20 ratio in cannulization.

21 So they measured the body weight,

22 vaginal opening and mammary gland development, they

23 tried to make this as close as possible to the Rayner

24 studies and significantly they found no difference in

25 vaginal opening in their study, the same strain of rats
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 93

1 but no difference. The dammed body weights were

2 significantly reduced during gestation and lactation.

3 And the body weights by treatment with atrazine.

4 The body weights of the high-dosed

5 atrazine in the fair-fed groups were similar during

6 gestation. They were different from the control. The

7 body weights of the high-dosed atrazine and the fair8 fed groups were also significantly less than that of

9 the control during lactation.

10 But significantly the fair-fed group had

11 a lower body weight than the high dose atrazine group

12 and this is all through lactations, so there was hardly

13 any change in body weight in the fair-fed group whereas

14 the atrazine group did increase and consistently

15 maintained a higher body weight during that period.

16 They evaluated end numbers in BrDU

17 labeling values at a variety of different times of

18 course roughly corresponding to those of the Rauner

19 study and found that there were differences at

20 postnatal day four in the high-dosed atrazine group in

21 terms of end-bud numbers and a BrDU labeling.

22 Respectively they end bud numbers were greater than the

23 controls in the fair-fed group. In the high-dose group

24 and the BrDU labeling and the high-dose group was

25 lessen out the controls.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 94

1 They also found that end-bud numbers

2 were different at the day of vaginal opening. With

3 end-bud numbers in the fair-fed group were greater than

4 the controls of the high-dosed atrazine group.

5 Interestingly, the Hobie papers states that the

6 atrazine at the 50 mg/kg group were also greater than

7 the controls but that was not the conclusion of the

8 public letter paper that was in our docket.

9 So the strengths of these were that

10 those response designs with fair-fed controls were

11 significant strength of the Howder paper and then the

12 use of the extensive objective extensive use of

13 objective image analysis approaches rather than these

14 subjective approaches used by Rayner.

15 Limitations for the methodology in terms

16 of life target cycle and the possible stress effects as

17 exemplified by dam aggression and cannibalization.

18 Further there were different, there was it could be a

19 lack of reproducity in body weight effects of atrazine.

20 This particular study found that there was an effect, a

21 significant decrease at the 50 mg/kg level whereas in

22 another study within this packet Coder C-study found

23 that there was no difference between the 50 mg/kg on

24 body weight of the dams.

25 Now the difference between these two
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 95

1 studies is that the Coder D, the one I'm just referring

2 to was that's a Long-Evans study and the Coder C-study

3 was Sprague-Dawley study so there's some question about

4 whether atrazine does have an effect on body weight in

5 their strength specific.

6 Furthermore, another limitation with the

7 conclusions that there was no effect--the no-effect

8 conclusion when it was clearly possible effects of

9 atrazine on end-bud numbers had postnatal day four,

10 that there were no effects of postnatal day four and

11 there were also effects at the day of vaginal opening

12 on both the 50 and the 100 mg/kg groups.

13 And there's no discussion with

14 significant differences in body weight during lactation

15 between the fair-fed and the high-dose groups. So

16 those are the evaluations of the strengths and

17 weaknesses.

18 So I move to the next question which is

19 to discuss your comments regarding factors which could

20 lead to the differences in findings. I've already

21 alluded to this, the Fenton group used a 14/10 cycle

22 and the Coder group use 12/12 and there was also

23 concern that the Coder study, the animals were stressed

24 and we've heard that atrazine produces corticosterone

25 increases and so potentially there's an undoubting
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 96

1 factor here as that the animals were already stressed,

2 you would not see the effects of atrazine, the same

3 effects that the Rayner studies saw.

4 Regarding the agency's conclusions of

5 these studies I think that both studies show

6 significant effects on mammary gland development, but

7 differences in procedures, differences in the results

8 and differences in possible adverse effects as stress

9 of the Coder study prevent concrete recommendation

10 regarding the use of the mammary gland to investigate

11 adverse outcomes of atrazine at this time and I think

12 that if you want to use this we have to have additional

13 studies.

14 DR. STEVEN HEERINGA: Thank you very

15 much. Our second discussant is Dr. DelClos.

16 DR. KENNETH BARRY DELCLOS: I think that

17 strengths and weakness has been pretty well covered in

18 the last statement. I think the differences clearly

19 not totally due to quantitative methods used by Coders

20 since Rayner also for the vaginal opening delay which

21 is not found in the Coder studies and also where Rayner

22 did use the quantitative measures in the mammary gland

23 area for example as mentioned by the treatment only

24 differences.

25 The differences in the quantitative
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 97

1 versus subjective measures have already been covered as

2 those are potential question stressed with the animals

3 I'm about to mention yesterday.

4 One thing included for study did fall

5 over the Rayner study is for its rat strain dosing and

6 life cycle as one difference that was mentioned. The

7 other control variable with difference in diet and it

8 is stated in the real report we were doing were based

9 on the diet using the previous studies injected with

10 atrazine with probable weight comes with the different

11 strain of diet used in the previous study and the one

12 with the current study was not expected to impact

13 animal health of the outcome of the study.

14 I don't necessarily agree with that the

15 absence of data because both of these diets are natural

16 ingredient diets of fiber where it is particularly high

17 in soy content, very variable phytoestrogen levels.

18 It's true both with both of the diets but they do

19 different analysis, they differ in the fat source that

20 the fiber using the animal fat and the 502 using corn.

21 But there has been a lot of--over the years bounded

22 with the rule of the soy content of the diet.

23 The phytoestrogen content in the diet is

24 exactly ten points so they only see data that lies

25 comparative study for the EPA and for the sharper
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 98

1 studies what can--with your appropriate answer and came

2 out with the recommendation that the levels of the

3 phytoestrogen should be tapped to a lower 350 mcg/g I

4 believe 350 parts per million which cleans the animals

5 even with that 30-40 milligrams of isoflavones per

6 kilogram per day.

7 Then the some weight--and phytoestrogen

8 has been what's interesting in the animal studies to be

9 used, so they're expecting the very endpoints of

10 serving both that vaginal opening and mammary glands.

11 And I also noticed this stuff that the--in the earlier

12 that the total vaginal opening will be different for

13 the maternal studies.

14 It was difficult to compare across

15 laboratories like this. There's going to be variation

16 of course with that of the Coder study that the vaginal

17 opening time did fall within the rate where Rayner were

18 in significant differences delay of vaginal opening.

19 So I think, you know I don't know

20 whether the phytoestrogens have an effect on atrazine

21 or it has been looked at, I don't know if what we're

22 aware of, but I think it's something that should be

23 considered.

24 And I think that EPA, it's my feeling

25 that they require the reporting of phytoestrogen levels
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 99

1 in the diet. It's certainly something if you're

2 currently clear cross studies on mammary gland that you

3 should be aware of and control force of about the same

4 diet should have been used and it's a potential--and

5 the thing is there's many reports and there's

6 disagreement in the literature on this effect of so why

7 the time determines the time of vaginal opening and so

8 forth that their maybe a strain difference of--there

9 are also a sporadic reports in the literature where

10 people move from one institute to another and somewhat

11 lose their effect and they attribute to the diet, so I

12 think it's a really important profounder in charging

13 the studies.

14 And I agree that as from the last

15 discussant, it indicated that the studies are not ready

16 for--I agree with the agency's inclusion that they're

17 not ready for the risk presented. That's all I have to

18 say.

19 DR. STEVEN HEERINGA: Thank you Dr.

20 Delclos. Dr. Horseman.

21 DR. NELSON HORSEMAN: So I'm not going

22 to comment on the strength and weaknesses. I just have

23 a general comment I want to make, so I think Jim

24 covered--this guy the strains in particular very well.

25 So there's a fundamental issue here that I think that
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 100

1 runs through this stage.

2 Neither of its various posts that breast

3 milk is good, and breast cancer that where the phone

4 number over these studies only address bigger than

5 smaller. And the outcome that's supposed to be

6 negative as smaller, and I don't see any reason to

7 believe that to be a legitimate outcome and particular

8 it's pointing out by Fenton in her presentation, the

9 issue of precocious mammary gland by augment which has

10 then recently reported as a potential human health

11 issue by trying to bear on his colleagues maybe

12 relevant. And here the paradoxes that bigger is a

13 negative outcome.

14 So I think just that the fundamental

15 level that the biggest weakness in this entire set of

16 studies lies on the presupposition that you can

17 automatically suppose that the more development at this

18 particular stage or at this individual stages, it is a

19 positive outcome and less development has made about

20 that.

21 Particularly it was regarded to

22 gestational studies. The most normal state at the

23 handling gestation for the mammary gland is

24 underdeveloped. This is a very small underdeveloped

25 only, slightly branched the mammary gland system.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 101

1 So I think that this--and when you come

2 back to sort of the summary of both of these sets of

3 studies, I think you'll not agree with this notion and

4 to maybe think about how we might be able to use the

5 mammary gland but be able to require examining these

6 fundamental assumptions first. That's my main comment.

7 DR. STEVEN HEERINGA: Thank you Dr.

8 Horseman. Dr. Leblanc.

9 DR. GERALD LEBLANC: I never ceased to

10 be fascinated by the frequency with which to hop in

11 labs typically one from Academia or the government and

12 the other being a supported lab addressing the same

13 question if you happen to seemingly despair results.

14 I've concluded that one of the reasons for this is that

15 the academic or the Government Labs are typically

16 seeking to discover new information or the industry

17 labs are seeking to confirm, to refute, or to address

18 alternative interpretations of the existing data. The

19 results that we get a series of checks and balances

20 from which hopefully the best science emergence and the

21 agents have been mixed on decisions.

22 In this case, we have the Rayner

23 studies, and Rayner et al. discovered basically that

24 atrazine were administered at a 100 mg/kg either

25 prenatally or postnatally, negatively impact with
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 102

1 mammary gland development.

2 The screens or the paper have been well

3 covered by my colleagues and I am not going to go into

4 that but I'd like to add a couple of points. One being

5 that this Rayner effort really represents three

6 studies. The two that we were asked to evaluate, and

7 then the Enoch studies which used a 100 mg/kg atrazine

8 as positive control.

9 And all three studies cooperate. They

10 all provide the same basic information with respect to

11 effects on mammary gland development.

12 This study has also provides the well13 tested atrazine effect that has increased the vaginal

14 opening supporting the efficacy of the treatments. The

15 measure used, scoring had been cited by my colleagues

16 as a weakness to the study and I agree that the

17 subjective nature of scoring can be viewed as a

18 weakness to the studies.

19 But I think it's important to also

20 recognize that it can be recognized as strength because

21 this really provides a holistic or an integrated

22 measure of mammary gland development rather than

23 looking at individual parameters in isolation.

24 The Coder studies sought to replicate or

25 extend upon the relevant aspects of the Rayner study.
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1 And again, the strengths and the weaknesses have been

2 well covered--I'm going to look at this quickly to see

3 if there's anything here that hasn't been discussed.

4 They've all well been covered. I'm not going to repeat

5 myself.

6 Since it presumed the tenth of the Coder

7 Study was to confirm, refute or extend upon the Rayner

8 Studies, I do find it surprising that a different

9 analytical methodology was used to assess mammary gland

10 development.

11 And the least I've learned to expect is

12 that both the qualitative scoring and the quantitative

13 movement or analysis would have been using this study

14 compared to provide us some interstudy comparison which

15 we could then expand to the Rayner studies which this

16 clause had done.

17 I consider it possible that small

18 difference between treatments if controls existed

19 anymore from metric analysis. Just looking at the data

20 that we had in the report, we can see differences, and

21 while these differences were deemed non-significant and

22 apparently not significant, it's certainly possible

23 that in combination they would have in fact the scoring

24 in the Rayner studies.

25 If the EPA may consider requesting that
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 104

1 the registrant have the slides generated in the Coder

2 study scored using the same scheme required by Rayner,

3 ideally the slides are to be scored of the same

4 histologist who scored the Rayner slides. This will

5 provide some direct comparison of results. There have

6 been two methods and this will be very informative.

7 Despite the different methodological

8 approach as used in the Rayner and the Coder studies,

9 several disease decision results exist at the atrazine

10 dose with is a 100 mg/kg.

11 Both studies reported a decrease in the

12 maternal animal weight, they both report a decrease in

13 pup weight, they both report an effect on live births

14 or death ones, and they do both report an effect on

15 mammary gland development.

16 The major discrepancy is that the

17 effects observed on mammary gland development are not

18 consistent with respect to magnitude and temporalities.

19 This difference may be due to the different methods and

20 analysis used which again, are views for a comparison

21 of the two methods, how to use the slides generated in

22 the Coder study.

23 DR. STEVEN HEERINGA: Thank you Dr.

24 Leblanc. Dr. Lowit.

25 DR. ANNA LOWIT: Thank you. I basically
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1 agree with everything that has been said so far and I

2 have just a few things that haven't been mentioned.

3 Yesterday, it was brought up by Dr. Coupe that they

4 made an effort to assess mammary gland development in

5 animals after vaginal opening on the same day at the

6 estrous cycle, and I think there's a lot of variability

7 in the data and I think in the adult numbers afterwards

8 in other measurement, hormonal measurements and the RU

9 measurements and so forth so I think that it might have

10 helped in the future if they do all their studies that

11 after vaginal opening, you have to check the animals on

12 a given date on the estrous cycle.

13 I found a couple of errors in the draft

14 paper in terms of their citations of that papers and I

15 have those in my written accounts. The only other

16 thing I want to add is I agree with the comments of Dr.

17 Leblanc that it would have been really nice to see the

18 comparisons between studies on the subjective scoring

19 using that method.

20 But my final comment is that what struck

21 me about these studies was that when they looked in

22 adults after and close to postnatal days 50 or 60.

23 There are some dates when I think each study looked at

24 mammary gland development, it had reverted. It was no

25 different from controls that happened.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 106

1 So even though there are--I think most

2 studies actually did find similar things in terms of

3 some delay in mammary gland development, it's

4 temporary. And I think they have to keep in mind that

5 atrazine treatment was also temporary.

6 It was limited to up to a period during

7 gestation or in postnatal or early postnatal, live

8 birth during lactation. And then it stopped. So once

9 it stops, once they're weaned, that postnatal day 21,

10 40-50 you know a couple of months later, things have

11 normalized.

12 It appears, I'm not--I mean they're not

13 frequently have points, there's just one in each study.

14 But it is a temporary phenomenon then maybe that's of

15 course affected, it was atrazine-caused and there's

16 something going on there. That's all, that's my

17 comment.

18 DR. STEVEN HEERINGA: Thank you very

19 much doctor. Other comments from our panel members

20 with regard to this question? Dr. Akana.

21 DR. SUSAN AKANA: In most of this view,

22 the endpoints have been morphological or structured and

23 I'm a physiologist, so I say--where is the functional

24 output for this?

25 And as one goes with milked mouse, you
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 107

1 can do these studies. And in Rayner study, they're both

2 on lactation challenges primarily focused on mom. But

3 the study designed, and they touched it on yesterday,

4 there was a possibility of measuring the--well she did

5 it. She weighed the depth and the liters after

6 separation that when they review grouped after 20

7 minutes of--well that's on the 20 minutes of lactation

8 will be weighed a liter.

9 Now at this age, infant mice or infant

10 rats do not excrete. They don't pee and poop on their

11 own. They need moms in relation to do it. So, what

12 they have is what milk they took in. Now, there was

13 reportedly no treatment difference in the weight gain

14 per liters. And I mentioned yesterday, however the

15 pups were less than atrazine-treated groups.

16 Therefore, they must have done it for milk.

17 So, I think there is a possibility of

18 exploring that kind of functional outlook from the

19 mammary gland. And there are a number of devices you

20 can use to collect milk from anesthetized females, so

21 there are possibilities.

22 DR. STEVEN HEERINGA: Thank you Dr.

23 Akana. Dr. Horseman.

24 DR. NELSON HORSEMAN: If I might just

25 make two comments on things since I'm not wise pretty
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 108

1 well, I don't know, but we don't advise pretty

2 regularly whereas that we either correct that that can

3 be done and it is a functional outcome.

4 We might come back to that later. I do

5 think I want to because by virtue of the way these

6 papers are presented and these studies are presented,

7 the concept of delay of mammary gland development is

8 intrinsic and we came to adopt that language when we

9 talk about these studies, and I think it's important to

10 challenge that notion. And I'll just give you one

11 specific case beforehand; Jim will be able to elaborate

12 on this.

13 So, why should it inherit MTV mammary

14 gland retrovirus? If you compare them with the

15 equivalent mice that don't inherit that or it might be

16 infected with that retrovirus, are always going to

17 score higher on any of these tests that we're talking

18 about here. Well, it's just simply observation

19 morphology or in measuring these things.

20 And so, it would be hard to say that

21 tumor virus is a better outcome than not having tumor

22 virus, right, and I think at some basic level. And so

23 the notion that this delay--that we adopted the

24 assumption of the delay is a negative outcome, I think

25 it is problematic.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 109

1 DR. STEVEN HEERINGA: Thank you. Dr.

2 McManaman.

3 DR. JAMES MCMANAMAN: So, I have just a

4 couple of thoughts regarding the effects of the

5 atrazine. It occurs to me that it's quite profound

6 given that what we heard yesterday about the

7 pharmacokinetics, that when you stop dosing at

8 gestation on day 19, that you can have an effect

9 several weeks later.

10 What I understand is that most of the

11 atrazine and metabolites, the area under the curve of

12 the data should have resulted in a clearing of the

13 active components within a few days after birth, yet we

14 still see an effect of both studies at the date of

15 vaginal opening on a number of end buds.

16 So, I think that has well, it's not, I

17 don't know how useful that is in terms of risk

18 assessment. I do think that it has a profound

19 biological question, it outraces profound biological

20 question so it's then how does that happened because in

21 tumor, I believe there is nothing left to induce these

22 changes. So, there has to be something happening early

23 on. And with that is a conundrum.

24 Furthermore, from what I've read, the

25 limited amount of information about how much atrazine
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 110

1 is actually in the milk is not well established. And

2 the data that I saw suggests that with using a tracer

3 study that about 2% of the atrazine that was induced by

4 the buds ends up in the milk.

5 So again, it raises questions about how

6 much atrazine is really there and what effect it's

7 having. Is it a gestational effect? Is it a

8 lactational effect?

9 And the questions are there were some

10 more questions and issues than they sought to address.

11 So, I think we know very little and I think about how

12 it affects the mammary gland and I'm--well, the data

13 don't support the use of the mammary gland at the

14 moment, I think the data support further study, because

15 I think that it has profound implications.

16 DR. STEVEN HEERINGA: Dr. Bucher

17 DR. JOHN BUCHER: First, the disclaimer

18 on Dr. Fenton works in my division, and I know that she

19 indicated the other day, we have an ongoing project to

20 look at the relative sensitivity and accuracy of the

21 subject of scoring mechanism versus this quantitative

22 scoring mechanism.

23 But I would agree with Dr. LeBlanc. I

24 don't think I have seen anything yet or I would

25 challenge I guess the group to produce data that would
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 111

1 suggest that quantitative scoring system that was used

2 is in fact more sensitive or more specific or more

3 accurate.

4 And until that has been shown, I am not

5 sure that the subjective system should be discarded as

6 it really has been and has a serious weakness as the

7 new case that's characterizing it in the document.

8 So, I think that this is really

9 important with respect to those levels in the Enoch

10 paper that generated changes in mammary development.

11 They are very, very low with respect to the combination

12 or a mixture of studies. Clearly the atrazine wasn't

13 tested on its own and with those low levels in the

14 Enoch studies has been.

15 But I think that this is something that

16 the panel should be fairly specific on, if you believe

17 that the sensitivity and accuracy of the quantitative

18 approach has been shown to be superior than this data.

19 But I'm not sure I've seen the data to suggest that

20 yet.

21 DR. STEVEN HEERINGA: Dr. Horseman?

22 DR. NELSON HORSEMAN: I'll just comment

23 that--I don't necessarily believe that the

24 quantitative, the objective--they both end up being

25 quantitative in the sense that you assign quantities,
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 112

1 the subjective scoring either is intrinsically more or

2 less valuable. I think they do.

3 They require a lot of different

4 approaches from the beginning in terms of how

5 individuals actually gather the data and we heard a

6 discussion about how people are trained and support to

7 gather the data which at that end, it's important that

8 they are different all the way up to the statistical

9 analyzing of the data.

10 And nonetheless we didn't discuss in

11 detail the statistical handling of these scoring data.

12 And I don't consider myself the internal legitimate

13 statistician, but I was not convinced that the most

14 robust statistical techniques were used there.

15 So there are layers of issues I think in

16 deciding preference of either the objective or the

17 morphology measurement base approaches versus a scoring

18 base approach. And maybe the argument that the one is

19 better than the other one, I would agree.

20 There is no reason to say that, but the

21 way they are handled obviously in this particular

22 studies, these measurements that the Coder study did

23 were handled in a much more apparently rigorous manner

24 from both measurement statistical sense, but that

25 doesn't make the approach more or less valid.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 113

1 DR. STEVEN HEERINGA: Dr. Fenner-Crisp.

2 DR. PENELOPE FENNER-CRISP: I just want

3 to follow up a little bit on John's last comment and it

4 was pointed out by Dr. Fenton yesterday that the

5 process by which the criteria were selected and are

6 applied is quite consistent with general evaluations of

7 pathology period, including some efforts to look at

8 severity of whatever the phenomenon is, which is a

9 reflection of this.

10 So, I was a little disconcerted by the

11 under currents of dismissiveness of using this kind of

12 technique when it kind of just bases of all of MTV's

13 data evaluations and a whole lot of other pathological

14 evaluations for studies of all of them.

15 So, the long history of having this kind

16 of process in which entities have agreed upon

17 appropriate criteria and that they agreed upon this

18 foreign mechanism and then do an internal and an

19 external peer review.

20 So I found it a little disconcerting

21 that you might want to just abandon that approach.

22 DR. STEVEN HEERINGA: Dr. McManaman

23 DR. JAMES MCMANAMAN: Yeah I don't think

24 that was our intention to suggest that it was--if one

25 had to say, how could we reconcile differences then and
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 114

1 I think that was the way I interpreted the charge is

2 that--if one has to evaluate in a blinded way and just

3 is able to use image analysis in which you have--

4 there's no Gestalt effect, you know.

5 And that is a very objective way that

6 would lead to less bias--potentially less bias, whether

7 this leads to a greater accuracy or not, I don't know.

8 But personally, I do the same kinds of studies that Dr.

9 Fenton does them in a sense that I look at them first

10 and find out what is the difference and then try to

11 come up and see viable lead determinants and try to

12 seek, you know to try to quantify that.

13 So in a way, I personally lean towards

14 the Fenton approach but I'd also do the image analysis

15 and it's difficult to try to match those things, but

16 clearly I wasn't able to just--if one had to evaluate

17 it from strictly an objective view the subject you've

18 approached that Dr. Fenton uses is if you were going to

19 try to have other laboratories reproduce this it's

20 harder to reproduce than the objective image analysis

21 approach.

22 So I think that it's a relative

23 weakness, it's not an absolute weakness. Furthermore,

24 the difference between the way a pathologist evaluates

25 samples in a way that Dr. Fenton's group evaluates
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 115

1 samples appears to be and that pathologist had

2 historical values for differences in a group.

3 And Dr. Fenton appears to take each

4 study and look for differences. And that's a different

5 means of analysis than what a pathologist would do. So

6 I think that--well that maybe just a practical way of

7 handling that.

8 I don't know if that is the best way so,

9 I would have to say thank you to Dr. Fenton--should be

10 encouraged to use historical data as a way of looking

11 at what's a four and what's a one and then from there,

12 try to finish one--each experiment into a one and four

13 paradigm.

14 DR. STEVEN HEERINGA: Dr. Bucher and

15 then Dr. Portier.

16 DR. JOHN BUCHER: So just one

17 clarification just to remind you that, Sue didn't say

18 that they included historical slides in the result.

19 Slides have each study so that there could be a sort of

20 a collaboration back to increase .

21 DR. STEVEN HEERINGA: Dr. Portier would

22 like to move on.

23 DR. KENNETH PORTIER: It's an

24 interesting discussion always to me, the qualitative

25 scoring and quantitative measurement. And what the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 116

1 argument want--suggested by actually having the Coder

2 slides score means that you can necessarily look at the

3 two.

4 And when I was looking at the--thinking

5 of the Rayner studies, I kept thinking what if they

6 have four or five measurements but they have done a

7 multivariate analysis.

8 They haven't looked at--because there's

9 some kind of latent structure here that would actually

10 lend itself to grouping, kind of like the coding and so

11 there's still some--it's very large and interesting

12 statistical analysis that could be done.

13 I did review the analysis of the data--

14 the scoring data and while the scoring is qualitative

15 and then they use any of methodology, they sum the

16 scores over individuals and litter to come up with an

17 average score for the mother. And then they went on

18 and did end over.

19 And I think that's probably pretty

20 standard approach. I don't think there's going to be--

21 in view of this that coming out of that approach.

22 DR. STEVEN HEERINGA: Well, very good

23 discussion on that. I'll thank the discussants and the

24 associates for the organization of the presentation.

25 At this point, I think I'd like to move
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 117

1 on to Charge question 2.2. Of course if other issues

2 arise back to 2.1, feel free to bring them in. But at

3 this point Dr. Lowit, if you can read the charge

4 question 2.2 into the record.

5 DR. ANNA LOWIT: In three of the studies

6 considered in Question 2.1 (Rayner et al 2004 and 2005;

7 Coder 2010), effects have only been observed an

8 Atrazine dose of 100mg/kg/day. In contrast, Enoch et

9 al. (2007) report delays in mammary gland development

10 as low as 0.09 mg/kg/day following exposure to atrazine

11 and mixture of several metabolites/degradates (DEA,

12 DIA, DACT, hydroxy-atrazine).

13 The Agency's review of this mixture

14 studies provided in Appendix A (Section A.10). Please

15 comment on the Enoch et al. study and the degree to

16 which the Agency's review accurately reflects the

17 strengths and limitations on the study. Please include

18 in your comments a consideration of the study design of

19 the mixtures experiment and how this designed impacts

20 the interpretation of results.

21 DR. STEVEN HEERINGA: Dr. Mumtaz will

22 discuss it.

23 DR. MOIZ MUMTAZ: I'm aq chemist by

24 training and became a toxicologist so I just then use

25 chemicals in the nervous systems and it was being
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 118

1 required to become a toxicologist.

2 So I'm more quantitative in the massive

3 extracts of the chemical. So then I came to EPA, I

4 realized to begin with mixtures of chemicals and so I

5 want to commend EPA for taking up this issue into

6 mixtures because often just because their complexity is

7 in born, that specific mixtures is shunt and it stay

8 with similar chemicals.

9 So my comments are the structures to top

10 it before the goals of this assessment. What are the

11 issues that make up this assessment and the common

12 relate it on the Enoch study, hopefully the rest of the

13 panel members will add to it.

14 So the goals of chemical risk assessment

15 in general could be multiple. It could be just

16 protecting public health. It could be ecological

17 health. It could be determining available levels, or

18 just improving the methodology or technology involved.

19 In case of mixtures, it could be just--to show that

20 mixture risk assessment is not an issue because a lot

21 of plant when you do a single chemical risk assessment

22 involving the community--what about mixtures, so it

23 could be just that and at least that it use the other

24 than the said sides.

25 We deal with hundreds of chemicals and
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 119

1 most of the time they find that at the low levels we

2 find chemicals and it's also important to look at

3 mixtures of chemicals.

4 But, it is just to show that being a

5 group we have addressed it on selective community,

6 where population is important. But the goal of these

7 insights here was to look at how they can improve the

8 frequency of standings, it was to get a risk assessment

9 of exposure. I believe that's my understanding of the

10 assignment we have here.

11 So this consist of chemical mixtures

12 goes through the process of hazard identification, it's

13 for your assessment, those response and then this kind

14 of question that John will lead some of that tomorrow

15 or later on this afternoon.

16 We'll see how that went to catch

17 everything into it and generate recommendation. And

18 they all recognize that exposure to chemicals is not

19 for a single chemical but all these mixtures of

20 chemicals.

21 To evaluate the mixtures risk

22 assessment, often a 104 is potency made dose or

23 response attitude which is but that assumes that the

24 slow interactions of humans between the chemical system

25 they go to the process in the human body or in an
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 120

1 animal body system.

2 But interactions do occur and there's a

3 lot of data to show that. That interactions in a

4 volatile system do occur because there are limited hot

5 place that human or animal body can dispose of

6 chemicals. And so we shall consider the role of

7 interactions in the joint process in the human mixture

8 risk assessment.

9 That at least to--hypothesis has been

10 raging to say, "Hey, if there are--these three can

11 cause present the same mode of action." That could be

12 possibly be of interactions and if that is the case, if

13 there's a limitation, that will recommend--that was

14 involved, it could either go down or you could it could

15 just not create limiting there could be--and that shows

16 separate enzymes so that there are--it could be more

17 than anticipated.

18 So, the consideration of interactions

19 helps us do the hypothesis testing for uses of

20 chemicals. While doing so, we should also consider

21 that the exposures occur either simultaneously or

22 sequentially and I'm stating all these issues about

23 mixture risk assessment because later on when we look

24 at the study, these are the issues we need to address.

25 I'm really happy that as toxicologist, we are moving
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1 away from genetic toxicity studies and we try to infer

2 data from such studies about mixtures. Then they don't

3 have the steps to go toward or there are levels to

4 address to get exposed.

5 But for a well designed facts of this

6 study towards improved paucity or risk assessment, it

7 prove teamwork as a that issue then improved the

8 experimental toxicologist, scientist, competition and

9 modelers and discuss systems as can be similar to this

10 part of a panel development. People have different

11 backgrounds in terms of scientific backgrounds and how

12 they are trying to enter their goal at.

13 And you might not agree today or in the

14 next meeting that what we're doing is right, but

15 ultimately when we get the answers then a consistent--

16 it could be the right one. Until then, we have to

17 depend on less than, I believed design studies and that

18 brings us to the Enoch et al. study.

19 This study was designed in context of

20 enlargement with preliminary exposures. Mammary gland

21 development as an indirect indicator of cross studies,

22 areas, mixture with appropriate proportions on them one

23 week was tested at three different concentrations of

24 0.09, 0.87, 8.72 in per kilogram levels, and an

25 appropriate positive and negative produced.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 122

1 This study addresses issues that

2 include, but limited to this specific mixture. They

3 don't administer dose. It is the same study of

4 simultaneous exposure to allergen and the three known

5 metabolites. There was no consideration given to the

6 genuine concentration of chemicals or the metabolites

7 as a functional scientific basis.

8 This was a mixture of component

9 chemicals of assumed similar mode of action. And if I

10 had looked at these structures of chemicals I would

11 have hypothesized that those additivity would be the

12 case in this part of the recognition.

13 I happen to have the knowledge because I

14 know that levels of low and the mode of actions were

15 similar. If the mode of actions were different, it

16 could have been a depression but that's just essential

17 right now about what you are doing in the study.

18 So as I mentioned, mammary gland

19 development was started as the part of the emphasis of

20 the endpoint. That dose response is a function of a

21 hundredfold change that we speak, as captured by the

22 EPA report. So it shows this endpoint or the weight

23 that was varied was really not sensitive to a

24 hundredfold the dose response was really not as good as

25 atrazine.
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1 But there was no correlation established

2 between that internal dose of the chemical and the

3 mammary gland development. So we don't know what are

4 the availability of this mixture as it was administered

5 to lodge.

6 We don't know how much of it is really

7 absorbed into the system and then what happens to the

8 proportions of this three or four chemicals during that

9 time period and that's something that really I just

10 want to leave on this afternoon.

11 Depending on the several components of

12 this chemical, Richmond was really causing these

13 effects, the proportions would change and then later

14 exposures also changes because some of this

15 metabolites, which were put in this mixture both from

16 multiple chemicals, so this might not really represent

17 what our patients are being exposed to or the rest in

18 this case are being exposed to.

19 And come to other points of what the

20 mixture itself even though we know this mixture will

21 identify it in the environment. This is a relative

22 mixture because we're looking at atrazine and its

23 metabolites. In any farm situation, we are exposed to

24 herbicides, pesticides, fertilizers, and bunch of

25 chemicals.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 124

1 So even though we are testing these

2 mixture and its doses, that has really help us identify

3 the real problem of the environment as in the concept

4 of public health.

5 I know that, CDC's in its studies go for

6 that. The USGS analysis of water shows that there's

7 bunch of chemicals present, I mentioned to you about

8 the herbicide and pesticides and nitrates in the same

9 sample of water.

10 So, that's something to consider. And

11 also, just because the chemical are present, it doesn't

12 mean that it's close through and it's something

13 standard and has been exceeded.

14 But CDC's enhanced data show that most

15 of us sitting here where they have more than 160

16 chemicals in our bodies, so by studying this limited

17 mixture, we are not trying to identify the sole problem

18 of what is happening in the environment but we have

19 specific and limited to the interpretation of this

20 particular mixture.

21 It has an interaction profile on

22 atrazine, and other chemicals present and what are the

23 causes that produce or relevant.

24 I'm going to stop here and let the panel

25 members comment and then we have some conversation on
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1 that.

2 DR. STEVEN HEERINGA: Thank you Dr.

3 Mumtaz. We'll go back to Dr. Akana.

4 DR. SUSAN AKANA: So this model design

5 of this study was made difficult to fit in with the

6 earlier types of studies. So I have two minor, minor

7 comments that might help frame something. And they're

8 both maternal factors.

9 The strains of rats that have been used

10 are the Long Evans and the Sprague Dawley and there is

11 literature that there's a difference on how the

12 maternal strains treat their young. And it looks as if

13 the Long Evans rat is more attentive on a number of

14 maternal behavior results.

15 Now, why is that significant? The whole

16 body of literature now, people giving micro meaning on

17 maternal behavior, the thing when we helped the

18 retrieval contact with siblings and mothers.

19 And they actually have a powerful

20 influence on programming my favorite access, the HPA

21 access, but that does--and there are some very

22 interesting evidence of the maternal that you really

23 have to strain the pup. The pup carries that on to

24 group pup, so it's transgenerational behavior. So if

25 you're looking at the long evidence strain and this
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1 might explain why there are some difficulties

2 supporting across different labs with a different

3 strains.

4 My second minor comment is that when you

5 order pregnant rats in, most vendors refuse to ship

6 them on their conception onward, towards or they say,

7 if you receive shipment you have a danger of losing

8 their pregnancy.

9 So as I understand it, with Dr. Fenton

10 in general, he had ordered in pregnant dams to be

11 shipped on gestation day tenth and if I understand

12 correctly, in the other studies, they come in on

13 gestation date of four, five, six, or seven I think Dr.

14 Coupe has said.

15 There are some studies saying that the

16 day of the shipment of a pregnant rat has an influence

17 on her maternal behavior because of the destruction in

18 early development. So, paying attention to the

19 strength and what day they had shipped might be the

20 nuance factors that have been blurring back some of the

21 interpretations.

22 DR. STEVEN HEERINGA: Thank you Dr.

23 Akana. Just a note to everybody on the panel, make

24 sure you hold the mic close and speak up even though we

25 just--really reason a good sound here in the back of
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1 the room to take. So, I just have to mention to all of

2 us to make sure that we project close to the mic.

3 Thank you Dr. Akana. Dr. Chambers?

4 DR. JANICE CHAMBERS: Thanks. My

5 interpretation of the strengths and weaknesses of the

6 study, I guess we're a little different from the two

7 other perspectives, so you got really the points. I

8 also agree with Moiz that doing mixture study perhaps

9 are more relevant.

10 And so I do commend that authors who's

11 doing that. However, there are a number of critical

12 points that the result of the EPA has pointed out, the

13 body weight of the pups in the mixture study were

14 increased but the atrazine which presumably is the

15 positive control were decreased.

16 This is included both. In the 4-0 type,

17 it addressed the point that earlier Dr. Akana about the

18 milk and the stuff or something that can be 60 or I

19 think that they would--that would be a legitimate

20 increase. So the increase evaluations are not toxicity

21 so I don't know how they interpret that.

22 Also, a thought has crossed my mind you

23 may or may not want to pay any attention to is if

24 there's any credibility to the other studies which

25 resort here in earlier that there's not lot of question
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1 much about the idea of low gestational age, kind of

2 close. You know, that is to correlate things to

3 increase in pup weight so there's just there between

4 the heavy such as they are the results in this.

5 I also kind to have questions in

6 experimentalist about whether atrazine should be

7 considered the positive control that still is an

8 experimental compound, I think. And these people are

9 still trying to gather data on that.

10 So if that's really a true positive

11 control, I kind of question that database is good

12 enough on that. Now, the mixtures, I guess one of the

13 things that is concerning about the study is the

14 incredibly low dose levels of which they got mammary

15 gland changes. And if those are true, that mean you

16 need to rethink your risk assessment.

17 There is one compound in this mixture

18 though that is not present in the other studies and

19 that's hydroxyatrazine which is an environmental

20 degradant, not a metabolite.

21 I'm not mainly in the metabolite. And

22 so if that were really the compound that was doing

23 something then that could explain why this very low

24 concentration result show something, but again I don't

25 think there's any database on what hydroxyatrazine
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1 might do.

2 Again, two internal ways to increase in

3 the mixture study not in the atrazine so they didn't

4 match the so called positive control. I don't know

5 anything about how variable this sort of measure is,

6 the control was rather low one occasion and in the same

7 range or some of the detrimental effects and some of

8 the mixture studies. So I don't know how you interpret

9 the validity of the animal group when the controls were

10 low on one occasion.

11 Dose responses as you guys pointed out

12 was not always present in the mixture study. If this

13 very low doses are real and worthy of consideration in

14 risk assessment process, because they are so much lower

15 than all the other studies that exist right now.

16 I think that really needs some

17 replication before you take that into the risk

18 assessment process. I'm a little uncomfortable with

19 the positive control that is so much higher in

20 concentration than the others to set certainly in

21 effect in pharmacokinetics with the animals involved.

22 And that's probably all the comments I had.

23 I think it is medical certainly on the

24 standpoint of being an environmentally relevant mixture

25 but it had the compounds that we don't know anything
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1 about. This, I think a little more verification

2 certainly replication if you're going to consider these

3 valid doses across the population.

4 DR. STEVEN HEERINGA: Thank you Dr.

5 Chambers. Dr. Krishnan?

6 DR. KANNAN KRISHNAN: It seems like

7 there's going to be as you would expect some

8 replication on comments. I was trying to eliminate the

9 words I heard and then however repeating but I'm not

10 coming up with anything. And I appreciate it so I'm

11 just going to go to comments that I have.

12 Initially, both of the other two, the

13 study design is fine even it's objective which is

14 essentially to rattle with the effects of composite

15 mixture here and focusing on an environmentally

16 relevant DCT mixture.

17 Furthermore, she said the doses where

18 designed to relate to the environmental acronyms in

19 terms of proportions and consultations which is

20 actually much better than most of the other mixture

21 studies that I have seen are far evaluated. But in

22 trying to use this are evaluated for the purpose for

23 which it was not, that there's no way evaluating it for

24 a different purpose.

25 Well, the side comments are currently
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1 clear and I concur with the evaluation with the agency

2 in terms of the shortcomings particularly the enclosed

3 from the hydroxy metabolites and the lack of individual

4 dose response information and possibly the scoring

5 process to evaluate the mammary system as well.

6 This tallying would have been

7 strengthened if the findings were--or if such findings

8 were ordered for other mixtures, let's say different

9 compositions affecting of temporal and spatial

10 reference are--there were those response information

11 for each of the compliments are the reproducibility of

12 these findings was there in other studies evaluating

13 the same endpoint of these kinds across levels.

14 Usually even just with the atrazine itself are within

15 the specific metabolites are--and if there was any

16 measure of internal dose or other parameters explaining

17 the basis of such an extraordinary low. Such effects

18 had low doses because possibly some interaction might

19 explain it.

20 So the thing that concerns me the most

21 is that there is no support from any of the other

22 search studies particularly even just with atrazine at

23 such low doses. Because giving atrazine individually

24 will result in essentially the same research with the

25 exception of hydroxy metabolite and you said that the
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1 proposal of the metabolites within the mixture is going

2 to be different, well, there's going to be temporal

3 changes.

4 And that's not in need to atrazine.

5 This kind of--once you give a chemical resulting in a

6 mixture, and chemical metabolite, it's not in each

7 atrazine, it's going to happen with every other

8 chemical that we know of, know all at most.

9 So considering these kinds of production

10 of metabolite and chemical mixture from atrazine

11 administration versus this mixture study, we should be

12 able to make some comparisons and conclusions to

13 strengthen the possible use and consideration about the

14 study. But I just don't gone away with the existence

15 of the weight an evidence for that.

16 So even though the study of weights is

17 concerned because the recorded didn't find such low

18 doses as being most cooperative.

19 The fact that individual dose response

20 at any of the compounds here are not known the mode of

21 interaction is not known, the internal doses were not

22 known which could actually shed some light about why

23 they don't expect to have or why the outcome was so at

24 such doses and in the absence of other studies that

25 such low doses collaborating or replicating these
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1 observations and concurrently with the agencies

2 inclusively regarding the non-use of this data set as

3 well as my colleagues who has worked with this.

4 DR. STEVEN HEERINGA: Thank you Dr.

5 Krishnan. Dr. Meek.

6 DR. BETTE MEEK: Well, there's not very

7 much left to say. I just wanted to make a couple of

8 points in the context. These are really a couple of

9 observations additional to as noted by the agency and

10 others related particularly to the limitations and

11 design relevant to testing the mixture which then

12 enables extinction of effects on the individual

13 chemicals that these are the--because I think that

14 point is going to be well made.

15 My comments relate to how you would use

16 a net layer like this in the context where we have

17 evidence in this assessment and it's important to

18 consider always the weight of evidence, the selection

19 of relevant studies as a basis to select those that are

20 critical explanation, dose response analysis, and that

21 includes consideration not only of the quality of

22 individual studies including internal consistency but

23 also the collective weight of evidence into finding

24 critical dose response in relationships.

25 So effects observed by one group with
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1 doses that are not consistent with the lead of evidence

2 should not be due to the influence selection of the

3 critical dose response in relationships or the point of

4 departure. That doesn't mean of course if you don't

5 raise questions that should legitimately be addressed

6 in an additional study.

7 Also, for individual studies, we

8 normally look for particularly in a critical study to

9 define the dose response relationship. It's always

10 desirable to have several complementary measures that

11 is left and they don't know that the issue is so much.

12 It's one better than the other or more sensitive than

13 another, it's really looking for a kind of a

14 accumulation of data that gives us confidence in the

15 results that we're observing.

16 Particularly where some are subjected

17 and again, this is a basis to measuring true

18 consistency of the observations. And so the pathology

19 findings that we normally look at, we look at it in

20 relation to the other results and studies as well, so

21 more than one kind of level or challenges is always

22 helping.

23 So again, for weight of evidence,

24 consistency of the result of an individual study with

25 those from other investigators considering assumed
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1 types of effects is absolutely an important

2 consideration and I think perhaps, maybe it would be

3 helpful and perhaps that the transparency if there were

4 some priority delineations of some of these generic

5 criteria in terms of how we look at weight of evidence

6 in determining critical studies with dose response

7 analysis. That's it.

8 DR. STEVEN HEERINGA: Thank you Dr.

9 Meek. Dr. Mumtaz, we'll come back to you.

10 DR. MOIZ MUMTAZ: That there's one thing

11 I thought was missing is that if you could look at

12 these various chemicals and you can always subtract

13 with the analysis contradiction in toxicology to see

14 what kind of potential this can cause because it's not

15 that we can act upon in terms of regulating it but just

16 to make the rate of evidences already mention so that

17 we can see these various metabolites, what kind of

18 product that they have and I know there's a big

19 competitions in toxicology at EPA and that would agree

20 with this report, but that would support that too.

21 But as everyone agreed, it's this one

22 story that we are looking at that should be sized if

23 there's one replication to be able to duplicate what

24 we're doing, so that's very important and the weight of

25 evidence is easier to--at lower levels as you already
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1 have been able to point it out. Thank you.

2 DR. STEVEN HEERINGA: That's a good

3 comment. I think in conjunction with the--mentioned

4 this morning, a sort of shorter term and longer term

5 recommendations and others--it's a longer terms but

6 important. Dr. Horseman.

7 DR. NELSON HORSEMAN: I think what I'm

8 hearing and what I think is a reasonable recommendation

9 is that this study per se isn't useful but it's clear

10 that it presents some challenges. Given the fact to

11 this very low doses and then the observation of effects

12 of these low doses, and the nature of this mixture

13 which I think Dr. Chamber has pointed out is

14 interesting because of the inclusion of

15 hydroxyatrazine.

16 So the notion that this thus need to be

17 followed up, I think these could be emphasized because

18 I don't think this is going to go away, right? So this

19 will come up again in the future.

20 So both replicating this study somehow

21 with--made you better informed or evolved sense of what

22 the endpoints might be and including endpoints that

23 could be directly related to this because I think we

24 need to keep that touchstone as well.

25 But also including physiological
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1 endpoints as Dr. Akana pointed out, so in dealing with

2 the challenges of this study I think at some thought it

3 needs to go into how the study should be designed

4 obviously, but those are some of the issues that ought

5 to be followed about.

6 DR. STEVEN HEERINGA: Thank you Dr.

7 Horseman. Comments from other members of the panel on

8 Question 2.2? Okay, I think that, Dr. McManaman.

9 DR. JAMES MCMANAMAN: I have one small

10 comment. In the charge it says that there were no

11 effects other than it's a 100 mg/kg/day and I think the

12 cover study shows that at 50 there was a significant

13 effect at the least on one parameters. So I think that

14 that needs to be amended or at least taken into

15 consideration. Thank you.

16 DR. STEVEN HEERINGA: Dr. Akana

17 DR. SUSAN AKANA: A comment on the

18 heavier pup weight on the early postnatal day in the

19 sixth week.

20 I actually think that's a hint that this

21 might be a model at these low doses of childhood

22 obesity where you see a heavy child and then at late

23 period normal weight and then heavier weight in

24 adulthood.

25 And, I do not find this necessarily
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1 inconsistent with other studies with a small chested

2 SGA, small pup or--because in models of neonatal

3 metabolic program, too big to--that's the problem if

4 you're not on the optimal curve both have long, both

5 are bad and have potentially bad outcomes in adulthood.

6 So I do not have a problem with seeing atrazine or even

7 mixtures at different doses producing heavier or

8 lighter pups, both are a problem.

9 DR. STEVEN HEERINGA: Dr. McManaman.

10 DR. JAMES MCMANAMAN: I agree totally.

11 It's well-known and she feel that low birth gestational

12 weights shift and might end up to have larger--it will

13 be larger with time and the idea is if there's a

14 rebound effect and I want to note that in the cover

15 study, during lactation, the dams on the atrazine were

16 heavier, 100 mg/kg, atrazine were heavier than the pair

17 of that controls again suggesting a potential metabolic

18 effect which would be consistent with the heavier pups

19 that are noted in this study. Thank you.

20 DR. RALPH COOPER: I just wanted to kind

21 of chime in on to Dr. Chamber's comment in stressing

22 the presence of the hydoxyatrazine mixture study. I

23 think it's a really important component of any future

24 studies and also just to comment about the idea of

25 doing mixture studies and one of the points in the,
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1 that by choice, with this mixture study, you can't

2 really determine which one, which pair are subset and

3 it is important.

4 That reminds me of a review in the

5 applications for PCB's projects and some proposals

6 would be talking about looking at congeners of PCB's

7 individually and try to figure out which one is most

8 important, one would then wish too, they're important,

9 and if there's a tendency to critic this as, well

10 you're missing potential interactions that could be

11 important because some PCB co-engineer shall be working

12 together. And the other flipside would be proposals

13 that we're proposing to study.

14 I thought I heard mixtures and there

15 would be a tendency for critics to say that would be

16 willing to make sure as you may be missing the

17 individual components that are most important, so I

18 think the same thing applies to this mixture study.

19 We're looking at environmental relevant set of

20 compounds that are apparently are in the water that are

21 there together that potentially a person is being

22 exposed to a drinking water. So I think that is in my

23 mind the strength of this study.

24 DR. STEVEN HEERINGA: I think it's a

25 productive morning and what I'd like to do is take a
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1 lunch break. Let's take a little over an hour and come

2 back here at 1:15. Can you do that? I appreciate the

3 progress we've made. Thank you.

4 (WHEREUPON, a lunch break was taken)

5 DR. STEVEN HEERINGA: Okay, let's plan

6 to resume. Welcome back everyone. I had indicated

7 that I wasn't going to be here all afternoon, and it

8 turns out I'll be here half. It will change the plans

9 and Dr. Portier will take over at about three o'clock.

10 We have completed at least the initial

11 discussion, Charge Question 2.2 and I believe we're

12 ready to move on to Question 2.3. If you could read

13 that into the record please.

14 DR. CAROL CHRISTENSEN: In light of

15 Panel discussion on Questions 2.1 and 2.2, please

16 comment on the manner on which the Agency has proposed

17 to use the mammary gland development studies in the

18 hazard assessment for atrazine.

19 DR. STEVEN HEERINGA: Dr. Horseman.

20 DR. NELSON HORSEMAN: A short answer is

21 yes. So our comments, so the Agency has proposed with

22 as little evidence on mammary gland development studies

23 that were summarized in the Issue Paper and presented

24 during the meeting based upon the analysis made in

25 response to Questions 2.1 and 2.2 that we did for
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1 lunch.

2 This does seem to be an appropriate way

3 of handling these studies currently. However, these

4 conclusions should not be construed to suggest that the

5 mammary gland is not either legitimate target for

6 further analysis and the recommendation does not

7 undermine the notion but there are good reasons to

8 believe that the mammary glands and the mammary gland

9 functions are important concerns for environmental

10 agents.

11 It remains true that particular features

12 of mammary gland development suggested as a valid and

13 potentially sensitive target for environmental agents

14 and I think Dr. Fenton made this case in a number of

15 ways.

16 These features include the fact that the

17 mammary gland development occurs at several distinct

18 phase of life including prenatal, puberty during

19 gestation and during the mother's pregnancy and during

20 lactation, and even during aging. And this distinct

21 phase of development offer opportunities to probe

22 agents that might have an effect. And also the point

23 should be made that the breast is a very frequent

24 target of cancer.

25 In addition, something we haven't talked
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1 about here too much is the importance of breast milk

2 for healthy development of the young, which is an issue

3 that was talked about in the epidemiology studies, and

4 the importance of breastfeeding for the healthy

5 metabolism of the mother are recently I think better

6 recognized but I think very well recognized and

7 documented in the literature. So it makes us aware of

8 this important area.

9 The aforementioned comments argue that

10 developing accepted methodologies and standard

11 operating procedures that would inform useful end

12 points related to mammary gland development and

13 physiology will be important.

14 It would be important for these methods

15 and procedures would address actual lactation end

16 points, and Dr. Akana's comments anticipated I think

17 what I'm going to say, and we'll do this both

18 qualitatively and quantitatively.

19 And this goal seems to present some

20 obvious challenges and it's interesting I think that

21 the studies here didn't make a lot of attempt to

22 address these kinds of real functional end points and

23 its characteristic I think of the law of literature.

24 However, very precise and comprehensive

25 lactation end points are routinely measured and used
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1 for specific goals, and here I'm thinking about an

2 invariance.

3 I think there's a legitimate analogy or

4 lesson to be learned because very small differences in

5 lactation outcomes can have very major economic

6 consequences there and therefore, there are approaches

7 routinely used that can monitor these small differences

8 in a physiological or a functional or an end point that

9 can be legitimately scored as adverse or outcomes.

10 So it should be possible to use this

11 rich literature and methodological resource to develop

12 approaches and standard operating procedures to

13 characterize the effects of environmental agents such

14 as chemical catalysts like atrazine on mammary gland

15 outcomes, and these methods should and could take into

16 account the importance of gathering both experimental

17 data and mapping that on human functional end points

18 related to the breast and breastfeeding that can be

19 meaningfully integrated.

20 Breast cancer outcomes are not on the

21 scope of this SAP but remaining vigilant about breast

22 cancer outcomes is obviously still important. And so

23 it seems that the Issue Paper, it concludes that the

24 few available mammary gland studies do not represent

25 sufficient body of evidence to be used in risk
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1 assessment.

2 It seems there's a correct way of

3 handling these studies. The suggestive evidence in

4 these studies does however argue with developing useful

5 and standardized methods for assessing the mammary

6 gland at a functional level that will be important in

7 the future. So I think that's -- I'll pass it on.

8 DR. STEVEN HEERINGA: Thank you, Dr.

9 Horseman. Dr. Delclos.

10 DR. BARRY DELCLOS: I have very short

11 and simple comments, I think. I agree with EPA's

12 determination that mammary data should be included as

13 part of the characterization. It was not used in risk

14 assessment. There's somewhat conflicting data limited

15 to the dose response data and the mode of action

16 information.

17 As discussed, many of the effects of

18 triazine over a failure of effects of the F2 generation

19 in each replication and long-term consequences need to

20 be evaluated.

21 The mixture of effects potentially

22 important, there's no clear mechanism to determine

23 those effects. Reported effects are particularly

24 significant because they're effects of developmental

25 exposure in current doses lower than is reported for
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1 the LH surge. Well, not if we're using quantitative

2 risk assessment. They should not be missed.

3 DR. STEVEN HEERINGA: Thank you, Dr.

4 Delclos. Dr. LeBlanc.

5 DR. GERALD LEBLANC: Data on mammary

6 gland development are informative with respect to

7 characterizing the hazard associated with atrazine

8 exposure. However, the existing data are insufficient

9 to warrant consideration in identifying the point of

10 departure for atrazine.

11 The single study is available that

12 reports on NOAEL for mammary gland development, that's

13 the Coder study, but I have reservations about making

14 regulatory decisions based on a single, unreplicated

15 study. As is often the case, these studies and

16 particularly the Enoch mixture study open new lines of

17 inquiry that should be pursued in future

18 considerations.

19 With respect to the mixture study, I see

20 three possibilities to explain results of the study.

21 One, the observed results are an artifact, that there's

22 some inherent flaw in this study design. The other is

23 that one or more active metabolites are contributing or

24 are responsible for the toxicity.

25 And here, we may be seeing additivity
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1 and presumably, this would be dose additivity among the

2 metabolites, the active metabolites. Or there's some

3 interaction among the metabolites resulting enhanced

4 toxicity, i.e. synergy.

5 From my experience, the likelihood of

6 significant interaction among these compounds would be

7 slim, and by significant here, I mean interaction

8 resulting in an order of magnitude increase in toxicity

9 of one of the constituents.

10 So I don't think synergy is a major

11 consideration here. Certainly, additional study should

12 be considered to address the possibility that the

13 results are now artifact, that there is a flaw in the

14 study design.

15 And that last possibility that there are

16 the existence of active metabolites should also be

17 pursued in future studies. I would concur with other

18 Panel members in that hydroxyatrazine would be a

19 candidate that should be considered here since the

20 hydroxy metabolite presumably isn't produced at

21 significant quantities in vertebrates.

22 So in the rodent studies where atrazine

23 is administered, hydroxyatrazine would not be

24 contributing to toxicity, so it may be the

25 environmental degrade that could be responsible for the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 147

1 enhanced toxicity that was seen in the Enoch study.

2 This should the considered.

3 DR. STEVEN HEERINGA: Thank you Dr.

4 LeBlanc. Dr. Legan.

5 DR. SANDRA LEGAN: Basically, I think --

6 my colleagues have stated in a very articulate manner

7 many of the things that I would at this point mention.

8 I think they've done a good job and I agree with them

9 that the Agency's manner of handling this is not to use

10 these studies, these very mind-blowing studies in risk

11 assessment at this time and that there needs to be

12 further issues addressed as my colleague said.

13 DR. STEVEN HEERINGA: Thank you very

14 much. Dr. McManaman.

15 DR. JAMES MCMANAMAN: Well, I agree. I

16 think that the data shows that the mammary gland is not

17 a direct target of atrazine due to the lack of dose

18 response. And consequently, it should probably not --

19 the mammary gland effect should not be used as a way of

20 looking at risk assessment.

21 However, having said that, it is

22 interesting that we can now -- we can show from a

23 variety of different pieces of evidence that because it

24 is not a direct target yet there's still an effect, it

25 suggests that perhaps the entire mode of action of
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 148

1 atrazine needs to be reexamined and that there may be

2 other targets that have not been considered.

3 For instance, there is some evidence of

4 interaction with neurons in the hypothalamus, and I'm

5 wondering --

6 I would urge the Agency to consider

7 looking at other sites as direct targets, which then

8 could -- their activity or alterations in our activity

9 could affect the mammary gland or the HPA axis or the

10 HPG axis. So I'm thinking that what we've seen today

11 is we've seen an analysis of the indirect effects of

12 atrazine exposure and not the direct ones, and we still

13 don't know what the direct targets are.

14 DR. STEVEN HEERINGA: Thank you, Dr.

15 McManaman. Comments from other Panel members on this

16 particular question? Yes, Dr. Akana.

17 DR. SUSAN AKANA: I absolutely concur

18 with Dr. McManaman that the hypothalamus would be a

19 very likely site to find some changes with circuitry

20 put in place in early gestational or postnatal base

21 which could very much give you the very late outcomes

22 you see in adults.

23 DR. STEVEN HEERINGA: DR. Fenner-Crisp.

24 DR. PENELOPE FENNER-CRISP: Well, we

25 were asked explicitly what to do about it because you
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 149

1 haven't reviewed it yet and that's been for review.

2 Tom and I would recommend that after you review the

3 Coder study that you put it in the same bin as these --

4 use it as a matter of hazard identification but it

5 wouldn't be ready for quantitative risk assessment in

6 there.

7 Female: Which study was that again?

8 DR. PENELOPE FENNER-CRISP: The Coder

9 study that Syngenta had on their mammary gland.

10 FEMALE: That study is in its -- well,

11 it's part of the learning package.

12 MALE: It was considered in 2.1.

13 DR. PENELOPE FENNER-CRISP: I understand

14 that, but you haven't received it and reviewed it.

15 Now, we have. We actually got it. Did you get that?

16 So although you didn't ask, I'll bring that right over

17 here.

18 DR. STEVEN HEERINGA: Dr Horseman, I

19 think you said it was in --

20 DR. NELSON HORSEMAN: I just want to

21 mention, that one was considered in 2.1.

22 DR. STEVEN HEERINGA: Other comments or

23 suggestions? Dr. McManaman and Dr. Akana on the

24 hypothalamus, the data we heard yesterday about primate

25 versus rodent, that same conclusion would apply. Dr.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 150

1 Akana?

2 DR. SUSAN AKANA: I would limit myself

3 to rodent studies sometimes.

4 DR. JAMES MCMANAMAN: Considering that

5 there's really no data about the primates regarding

6 this, I would say I would have to limit it to rodent

7 studies, too. But nonetheless, there is -- it's well8 known that the hypothalamus plays a key role in mammary

9 gland function. Ask any woman who's trying to

10 breastfeed right after giving birth and things are not

11 just working quite right at that time, and so it's

12 difficult.

13 So I think that there's a lot of data

14 showing the functional relationships between

15 hypothalamic activity and breast function and it should

16 not be excluded.

17 But I think that there is a good amount

18 of data in animal study showing that alterations on

19 hypothalamic activity can affect mammary gland -- late

20 term mammary gland function, as well as the

21 aforementioned metabolism effects observed with

22 atrazine.

23 DR. STEVEN HEERINGA: Dr Akana?

24 DR. SUSAN AKANA: On this section, I

25 think there might be some data in primates by Susan
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 151

1 Smith and Kevin Rowe and I shall find those references.

2 DR. STEVEN HEERINGA: Thank you. I just

3 sort of wanted to make -- Dr. Horseman?

4 DR. NELSON HORSEMAN: Just one quick

5 comment on that if I can -- if I think I understand the

6 original question. I think there is probably a better

7 mapping of the physiology of hypothalamic and neural

8 control of mammary gland development, milk gland and

9 all of that between the rodent and the human than there

10 is for the regulation of the ovarian cycle in terms of

11 the role of12 DR. STEVEN HEERINGA: Thank you. I was

13 confused on that.

14 DR. NELSON HORSEMAN: Right. It may be

15 a closer map than in the other case.

16 DR. STEVEN HEERINGA: Thanks. I think

17 that's valuable for me and maybe for some others in the

18 room, too. Okay, at this point, we -- I'll turn to EPA

19 to see if there's -- a few of you I think have a pretty

20 clear response on these questions.

21 FEMALE: Very good. Thank you.

22 DR. STEVEN HEERINGA: So we move on to

23 the next question, which I have as Question 3.1 and if

24 we could read Question 3.1 into the record. Now, just

25 to -- let me check with the Panel. Dr. Greenwood, you
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 152

1 suggested potentially answering A and B together, and

2 Dr. Krishnan raised to that, too. So if we could read

3 Part A and also Part B into the record.

4 DR. CHESTER RODRIGUEZ: Thank you. I

5 guess I have to read this. So Question 3.1 Part A,

6 please comment on the analysis of the pharmacokinetic

7 data that is obtained in the Thede 1987 study to

8 estimate internal dose reflective of atrazine and its

9 metabolites in rat plasma.

10 DR. STEVEN HEERINGA: And then also Part

11 B.

12 DR. KANNAN KRISHNAN: And Part B, so

13 based on Figure 5.5 in the Draft Paper, the Agency has

14 concluded that plasma levels of atrazine and its

15 metabolites reach or nearly reach pharmacokinetic

16 pseudo-steady state levels in rat plasma after

17 approximately four daily oral exposures over a wide

18 range of doses. Please comment on the extent to which

19 the data do and do not support this finding.

20 DR. STEVEN HEERINGA: Okay. I will

21 follow the order for Question Part A in terms of our

22 discussion we covered with Dr. Greenwood.

23 DR. RICHARD GREENWOOD: Well, first of

24 all, I think we're in agreement when we discussed this

25 amongst the three of us involved that the use of area
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 153

1 under the plasma concentration time curve is a totally

2 appropriate measure of internal exposure. We feel it's

3 probably the best way to approach the problem of trying

4 to relate exposure in rats to response and then

5 extrapolating or predicting human exposure of this

6 ideally for the physiologically based pharmacokinetic

7 model but in its absence, this is a good start. We

8 feel this is going in the right direction.

9 Now, it's particularly useful because at

10 the minute, we have no idea really of the exact

11 critical sight of action and what this area under the

12 curve does is to provide a measure of the opportunity

13 of a material to be distributed to other tissues,

14 target and non-target tissues. And certainly, if you

15 look at the studies of Dulley what he published in 2008

16 looking at rat pituitary in the brain, he looked at

17 preoptic area, medial basal hypothalamus cortex and I

18 think he found some 19 proteins leveled in the

19 pituitary between two strains of rats, and about 30 in

20 the brain, and these are just an indication. He gives

21 no idea of whether the binding of the Di-D agent

22 atrazine or the binding has any impact at all. We

23 don't know if it's anything to do with the mode of

24 action; we don't know. All we know is that it binds.

25 It could activate proteins, deactivate them or do
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 154

1 nothing at all. We haven't got a clue but we do know

2 that it's scattered everywhere.

3 So the area under the curve is a good

4 approach and I think in the absence of good data on the

5 plasma concentrations of phenol compound and individual

6 metabolites, then we give it on its way with elapsed

7 time. Then the use of total chlonotriazine based on

8 the C14 compounds that's in the study is a very

9 reasonable first step, particularly as there's an

10 absence of information on the relative pharmacodynamic

11 activity of the parent compound and some of the

12 metabolites.

13 However, having said that, the data of

14 Thede from 1987, there are some limitations and these

15 need to be kept in mind when interpreting the

16 pharmacokinetic analysis. One of the big problems with

17 it is that the plasma samples were taken every 24

18 hours, so the data sets are really sparsely populated

19 and it means there's a big gap of 24 hours between each

20 points, each turn points.

21 This means that the profile is very

22 defined. And because of the very widely separated

23 sample points, it's bound to appear smooth. There are

24 a few points on the washout period really to obtain

25 sound estimates of the elimination properties. That's
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 155

1 one of the problems.

2 The Agency's interpretation of the data

3 involves the assumption that the parent compound and

4 individual metabolites, DACT, DEA, DIA, are all

5 toxicologically active. And there's no evidence to

6 gain some. There is evidence that the DACT and

7 atrazine, DACT is the way you want are actually

8 important in some areas.

9 So it doesn't need to be borne in mind

10 when looking at the interpretation. But when we look

11 at the way that atrazine behaves, atrazine on the two

12 model of the Alcott is done are very rapidly depleted

13 by primary detoxification. And the major end product,

14 the DACT dominates the plasma profile. So in

15 interpretation, and I think it's fair, there is an

16 assumption that none of the pharmacokinetic processes

17 such as absorption, distribution and the detoxification

18 and elimination is saturated.

19 And so, it may be assumed in the first

20 instance that the proportions of parent compound

21 metabolites are in dependable dose. But of course, the

22 composition of the total radiolabel will vary with

23 time. So as soon as you've administered the next order

24 of gavage, then there will be atrazine present to model

25 the outcome agent compounds that biochemically, we get
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1 four hours and it will be virtually all back to the

2 DACT, and then you give another gavage.

3 So during that time, it's not a constant

4 dose in terms of this constant mixture that is being

5 distributed around the body. I think what this pseudo6 steady state implies, there's good evidence it is being

7 reached. These absorption and distribution processes

8 have been matched by elimination. And so, if we look

9 at the levels presented in Figure 5.5, this is why we

10 wanted to consider the two together; they are linked.

11 It's a bit misleading in the way that they're being

12 described because all of the samples were taken at 24

13 hours after the previous dose.

14 So the points we find in the profile on

15 Figure 5.5 or the minima in this whole truth. They're

16 not the average; they're the minimum because they're

17 taken just before the next dose. So those will be

18 underestimated in the area under the curve and if you

19 look at the figures from Syngenta's study where they've

20 got a much better definition of the individual spikes,

21 what you'll see is that there is quite a large area by

22 all of gavage, there's a dietary application around

23 that to a large extent. But by all of gavage, there's

24 a large area in that too, both the level of which being

25 measured by the team.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 157

1 And in fact, if samples are being taken

2 say 12 hours after dosing, you would put a different

3 profile with a different problems and mistakes, if you

4 take them randomly, you probably wouldn't have seen a

5 plateau at all with those few data points that would be

6 available.

7 So I don't think we're arguing that

8 pseudo-steady state is not being achieved. I think

9 it's the way that that plateau is being interpreted

10 because it's not like the theoretical figure that is

11 shown in Figure 5.5 of the paper because all of the

12 points, it's much more spiky than you would think from

13 that theoretical picture.

14 The good thing in a way is that although

15 there is a bias there, it's going to be approximately

16 across the plateau. So actually, your conclusions are

17 okay. It doesn't affect the goodness of fit of the

18 area under the curve when you've plotted it against the

19 administered dose. But it could give you a false sense

20 of security, so I think we need to think about that and

21 something will come back to Part C of Question 3.1.

22 I think the other problem -- so getting

23 back to the Part 3.1 A, is that the estimates of the

24 elimination were in constant on the basis of the last

25 three points. Remember it's a single animal that is
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 158

1 going to be associated with some uncertainty because

2 there is a very rapid fall from the level -- at the

3 time of the last dose. And actually, if you consider

4 that that would have risen before it fell, again after

5 that time, it's just unfortunate releasing the data

6 you've got, you've done the best you can with it, but

7 it's difficult because of the lack of definition I

8 think in the watch-out period.

9 But having said that, you do what you

10 can. You've got the best estimates you can from those

11 data. But this morning when I questioned Syngenta,

12 they said in fact it is -- they've got a study out of

13 the four and they felt it was first order rather than a

14 double exponential process. So maybe what you've done

15 is actually giving you a reason to investigate and do

16 an elimination of being constant. But I think it's

17 something that needs to be looked at carefully that

18 there are in fact two phases in the elimination.

19 So I think to some -- I think the

20 approach taken by the Agency is not the correct way but

21 I think you just need better data than the Thede's data

22 set before you start to take it seriously in terms of

23 regulatory matters.

24 I think the pharmacokinetic study that

25 Syngenta have got underway -- and the preliminary
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 159

1 results are in Report TK0040016. We'll provide the

2 data of the quality you need because it uses frequent

3 sampling and mass spectrometry for identifying, which

4 is the method of choice for the different analites and

5 it's going to give tight pharmacokinetic profiles for

6 individual compounds. And this type of study will lend

7 itself to the analysis of relationships between some

8 area under the curve for the different metabolites and

9 the parent compound, and trying to relate those two

10 biological responses.

11 So I think this, combined with good

12 estimates of plasma partition coefficients, should

13 allow really a good physiologically-based

14 pharmacokinetic model being developed and I'm quite

15 excited about that. I think it could well provide a

16 good basis for extrapolating to humans once that's

17 developed, but that we'll see what pre-judges that.

18 It's an exciting turnout.

19 Now, there is another limitation in

20 terms of interpreting the pseudo-steady state because

21 in the pharmacokinetic experiments that the team has

22 determined, the same dose is used throughout, so you've

23 got a constant level in the area of pseudo-steady state

24 over four days, and that's proportional to the gavage

25 dose which is shown in Figure 5.6. But when you come
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 160

1 to interpret this, really the conclusions you can draw

2 are limited by this experimental design. It's very

3 difficult because if you are trying to use that pseudo4 steady state to get some area or area under the curve,

5 it doesn't tell you in the way the experiments are

6 being done whether there's a critical time. And then

7 if there is and you have to maintain a constant level

8 over a critical time, say three days, two days, four

9 days, then what you're going to do when you calculate

10 the area under the curve, you just multiply the

11 concentration by a constant. So you're back to the

12 plasma concentration rather than the area under the

13 curve. And this is kept with time because all you're

14 doing is multiply it by a constant if there is a fixed

15 critical period of exposure.

16 We don't know that that's the case but I

17 think it's something that's worth investigating. Would

18 you get a different result if you had a high dose on

19 day one, low doses on day three and four, you establish

20 a pseudo-steady state at the low level, and then you're

21 putting a big spike on the two or three? Measure the

22 area under the curve. IS it the area under the curve

23 that's related to the responses we get or not are

24 equally susceptible on all this exposure? There are

25 lots of questions about that.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 161

1 The only interpretation you can get

2 really, you're limited by the way in which the

3 experiment is done. To do it in the other way would be

4 quite difficult and quite challenging. But if you

5 really wanted to tie it down to an area under the curve

6 rather than plasma concentration for a fixed time so

7 the time is just constant and the area under the curve

8 was triggered with calculation, you really need to know

9 whether if you change the time window and change the

10 area under the plasma period for that timetable whether

11 you'll actually get a different response, or if you

12 have the same area around the curve achieved in

13 different ways, do you always get the same response?

14 So at the minute, I think there are some

15 limitations in there. But I think maybe the way to go

16 in future studies, I think it is clear with dietary

17 administration, it's possible. One, you get a smoother

18 plateau. That's what is needed. And certainly, I

19 think you can avoid some of the stress associated with

20 daily oral gavage, unless of course there are problems

21 with the dietary in oncology. And it goes to those

22 cells, you get a bit more realistic in terms of what

23 you might expect to see with the human exposure to

24 drinking water. But even there, depending on the

25 presence of food in the digestive tract, it could be
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 162

1 sort of good between the equivalent of all of gavage

2 and dietary exposure. So I'll leave my comments there.

3 DR. STEVEN HEERINGA: Thank you Dr.

4 Greenwood. Dr. Krishnan.

5 DR. KANNAN KRISHNAN: I think he

6 essentially covered everything. All I can do is say

7 the same thing in a different accent. Well, maybe in

8 different words.

9 First of all, to begin with, I think it's

10 important that I do say in public as well that I agree

11 with my colleague's conclusion that EPA is taking an

12 approach of using the daily AUC as a matter of internal

13 dose. That is quite reasonable and it's definitely the

14 right direction. As it reiterates their relevant

15 pharmacokinetic exposure to atrazine. And this section

16 was a pleasure to read, not that the EPA and others

17 weren't. It's very well written and I appreciate the

18 clarity and the presentation, even though we share the

19 same concerns.

20 Number one, about the steady state, once

21 again thinking about the steady state pharmacokinetics,

22 particularly in the context of chronic assessment is

23 relevant and it's in the right direction that it allows

24 you to perform simple calculations that help go a long

25 way with less uncertainty essentially. When you think
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 163

1 about for example rat to human extrapolation, you're

2 thinking that it's essentially the ratio of clearance

3 and adjustment for bioavailability if that's relevant.

4 But for a chemical like atrazine, the dosing

5 with humans are under consideration. It may not result

6 in the constant steady state concentration. I think

7 you already alluded to it that there's a peak or a

8 maximum concentration and a minimal on a trial. On the

9 12 concentrations which were essentially recorded or

10 that you got from the key paper, it most likely

11 represents concentrations of DACT on the metabolites

12 rather than the others. Still, the fact that these 12

13 concentrations that were part of the new figures that

14 did not vary between the days clearly indicate that

15 there's no increased accumulation due to subsequent

16 doses. I think that's -- that comes out clearly in

17 there.

18 So no Sequa non increase in accumulation are

19 performed by days. That's essentially like getting

20 into the steady state, and the document also, the key

21 paper I think brings to line that plasma is a

22 reasonable biological metric that reflects the internal

23 dose. They do present some ratios of concentrations

24 including plasma and tissues or RBCs.

25 An RBC is essentially it would represent a
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 164

1 challenge given the extent of binding and so forth. So

2 the measurements in the plasma would appear to be

3 reasonable and the consideration of steady state would

4 appear to be fine. And the conclusion of linearity on

5 Page 68 of Section 525, that figure on the associated

6 discussions, I'll make it clear that there's no

7 saturation in those ranges. That's what I see rather

8 than, in your terms, linearity. You know there was a

9 problem with an aggressive attempt as we talked about

10 this.

11 And there's a concern that in those different

12 points that the proportions of metabolites might vary

13 since it's a total of C14. There are very high doses

14 that are not brought up in this figure. Maybe those

15 proportions could be different. And also, you may be

16 aware the DACT, the higher concentrations, and one has

17 to appreciate the difference in molecular weight. In

18 the study we heard that that might not be a problem,

19 but that might be a problem. Atrazine molecular weight

20 is 215.7 whereas DACT is 145.5. It's 50% or so in

21 difference. But for others, it may be much less of a

22 difference.

23 So once you take -- once you consider them on

24 the basis of molecular equivalence of atrazine, I think

25 you always have to be conscious of the molecular weight
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 165

1 differences, especially when you're taking the numbers

2 down like 24 hours of exposure and assuming that to be

3 atrazine, milligrams of atrazine. It would not be

4 appropriate.

5 And I think I've got two more comments. One

6 -- the next one is about the internal dose, the choice.

7 Here, the measures are presented based on triazine

8 equivalence, which time to be conservative, which I

9 thought was not warranted. In this context, I can see

10 rather -- it's essentially a letter to the current

11 extent of the knowledge or the database; that's what it

12 needs rather than conservatism.

13 Here, the choices are the total metabolism of

14 atrazine, total amount of calories produced. I mean in

15 terms of the semantics that you can think of. Our

16 total metabolism minus the GSH conjugation put a sum of

17 DIA, DEA and DACT or the sum of DIA and DEA. You

18 haven't tried to make any differentiation among these

19 potential dose metrics. One thing that will be useful

20 is to state very clearly what the assumptions are,

21 which is basically counting atrazine and the GSH

22 conjugate out, calculating the total. At least that's

23 what it represents in most cases. The other two would

24 be very minor.

25 But some use of the observations from the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 166

1 papers of Susan Lars in 2003, Stopper 2002 and McMullen

2 2004 might help co-add some collaborative or non3 contradictory evidence for using some of the

4 metabolites in atrazine or for using the atrazine

5 equivalence. Like there'll be some things there that

6 could be drawn upon to strengthen the use of the kind

7 of dosing already.

8 My last comment relates to the potential use

9 of this kind of -- here, the calculations are made

10 using the dose surrogate which is the total triazine

11 equivalence. I think you have to keep in mind as to

12 the ultimate use of that, then how would one do the

13 inter-species extrapolation based on the kind of

14 totality of the triazine equivalence. So hopefully

15 there will be more and better information to

16 characterize that rather than summing all of them, or

17 you have to define more clearly one of these

18 alternative dose metrics.

19 If one does that and uses a PBPK model for

20 human or some PK information or clearance information,

21 I think consideration has to be given to differences

22 and bioavailability, the GSH conjugation in rats versus

23 humans. We didn't hear very much about it yesterday,

24 the role of intestinal metabolism and then the red

25 blood cell to plasma partition coefficient that I
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 167

1 didn't see here at the meeting, but there was one in

2 the paper. That seems to be critical, at least with

3 the metabolites. I think some of those considerations

4 would be very critical if the position used to derive

5 HEDs or the human equivalent doses weighing from any of

6 these internal dose measures that lump together.

7 At some point I was confused, so let me talk

8 about that before I conclude, which are some of the

9 pharmacokinetic data and so on were on the Sprague10 Dawley rats and there were some presentations about

11 Long-Evans being more sensitive than the Sprague-Dawley

12 rats particularly to the measures of LH. I tried to

13 get some clarification, but I should admit that I may

14 refuse. So I do see that there is some increase to

15 susceptibility that came through in reference to the

16 2000 paper and so forth. But then in your plots where

17 you put everything together, it seems like there's no

18 difference in sensitivity and so forth.

19 The reason why I bring that up is that that

20 shouldn't, at the end of the day, become a factor -- a

21 limiting factor for the use of the PK data because at

22 the end of the day, if the key study ends up being

23 along the Long-Evans rat considered more sensitive than

24 the SD rats and then you end up having a lot of PK data

25 in the SD rats, which is the case of Thede as well as
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 168

1 the presentations of the other scientist.

2 So in conclusion, the use of the daily AUC

3 seems to make more sense. It's excellent. And the use

4 of total triazine equivalence or refinement data based

5 on some of the alternative dose metrics that we talked

6 about, which you might be able to do if there is a PBPK

7 or another PK model that use those alternative metrics.

8 So the use of that kind of triazine equivalence

9 consistent with the assumptions and use of plasma as

10 metrics seem fine, whether it's based on -- and when I

11 went to that, I said to myself these calculations and

12 the strategy can be followed either using the PK data

13 or PK model but that assessment would be determined by

14 what's going to be available to allow you to resolve

15 the level of detail of the internal dose level because

16 you don't have to resolve it more than what the data

17 would allow me to do at this point.

18 DR. STEVEN HEERINGA: Thank you, Dr.

19 Krishnan. Dr. Meek and then we'll go to Dr. Mumtaz.

20 DR. BETTE MEEK: Well, I don't think

21 there's a lot more to say. We were in conclusion

22 before these comments. But I just wanted to add that I

23 very much appreciate the work the Agency is putting in

24 this particular aspect. Pursuing dose response

25 analysis based on the internal dose metric as an
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1 alternative to the ministered dose metric we may just

2 reduce it. It's really, really important. It's really

3 -- you've attempted a maximum use of limited and

4 available data. You've made many conservative choices

5 in the absence of information and you verified the

6 estimates that stand possible. I think that the issue

7 really here is, how do you deal with the residual

8 uncertainties associated with the constraints of the

9 data that were available to you.

10 And I think it would be great to have a

11 PBPK model. I think we all heard of it. We all do it.

12 But I think that understanding well the constraints

13 with regulatory programs having been employed in one

14 most of my, I think -- I wonder if there is some way to

15 prioritize the collection of data that most inform in

16 the current analysis, so to move us forward, kind of --

17 you may not get to the whole PBPK model at the time

18 that you need to do current decisions, but we might be

19 able to do something in current analysis and the

20 information that's being collected.

21 So I would strongly suggest that perhaps

22 that would be considered. I would also suggest that

23 the analysis has been extremely valuable in another

24 context, just making it distinct about what's actually

25 driving the dynamics of -- so just think about how we
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 170

1 relate all these things.

2 DR. STEVEN HEERINGA: Thank you, Dr.

3 Meek. Dr. Mumtaz.

4 DR. MOIZ MUMTAZ: Kannan did it, so I

5 will give you the accent, too. I I think I agree with

6 what has been presented in the report, is that the AUC

7 -- what we're trying to do is a concept which we can

8 live with. And the point or order of the mixtures

9 issue that is -- it would be interesting to see what

10 happens if we could get an SCR analysis to give them

11 the potency of other chemicals and use that in the

12 greater that all these are equal. So to define a

13 common currency or matrix is an issue that makes us,

14 people always deal with, in this particular time that

15 might be useful. So I would suggest that that can be

16 used as a data to support what we're going to use.

17 THE CHARIMAN: Thank you Dr. Mumtaz.

18 Dr. Schlenk.

19 DR. DANIEL SCHLENK: I'm going to sort

20 of contrast. I think that his comment here being --

21 I'm going to claim na Academic isn't here for this but

22 in terms of the politics, in terms of coming up with a

23 PBPK, I just -- sitting on this Panel, we've seen PBPKs

24 from other compounds that are probably less data rich

25 in terms of what we have for atrazine. I just find it
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 171

1 actually a little disappointing that there hasn't been

2 pursuance towards the PBPK model since 2007, which

3 since -- at least that's when the last McMullen model

4 is published.

5 So again, I would recommend that that

6 would be a high priority. I think that you'll

7 definitely be able to reduce a lot of the uncertainties

8 that have been raised already by some of the other

9 comments that have been made. And I guess at least my

10 hope that in putting this at least in writing that

11 maybe within five years from now that maybe when re12 registration comes in that maybe we might have a PBPK

13 model. So at least again, I would push towards that.

14 I'd also say to that, given the

15 differences, species differences with the Long-Evans

16 and Sprague-Dawley rats and its LH response that that

17 particular species be targeted for the PBPK model. I

18 think most of everything I've seen has always been

19 Sprague-Dawleys but there's been a limited sort of

20 focus on Long-Evans. There are obviously some species21 specific issues there which may be related to dynamics.

22 I don't know, maybe kinetics, too. But I think that

23 needs to be explored. I would also recommend that

24 again, intestinal biotransformation be considered in

25 this metric when the model is actually put forward.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 172

1 This again could either be done through Caco cells or

2 through microsomes, although I would warn based upon

3 the podocyte data that Syngenta presented that there

4 are some differences in microsomal metabolism,

5 particularly when you don't see DACT formed in

6 microsomes, but you do see it form in the podocyte,

7 which I think is very interesting.

8 So therefore, the Caco cells

9 particularly in the intestinal system may have actually

10 warranted, particularly given this microsomal

11 inconsistency in the form of the DACT, and DCT seems to

12 be this ultimate metabolite that seems to be formed. I

13 think that's probably important. And again, I think

14 with these models, particularly using the podocyte,

15 even now I think you could probably do in vitro studies

16 with mixtures to get an idea of what's going on in

17 terms of an addition and at least in the max KM data,

18 it can actually be employed in certain model that's

19 already present. At least Syngenta has presented it.

20 And again, I would highly recommend that there would be

21 some interaction there perhaps and maybe that model

22 could be again modified and refined to something that

23 might be useful here in the near future. If not, next

24 year at least. Again, whenever the next assessment

25 takes place.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 173

1 Again, I think this is a great system,

2 particularly you got a target that you already

3 identified, so I think it's a very powerful tool that

4 can be used not only in low-dose extrapolation but also

5 I think maybe in your toxicity assessments, too.

6 DR. STEVEN HEERINGA: Thank you, Dr.

7 Schlenk. Comments from other Panel members? Okay.

8 Well, thank you very much for that presentation. Let's

9 turn to Question 3.1.c, and if we could read that into

10 record, Dr. Rodriguez.

11 DR. CHESTER RODRIGUEZ: So Figure 5.8 of

12 the draft paper shows a plot of the LH attenuation

13 versus those of atrazine across different exposure

14 durations which range from four days all the way up to

15 six months. This data contained on this figure were

16 collected from several different labs and involved two

17 modes of oral administration of the dietary versus oral

18 gavage in two different rat strains. Attenuation of LH

19 as measured by percent of control is remarkably similar

20 across studies, strains, labs, again most notably of

21 the duration of exposure. The Agency has concluded

22 that the findings on Figure 5.8 strongly support the

23 hypotheses that pseudo-steady is achieved or nearly

24 achieved in adult rats after four daily consecutive

25 oral exposures.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 174

1 The Agency also believes that pseudo2 steady state is strongly associated with the

3 attenuation of the LH surge following atrazine exposure

4 in rats. Please comment on the analysis in the

5 Agency's preliminary conclusions as it relates to these

6 findings in rats.

7 DR. STEVEN HEERINGA: Dr. Akana.

8 DR. SUSAN AKANA: Hello, this is Figure

9 5.8 on page 71 of the Issue Page and our working group

10 met and I believe it's correct to say we have a general

11 consensus that this is a good start, a model to work

12 with. It's nice to have a visual graphic to start to

13 test ideas.

14 And the group as a whole found that they

15 would like to really reconsider how it's scaled above

16 the X and Y axis because it's linear here.

17 Also, that there was some desire --

18 actually, quite a lot of desire. They have the highest

19 -- the higher dose is spotted on this, the ones that

20 were not included in the study. And we had a very

21 eloquent group and they had very specific comments of

22 the scaling and on the quality of the LH data. So I

23 would prefer to differ to the other members of the

24 group who are very expert in that.

25 So on personal comments that are unique
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 175

1 to me, one of my concerns was looking at the Y-axis, LH

2 surge attenuation presented and unfurled and I did ask

3 this to the group, and the answer was -- the question

4 was, "Was this peak LH?

5 Was this LH at 1800 hours or was this

6 varying under the curve?" because the answer they gave

7 us, the peak -- or excuse me, the amount of LH at 1800

8 hours, what the studies included include three older

9 studies in Morseth, Minima, and McMullen, and then the

10 more current Cooper study. And my apologies and

11 sympathy to everyone on the committee because I did

12 request the two older studies, which was an additional

13 1500 pages.

14 Now, why did I do that? Okay, my

15 personal interest was I was very concerned about the

16 food and body weight. These are legs, which mean that

17 I can interpret well, and my eyeball scan -- my eyeball

18 scan looking over the more set Minima data and the

19 McMullen data that were available, is that food and

20 body weight were not touched, were not different in the

21 doses shown on this spot. And so that was very

22 reassuring to me. However, I do want to add the

23 comment that they are very much hit when you hit the

24 higher doses.

25 So when you look at this curve which is
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 176

1 LH attenuation to atrazine, there's actually a mirror

2 image curve that emerges for food and body weight in

3 the other direction and I think that's something to

4 keep in mind to not just look at the LH surge

5 attenuation and isolation.

6 My second point of concern was the --

7 oh, I mentioned the Y-axis, the actual nature of the

8 LH. You go back to these older studies. If you look

9 at LH at 1800 hours, in some space, that is a peak. In

10 many studies, it's not. So sometimes the peak, the

11 highest level is at 1600 hours or 2000 hours.

12 Now, why that's significant to me is how

13 much precision do you have at these very low points.

14 Now, if you were to look at it as the

15 absolute peak overall time, you're going to get a

16 different figure. And since these points are so

17 sensitive, you get a different body. Those are my

18 unique points that I wanted to raise and I wanted to

19 let the other Panel members expound.

20 DR. STEVEN HEERINGA: Thank you Dr.

21 Akana. Dr. Delclos.

22 DR. BARRY DELCLOS: I conducted a

23 sophisticated pattern analysis and noticed that the

24 discussions were listed by alphabetical order rather

25 than whether they contribute to the questions, and the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 177

1 other members who are eloquent

2 DR. STEVEN HEERINGA: Does eloquence

3 increase in alphabetical order?

4 (Laughter.)

5 Okay. Well, Dr. Greenwood, you're going

6 to own up here.

7 DR. RICHARD GREENWOOD: I think what we

8 were looking at was normally instead of percentages,

9 you would expect to see maybe a normal probability

10 scale used on the Y-axis to replace those percentages

11 which will, if anything else, spread out to the points,

12 but also making quite clear that the confidence

13 associated with the points of the upper or near the

14 upper percentile, 100 percentile are very, very much

15 less reliable with those in the middle.

16 And I think maybe it will be clear to

17 see also if we used a log scale and included some of

18 the higher dosage. But I think if we would need to do

19 that scaling in order to build it and then hopefully,

20 get the data around that plot in a scale that is useful

21 and that we could actually see what's going on.

22 I think it is important to remember that

23 when there is a lot of uncertainty anywhere around some

24 of these influences that already Dr. Akana has pointed

25 out, it makes a big difference maybe sometimes of two
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 178

1 hours when you look at the LH surge.

2 It's important to remember that when you

3 are trying to the upper and lower percentiles, you do

4 have a very large uncertainty and I think you've got to

5 think in terms of the probability scale and there, I've

6 been wrong.

7 DR. STEVEN HEERINGA: Thank you, Dr.

8 Greenwood. Dr. Legan.

9 DR. SANDRA LEGAN: I have some specific

10 comments relative to my numerous years in measuring LH

11 surges. But first, I'd like to also mention first, a

12 formal data said that this construct is really

13 appealing to me and I really like the way this approach

14 has been attempted to carry out to see if in fact there

15 is a pseudo-steady state and call data from other

16 experiments.

17 That's really a nice approach here. But

18 as you said, we feel that it really maybe doesn't -- it

19 isn't the strongest support and it's probably because

20 you're having a pool of data from different studies.

21 In any case, aside from the differences in rat strains

22 and so forth that were mentioned, when I got those

23 thousands of pages for the Morseth and Minima studies

24 that had not been originally provided, and I had also

25 sort of wanted to have -- get a hold of those but I
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 179

1 started on the other mammary gland question first. I

2 didn't get that question out.

3 But I started to really look carefully

4 at the LH data and yes, there is a great deal of

5 imprecision in measuring LH peak as I talked about

6 earlier and it seems

7 So I looked at the LH data and it's

8 great to see ends of 20 in a group and I looked through

9 the data in both of those group studies. And when you

10 look at the individual rats, there are tables you could

11 use and columns of LH. So whenever you're measuring an

12 LH surge and looking at your data, all rats' biology,

13 all the rats don't surge all the time.

14 So you first have to define what data

15 you're going to accept as -- you have to define the LH

16 surge within the data set. And the most common in the

17 literature that has been used is its -- you look at the

18 level of LH at the first time point at this baseline

19 and you do the main standard deviations of that and you

20 take two standard deviations of that mean, which is the

21 95% limit.

22 And you say any animal that's had LH

23 levels up at any -- above that for at least two

24 consecutive time points because we're talking about a

25 68-hour duration to that, will be defined as an LH
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 180

1 surge.

2 And so I went back and I saw a lot of

3 zeros and 1.2s in the data and I -- so that's what

4 triggered this whole comment in my going back, so I

5 calculated the mean and standard deviation and so forth

6 and did this.

7 And what happens is that a significant -

8 - a large number of rats in some cases, 10 out of 20 in

9 one group -- and I just went through the controls. I

10 didn't have the time to go through every dose levels

11 and so forth. But there were a number of animals whose

12 LH didn't achieve LH sequence of data, did not over the

13 afternoon achieve an LH surge under that definition

14 that I just described. And those data should have

15 dropped from the analysis.

16 The problem is maybe they were but I

17 couldn't -- there was no description of that that I

18 could find in these thousands of pages. It was hard to

19 know where to look in the sets of information. So that

20 needs to be corrected.

21 Then for the McMullen data, it's not

22 possible to tell whether this is done. And in the

23 Cooper study, they, as everyone heard, have three

24 conditions to determine whether the animals were

25 crossed. This was a sacrifice study so they have
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 181

1 sequential samples.

2 So when they went and -- before they

3 sacked the rats, they had to know whether they were in

4 process or they were not. So that is not a problem

5 with the Cooper study because they looked at

6 progesterone. They looked at vaginal smears, right?

7 They didn't have the levels of progesterone they had

8 the uterine wave and vaginal smear.

9 And later on, post-op, I think they

10 verified their progesterone levels when they got them

11 to make sure all the data were corrected and all the

12 animals have surge.

13 So when we mentioned about the precision

14 around the values at the low end of the curve here, the

15 precision, it may well be quite a bit tightened up if

16 those animals who have been surged are taken out of the

17 data set, if in fact that is true of those two data

18 sets.

19 What that does in addition however is

20 that changes -- that may change where the NOEL and the

21 LOELs are. It might be a different data because the

22 numbers that I calculated -- and I can show you. I

23 brought some xeroxes and calculations with me. I don't

24 want to go into the details, but I can show you later

25 that instead of having an LH peak value of 3.4, it goes
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 182

1 to 4.5 or 6 or such. In a standard deviation, it gets

2 smaller.

3 So this data, if they re-plotted mine

4 and maybe on a log scale as was suggested, you might

5 get a better estimate of what this -- where this data

6 fit and if they support the pseudo-steady state. You

7 just couldn't -- you could maybe tighten up the data.

8 Having said that, if these data are incorrect and in my

9 mind -- I looked at this and said I can't count on the

10 3 Ms data, the Morseth, Minima and McMullen studies

11 because of this issue.

12 But the Cooper study, they were counting

13 data from the animals that had it. And so, that's the

14 only data on the graph, so that weakens it and I think

15 you need more data. The other thing is that also,

16 according -- Dr. Akana mentioned that the better way to

17 analyze it is to take the highest level in each rat

18 rather than just give the 1800 time point.

19 If there are a significant number of

20 rats, he'd get 1600 or 2000 or whatever is available

21 and the data consider these, you should use the peak

22 level and that will tighten up this data set also. And

23 then, you should use more midrange doses and at the

24 higher, and maybe you would get a whole lot better

25 construct from which you can much more strongly say
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 183

1 that the pseudo-steady state is associated with these

2 types of attenuations of LH surges.

3 I guess I was a little confused about --

4 in the Cooper report, the NOEL level was 1.56

5 milligrams per kilogram per day when you use the data

6 from all the times in those groups in the analysis.

7 But if you only use the 1800 time point, the NOEL was

8 doubled, brought into the next dose, 3.12 milligrams

9 per kilogram per day.

10 So I didn't know which ones were -- I

11 guess I could have figured it out. But if the NOEL can

12 change, having on which groups were put into the data

13 set, you have to come up with a decision about that,

14 which is, do you want to use all the LH data from the

15 whole afternoon or do you just want it as the peak

16 level? Or do you want -- I don't think you should use

17 the 1800 only.

18 I think you should use the peak level,

19 which could occur at 1800 plus or minus two hours, and

20 you could read through this graph and it might come out

21 stronger because of those data sets, but I think you

22 still need midpoint levels between say 5 and 25.

23 There's no -- there's not much data. And maybe beyond

24 that, there are some data or -- I don't know, the

25 higher doses. I mean we have a -- I think it would be
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 184

1 better. So that was my main point.

2 DR. STEVEN HEERINGA: Thank you, Dr.

3 Legan. Dr. Roby.

4 DR. KATHERINE ROBY: Okay, let me jump

5 in and respond first to a couple of these comments

6 before I forget them. First of all, in regard to

7 normalizing to the peak, I do think that is a good

8 idea. I think you might enhance your sensitivity if

9 you can do that.

10 However, in regard to kind of throwing

11 out the data from the animals that do not surge, I

12 think in fact you would be reducing your sensitivity

13 because part of the response is the lack of the surge

14 and just maybe it means that the data needs to be

15 thought about in a different manner instead of cooling

16 20 animals and averaging their surge.

17 Maybe it needs to be reported such that

18 15 animals did not surge and of the animals that did,

19 there was a surge. I think that will allow you to

20 really make a strong point that a significant number of

21 animals did not surge. I think that will strengthen

22 the overall data.

23 I think like several of us, I would also

24 like to see the higher data points included in part

25 because those are the dose levels that are more closely
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 185

1 associated with a downstream fertility end point. But

2 secondly, I think you made a comment in your discussion

3 yesterday or the day before that that after about five

4 or milligrams per kilogram, the curved plateau -- and

5 I'm not entirely sure if all those higher data points

6 are added that that will actually be the case.

7 Further, I think that if it's not the

8 case, it might represent either a different or

9 secondary mode of action that needs to be addressed or

10 a different PK profile at higher dose levels and that

11 would be very interesting.

12 I guess my other point was that we're

13 thinking about different life stages throughout this

14 whole assessment and most of this data really is in

15 mature cycling animals.

16 I think it's been showing a little bit

17 of data with prepubertal exposure and I think no data

18 actually under the aging or senescent or perisenescent

19 setting, and I wonder if this representation will look

20 different in those studies. This kind of associates

21 with the discussion about the LE versus the SD strains

22 and maybe some of the information we saw with the

23 prepubertal exposure, the results might look different

24 if it was done in the LE strain first.

25 And then secondly, again repeating
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 186

1 myself, this response curve might look different in a

2 perisenescent animal where we believe anyway the

3 sensitivity to the estrogen and the response of surge

4 is a little different, and that might give us some

5 information later on to human exposure and sensitivity

6 at different life stages.

7 DR. STEVEN HEERINGA: Thank you, Dr.

8 Roby. Any additional comments on this particular

9 charge question? Dr. Greenwood.

10 DR. RICHARD GREENWOOD: Just really

11 again to remind you, I wouldn't get too hung up on the

12 pseudo-steady state per se. That may not be important.

13 What might be important is just maintaining a minimum

14 concentration of the--

15 We keep saying pseudo-steady state but

16 that may not actually be what is needed. It can then

17 just be -- as long as you got the exposure of a

18 critical field, a minimum exposure, you're going to get

19 an attenuation of surge.

20 DR. STEVEN HEERINGA: Dr. Horseman and

21 Dr. Roby.

22 DR. NELSON HORSEMAN: Maybe this --

23 maybe I can be educated here. It seems to me that all

24 we're being asked to do here is based on the cooling on

25 a number of these studies, is to draw a line to these
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 187

1 data ourselves. Imagine a kind of uncertainty around

2 that one. I wonder why the Agency didn't do that for

3 us.

4 (Laughter.)

5 And maybe the statisticians here could

6 talk about the strengths and weaknesses of maybe doing

7 that kind of analysis, but what it seemed to have done

8 is draw a line through one -- the data from one

9 experiment and dribble other data on to the same graph

10 and ask us to interpret the strength of that kind of

11 enhancement. So I'm sorry if that may be a crass

12 characterization.

13 (Laughter.)

14 Maybe you can help me here.

15 DR. KATHERINE ROBY: I think you're

16 asking me to respond or affirm to this one. In

17 response to Dr. --

18 DR. NELSON HORSEMAN: I may be asking as

19 well for a response from some of the other more

20 statistically apt members of the panel.

21 DR. CAROL CHRISTENSEN: I'm sure they

22 would be obliged because they're always willing to --

23 the statisticians will always step up.

24 (Laughter.)

25 DR. STEVEN HEERINGA: Dr. Portier and
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 188

1 Dr. Lee are thinking right now.

2 DR. CAROL CHRISTENSEN: I can tell from

3 their faces. One comment before we get back to that,

4 Dr. Roby, on that plot, the splattery one, there is --

5 the more set data set on there is six-month because

6 those are the six-month graphs.

7 So you're implying that they're aged --

8 aging, except they were exposed over that six-month

9 time frame.

10 DR. KATHERINE ROBY: Yes.

11 DR. CAROL CHRISTENSEN: And I think one

12 of our goals is to understand an exposure time frame

13 related to an effect or response, and this is really in

14 my opinion, very long-term exposure. And even though I

15 think some call four-days a long-term, you could also

16 consider it short-term, especially in relationship to

17 the event that it can cause biologically.

18 So I guess what I was talking about is a

19 lot of these studies are normalized to basically

20 inducing a surge exogenously or looking at the

21 endogenous surge and impact of atrazine over a pretty

22 short period of time, over one cycle in fact. And this

23 is really a long-term study.

24 I think the data is -- I'm not

25 questioning the data and I think that the results are
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 189

1 very realistic, but I was thinking more along the line

2 of kind of repeating the studies that had been done in

3 the adult cycling mature animal in a kind of

4 perisenescent animal where at least the data we have

5 already would indicate that the responsiveness should

6 be already altered based on aging.

7 So you actually might see -- you might

8 hypothesize that there would be a greater sensitivity

9 in the perisenescent, i.e. perimenopausal setting. I

10 guess that's what I was --

11 DR. KATHERINE ROBY: Thank you for that

12 clarification because I didn't hear that point --

13 DR. CAROL CHRISTENSEN: Okay. But I'm

14 going to add one more before -- and that is, as Dr.

15 Greenwood reminded me, I think the other important

16 issue that needs to be thought about and that also

17 relates to the six-month versus the four-day exposure,

18 and that is period of exposure with relation to stage

19 of the cycle.

20 And I think that it's very clear that

21 exposure throughout one entire estrocycle has an

22 effect; that's clear. But that's exposure through two

23 days of the estrocycle. What about the first two days

24 versus the last two days?

25 Because I think there is data to show
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 190

1 that on pro-esterase, there is not an effect. A dose

2 given earlier in the cycle that are very high dose will

3 have an effect. This kind of gets to the area under

4 the curve versus for a six-time duration exposure

5 issues also.

6 And I only -- I think it's very

7 difficult in a rodent because of the short cycle, but I

8 think it's relevant when you start to address questions

9 related to exposure in humans or women with a longer

10 cycle and it doesn't matter if the first five days of

11 the menstrual cycle have any exposure versus days 8 to

12 14, and that's where I'm thinking not just the duration

13 but the timing within the cycle is maybe an important

14 issue to think about.

15 DR. KATHERINE ROBY: I hope you hold

16 those thoughts for the last question.

17 DR. CAROL CHRISTENSEN: I know. They're

18 all really tied together.

19 DR. KATHERINE ROBY: So in response to

20 Dr. Horseman, if you recall Dr. Rodriguez's

21 recollection of the events that led up to this question

22 you have in front of you, after the April SAP, we heard

23 from the Panel these deliberations to think about

24 moving to an internal dose metric.

25 We heard that loud and clear, and
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 191

1 proceeded -- I mean, as you can tell, fairly

2 aggressively after that point. And he began those

3 plots with the Thede study to show this very nice

4 plateau and we all simultaneously have the same

5 question, what about the LH data? And started out with

6 a plot and we drew on another plot and drew on another

7 plot.

8 It's actually not far from the way you

9 described it in fact, which is really funny. But

10 that's not a bad thing to create a process that works

11 in different ways.

12 And after the fact, we've asked

13 ourselves those questions. Does it make sense to do --

14 for example Dr. Lucciani has benchmarked those analysis

15 as a single end point.

16 Our program, we have a precedent for

17 taking multiple studies and combining these single

18 analysis across -- particularly things at the steady

19 state. We have a strong -- we've done that on 30 some

20 chemicals and organophosphates. Is this another case

21 for doing that? Actually doing a more sophisticated

22 analysis across, which would involve calculated

23 confidence balance.

24 We withheld doing that analysis until

25 we've got some feedback on the direction that we were
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 192

1 headed not only on the pseudo-steady state ideas but

2 thinking about looking at the LH data in this way

3 because it's a unique way that people have taken.

4 We're not aware that anyone else has taken all this

5 atrazine, LH data and done this before.

6 So we were reserving a lot of

7 sophisticated analysis until we got feedback on where

8 we were at that point.

9 DR. STEVEN HEERINGA: Steve Heeringa,

10 just maybe to start the discussion, I think it deserves

11 a little bit of discussion, and that is -- I'm actually

12 glad that they did it this way because as Dr. Legan has

13 pointed out, we really don't know much other than the

14 Cooper data, but what happens between 5 milligrams per

15 kg and 25 milligrams per kg.

16 And if you remove the Cooper data, which

17 is the Long-Evans rat, so the first covariate, I would

18 put in this analysis. If I was going to try to combine

19 this, it's the rat type and immediately, you get two

20 different curves.

21 And if you had -- so in short answer to

22 you and maybe it's a copout is that I think that the

23 covariate space is partitioned here. We say

24 essentially you don't have overlapping covariate space.

25 Dr. Legan says you don't have enough
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 193

1 observations on the two different types of rats between

2 5 mg and 25 kg. So I think it's a great idea. I think

3 that the way that they presented it now actually

4 generates better statistical hypothesis about the next

5 stage than if they try to combine them.

6 If we had enough data pool -- I mean

7 theoretically, we like to not only know the variances

8 of these estimates, but we'd like to know their biases

9 relative to some pure measure, and we don't know the

10 latter and we never will but we could maybe assume some

11 of those away.

12 But I think this rat strain covariate

13 and the fact that it's only one study that's defined in

14 the shape of this curve between 5 kg and that it's a

15 unique rat type, to me biologically I don't know if

16 there's anything to that at all. But statistically, I

17 couldn't sort out the rat strain from the shape of this

18 curve at this point. I don't know, Dr. Lee, do you

19 have any thoughts?

20 DR. HERBERT LEE: I agree that the rat

21 strain is an issue here, although one approach we did

22 at the hierarchical model, it certainly gets more

23 traction that way combining across the two strains.

24 But as you pointed out, there's not any information

25 though between the 5 and 30 on the Sprague-Dawley rats
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 194

1 so that the shape of the curve is what would be

2 determined by Long-Evans rat study.

3 And with only six different levels, even

4 there you're not going to get a lot of information

5 about what the shape should be to get a model with

6 actual confidence. Hence for a whole curve, you're

7 going to have to make some fairly strong assumptions

8 about the shape of that curve. And then of course all

9 your results depend on those assumptions.

10 DR. STEVEN HEERINGA: I think if you

11 look at the figure, there's a faint dash line which

12 draws a straight -- between two points for the Sprague13 Dawley rats, and my guess is that we did some sort of

14 merged analysis that what you would see emerge as a

15 curve somewhere between that straight line and the

16 sublinear curve that you're seeing for Dr. Cooper's

17 data. It's a great idea but -- other thoughts on that?

18 DR. KENNETH PORTIER: Well, I think

19 since I've recommended this study, Dr. Cooper should go

20 do a six-month exposure --

21 (Laughter.)

22 It will only take, what, three or four

23 more years, right? But I'm looking at this and I'm

24 thinking the same thing. It would be really nice to

25 know -- I wish to see this, but it would really be nice
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 195

1 to know for more dose levels for the six-month exposure

2 exactly what we look at in the Long-Evans rat.

3 And right now, we just don't -- what was

4 the word used? Gestalt this map gives you a gestalt

5 but it's definitely not going to provide you confidence

6 bands on some kind of dose response thereof.

7 DR. STEVEN HEERINGA: Other

8 contributions on the Question -- I guess 3.1? Well, I

9 know that Dr. Meek has a plane to catch later and we

10 want to get to her charge questions, so let's turn to

11 Question No. 3.2 before our break. Dr. Rodriguez, do

12 you have a comment? Please. I'm sorry.

13 DR. CHESTER RODRIGUEZ: Yeah, thank you.

14 I just wanted to add that I tried plotting this data in

15 many different ways and this was the way that should

16 make the most sense to at least four other scientists

17 here. So yeah, we think that I should feedback sort of

18 the best way to show this data.

19 But for us, I think it's remarkable how

20 similar the four studies are in open plotted, as per

21 se, control and how that correlates within

22 pharmacokinetics. So we were partly ecstatic when we

23 saw this because it gives you a chance to link the PK

24 and PD and that's something that is not commonly seen

25 in science.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 196

1 DR. STEVEN HEERINGA: Stephen Heeringa.

2 I agree it's a nice picture and I think we all agree

3 the picture would change. We would be less ecstatic if

4 at 20 milligrams per kg, there was 85% controlled LH

5 value sitting out there for four different types of

6 rats.

7 I think that's what we're -- but

8 nevertheless, I think it's effective and the

9 presentation certainly stimulated the right type of

10 discussion. Thank you.

11 At this point, if we could read Question

12 3.2 into the record.

13 DR. CAROL CHRISTENSEN: Please comment

14 on the BMD analysis summarized in Section 5.3 of the

15 draft Issue Paper with details provided in Appendix C.

16 DR. STEVEN HEERINGA: Dr. Meek.

17 DR. BETTE MEEK: Michael has the

18 shortest question, right? Okay, so this is a great

19 general question and I think just to provide some

20 general comments to start, I am very much in agreement

21 that it's important to consider early key or precursor

22 events that might quantify modes of action as the basis

23 for characterization, the critical response,

24 relationships, more relevant to prevention of adverse

25 effects in the human population.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 197

1 In fact, we have evidenced consideration

2 the mode of action the context of this source to

3 adverse outcome have we really assisted in

4 transitioning our focus in toxicology and risk

5 assessment from the lead adverse effects which occur at

6 high doses to the earlier precursor events of doses

7 that are more relevant.

8 Often data on these earlier precursor

9 key events lend themselves to much better

10 characterization in the response analysis as well.

11 Then have in traditionally -- and that's really owing

12 to the availability incidence data and more involved in

13 these levels under the commission could have more

14 return studies as we've seen on the plot.

15 So I appreciate very much that EPA is

16 following up on the recommendation of the previous

17 Panel in this context. And the advantages of

18 developing benchmark doses over effects are also well

19 documented and all the dose response data are much more

20 robust. There's accounting for those selections based

21 in the sample size.

22 A benchmark dose based on special LH

23 surge also appears to be protected where the end points

24 occurring more doses that have a wide range of effects

25 investigated in a rather extensive toxicological
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 198

1 database, so it has many advantages.

2 However, and there's always a however,

3 the biological significance and adversity of the effect

4 for the benchmark value in my mind remains to be

5 robustly addressed and necessarily will need to be

6 considered carefully in relation to more severe end

7 points.

8 It seems important to consider levels of

9 effect which would be considered adverse on continued

10 exposure based on the totality of the data, including

11 from the more traditional studies. And this would

12 permit consideration of the degree, a protection

13 offered by this proposed point of departure compared to

14 those based on traditional toxin for which we all can

15 have a larger database.

16 I think that -- and you haven't gotten

17 there yet in the assessment. I think that that will

18 need to be taken into account and consideration the

19 application of example and certainty factors, as well

20 as the advocacy, particularly kinetic and also dynamic

21 data to inform intermediate species extrapolations.

22 It may be helpful then to rank potential

23 points of departure for various ends points with a view

24 to characterizing their degree of protection in

25 relation to biological significance of the observed
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 199

1 effect and associated uncertainty.

2 This is rather critical as a basis to

3 interpret the derived benchmark dose in context not

4 only a biological significance but the traditional risk

5 assessment construct as a basis to comparatively

6 consider uncertainties associated with various options.

7 So comparative uncertainty analysis would include

8 consideration of appropriate intraspecies and

9 interspecies adjustments in the context of their

10 associated degree of protection and available and some

11 of us involving kinetic and dynamic information at both

12 ends, so it would be a better basis for comparison of

13 the benchmark doses overall. I'll leave it at that.

14 DR. STEVEN HEERINGA: Thank you, Dr.

15 Meek. Dr. Delclos.

16 DR. BARRY DELCLOS: My slight

17 interpretation of this question is to whether the

18 analysis have been chosen the way the end point, the

19 right study to use for the analysis, and I think that

20 coming in here, I thought that the day we submitted our

21 case that this is, right now the most extensive end

22 point and the most protected end point as you enroll

23 the level of change that's unclear what it would mean

24 in terms of adverse output.

25 But it's clear that this sensitive end
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 200

1 point study that was selected is certainly -- again,

2 the Agency made a good case in the document that it was

3 the best study to use. It's been criticized here and I

4 don't know what would make that criticism.

5 Frankly, I think that's a matter for the

6 statisticians and the Agency to work out what to do to

7 with the blocking time of the experiment, whether there

8 are certain chores. I don't know how to evaluate those

9 criticisms, but certainly it would need to be ironed

10 out. That's really all I have to say.

11 DR. STEVEN HEERINGA: Thank you, Dr.

12 Delclos. Dr. Penelope Fenner-Crisp.

13 DR. PENELOPE FENNER-CRISP: I want to

14 confirm my opinion. I agree with what Bette said and

15 what Barry said about what I thought before I got here.

16 And in fact that there were some discussions of

17 criticism offered and of the day that it had been --

18 well, it's clarified some of them and one of the

19 criticisms was that the subset of controlled data

20 weren't included in the analysis but it says it was.

21 So if that's the case, then that criticism is not

22 appropriate.

23 At that time, I was looking if that was

24 a valid criticism and if it were appropriate to

25 criticize the wide range of age of the animals and the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 201

1 three blocks, then what would the analysis look like if

2 you only use the third block compared to what it would

3 look like using all three provided agents what happened

4 if you used all three combined, so that was just a

5 curiosity.

6 And then of course we've only seen the

7 summary of a new data set from Syngenta and I don't

8 know exactly what that means in the context of the

9 Agency's data because of the different results. It's

10 interesting to see the analysis of similarity and

11 differences between those two data sets and how they

12 make their way in the quantitative assessment.

13 You would think that they would want to

14 echo it is appropriate to use the benchmark dose rather

15 than an old break level. You get a lot more

16 information out of it. It's right for you to know.

17 DR. STEVEN HEERINGA: Dr. Cooper.

18 DR. COOPER: Yeah, I would like to make

19 a clarification about what Dr. Legan and the Sulken

20 paper discussed in the statistics. That was on the

21 analysis of areas that was included in my report to the

22 program office.

23 Those statistics are not being used in

24 benchmark dose. I mean the same data is being used but

25 it's a different -- it's the same data but whether that
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 202

1 third control group was included or not is moved now

2 because we're not using my graph pad analysis. That

3 was I think the second point to whether we should

4 analyze different blocks separately. That I think is a

5 matter of cranking through different data sets.

6 DR. STEVEN HEERINGA: Thank you, Dr.

7 Cooper. Dr. Roby.

8 DR. KATHERINE ROBY: Okay, just quickly,

9 I concur with everyone. I do believe that the

10 benchmark is the appropriate approach and I think given

11 the body of data that is out there right now, LH is the

12 appropriate end point at this point. I think

13 everything else goes into next question.

14 DR. STEVEN HEERINGA: Dr. Legan.

15 DR. SANDRA LEGAN: I agree about the

16 benchmark dose in being the appropriate measure to use

17 as opposed to NOEL end support. And I took this

18 question sort of like delicate in terms of this is the

19 right end point for calculating a benchmark and that

20 was mentioned by the other commentators on this

21 question, on this issue.

22 And of course, I had talked about -- I

23 have raised an issue a couple of days ago about whether

24 a small decrease in the amplitude in the LH surge it is

25 really physiologically meaningful thinking in terms
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 203

1 back to the mode of action of atrazine in attenuating

2 LH surge or the LH peak. I guess we've had a lot of

3 discussion about that and I guess some of those

4 comments should be I think seriously taken in

5 consideration.

6 Right now, based on all the historical

7 data and the way this was developed, the LH surge

8 attenuation continuation is in fact at this point the

9 most sensitive. So I agree with having pursued that

10 and maybe even continuing with it a bit.

11 However, in light of the evidence from

12 20-planned that the LH surge is controlled differently

13 in the rat and the human. We might think - and even

14 before I heard these comments, I was thinking the LH

15 surge peak is maybe not the best end point partially

16 because it's so variable.

17 And you're trying to pull out a

18 significant small difference which may not be

19 physiologically important but it's a variable. You're

20 fighting this large variability in the event that

21 you're measuring.

22 I kind of hate to say this, but basal LH

23 which is controlled by estrogen negative feedback might

24 well, as Tony suggested -- I agree with him that that

25 might be something you should start looking into as
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 204

1 another alternative marker to derive

2 DMD from.

3 And there were two pieces of evidence.

4 I was concerned about the small attenuation in the LH

5 surge at the lower levels whether - I mean that's not

6 enough to stop the cycle. But Dr. Akana and Dr. Cooper

7 both have said in the last couple of days that they

8 agree this may not be physiologically important thing

9 and that may not be driving the mechanism of what mode

10 of action is beyond this.

11 But it's part of that mode of action.

12 It's a small part and maybe there are the places in the

13 brain and other things that we're not looking at. It's

14 like you've got a ton of vision about this. It's a

15 very sensitive end point that we have -- or that you

16 have found and I think to this point, it's valid but

17 you can't keep the blinders on. I think there are

18 little bits of the data that don't all fit into this.

19 For example, we know that there is a

20 decrease in general pulsatility as atrazine's effect.

21 That certainly can explain the decrease in amplitude of

22 LH surge but there's no change in a couple of papers.

23 There was no change in FSH and that's odd because when

24 you suppress the GnRH frequency, that is a change

25 generally. So there is a little bit of red flag there.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 205

1 There is a paper that came out this year

2 that indicated that atrazine finds or competes with

3 finding growth hormone releasing hormone to the growth

4 hormone releasing hormone receptor.

5 So if atrazine -- and there's no data

6 other than that that I knew of, and that was in vitro

7 data and there may be problems with that data. You can

8 always criticize some papers and experiments. However,

9 there's a hint there that maybe there's a decrease in

10 growth hormone as a result of atrazine.

11 There might be. I don't know that there

12 is. No, I don't know of any. But if there's a change,

13 as an endocrinologist, if there's a change in growth

14 hormone, it has to do with onset of puberty.

15 The growth hormone has a lot to do with

16 GnRH pulse frequency and LH growth. Growth hormone has

17 a lot to do with metabolism, mammary gland development,

18 etc. So I think that we can't just look at the LH

19 surge attenuation. I don't think we should throw it

20 out or stop with that.

21 But I like the Agency to keep looking

22 for other things that might -- I know it's very complex

23 but I guess I was just -- keep your open. And the

24 growth hormone receptor paper, actually what triggered

25 that study according to the introduction was they did
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 206

1 not change it. They treated some animals with atrazine

2 and they did some kind of change of analysis and they

3 found this transcript that they blasted and figured out

4 they have the most similarity to growth hormones

5 receiving receptor and they jumped on that and did

6 several different studies.

7 Well, this is kind of a relevant

8 approach. There's not been any molecular data here. I

9 think maybe someone should be involved in generating a

10 BMDI. I know increasing workloads and -- and I realize

11 that, but it's a thought that one should keep in mind.

12 Basal LH is going to be something and I'm not sure

13 that's a realistic suggestion, but stuff like that has

14 to be kept in mind.

15 There are other possible mechanisms that

16 certainly have been ruled out, and it also could -- to

17 completely explain much of the changing -- many of the

18 changes that we have seen. Totally, I think easily if

19 the growth hormone story is the one I'm thinking about

20 is -- so I'd say don't throw away the LH surge but I

21 think there's probably many other issues and it's

22 probably one component among a number of things.

23 Like Bob Hom's data on the POS

24 localization, in generic terms, he had to see changes

25 in the CRH event. But you know, if there is an effect
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 207

1 -- if he still got sections, then he's not prepared and

2 I would want to get up and look at him because maybe

3 there's a cause related activation of neurons elsewhere

4 in the brain that would give us another hint.

5 Things like that that could be explained

6 and keeping our eyes open on what is a sensitive end

7 point that can also be used not only in the LH surge.

8 And I think we all agree and you said earlier that

9 these changes may be controlled in the brain. You said

10 the hypothalamus. Maybe other neurons that have input

11 to the GnRH or the CRH growth hormone releasing hormone

12 bias and those kinds of things.

13 So you know, the catecholamine and

14 energy sort of from and you do see changes in

15 catecholamine with atrazine. We haven't even -- I

16 don't remember that you said that, but once -- so there

17 are these little bits of pieces of data in your draft

18 paper that maybe -- and again, in a literature, it

19 wasn't mentioned there. I don't know if it did come

20 out afterward, but keep looking for other mechanisms if

21 possible.

22 DR. STEVEN HEERINGA: Dr. Legan, you can

23 provide the reference for the --

24 DR. SANDRA LEGAN: Absolutely. That

25 paper is actually included in our analysis.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 208

1 DR. STEVEN HEERINGA: Okay.

2 DR. SANDRA LEGAN: And the big appendix.

3 DR. STEVEN HEERINGA: Thank you. Any

4 other comments or response to Question 3.2 for the

5 Panel? Yes, Dr. Krishnan.

6 DR. KANNAN KRISHNAN: I just want to add

7 a couple of minor comments in some of the analysis.

8 Some of the BMD analysis also are based on the internal

9 dose just for the record since we mentioned it

10 yesterday, but there is a negative intercept in the

11 analysis culminating with the administering of internal

12 dose and that's what fits in to the BMD analysis, so

13 it's a bit problematic but it's coming with that, and

14 number two, the internal dose measure once again as

15 it's used is residual or minimal triazine equivalent in

16 the system for all the reasons that Richard and I

17 discussed before.

18 The AUC on the concentration that's

19 being used in the residual concentration were a minimal

20 one during the steady state condition. One thing that

21 was a bit surprising is to see different BMD models on

22 the basis of administered versus internal dose, even

23 though those are supposedly linear or proportional, so

24 maybe that needs some ratification. I haven't been

25 able to rerun the models but that's just -- that caught
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 209

1 my eyes.

2 DR. STEVEN HEERINGA: Thank you very

3 much Dr. Krishnan. Other comments on this particular

4 one? We could come back afterwards, but let's take a

5 15-minute break and reconvene at 3:20. Again, I would

6 have to excuse myself for the balance of the afternoon,

7 but Dr. Portier will take over chairing the meeting.

8 So back here at 3:20.

9 (WHEREUPON, a Break was taken.)

10 DR. KENNETH PORTIER: Okay, I'm Ken

11 Portier. I'll be chairing this last session. My plan

12 is to go until about 5:10, 5:15. It's 3:20 at this

13 point. I'm hoping we'll get through 4.1 and of course,

14 if not all, or 4.2 today to get us back on schedule, so

15 just kind of warning, hydrology and modeling people.

16 I think we've kind of started actually

17 the discussion of 3.2 in some of the last

18 conversations, so why don't we read question 3.2.b and

19 we'll continue with the conversation on that question.

20 DR. CHESTER RODRIGUEZ: Question 3.b

21 says to please comment on scientific factors important

22 for establishing a benchmark response for attenuation

23 of LH as part of the BMD analysis for atrazine. Notice

24 that that's not what is there -- what's here, sorry.

25 DR. KENNETH PORTIER: Okay. Dr. Roby,
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 210

1 you're the lead.

2 DR. KATHERINE ROBY: Okay. So I think

3 this is a key question and maybe paradoxically it can

4 be answered or addressed really simply or really

5 complicated -- complexity.

6 So the question is what's important in

7 establishing a benchmark response specifically with

8 regard to LH, and I think the short answer is we really

9 do not know. We do know that -- well, personally --

10 first let me back up a second.

11 We have to establish events with a

12 benchmark response. That's key to the analysis. But

13 in specific regard to LH, which again I think is the

14 key end point to be measuring in this situation, we do

15 know that various significant changes in the LH surge

16 result in significant downstream effects related to

17 fertility.

18 It's well established what the LH surge

19 controls from rodents up to humans and we know that if

20 the LH surge is lost, there are significant effects on

21 fertility. That's key. That's very clear from a huge

22 body of data.

23 What we really do not know is how much

24 of the LH surge is important. Actually, we kind of

25 know. We know that we need 20 something percent of the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 211

1 LH surge. We know that but that means shutting down

2 the LH surge at 80% and that's significant.

3 What we don't know is what is the effect

4 of diminishing 30%, 40%, 25% -- actually, there's

5 really no information on the literature to indicate

6 there's a significant downstream effect of loss of LH

7 surge peak or area under the curve or duration really

8 by -- what I'm going to say is a small effect but

9 statistically, it's a big effect.

10 It's a statistical effect but

11 biologically, there doesn't seem to be a downstream

12 effect. So that's what makes this question simple and

13 complex. So the simple thing is we can't really

14 establish X percent decrease in one LH surge, this is

15 also a key, having a biological downstream adverse

16 effect on fertility.

17 Now, my bias -- my personal bias is this

18 is a measure of an effect, an ultimate effect on

19 fertility. Now, if you would hit at one LH surge, it

20 doesn't matter in the scheme of things. One LH surge

21 doesn't matter.

22 I believe if you would hit the LH surge

23 over the duration or over a critical time, onset of

24 puberty or during critical time during one cycle, it

25 might produce one LH surge but over a critical period
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 212

1 of a lifetime, there may be an adverse effect on say

2 onset of menopause or change in its timing of onset of

3 menopause.

4 But that's my personal bias. There is

5 no literature to suggest that that's true. That's

6 where the difficult thing comes.

7 So really, I think the question, the

8 scientific importance of establishing an actual percent

9 change in the LH surge I think cannot be addressed

10 directly because there's no literature to help us look

11 at that.

12 So I think based on your guidance that

13 you have to go to the approach that you've taken and

14 look at the mean and the standard deviation, again,

15 with the caveat that one or two standard deviations

16 from the mean may not have a biological type than that

17 one cycle that you're looking at, it might have a long

18 term biological effect.

19 So there's really a big disconnect I see

20 in looking at ammunition of the LH surge and what's

21 happening downstream. That's just in these small yet

22 significant changes in the surge amplitude.

23 And if I should -- I've already

24 indicated the idea that change in effect throughout the

25 lifetime is also a consideration and there's no
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 213

1 information. We already know that the spike in the LH

2 surge changes with aging in women and in rodents.

3 But again, there are changes in the

4 amplitude of the LH surge as a woman enters menopause,

5 that perimenopausal period where there is still

6 ovulation with every cycle. So again, that means that

7 the downstream -- what we would consider negative

8 effect is not there with changes in the amplitude of LH

9 surge.

10 So again, there's a big disconnect and

11 now I'm repeating myself, and it takes you to the

12 conclusion that the Agency came to in using the mean

13 and so much data..

14 DR. KENNETH PORTIER: Okay. Dr. Roby,

15 are you done?

16 DR. KATHERINE ROBY: I think that would

17 be it for now.

18 DR. KENNETH PORTIER: Dr. Bucher's got a

19 question for you.

20 DR. KATHERINE ROBY: Oh, for me? Yeah.

21 DR. JOHN BUCHER: So I'm reminded of the

22 cancer study long ago that started this whole thing

23 about the LH suppression and the fact that the LH

24 suppression was seen early in the Sprague-Dawley rat

25 that caused early senescence and caused the --
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 214

1 according to the dogma, an increase in estrogen

2 stimulation and mammary onset the onset of mammary. So

3 are you speaking only in terms of the humans or are you

4 speaking in terms of the fact that there's no adverse

5 effect of LH surge suppression through our lifetime?

6 DR. KATHERINE ROBY: Well, no. In fact,

7 the opposite. I think that there is evidence over the

8 lifetime but all of this is looking at short-term

9 exposure and then short-term effect. In other words,

10 can we relate a change in LH surge in one cycle in an

11 adverse effect?

12 Now, if we shut down the LH surge and

13 have no LH surges, the data is there. If there's a 50%

14 reduction in the amplitude of the LH surge, the data is

15 not there. That's where I think the difficulty comes

16 in. Does that make sense? No?

17 DR. JOHN BUCHER: Well, if you're

18 speaking -- if you think there's a distinct difference

19 between short-term and the long-term exposure, yeah, it

20 makes sense.

21 DR. KATHERINE ROBY: Well short, and

22 then -- but I think the issue question is to define a

23 percent change that we're going to call a significant

24 change related to subsequence fertility event in this

25 situation, or it can be cancer event, too.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 215

1 And that's where the problem is. I

2 don't think that we have information on the given

3 percent change that will elicit that secondary effect.

4 DR. KENNETH PORTIER: I think what

5 you're saying is the percent change for how many cycles

6 is going to produce what downstream effect, right? So

7 you're saying a percent change in one cycle, it's

8 unlikely to see anything.

9 But a percent change in the next 15

10 cycles may produce cancer or other effects underlying.

11 DR. NELSON HORSEMAN: Just for

12 clarification, this model or notion suppression of the

13 cycles leading through the sequence of events to cancer

14 is complete suppression in the cycle or a large

15 fraction of the cycles.

16 In other words, a large fraction and

17 other cycles I believe as opposed to just the issue of

18 decrement in the LH surge. At least that's my

19 understanding.

20 I'm sorry, John Bucher, this is -- I

21 look at it in a different way. I look at it sort of in

22 a way that you're going to have senescence in a

23 Sprague-Dawley rat no matter what rat's life is like.

24 Any addition in the LH surge could bring that

25 senescence on earlier.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 216

1 DR. KATHERINE ROBY: Well, I think you

2 can hypothesize that but I don't think there's any data

3 to support that hypothesis. Is that -- does someone

4 else have -- yeah?

5 DR. KENNETH PORTIER: Dr. Akana, I'm

6 going to ask you to wait in on this discussion.

7 DR. SUSAN AKANA: I tried. I thought

8 real hard about this and I honestly have nothing to

9 add.

10 (Laughter.)

11 DR. KENNETH PORTIER: Okay, Dr. Legan.

12 DR. SANDRA LEGAN: I think Dr. Roby put

13 it in a very articulate way and I think it is obviously

14 generating a lot of thought about this, and it's really

15 tough. It's a tough issue but I have some additional

16 thoughts.

17 In response to your question, if a

18 diminution in the LH surge happens naturally in the rat

19 physiologically with aging, doesn't that mean that over

20 time that that would shut off cycles?

21 And think a way to think about this, not

22 to figure -- so we as measuring LH, when you have 10%

23 or whatever continuation at peak physiologically within

24 that cycle and it just doesn't change the compliment of

25 the old ovulation, the number of eggs ovulated -- in a
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 217

1 short-term, that is -- so the problem is we keep

2 running up against it.

3 This isn't physiologically important to

4 the rat cycle or to the fertility probably because some

5 rats only have a very small LH surge and they function

6 fine through their whole lives. Okay, and that

7 individual variation. Maybe that's not important, but

8 the way I think of it is that this attenuation of the

9 LH surge that occurs in rats as they age and in women

10 as they become perimenopausal is the canary in the

11 coalmine.

12 Forget about the absolute attenuation of

13 the LH surge and how much that's going to affect

14 whatever. It's part in nature, in biology. It's part

15 of a process in both species that we know this is just

16 one of the steps that we've been measuring, the

17 mechanism.

18 There's something else going on that

19 started this that is now going to overtime result in

20 infertility and possibly cancers, and possibly all

21 these things we're thinking about because atrazine is

22 maybe sometimes causing this in a non-destructive way.

23 So with the loss of cyclicityin the

24 middle aged rat, the theory has been proposed, although

25 I don't know if there's -- there's not real solid data
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 218

1 for it that the reason that the LH attenuates is that

2 there is a decrease in response to the positive

3 feedback of estrogen and that's happening up in

4 hypothalamic and higher centers.

5 So in my mind, that's the kind of thing

6 where we can't measure those things easily yet and we

7 haven't identified what that is because this is really

8 a complex process.

9 So atrazine may be doing some of those

10 things that cause that attenuation of LH surge

11 naturally in the rat and naturally in women, and that's

12 the only -- that's our canary. And so there is

13 something else here where we're measuring the canary's

14 survival rate. Does that help?

15 It's another way to look at it. I think

16 you alluded to that with talking about the drop in the

17 amplitude of the daily surge in one cycle where we have

18 a short term. It isn't physiologically -- so for us to

19 say here's a dose of atrazine with a certain percent

20 attenuation that's probably -- we can't because the

21 measurements haven't been done over in the long term

22 and we don't know what caused this attenuation.

23 In that thing, we may be able to connect

24 better. But it's chronically going to take place over

25 a long period of time.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 219

1 The problem with -- maybe you could

2 design that experiment new with chronic atrazine and

3 you could define thresholds, so we've talked about

4 that.

5 But in the human who's drinking water in

6 the spring or a few days they have hot and you know,

7 you have all these kinds of different patterns and the

8 chemographs that we're going to talk about, I don't

9 know how we'll ever get that experiment done. But to

10 benchmark those analysis, getting back to the question,

11 I think you go with the standard D.

12 I agree with Dr. Roby. We don't have a

13 number we can put on this. We could lose the event as

14 a measure and we should go with the standard deviation

15 percent as the benchmark.

16 DR. KENNETH PORTIER: Dr. Mumtaz, I

17 think you're next up.

18 DR. MOIZ MUMTAZ: I'm going to defer my

19 time to Kannan Krishnan.

20 DR. KANNAN KRISHNAN: I'm just going to

21 ask a question. One standard deviation or two?

22 DR. SANDRA LEGAN: We're just

23 reiterating the problem in that there is no information

24 to say what a significant change is, one or two or

25 three, until we get to about 80%.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 220

1 And I don't -- I think in the desire to

2 be conservative, which I think is the right approach, I

3 don't think that you can go to that level. And I do

4 think that the small inhibition ultimately may have an

5 effect but we do not know what that effect is and we --

6 there is no literature to support that hypothesis.

7 I suppose studies could be designed to

8 over a long-term inhibit 30% or 50% and look at some

9 long-term effects. Those studies could be designed and

10 carried out. They're very long term studies.

11 I do believe that inhibition is a

12 signal. It's a biological response to trigger. So you

13 have to -- you do have to set a benchmark. That's the

14 difficulty in the question and there's no biological

15 reason to say one or two SD, but you have to set that.

16 You have a guidance and you need to set it, and you

17 already have a guidance in place to use, right?

18 And since the other approach can't

19 really be supported by any data, then you take the

20 guidance with that, unless other studies are carried

21 out in the long-term. It's a long-term side effect.

22 DR. KENNETH PORTIER: I'm going to let

23 Dr. Schlenk come in, but we also -- Dr. Krishnan, you

24 asked a question rather than made a statement, so I do

25 want to come back to your question.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 221

1 DR. DAN SCHLENK: Yeah. Actually, this

2 reminds me. Carrie and I were talking about this

3 during the break and it reminds of the OP choline

4 esterase sort of story. I mean, how much choline

5 esterase inhibition do you need before you actually see

6 development neurotoxic effects or something like that.

7 It's the same question.

8 I think the relevance here, the positive

9 sides of this is that you did a very nice dose response

10 curve that you can actually somewhat get a nice

11 calculation of a BMD.

12 So the logic is there in terms of its

13 approach but again, quantitative and qualitatively it's

14 there because it fits the theory in terms of what it's

15 -- the question really boils down in actually being

16 more of an ecological person. One of the things we

17 always deal with is asking so what because we have

18 biomarker data all the time and says, "Well, so what?

19 We see Sequa-non induction in animals.

20 So what? What does that really mean?"

21 And this has all been kind of fascinating to me,

22 particularly with the choline esterase thing as well.

23 But I think in terms of for 2011 and what the time

24 frame has actually required here, it seems to fit a

25 nice dose response curve that allows you to do a BMD
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 222

1 response.

2 Now, in the long term, I think you got

3 to calibrate that response to a higher apical end point

4 at some point somehow, either using again a

5 gonadotropin antagonist or something like that with a

6 low enough dose that you could say, "Okay, if we

7 inhibit 20% or 30% or 40%, what does that do to vaginal

8 opening or what does to do the reproductive fertility

9 end point that's you're actually concerned about?" and

10 you can calibrate to that then you'd be much more

11 certain about what your data mean and what your

12 benchmark is at that point.

13 But anyway, it seems to me that it seems

14 very similar to the choline esterase OP situation.

15 DR. KENNETH PORTIER: Dr. Krishnan, did

16 you want to elaborate on the one or two standard

17 deviation? And then we'll come back --

18 DR. KANNAN KRISHNAN: Well, I could just

19 ask --

20 DR. KENNETH PORTIER: Dr. Roby, would

21 you turn off your mic?

22 DR. KANNAN KRISHNAN: I don't have any

23 particular task but rather I just wanted to see your

24 real view point on this, and some of it is pertinent

25 because it's not an adverse effect for states of
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 223

1 precursor and there's no calibration on the precursor

2 levels with the downstream effects, particularly at the

3 organism level of things.

4 Now, the science doesn't allow us to

5 really define the level or the delta of the precursor.

6 Personally, we're here actually talking about the NOEL

7 instead of the LOEL that the Agency has started this

8 for several years. But it may be transcending in light

9 of the recent NAS work and so on.

10 Is there any way that data from several

11 studies in terms of the controls and so forth could

12 somehow allow a characterization of how much deviation

13 might be still normal or that's not a realistic thing

14 in such an analysis --

15 DR. KATHERINE ROBY: You mean to

16 standardize a normal surge? Is that what you mean, to

17 standardized a normal surge? Well, I think there's a

18 lot of data in the data packages we've received right

19 here to indicate that there's a huge range of what

20 normal is. That's apparently the problem in defining a

21 percent change. The surge can be very small and

22 minimal or very large and still result in normal

23 fertility and that's true in rodents and women.

24 DR. KANNAN KRISHNAN: So there's no way

25 of normalizing them and pulling them --
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 224

1 DR. KATHERINE ROBY: Not when you're

2 talking about absolute level. If you have --

3 DR. KANNAN KRISHNAN: No, I mean

4 somehow --

5 DR. KATHERINE ROBY: If you had

6 sequential cycle serial samples, yeah. It's possible.

7 DR. KENNETH PORTIER: But that implies

8 then that the one standard deviation means it's going

9 to drop it right there, right? If the standard

10 deviation is big, one standard deviation in the mean is

11 going to take you on your way out.

12 DR. KATHERINE ROBY: Well, you see in

13 the previous figure we were talking about, you see the

14 standard errors. That's because that's inherent of the

15 data that's on there.

16 DR. KENNETH PORTIER: Dr.Legan?

17 DR. SANDRA LEGAN: I was going to answer

18 that question, one standard D or two. My comment was

19 that given the highly variable nature of the

20 measurement of the LH surge, the amplitude of the LH

21 surge, I have just an opinion about that that normally,

22 I would -- to see a significant difference, I would go

23 with two standard Ds because it's a 95% content limit.

24 But when I think about LH surge peaks and how they're

25 measured and how variable that is, one standard D is
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 225

1 more than enough in my opinion to use from -- to have a

2 difference from the controls.

3 Plus adding in a thought about the fact

4 that the doses that are being tested just could get the

5 lowest dose -- benchmark dose, right? How many orders

6 of magnitude below what humans are exposed to, so given

7 those two things in my head, I think one standard D is

8 fine with this specific instance. And everything else

9 I do in science I will use, too.

10 But when I think of the variability of

11 the LH peak and measuring it and I say -- and we're so

12 far below a dosage, it's probably going to be unhinged

13 upon, or that any human would be exposed to-- is that

14 not correct? I kept hearing that. Then I think one is

15 fine, and that's just my reasoning and I can be

16 overruled.

17 DR. KENNETH PORTIER: Yeah. I think you

18 mean the human dose is much lower than what the animals

19 are exposed to, right? You had it -- you were saying

20 it backwards but --

21 DR. SANDRA LEGAN: Well, that's what I

22 meant.

23 DR. KENNETH PORTIER: Yeah. Dr. Lowit,

24 do you want to jump in at this point?

25 DR. ANNA LOWIT: Yeah. I had one
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 226

1 question on -- I'm pretty sure I know the answer but I

2 just want it on the record to make sure it's part of

3 the question. I've heard a lot of conversation about

4 the female fertility and the cycling.

5 Can we take a minute and cover the late

6 puberty and anything in the male? I'm sure I know the

7 answer. I just want it on the record.

8 DR. KATHERINE ROBY: So same thing with

9 the delayed puberty. Again, we don't really know. You

10 can shift to puberty by shifting when -- let's see, I

11 guess the frequency of the generic surges increase,

12 pulses necessary. But is there an amount change that

13 relates to a shift on the onset of puberty? And did

14 you say "male" at the second part?

15 DR. ANNA LOWIT: Yeah. I think you

16 covered it. I just want to make sure that the response

17 to the question is a little broader than just the

18 cycling.

19 DR. KATHERINE ROBY: Yeah. It goes over

20 also over life span and onset of menopause or

21 senescence.

22 DR. KENNETH PORTIER: Did I hear a

23 discussion on the male?

24 DR. KATHERINE ROBY: Do you want me to

25 come on the male? The male is going to be the same.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 227

1 DR. SANDRA LEGAN: Correct, that's

2 right.

3 DR. KATHERINE ROBY: A little change in

4 LH is not going to have a significant change in

5 testosterone, which is going to be driving the

6 spermatogenesis and the secondary sexual

7 characteristic.

8 DR. SANDRA LEGAN: As it relates in the

9 question, there's an unknown fact, there's an unknown

10 amount of change that work into those stuff.

11 DR. KATHERIN ROBY: Correct.

12 DR. KENNETH PORTIER: Anyone else who

13 want to jump in on this? That was a good conversation.

14 I learned a lot. Dr. Greenwood.

15 DR. RICHARD GREENWOOD: I just don't

16 think it would be an appropriate -- I know that this

17 is a little out of place just at the moment but it's

18 just for the record really to go back to our previous

19 conversation where a growth hormone was introduced.

20 And actually, this was discussed at length and in the

21 ending minutes of the April- it was in response to

22 Question 1.3.

23 There was a discussion around growth

24 hormone and how that might be actually something we

25 should look at because it does have impacts for both
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 228

1 males and females, the very general effects that might

2 actually, it's some of the general syndromes with

3 atrazine exposure.

4 DR. KENNETH PORTIER: So Dr. Roby and

5 Dr. Legan, you weren't at that meeting but we probably

6 had the whole afternoon of discussion on that. That

7 doesn't mean that -- I remember it. I didn't

8 understand any of it, but I remembered.

9 Dr. Horseman.

10 DR. NELSON HORSEMAN: This could be an

11 instantaneous response. Is there a program or some

12 follow-up to that? I think that's the critical issue

13 in this, is whether given that discussion at the April

14 meeting, has the Agency program into their planning

15 actually addressing this growth hormone issue? That's

16 all. I will --

17 DR. KENNETH PORTIER: Dr. Lowit.

18 DR. ANNA LOWIT: That's new. I would

19 just reiterate what I started out this morning around

20 9:00 or 9:15 that there are many investigators

21 worldwide who continue to do atrazine research on a

22 variety of topics.

23 I'm not aware that EPA is pursuing that

24 per se and certainly by 2011, those ideas will -- it's

25 highly unlikely it will be mature to an extent. It
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 229

1 would be -- the science is immature enough for moving

2 into the regulatory arena. But as that grows, we'll

3 continue to watch it

4 DR. KENNETH PORTIER: And I think if

5 there's one guarantee a year from now, there would be

6 150 more papers on atrazine out there for us to -- any

7 additional comments? Very good.

8 Dr. Lowit, do you think you got it from

9 this? I think all that discussion kind of came down to

10 your characterization a minute ago that no, we can't

11 tell you what percent is the threshold.

12 DR. ANNA LOWIT: At least we know why we

13 had trouble coming up with the value.

14 DR. KENNETH PORTIER: Okay. I think at

15 this point we're going to kind of change gears on the

16 other side of the table or the far end of the table,

17 and this side is going to get more involved in the

18 discussion and we're going to start talking about the

19 water sampling strategies. So if you will read -- I

20 guess Mr. Nelson?

21 DR. NELSON THURMAN: Okay. The ultimate

22 utility of pesticide monitoring data for drinking water

23 exposure estimations in human health risk assessments

24 depends on how well the monitoring data characterize

25 the spatial and temporal variability of pesticide
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 230

1 concentrations in drinking water with an emphasis on

2 the upper end of the exposure distribution.

3 In addition, the method used to estimate

4 concentrations from monitoring data depends on the

5 duration of concern and how critical it is to estimate

6 peak concentrations.

7 A well-designed drinking water

8 monitoring study takes into account both spatial and

9 temporal patterns of exposure.

10 Please comment on the USEPA's

11 recommended framework for designing a monitoring study

12 by targeting the most vulnerable areas, targeting

13 seasonal times for more intensive sampling, basing

14 sampling frequency on the toxicological exposure

15 duration of concern and using autosamplers to

16 supplement monitoring data.

17 DR. KENNETH PORTIER: Dr. Coupe.

18 DR. RICHARD COUPE: First off, let me

19 apologize. You think after sitting here every week I

20 have something profound to say.

21 I really don't. I just want to

22 compliment the USEPA in the April meeting. It sounded

23 like things were disorganized and this time, it looks

24 like we're on target. So what we want to do is compare

25 it to treatment with atrazine, make some dose that does
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 231

1 not do anything to rats or whatever.

2 Sampling for atrazine in the water is

3 really --

4 DR. KENNETH PORTIER: Dr. Coupe, would

5 you pull your mic a little closer? I think people in

6 the back might be -- they're not able to hear you.

7 DR. RICHARD COUPE: Okay. Compliments

8 to Syngenta, too. I think Paul went through his

9 presentation pretty quickly. They've kind of lowered

10 the impact of Syngenta on the time frame, but he had

11 some really good information in there and they had some

12 really good suggestions even when we look at it

13 critically, you did a nice job on that.

14 Just a little note, I saw on the heading

15 for Section 7.2 as a General Strategy for Designing a

16 Monitoring Study to Characterize Drinking Water

17 Exposure and that's a little different from the stated

18 purpose on the Issue Paper in Section 3.7.3.2 that

19 gives the rationale for sampling source water versus

20 finished as we get more protection.

21 I think if the purpose of the monitoring

22 program because it is truly estimating exposure, then

23 you might have to sample the finished water. It's

24 probably more appropriate.

25 The work I've seen generally shows the
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 232

1 statistically reduction of atrazine between the source

2 water and finished water, although there's atrazine

3 still left in the finished water after it goes through

4 the treatment plant most of the time.

5 But I would -- in general, I would like

6 to see the source water completely sampled. We could

7 only do one but if you want to, you'd be able to go

8 back and do a study on the population that's drinking

9 the water and probably even the finished water.

10 So this question is broken into four

11 parts, so it's targeting and monitoring the most

12 horrible areas. Yeah, this is certainly appropriate as

13 an additional method for inclusion here, inclusion into

14 the monitoring approach.

15 So right now, it's included when the

16 community water system trigger currently 1.6 parts of a

17 billion in the Source Water Drinking Act and that's a

18 good one. So including, when we borrow the ones, using

19 the models and looking in front of a community water

20 system certainly is a way to do it.

21 Additionally, I just looked at where

22 atrazine is used, applied at the old layer of the cross

23 drainage bases and see which bases have an awful lot of

24 atrazine use in them and make sure that they're

25 included in the variability model and also Source Water
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 233

1 Drinking Act.

2 As I mentioned before, agriculture is

3 not a static enterprise that can change quickly, much

4 more quickly than you would think and in any given

5 year. So any monitoring program I think needs to have

6 a little flexibility to be able to move around.

7 The second part of this target intends

8 at sampling to the high occurrence period. I think

9 that's definitely a good idea and you get the most

10 tainted blood by sampling one that have pesticides out

11 there.

12 But in addition to following up on that,

13 you can also target the timing and the amounts of

14 samples of a tiered approach based on expected

15 concentrations in the screening. So as we are

16 targeting certain vulnerable areas, you can also target

17 your sampling regime and having that flexibility with

18 who's collecting the sample.

19 So if you think about it, atrazine in

20 water can be explained by sort of a function that's

21 kind of like source strength which is a function of

22 time. So that the amount of atrazine that's been

23 applied and even how long it takes for water to move it

24 off, and then the second part would be hydrology.

25 Using these two terms, you can predict pretty close to
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 234

1 where your atrazine is going to be moving into your

2 screening.

3 You can also use things like the low

4 percent of use. We've talked a little bit about how

5 well characterized atrazine is, how many studies there

6 are of it, the ecological part of it.

7 But man, there are a lot of studies on

8 the safe transport of atrazine, so it's pretty well

9 characterized. We know how much atrazine is going to

10 move off, know how much water moves off, so you kind of

11 look at it in the backwards way and say, "If I have

12 this much atrazine being applied in this basin, how

13 much are going with my range of concentration?" They

14 kind of worked those anyway.

15 And then you can do sampling based on

16 flow, so I think Syngenta had a little suggestion of

17 this. You can kind of look at--we've gota small screen

18 and it's really flashy. Then you could sample when it

19 first comes through and you got a small screen.

20 If you got a larger screen and the

21 screen takes a long time to reach the peak flow and it

22 takes a long time to go down, you need to sample a lot

23 less. One the smaller screen, they up and go down on

24 one day or two days. You could actually set your

25 sampling regime to sample twice the first day, twice
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 235

1 the second day and give you a little bit more

2 characterization on how far up that sample was.

3 If you take a sample in the middle of

4 the day, facilities water base, if we dug in that, it

5 would give you the peak concentration. It could change

6 quite a bit. The third part and fourth part really was

7 base sampling frequency on the toxicological exposure

8 duration of concern.

9 That's a good idea if we actually knew

10 what the duration of concern was. And then, use other

11 samplings to collect data for exposure of impurities,

12 that's a really good idea if you can do that, but the

13 only thing I wanted to point out here was that you

14 actually don't even need to get that complicated.

15 If you have a water treatment plant

16 operator who's collecting the samples for you, you just

17 have them collect one sample everyday at the same time.

18 He just has to go to the water tap and pour it into a

19 bottle. If he could just put that in a refrigerator or

20 a freezer, you could just hold on to the sample and

21 decide later if -- I want to look at this four-day

22 period, so I'm going to take these four samples and

23 post it together in a subsample and I'd get an average

24 concentration on those four days, or I could take one

25 sample every four days and something like that. You
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 236

1 can do a lot more.

2 You don't need to be as complicated with

3 all the samples if you don't have the opportunity.

4 Usually, automatic samplers is a little bit more

5 tricky, so you need a little more experienced personnel

6 to -- that's all I have at this point.

7 DR. KENNETH PORTIER: Dr. Lee.

8 DR. HERBERT LEE: I don't have too much

9 to add to that. I just want to reiterate something we

10 brought up in the April SAP meeting, which is if you

11 are trying to estimate shortened time periods or trying

12 to estimate an area under the curve or some sort of

13 average over time period, you really need at least sort

14 of two points per time period unless you're going to

15 use some really enhancing modeling, which we could talk

16 about in a bit.

17 So the basic idea is if you can get two

18 samples per time period, that will get you pretty much

19 all the way there, not quite but most of the way there.

20 And so, the idea of basing a number of samples on the

21 exposure duration concern seems like a really good

22 idea.

23 DR. KENNETH PORTIER: Mr. Stone.

24 MR. WESLEY STONE: Yeah, I don't have

25 too much to add other -- one point that Dr. Henley
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 237

1 brought up yesterday was source of the water is very

2 critical, whether -- and it's something that I had

3 overlooked. I've looked at the AMP data as well as the

4 AEEMP data.

5 But whether it's flowing, static, I

6 think you had bio up there, that's going to be critical

7 as far as designing your plan to get representative

8 sites that you can then relate to other clean water --

9 or excuse me, drinking water and clean water supplies.

10 The other thing in the sampling plan was

11 the period of application or the growing season. And

12 typically if you think about that in terms of corn now,

13 you should be looking at April through August for your

14 sites, start turning south.

15 You're going to need to move out a

16 little earlier in the year. The case in point would be

17 -- I think I've seen data from one of the studies. I

18 think it's ATEMP sites in Tennessee that April wasn't

19 quite early enough to characterize a chemograph there.

20 My colleague is from Mississippi, so he might have to

21 move further south.

22 I think they've covered the issue about

23 the exposure duration. Just to add to what Dr. Coupe

24 was saying, you could consider using mineral assays at

25 the stream level if you have multiple samples to look
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 238

1 at it and see which ones you may want to target.

2 Having done some of these transport studies, it gets

3 frustrating to be sampling a high intensity when you're

4 at base level for a long period of time.

5 But also note it's very hard sometimes

6 to capture when things missed its off of the function

7 of the street sites.

8 The only thing about the other samplers

9 having used them, I do see that they were a great help

10 in the AEEMP study, adding to the data by triggering

11 on, I believe it was flow and the data deposits.

12 I think the point that Dr. Coupe brought

13 up about it essentially different because there's a

14 facility there. Maybe they're not -- may not be as

15 needed. And whenever you involve more technology,

16 there are robots collecting samples.

17 Odds are pretty high that something will

18 go wrong when you don't want it to go wrong and that's

19 what we're seeing. That's all.

20 DR. KENNETH PORTIER: Any other

21 comments? This is getting pretty I wanted to point

22 out when I've talked to these guys about this thing,

23 one of the things that comes up is that what you're

24 really proposing here kind of deviates from the typical

25 sampling designs that we've done probably over the last
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1 50 years which is to take your sampling effort and kind

2 of uniformly distribute it over the growing season and

3 tell the operator, "Give me a sample every seven days."

4 But what we're really seeing now is if

5 you're going to take that sampling effort, you want to

6 be smarter about where you want to put it. And in this

7 situation, the amount of data, amount of understanding,

8 the amount of modeling that's going on for these

9 situations means that we're very smart about knowing

10 what goes on. And we really don't want to put samples

11 at times and situations where we expect nothing to

12 happen.

13 Why would we want to spend our effort

14 there? And so, all of these things you're recommending

15 seem to indicate that you're moving toward a much more

16 -- what statisticians would call an adaptive sampling

17 design and the adaption means you're adapting your

18 sampling to which you know or expect in the system.

19 And a model doesn't have to be perfect for this to be a

20 very good sampling design.

21 In risk assessment, you're working on

22 probabilities and all we're saying is that when you're

23 doing your sampling, you're going to be working on the

24 same probability. So there's going to be a chance that

25 you've going to miss a peak every once in a while.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 240

1 But maybe from a public health point of

2 view, that's not as critical. It's not wasting a lot

3 of dollars, on the other end, sampling and tiring up

4 your workforce collecting samples that don't produce

5 results. So I'm personally glad to see this moving in

6 this direction.

7 Any additional comments on the sampling?

8 Okay. Nelson, does that kind of answer your question?

9 I see a smile, so --

10 DR. NELSON THURMAN: Yes.

11 DR. KENNETH PORTIER: Let's go on to

12 Question 4.2.

13 DR. NELSON THURMAN: Okay, there are two

14 parts, so I'm going to read one part at a time so you

15 could actually ask two different things.

16 Please comment on the Agency's proposal

17 to use chemograph shapes (number, duration and

18 magnitude of peaks) to manage Community Water Systems

19 with more intensively monitored data sets.

20 In particular, the Agency is interested

21 in recommendations on approaches for matching

22 chemograph shapes given the loss of short-duration

23 peaks that occurs with less frequent sampling.

24 DR. KENNETH PORTIER: Mr. Stone.

25 MR. WESLEY STONE: You know, chemograph
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 241

1 shapes influenced by many factors other than the

2 watrershed characteristics. They're influenced by

3 factors that vary annually such as precipitation,

4 amount of pesticide applied spatially. And so, you can

5 have chemograph shapes, the magnitude, length of -- the

6 foresight varying from year to year.

7 So it's not really something that I've

8 ever considered kind of a fingerprint foresight or an

9 identifier. So the concept of trying to match to that

10 -- when I first read the question, I never really ever

11 considered it before and I thought, "Well, maybe that's

12 something we should look at."

13 The other thing with the chemograph

14 shapes is the question itself kind of brings up the

15 larger questions. You don't know if you're missing

16 something when -- you have a beautiful match between

17 high frequency, high -- a site of sample with high

18 frequency to less frequency but it still didn't tell

19 you if you missed them.

20 So alternatively, I would think more in

21 terms of matching these sites based on first, the

22 source water where there's flowing or static or

23 whatever, any hydrologic information that will hold up,

24 water shed, pesticide use, climate, precipitation

25 characteristics, as well as their geographic locations.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 242

1 And you create these groups because you

2 should be able to get a better understanding of what

3 you're looking at there and getting an idea of when

4 these sites fall into these troops, what the magnitude

5 of a less frequently sample site might miss.

6 In case of static waters, I think it's

7 also important to look in terms of reservoirs and in

8 terms of retention time, turnover time because if

9 you're looking at a site's impoundment that actually

10 it's a low dam or something that the water is flowing

11 by quickly, it may behave like a stream water, so

12 residence time is very, very critical. That's all I

13 have.

14 DR. KENNETH PORTIER: Dr. Coupe.

15 DR. RICHARD COUPE: I don't have much.

16 DR. KENNETH PORTIER: I can quote you on

17 that, okay. Dr. Lee.

18 DR. HERBERT LEE: I just want to add a

19 little bit more on the idea of matching the shapes.

20 This relates to the literature on registration and

21 functional data analysis.

22 In registration, it's lining up curves.

23 It happens a lot in biological events or neurons and

24 stuff like that where they have a pulse at a certain

25 time and you want to line up similar curves based on
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 243

1 them starting at the same time.

2 And I think there's the same idea here

3 in that you have an application and you have rainfall

4 and you get a particular pulse.

5 And so I would recommend in the

6 literature the functional data analysis. For example,

7 the Bransen and Silverman have two books out that are

8 listed in the April 2010 SAP report.

9 And so, those references are already

10 there. The key to keep in mind is that here, you have

11 missing data. You know that people are only sampling

12 once a week. You don't have measurements for the next

13 six days. So you may completely miss the peak. So

14 when you're talking about an actual shape, you got to

15 keep in mind that you may not know what your shape

16 looks like if you're trying to match.

17 So I'm a little bit concerned from the

18 presentation. For example, when Figure 7.7 is on the

19 112 of the Issue Paper that's put up, it was discussed

20 as -- you still get the general shape, even sampling

21 once every seven days. But for that particular one,

22 you're really missing the main peak. You get the

23 initial peak but you miss the second peak, which is the

24 large peak of the two.

25 And so, if you're just looking at
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1 general shapes of chemographs, that one is going to

2 look quite different from what you actually want to

3 match it to. And so, just keep in mind that you have

4 missing data and you may -- that can affect.

5 You can still match things with only

6 partial data, but you're going to have more

7 uncertainty. You're going to have a range of different

8 shapes which could match. And so, you can build that

9 into your confidence assessments based on the ranges of

10 shapes which could match.

11 DR. KENNETH PORTIER: Anyone else who

12 wanted to comment? Anybody?

13 You know, when I think about this idea

14 of pattern matching as a statistician, I'm really

15 thinking of that shape as a function. It's

16 parameterized.

17 In 2007, we saw a lot of these

18 chemographs but they don't have the same shape. Where

19 they're different was in peak and duration or the main

20 body, area under the curve, all these kinds of things

21 and to me, you're never going to get enough data to

22 really do a great job of matching not just the pattern

23 but putting it in the right location and time.

24 And really, this pattern matching has to

25 link in with the modeling that's being done because the
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1 modeling is going to tell you kind of where it fits in

2 time and roughly it's going to give you half your

3 parameters and if you're lucky, the data you're

4 actually collecting and monitoring is going to help you

5 fill in the other half of the parameters to put the

6 shape into place.

7 I think I did for a little bit for them

8 in that typically, they're thinking kind of a little

9 more non-parametric the data in some kind of a

10 smoothing process is going to give me that shape.

11 And I tend to think more in terms of a

12 parameterization of a function that you're trying to

13 estimate those parameters for a certain location,

14 certain situation or a certain point in time. And I

15 find it much easier for me to think about it in that

16 way. Dr. Lee.

17 DR. HERBERT LEE: I think we're going to

18 come back to that because I think that's essentially

19 what the SEAWAVE model is doing. And so, we'll discuss

20 that in the following two questions.

21 DR. KENNETH PORTIER: So I think we can

22 move on to B.

23 DR. NELSON THURMAN: Please comment on

24 the strengths and weaknesses of using more intensively

25 samples data sets from Heidelberg and the Atrazine
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1 Ecological Exposure Monitoring Program for analysis of

2 flowing water and PRZM/EXAMS for lakes and reservoirs.

3 DR. KENNETH PORTIER: Dr. Lee.

4 DR. HERBERT LEE: Alright, so first, I

5 do want to say that the analysis given and it's deep,

6 with all the chemographs looking at weekly samples from

7 a daily analysis of Heidelberg data and AEEMP data very

8 nicely shows the problem of trying to draw inference

9 for an exposure when the interval is only sampled with

10 a single point per interval.

11 Sometimes you get lucky and your timing

12 is good and you get a good estimate, and sometimes you

13 get unlucky. You miss the peak and you just grossly

14 underestimate your exposure. So it is really important

15 to have true daily data when developing methodology, or

16 I guess on some flashy streams, you may even want more

17 than daily data.

18 But in general, having true daily data

19 is really important to be able to understand what

20 you're trying to estimate and be able to test the

21 methodology. And so, what I learned in the AEEMP data

22 sets are good for that, although they are representing

23 particular types of watersheds.

24 The PRZM/EXAMS modeling can also be

25 useful providing Halberd curves for simulated daily
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1 values. Again, that's giving a more parameterized

2 approach to giving a curve.

3 It should be noted that the PRZM/EXAMS

4 model is not necessarily true data. It's simulated

5 data, so any conclusions drawn should be taken as

6 approximation. But they certainly would be expected to

7 give more accurate results than say linear or stair8 step interpolation of less frequently sampled data.

9 There is a substantial literature on the

10 use of pure models to augment physical data and were

11 properly calibrated, so meaning adjusted to match the

12 available partial real data. In the population, it can

13 be very good, and so I think that the concept in using

14 PRZM/EXAMS is a good one with a lot of backing in the

15 statistical literature.

16 However, my understanding -- and I think

17 my colleagues can contest to this further, is that

18 PRZM/EXAMS is not built for later reservoirs and that

19 may not be the right model. Dr. Henry brought up the

20 very late data set and that might be a more relevant

21 one for reservoirs, and depending on what the reservoir

22 is. Some reservoirs are essentially stream-based and

23 there's a dam or something.

24 And so, they do exhibit a behavior more

25 similar to flowing water whereas larger reservoirs
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1 generally have multiple input sources that would be

2 blending different inputs, different concentrations of

3 pesticides that also tend to have exposure over time.

4 And so, the pesticide concentration tend

5 to be much more smooth, lot less spiky in a larger

6 reservoir compared to a flowing stream, which is what

7 most of the data we've been looking at as also flowing

8 stream data. In such a case, the sparser such as

9 weekly sampling may actually be representative enough

10 of the overall chemograph as was demonstrated by the

11 Perry Lake example that Dr. Henley showed.

12 So you can think of that maybe in those

13 cases and you had to take it in a case by case basis.

14 But for certain reservoir base systems, weekly may be

15 sufficient.

16 On the other hand, you can also think of

17 -- if we're always thinking about flowing water sources

18 that can be seen as a conservative approach, being able

19 to predict the peak of the flowing water source will

20 generally then -- whatever methods will work for that

21 should give you good enough results as well on the

22 larger reservoir.

23 DR. KENNETH PORTIER: Dr. Coupe.

24 DR. RICHARD COUPE: Is it always in

25 alphabetical order? Sorry. So just a comment on the
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1 Heidelberg data, certainly it's a great data set and

2 one that's used, but it's kind of unique in the sense

3 that you never see a scale difference in it. Usually,

4 when you go from a very small base to a larger greater

5 base, you have a reduction in the load or the percent

6 of use there.

7 Well, now there's not a reduction there

8 but a reduction in the amount that's coming because you

9 incorporate more areas that are not agricultural.

10 In the Heidelberg data set, they

11 actually manage to keep the almost 80% to 90% of the

12 agriculture from the very small base into the larger

13 ones. You kind of miss out on that scale difference.

14 There's a little -- we don't match up always with some

15 of the stuff we see elsewhere.

16 Most of my comments, I think I'm going

17 to stay out with PRZM/EXAMS or PRZM -- I have used PRZM

18 and it's a very detailed model. It's not an easy model

19 to use. There's another talk about using SWAT. I

20 mostly use SWAT. SWAT is a nice model.

21 I don't know that you want to or have

22 the people available to put together a PRZM or a SWAT

23 model on all of the reservoirs that you have in

24 Midwest. They're very data-intensive, take rainfall

25 slower. They're just taking an awful lot of work to
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 250

1 get going.

2 Additionally, I don't really think you

3 need a model of that scale to do what you need to do.

4 One thing you need to keep in mind with reservoirs is

5 to make reservoirs different.

6 So it makes a big difference whether

7 it's a small -- one of the river reservoirs, or it's

8 just a reservoir with a low head dam and the water is

9 backed up, or whether that only has a small retention

10 time of just a couple of days, or the large reservoir

11 which has a large retention time of months and years.

12 Or even they some that are offstream storage reservoirs

13 where they can pump from the river into an offstream

14 storage that they could control when the water comes in

15 the reservoir.

16 To kind of finish that up, I think you

17 could actually get a better idea of how a reservoir --

18 how pesticides act in the reservoir by just looking at

19 random basin characteristics and reservoir

20 characteristics, potential time and size, those kinds

21 of things.

22 I think you can do a much better job

23 with that given the idea that reservoir tends to lower

24 the peak concentration and attenuating concentration.

25 So given the reservoir's -- the different reservoirs
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1 and streams, I think you have a much better opportunity

2 to use a model of the reservoir to do a screen. You

3 should be able to do it fairly easy.

4 DR. HERBERT STONE: I'm going to leave

5 the reservoir stop with Dr. Coupe. I'm going to just

6 talk about the Heidelberg and just kind of switch back

7 and forth. Heidelberg data set is a very good data

8 set. I've used it in the work I've done with wash

9 aggressive pesticides. It's very intensive.

10 There are some limitations in fact that

11 only represents a select geographic area that atrazine

12 was used in, and this is kind of highlighte when the

13 AEEP -- and I always get stuck on the acronym. It's

14 too easy, but I think they understand what I'm saying.

15 When it spread throughout the entire

16 corn belt, one thing that's very, very important to

17 understand is that it highlighted some watershed

18 characteristics and I'm specifically talking about the

19 depth to limiting or a restrictive layer that really

20 hadn't been thought of or seen, and that's an issue of

21 not having a certain data.

22 Certainly, the folks in Northwest or

23 Northeast Missouri knew about it and wrote quite a bit

24 of papers about it. I mean, that's a case in point

25 that Heidelberg may not represent all of the areas, all
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1 the water supplies that could potentially be that and I

2 think that's a limitation that if you look at the

3 Heidelberg and draw conclusions, you run the risk of

4 being overconfident, which is being shown here.

5 DR. RICHARD COUPE: Let me add one more

6 thing. Some USCS people have done work on pesticides

7 on reservoirs, especially atrazine.

8 DR. KENNETH PORTIER: Anyone else who

9 want to comment? I don't know why the physiologists

10 aren't just jumping out.

11 Okay, let's -- question?

12 MR. WESLEY STONE: Actually, I have a

13 couple of follow-up questions. Dr. Coupe struck me

14 because we've been talking about size of the basin and

15 I'm wondering, is it really the size or is it the land

16 use? Should that be your focus?

17 DR. RICHARD COUPE: Well, I think you've

18 got to do both-- I mean unfortunately -- I mean, the

19 size of the basin is going to control the water. The

20 land use is going to control the input of the atrazine.

21 So that's why I had to two functions, the source

22 strength and how the hydrology relates to the basin.

23 You probably need both.

24 MR. WESLEY STONE: This is Wes Stone.

25 One of the first iterations actually looked at basins
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1 or reservoirs. In the percentiles, it largely aired on

2 being biased as far as underpredicting because the

3 residence time.

4 Reservoirs may not have a sizeable peak

5 but they can carry their concentrations for a longer

6 period of time in the stream.

7 So being in terms of the chemograph that

8 it's a wider and a sharp peak, but you can look at that

9 as well. You might have some insight into those

10 characteristics.

11 DR. NELSON THURMAN: And then one last

12 follow-up. One of the reasons we haven't focused too

13 heavily on Perry Lake is because it never really got

14 concentrations greater than four parts per billion, so

15 didn't really think there was a high exposure example.

16 I know we're aware of the USGS monitoring and weekly

17 sampling for some reservoirs, but in terms of anything

18 in between there, that's one of the problems we've

19 struggled with on that. So if you're aware in

20 providing a report of anything that might be more

21 frequent than that, we'd appreciate that.

22 DR. RICHARD COUPE: So reservoirs are

23 really ugly animals so no one really wants to do any

24 work there.

25 DR. NELSON THURMAN: I've noticed that.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 254

1 DR. RICHARD COUPE: There are a lot of

2 issues involved with the reservoir sampling, where you

3 sample, what you sample. It's very difficult to know

4 because there's not a lot of work has been done there.

5 DR. KENNETH PORTIER: Why don't we just

6 finish Question 4.b?

7 DR. NELSON THURMAN: You know what, I'd

8 like that. Please comment on the strengths and

9 weaknesses of proposed -- wait a minute, I think there

10 may be -- okay, I might give you the preamble part of

11 that question to put some context.

12 Once the magnitude and duration of

13 exposure of toxicological concern are identified, USEPA

14 will determine which methods to use to analyze the

15 existing atrazine Community Water System monitoring

16 data for use in drinking water exposure estimates.

17 Based on the April 2010 SAP recommendations, the Agency

18 is focusing on regression-based modeling combined with

19 random function modeling.

20 For example, WARP model coupled with

21 kriging the USGS SEAWAVE-Q Model.

22 Please comment on the strengths and

23 weaknesses of these proposed approaches and provide any

24 recommendations for improving the model applications.

25 DR. KENNETH PORTIER: Dr. Lee.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 255

1 DR. HERBERT LEE: Well, really the

2 easiest thing to do is just sampling twice for each

3 period of interest, then you're fine. If they conclude

4 that the duration of interest is 14 days or longer,

5 then we've got two samples per interval and we can just

6 go ahead with everything and not worry about modeling.

7 On the other hand, supposing that we get

8 an interval of concern which is shorter than 14 days,

9 then we're going to need to go to some sort of modeling

10 and the idea of combining deterministic model like

11 SEAWAVE or PRZM, PRZM with a regression-based model

12 like WARP appears to be the most promising approach to

13 deal with sparse data with flowing water, which is the

14 scenario that these models were designed for.

15 This approach makes use of information

16 about halfway as an aggregate data that are not

17 incorporated by purely statistical methods such as

18 creating our neural networks. And so, bringing in more

19 information should give you better results of course if

20 you use the models correctly.

21 On the other hand, combining a model

22 such as WARP with a purely statistical method like

23 kriging will still give better interpolations than

24 simple methods like a linear interpolation but it fails

25 to make use of the information about the expected shape
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 256

1 of the concentration curves that could be predicted by

2 deterministic model.

3 Oh, there's a trade-off in the

4 difficulty implementing these methods. It would likely

5 be easier to implement a combination of WARP and a

6 relatively simple statistical approach such as kriging

7 instead of combining WARP with a deterministic model.

8 So this may be more practical to do in a short amount

9 of time, or if say this needs to be done more out in

10 the field, I think it would be nearly impossible to get

11 WARP calibrated with SEAWAVE for every community water

12 system in the field.

13 Something like rigging doesn't allow

14 this small estimation of compost vents and that can be

15 useful for guessing what might be happening in between

16 observations. But please do note that empirical

17 diagrams are highly variable.

18 I saw you looking at empirical diagram

19 and you're estimating something from it and that's

20 sometimes the best you can do, but the variability in

21 variograms is much higher than most people think it is.

22 So one thing to do is you take a

23 variogram, generate data from it, and then fit the

24 empirical diagram and see what you get on that. If you

25 do this ten times, you'll get ten weekly different
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 257

1 variogram estimates.

2 DR. RICHARD COUPE: That's fun to do.

3 DR. HERBERT LEE: Okay, it can be

4 enlightening, let's put it that way. Slide 94 in the

5 presentation shows -- it shows looking at different --

6 doing rigging at different sampling intervals and this

7 shows -- for the interval, you get some instant results

8 but when you get to 16-day sampling, you hardly have

9 anything there. I think this really highlights the

10 need for doing joint estimation of variograms. You can

11 learn a lot about the shapes and the correlation

12 structures approximately more than one water system at

13 a time.

14 I think it's not necessarily that

15 they're all going to have the same shape but

16 correlation structures will be more similar across

17 different watersheds than individual shapes are.

18 And so, I think there is a lot more to

19 be learned by providing information in correlation

20 structures across watersheds and that's going to be

21 really important to get good kriging estimates because

22 you need to have that correlation structure to get

23 anything out of that 16-day sample. And if you do have

24 a good estimate, what you're going to get there is

25 really Y-competence bands.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 258

1 You'll get the same mean as you got

2 right there but your confidence should be a lot wider

3 and that should give you some more reasonable way of

4 estimating what's probably exceeding a threshold over

5 four days or something like that.

6 The last thing I want to say about

7 rigging is that correlation structure here seems

8 clearly non-stationary. Earlier I asked a little bit

9 about the Galian correlation structure that was

10 estimated and I didn't think that that was appropriate

11 for the peak. And on further the question, I think

12 what's going on is that it is non-stationary.

13 Around the time of the peak, it has one

14 correlation structure and then sort of the tails which

15 comprise much of the curve where you're getting very

16 low levels or close to zero or zero levels, it's a

17 different correlation structure. It's very smooth at

18 these low levels for most of the time period of

19 sampling.

20 And then, there are these short

21 intervals where there are peaks. And so, the

22 correlation structure is actually quite different

23 around the peaks than it is for the low level areas.

24 And in fact, you got a lot more points at the low

25 level.
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1 It's driving you to estimate a much

2 smoother correlation structure than you want around the

3 peaks. And so, if you use that sort of correlation

4 structure, it's going to underestimate the peaks.

5 So you may want to actually estimate a

6 correlation structure from shorter snippets of the

7 chemographs concentrating more tightly around the peak

8 areas and use that correlation structure.

9 You could use that for the whole thing.

10 It will give you sort of wider bands than you need on

11 the boring areas. But those are the boring areas.

12 You're not really worried about it as much anyway.

13 DR. KENNETH PORTIER: Dr. Coupe.

14 DR. RICHARD COUPE: So I'll leave the

15 fun stuff to Herbert. I don't have too much to say.

16 Not to be a naysayer but after this week, the genesis

17 of toxicology is coming up where there is a concern on

18 -- we know it's less than 90 days, probably more than a

19 day.

20 I think -- I think if you go forward

21 with the dark wave sampling that we're talking about,

22 you can actually lead the way for toxicology. You can

23 actually have the characteristics of the atrazine in

24 these streams and community water systems already done

25 by the time they come up with the duration of concern.
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1 And so, they'll be able to move right into it.

2 DR. KENNETH PORTIER: Mr. Stone.

3 MR. WESLEY STONE: I'm going to talk

4 more just about WARP, SEAWAVE view and some of the

5 efforts with that. But I want to first say to watch

6 your aggression for pesticides to the WARP, the intent

7 there is this national, it's nationwide -- it's more or

8 less the guide, more intensive efforts of sampling to

9 show you where you might have a probability of a

10 seeding criteria, so you should look more closely at

11 it.

12 So in terms of guiding more intensive

13 efforts, it's a good approach. Now, it is national to

14 look at in terms of what you're keying in perhaps. It

15 would be more appropriate to scale it to the area of

16 interest such as corn mill and that's been talked about

17 and the progress is on its way. It's being worked on.

18 But a part of that would be adding in

19 the data from the AMP program and we'll try to do that.

20 But I think that the issues there, it's trying to get

21 the watershed ancillary data.

22 This is a huge enough data when you add

23 in all the 40, 50, 60 traditional variables for all of

24 the water supplies together and put them all into one

25 package.
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1 Earlier on in work development, I think

2 they looked at doing data simulations report and it's

3 in the references I believe. I'll make sure it gets

4 there. It's back in Crawford 2006. They've built the

5 time series model and that simulated daily pesticide

6 concentrations.

7 They did use the Heidelberg data set as

8 part of the development and checking of this model.

9 And it performed fairly well but one of the keys in

10 that effort was that their intent was not to predict

11 daily concentrations. They wanted to just do that in

12 order to look at long term trends and that's the key.

13 Dr. Vecchia went on to develop the

14 SEAWAVE-Q models that we've talked about more recently

15 and again, the intent of that model is to look at long16 term trends. It does generate daily pesticide

17 estimates but it's more intended to look over trends in

18 years.

19 The next logical step would be to go

20 back or revisit the joining of those two models again

21 then maybe change the focus back to more of a data of

22 the time set. That has a lot of promise.

23 One of the key weaknesses I think Dr.

24 Henley brought up yesterday is that these models, the

25 empirical models are based on the data collected on the
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1 stream and if our data that we're using in the models

2 is not adequate to address the short-term exposure,

3 then the model is not going to behave that well.

4 So if we're looking at short-term

5 durations, the models are set to do that such as WARP

6 and its models.

7 Adding in data such as the data which is

8 collected on weekly basis will help it, and that will

9 certainly key it that way, but that's one of the

10 caveats, it's making sure. And Dr. Lee was referring

11 to that, using it appropriately in the discussion.

12 To build in this adaptive or iterative13 as this data becomes available and incorporating them

14 back into the model's improvement and to keep this

15 phase going. That's all I have.

16 DR. KENNETH PORTIER: I've been giving

17 this some thought, too. This is Kenneth Portier and I

18 wanted to kind of read what I've got here because it

19 took me a while to kind of put this altogether. So I

20 recognize that the focus of the exercises and atrazine

21 concentration, modeling atrazine concentration and

22 community water system source and output of treated

23 water is the support that's brought risk assessment

24 goals, right?

25 But there's also been a discussion about
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1 how this exercise will inform broader monitoring

2 efforts around atrazine in the environment as we move

3 out. So you're going to do the risk assessment.

4 A lot of the data we're collecting is to

5 try to help set these thresholds of concern but at some

6 point, you can also move this out into how we monitor

7 atrazine in the environment to ensure from a public

8 health point of view that we're not exceeding these

9 levels.

10 And I would point out that -- with

11 apologies to Dr. Bailar that in this case, we have vast

12 and complex data but it's exactly the data we want.

13 It's not the data we don't have. We have the data that

14 we want here. We just need to figure out how to use

15 it.

16 At the April 2010 SAP, the Panel

17 recommended the use of statistical models incorporating

18 environmental explanatory variables and geostatistical

19 correlations that simulate chemographs for input waters

20 for community water systems.

21 With such a system, the exposure end

22 points of interest can be estimated in proposed

23 sampling procedures evaluated for their statistical

24 properties. It's clear for the presentations before

25 the Panel that both the AUC and Syngenta continue to
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 264

1 pursue this approach and we're happy to see that and we

2 continue to see progress.

3 It seems that a lot of time and effort

4 is being concentrated on properly modeling the

5 magnitude of concentrations in source raw waters

6 realizing that the source waters will eventually be

7 modified through the community water system treatment

8 process and that the ultimate exposure metrics for the

9 human risk assessments are derived from the levels of

10 atrazine in community water system output or drinking

11 waters.

12 It's comforting to see that again, EPA

13 and Syngenta are making advances in characterizing the

14 community water system's water treatment processes as

15 they affect of atrazine concentrations.

16 As EPA and Syngenta continue to explore

17 sampling plants for monitoring, some thought should be

18 given to actually using these simulation models and

19 community water system characterizations as part of the

20 monitoring process.

21 In particular, it's feasible that models

22 will eventually be accurate enough to provide

23 predictions of atrazine concentrations in source waters

24 to community water system for the coming crop season,

25 so I'm thinking in terms of being able to use these
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 265

1 models to kind of predict ahead.

2 Instead of requiring a community water

3 system to collect and analyze water samples in their

4 output drinking water stream in some predefined

5 frequency, say daily or weekly in the case of some

6 sites, it should be possible to use these models to

7 facilitate targeted sampling for periods of time most

8 likely to experience an exceedance. The sampling plan

9 might look something like the following.

10 A prediction of expected concentrations

11 for raw and drinking water for a CWS would be running

12 at the beginning of a crop year using available long13 term climate predictions within the area of the water

14 system and expected cropping practices in this area and

15 the CWS site-specific characteristics.

16 It's not exactly required that one know

17 the exact days of rainfall or atrazine applications.

18 Just having distributions for the expected number of

19 heavy rainfall events, expected duration and likelihood

20 that these events would coincide with new atrazine in

21 the field would be sufficient data probably for this

22 exercise.

23 For most community water systems, the

24 expectation would be that there would be no predicted

25 exceedances. Sites with high likelihood of exceedances
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1 might be required to implement interventions under

2 specified conditions to mitigate these expected

3 exceedances.

4 The CWS would be required to collect

5 water samples on a periodic basis, say daily, and store

6 them in a form that would preserve atrazine

7 concentrations. At the end of the season, the model

8 would be again run but this time, the actual rainfall

9 patterns and crop and chemical application and used

10 information would be used.

11 The post-season model run will identify

12 periods in the season just past where high atrazine

13 concentrations would have been predicted. The stored

14 water samples in this period are then retrieved and

15 analyzed to get actual atrazine concentrations in the

16 CWS output waters to determine actual concentrations

17 for exposure.

18 This information can then be used to

19 assess the water system's performance and the need for

20 further system management changes to ensure atrazine

21 concentrations in the future are below maximum

22 allowable concentrations. The plant follows closely

23 sampling protocols that are typically used for

24 obtaining information on rare events. It has the

25 benefit that sampling effort can be more easily
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 267

1 controlled and planned for.

2 The community water systems would know

3 that they would be responsible for say only X water

4 sample analysis for any one year so they could do

5 better planning.

6 But they would know exactly which time

7 period is going to be analyzed, so there is always

8 this, "I have to do a good job because I'm not sure

9 when they're going to ask for my data and grade me."

10 They would be responsible for collecting and storing

11 water samples for the season, but I think this is more

12 of a minor clause than anything else. This is an

13 economic issue that we heard of in the public comments

14 and it's of high concern for community water system

15 managers.

16 You control sampling effort and at the

17 same time focus the sampling on periods with the

18 highest likelihood of actually seeing measurable

19 concentrations. It acknowledges the fact that for most

20 community water systems and for most times, there is no

21 atrazine concentration in output waters.

22 It also allows for random selection and

23 analysis of stored water samples to estimate the extent

24 to which the models might be missing peak periods or

25 high concentration areas, if any.
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1 It's recognized that this method of

2 organizing sampling effort deviates from the

3 traditional approach which specifies sampling effort to

4 be distributed uniformly over a time period of

5 interest.

6 But it has been made clear in the

7 presentations before the Panel, and I'm talking about

8 the SAP in 2007, the SAP in April 2010 and today's SAP,

9 that this traditional thinking will lead to intensive

10 and expansive sampling with high frequency with the

11 expectations that the vast amount of the analysis will

12 indicate no detect concentrations.

13 While this may provide confidence to

14 many that there is little or no atrazine in these

15 community water system output waters, it also

16 represents an unnecessary expensive approach that will

17 not provide better results than this more focused and

18 less expensive sampling that I'm suggesting here, so

19 I'll include that in.

20 Sorry for reading the whole thing but it

21 was really -- I wanted to get it right. Any additional

22 comments? Yes, Dr. Horseman.

23 DR. NELSON HORSEMAN: Well, since you

24 can't challenge this side of the table, they have

25 something from this.
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1 I'm welcome to contribute. We did have

2 some information and quality information can be argued

3 and so forth that one or more environmentally generated

4 metabolites, in this case since hydroxyatrazine came up

5 a couple of times, might end up having some relevant

6 role.

7 And I haven't heard anything in the

8 discussion of characterizing these systems for that

9 kind of metabolite. I'm just wondering if -- and

10 obviously, we're not going to recommend based upon this

11 little bit of data that every sample being measured.

12 But even the possibility that the profiles, and we're

13 talking about these profiles in shapes and curves,

14 might be quite different for a metabolite that's

15 leeching from a validly generated source.

16 It might be quite different. Is there a

17 reason to include a subject to amount possibility of

18 measuring that sort of product? Did I make any sense?

19 DR. KENNETH PORTIER: Mr. Stone?

20 MR. WESLEY STONE: Yeah. I think we're

21 if I hear what you're saying correctly, is the

22 chemograph or degradation product the same as the

23 parent product.

24 And in some of the smaller systems, like

25 you said, I've seen certain soils in a degree and you
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1 get different phases of water movement through soils

2 and you may see more of one component coming through

3 than the other.

4 But I think that needs to be looked at.

5 And if that becomes something of an interest, one in

6 particular comes more interesting then you have to

7 target it.

8 DR. RICHARD COUPE: A comment to that9 Nelson we only regulate the atrazine, not the

10 components, correct?

11 DR. NELSON THURMAN: We are modeling for

12 all the chlorotriazine which includes atrazine,

13 cenozine and the DEA, DIAA and DACT. As far as I know,

14 hydroxyatrazine was not included in the monitoring.

15 DR. RICHARD COUPE: So as far as the

16 AAEMP program, which --

17 DR. NELSON THURMAN: It's part of --

18 yeah, the community water system data, we get more of

19 those metabolites as well, so we get it. All the

20 components are measured individually, at least for the

21 last --

22 FEMALE: 2005.

23 DR. NELSON THURMAN: Since 2005, yeah.

24 DR. RICHARD COUPE: Do you have DACT

25 also?
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1 DR. NELSON THURMAN: Yes.

2 DR. RICHARD COUPE: Well, just to finish

3 up, the metabolites of atrazine are not really easy to

4 analyze. The diethylatrazine is -- our labs had

5 trouble with it and its recoveries are 20%, 30%, 40%,

6 something like that.

7 So we don't have a really good idea of

8 what diethylatrazine is the one that you normally see

9 in surface waters. We don't do DACT on a regular basis

10 and we don't do hyrdoxyatrazine on a regular basis

11 also. They're very expensive and you take it little

12 different approach to them.

13 DR. KENNETH PORTIER: Dr. Chambers.

14 DR. JANICE CHAMBERS: Really, the reason

15 we're not speculating about the hydroxyatrazine is

16 because of that one study that showed extremely low

17 biological effects and the only real good explanation

18 is the hydroxyatrazine was never been studied and the

19 other studies was the actor, but I don't think there's

20 any database at this point to suggest that it would

21 really be worth studying at this point.

22 DR. KENNETH PORTIER: Dr. McManaman?

23 DR. JAMES MCMANAMAN: So would you

24 expect that the hydroxyatrazine would be really

25 different in the community water system given the way
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1 that water is handled versus the catchment areas

2 because it's dehydroxylated and that's a chemical --

3 I mean, that form of atrazine is being

4 generated by chemical processes rather than biological

5 processes, so it might actually be harder to measure.

6 But it might actually really give you some interesting

7 information with regards to what is going on.

8 DR. RICHARD COUPE: Yes, that's very

9 true. But I don't have any information

10 hydroxyatrazine. We didn't measure for that, just the

11 diethylatrazine. My only response to Dr. Chambers is

12 if we can dismiss that result out of hand, then we can

13 ignore the possibility of measuring this.

14 But I don't know that we can dismiss

15 that result out of hand at this point. That's a matter

16 of science whether or not we can.

17 Like I said, I'm not -- certainly nobody

18 is to construe this as advocating that, you know, run

19 out and measured this metabolite on every sample, but

20 they are getting some data that might inform that

21 analysis. That's all.

22 DR. KENNETH PORTIER: Dr. Chambers, a

23 follow-up?

24 DR. JANICE CHAMBERS: But what I'm

25 hearing from Dr. Coupe and others is that their
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1 technology is not really in place right now. It would

2 be very expensive and it really might not be a very

3 practical approach until there is some biological

4 information that that degradate is really atrazine.

5 DR. RICHARD COUPE: So I -- I'm sorry,

6 Richard Coupe. I'm just talking about our lab and our

7 lab tries to package together some 50 pesticides

8 together, so we're doing our best to do a solid phase

9 extraction. I think there are lots of techniques that

10 would be better as you go for it.

11 This is a really cheap one, cheap in the

12 sense that it's not very expensive but it's a good way

13 of doing it. There are other ways to do it.

14 DR. KENNETH PORTIER: Dr. Lee.

15 DR. HERBERT LEE: So my personal

16 speculation on these chemographs would be that in

17 flowing water systems like most of these water systems,

18 you're going to see similar shapes because it is a

19 flowing water system.

20 There's continual turnover in the water,

21 whereas the reservoirs would be a whole different ball

22 game and no idea what they're going to look like.

23 DR. KENNETH PORTIER: Mr. Stone.

24 MR. WESLEY STONE: At one -- and this is

25 kind of moving off subject a little bit, but we spent
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 274

1 several days talking about lab PC and QC in

2 experiments, quality assurance and that was one of the

3 things that we didn't touch on in framework of the

4 study for the sampling, is we can do a lot of work on

5 characterizing how well we're capturing a certain

6 exposure duration by changing our sampling frequently.

7 But there also needs to be a component that tells you

8 how confident you are in that sample that you took,

9 whether it's through applying some replicates. So I

10 think the QC plan should be used in all of that. I

11 mean, I don't think that was in there.

12 DR. NELSON THURMAN: This is Nelson

13 Thurman, I just want to add, I know they do -- Syngenta

14 have that analysis issues. I'm not sure we've got a

15 final report on the paper report but I know that's part

16 of what is collected on there.

17 DR. KANNAN KRISHNAN: I just want to

18 make a comment on the hydroxy metabolite. The study

19 authors used some of the survey results, I could be

20 wrong , their effort to see the monitored results when

21 they chose the percent that they were using in their

22 study, which was about 20% I think.

23 But again, stepping back and looking at

24 this assessment, essentially it's an assessment of

25 atrazine and you could within the context include the
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1 metabolites that are found within the mammalian system.

2 I don't necessarily see us leaving any more talks to

3 the current evalutation of the hydroxy metabolite, even

4 though that could be on its own.

5 DR. KENNETH PORTIER: Dr. McManaman?

6 DR. JAMES MCMANAMAN: Given the fact

7 that the hydroxyatrazine looks like it's implicated

8 from the mixture study, it would seem like it would be

9 something recommended to do. And given that the

10 fragmentation pattern of the hdyroxyatrazine versus the

11 chloroatrazine, mass spec would be greatly different

12 that it would just seem like a -- as a mass spec

13 analysis, you should be able to pick it up pretty

14 easily but I don't do that sort of thing.

15 I mean I do mass spec but I don't do the

16 kinds of things that you guys do. So just a comment

17 whether is it really -- could it be that hard to

18 develop those kinds of operating procedures that's the

19 expensive part of it, or is it you have to give an

20 analysis?

21 DR. RICHARD COUPE: Richard Coupe, so we

22 have a standard analysis to do it. I'm not a

23 laboratory person so I'm not an expert on the aspect

24 and we have analysis to do it. I just know that when

25 you add in hydroxyatrazine, you add a lot of ammonia on
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1 your analysis, so we have done a lot of that.

2 To kind of answer the question about --

3 I wasn't really sure. I was surprised when she said it

4 was a mixture from USGS.

5 I just quickly went through and looked

6 at the references and I'm not sure she could have

7 gotten all of those -- or the authors could have gotten

8 all of the mixtures from USGS references they put in

9 the table. There were some other papers that they had

10 listed as far as this where we have a mixture of only

11 one USGS and I think there were other mixtures. So

12 they might have gotten the hydroxy from someone else.

13 MR. WESLEY STONE: This was on the --

14 recently in the last couple of years, we spent some

15 work, Greg Ellsworth at USGS looking at treated and

16 untreated drinking water. And I believe

17 hydroxyatrazine is one of the components they're

18 looking at.

19 I could find the reference and try to

20 get it, if it's published. It could provide more

21 information about hydroxyatrazine, but they looked at

22 sampling from on the river, at the intake and then at

23 the finis. And then they also tried to time the

24 samplings and get to cohort so you have the same water

25 coming in and out, which is extremely difficult to do.
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1 It sounds easy but it's not. I'll see if I can find

2 the reference.

3 DR. JAMES MCMANAMAN: First I want to

4 apologize for even bringing this up due to the amount

5 of discussion, but I guess in the back of my mind, the

6 reason for bringing it up is I just hate for people to

7 be put in a position of an issue being brought up but

8 appears to be toxicologically an interesting outlier in

9 terms of the data we've seen.

10 I think we can agree to the stat, right?

11 And if there's any confirmation that -- if someone

12 asked whether you ever looked at it and the answer is

13 "no", that would be sort of a hard answer to get.

14 That's my own reason for bringing it in.

15 MR. WESLEY STONE: I think bringing it

16 up is actually very useful.

17 DR. KENNETH PORTIER: Dr. Lowit.

18 DR. ANNA LOWIT: Yes. Of course, the

19 one reason I didn't bring my bag of atrazine thinking I

20 would just sit here all day and take notes. In 2003,

21 atrazine risk assessment does have a component of it

22 where we're evaluated hydroxyatrazine. I don't know

23 that data off of my head. I don't see anyone in the

24 room who have knowledge -- you do.

25 There are some guideline type studies
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1 submitted for hydroxyatrazine. It's what we would

2 consider a limited database for food use but we've got

3 a good amount of data for a lot of other chemicals in

4 the world.

5 And there's at least one study from ORD

6 looking at I think the puberty onset of hydroxy, but I

7 do know that none of those studies have shown hydroxy

8 to be more potent than the parent atrazine. But

9 anyway, I have it referenced right here on the

10 computer.

11 DR. PENELOPE FENNER-CRISP: I do. I

12 have the RFD for hydroxyatrazine here. Its NOEL was

13 actually lower than the NOEL for the chronic number for

14 atrazine. It's 1.0 with an uncertainty factor of 100

15 is based upon results from the chronic carcinoegenicity

16 study in a rat and the end point are histopathological

17 lesions of the kidney.

18 But the tox profile isn't in here. I

19 was looking for the results from the double battery of

20 the registration but you don't have them in here

21 anymore.

22 DR. ANNA LOWIT: I think you need to be

23 careful about saying that the one is different than one

24 point at using the rat. I think they're in the same

25 line I think.
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 279

1 DR. PENELOPE FENNER-CRISP: Exactly, but

2 -- it's near it but the point is -- this conversation

3 has been, "Oh yeah, hydroxy is so much less toxic."

4 Then you look at the choice of studies for the RFD

5 andit's comparable. What id doesn't say anything about

6 is what you know about it in terms of being hormonally

7 hormonally active. None of those, it's just plain,

8 old --

9 DR. KENNETH PORTIER: That was Dr.

10 Fenner-Crisp and Dr. Lowit.

11 DR. PENELOPE FENNER CRISP: Oh, sorry.

12 DR. KENNETH PORTIER: Are you going to

13 bring up another topic here at 5:00?

14 DR. MCMANAMAN: No, I just want to make

15 a final statement about this. Don't ever ask for

16 comments from physiologists.

17 DR. KENNETH PORTIER: Dr. Legan.

18 DR. SANDRA LEGAN: I just want to

19 comment on what you said Penny. It may be that a given

20 chemical has a totally different effect at different

21 dose levels in different tissues.

22 So it doesn't matter that in the kidney

23 toxicity study or whatever, the dose -- how they drive

24 an RFD you said, they use that data. In the mammary

25 gland that's developing, it made it a totally different
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 280

1 -- you might get a totally different RFD.

2 DR. PENELOPE FENNER-CRISP: Well,

3 absolutely. You might -- well, if you have ever tested

4 this, this is some of the systems that Ralph has used,

5 you will probably get are very different numerical

6 profile when you do it for the parent and the DIADA. .

7 DR. ANNA LOWIT: I think an important

8 point that gets lost in the numbers is that the agency

9 has put some effort into thinking about hydroxy so we

10 haven't forgotten it or ignored it in any way.

11 DR. KEN PORTIER: And we've gotten all

12 of the topic. Somehow we got packed to the physiology.

13 We're finishing up the questions on sampling. Any

14 additional comments on Question 4 or any components to

15 report? Yes, Mr.Thurman?

16 DR. NELSON THURMAN: I'd actually like

17 to go back to Question 4.3 and just ask just a little

18 bit different because some of the recommendations I've

19 heard are things people are thinking about, some are

20 going to be taking a little bit to develop.

21 Given what Dr. Lowit said earlier that

22 we're probably looking at -- at least for determining

23 doing more frequent monitoring or how we're going to do

24 the monitoring, I know the recommendation, the points

25 of what you're talking about is.
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1 But in terms of some of the modeling and

2 some of these things we look at is possible exposure

3 estimation methods, possibly saying take -- here's what

4 we have, here's how we might estimate the exposures by

5 some type of a comments bound or a safety factor, and a

6 spring 2011 time frame. What would you -- would your

7 recommendations stay the same as what you've done or

8 what would you recommend?

9 MR. WESLEY STONE: This is Wes - How

10 exactly are you going to use these models?

11 MR. WESLEY STONE: In terms of doing

12 exposure estimation for the drinking water, there are

13 different ways of looking at it. A lot of it is going

14 to come down to what the duration of concern is.

15 If we end up with a short, much shorter

16 duration we have now and we're trying to estimate have

17 we -- and not knowing what the magnitude is, but do we

18 have sites that based on their existing weekly

19 monitoring may actually exceed the level of concern?

20 We'd like to do some way -- at least

21 doing a -- one of the ways we might do is a triage of

22 looking at we have sites based on how we -- given a

23 weekly sampling in terms of estimating exposures, we

24 don't necessarily want to do linear interpolation in

25 that regard.
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1 So we would probably be looking at in

2 terms of evaluating maybe as a first shot. Given the

3 monitoring we have on these sites, these high, more

4 vulnerable sites, are we seeing anything based on a

5 weekly monitoring that might suggest that we could

6 triggering.

7 Exposure duration is a concern because

8 we're not on anything that might lead us in one

9 direction. But if there is something that may be

10 exceeding that level of concern, it might lead us in a

11 different direction.

12 So I think that's kind of giving us a

13 way of how confident are we that given the estimates

14 that we might be exceeding or not exceeding whatever a

15 duration and timing comes up to be.

16 MR. WESLEY STONE: So let me ask in a

17 slightly different- this is Wes Stone again. So if you

18 run a model and it shows that a site has a higher

19 probability to perceive, what does that mean to you?

20 What is the subsequent end? What is the use of --

21 DR. NELSON THURMAN: I think it means of

22 a more intensive monitoring a little more intensive

23 focus on those sites.

24 MR. WESLEY STONE: Okay, and we were

25 talking about this before this morning to make it more
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1 obvious to ourselves. Now, to get back to your

2 original question, if your exposure duration as Dr. Lee

3 pointed out, it turns out to be anywhere from three

4 weeks or greater and I think we're pretty good. Once

5 that exposure duration starts getting shorter than

6 that, then at least in terms of the WARP models, they

7 would need to be reevaluated.

8 But the data that will support that type

9 of estimation, if it got down to a four-day, then we're

10 talking about we would need the data from your first

11 study to try 7.2 to go back.

12 So all of this is a feedback mechanism

13 as far as all are connected including data you're

14 collecting back into the models and development to

15 improve that. So in the time frame that you're talking

16 about, you're not having data by spring obviously.

17 So depending on what exposure duration

18 you come up with, you're going to have to look at-- I

19 haven't had a chance to talk with Dr. Vecchia on the

20 SEAWAVE-Q and what can be done with that. And moving

21 back to this page on where we talked about chemograph

22 shapes but it's loosely based on atrazine.

23 It's not giving you what you would --

24 there has to a refinement of that to actually zero in

25 on that daily estimate to better characterize that.
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1 And from earlier toxicology2 So break it down to a further -- in

3 terms of the corn belt, we can move there and hopefully

4 have something to use for at that time. But the other

5 parts, I just don't have an idea right now as far as

6 characterizing for those.

7 And one of the key unknowns with these

8 models, if you're doing a type of that exposure

9 duration and it's real down tight, it's really going to

10 change the model and throughout the plots data that

11 don't support that type of model.

12 Did that answer your question?

13 DR. NELSON THURMAN: Probably not.

14 MR. WESLEY STONE: Did I talk long enough

15 to confuse you?

16 DR. RICHARD COUPE: This is Richard

17 Coupe. I'll reserve the right to come back tomorrow

18 morning after I've thought about it.

19 But are you familiar with what Patagalan

20 did with the reservoirs and what Gould and Patagalan

21 did? That would be some low hanging fruit because they

22 developed a regression equation based on a number of

23 variables in the 90's. You should be able to pick that

24 up and move right with it.

25 DR. NELSON THURMAN: I'm familiar with
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 285

1 some of the work. I have to go back to --

2 DR. RICHARD COUPE: Yeah, that was a

3 while ago.

4 DR. KENNETH PORTIER: I think we're

5 reaching the witching hour here, ten after five. What

6 I'm going to do is I'm not going to end this question.

7 I'm going to allow a couple of these thought to

8 percolate over the evening and we'll revisit just

9 briefly in the morning just in case you --

10 A new question came up or whether the

11 trio on the end over here comes up with some new areas.

12 I think at this point, we're going to call it quits for

13 the day and we're starting again tomorrow morning at

14 8:30 with finalizing Question 4 and Questions 5 and 6.

15 Thank you very much.

16 (WHEREUPON, the meeting was recessed at 5:07 p.m.)

17

18

19

20

21

22

23

24

25
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 286

1 CAPTION

2

3 The foregoing matter was taken on the date, and at the

4 time and place set out on the Title page hereof.

5

6 It was requested that the matter be taken by the

7 reporter and that the same be reduced to typewritten

8 form.

9

10 Further, as relates to depositions, it was agreed by

11 and between counsel and the parties that the reading

12 and signing of the transcript, be and the same is

13 hereby waived.

14

15

16

17

18

19

20

21

22

23

24

25
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 287

1 CERTIFICATE OF REPORTER

2 COMMONWEALTH OF VIRGINIA

3 AT LARGE:

4 I do hereby certify that the witness in the foregoing

5 transcript was taken on the date, and at the time and

6 place set out on the Title page hereof by me after

7 first being duly sworn to testify the truth, the whole

8 truth, and nothing but the truth; and that the said

9 matter was recorded stenographically and mechanically

10 by me and then reduced to typewritten form under my

11 direction, and constitutes a true record of the

12 transcript as taken, all to the best of my skill and

13 ability.

14 I further certify that the inspection, reading and

15 signing of said deposition were waived by counsel for

16 the respective parties and by the witness.

17 I certify that I am not a relative or employee of

18 either counsel, and that I am in no way interested

19 financially, directly or indirectly, in this action.

20

21

22

23

24 MARK REIF, COURT REPORTER / NOTARY

25 SUBMITTED ON SEPTEMBER 16, 2010
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 288

0

0.09 117:10

121:24

0.77 40:1

0.79 39:22

0.87 121:24

1

1 13:7 59:24

1% 13:8 62:6 62:7

1.0 278:14

1.1 57:19 57:19

1.2 57:20 58:4

58:6 65:1 65:4

1.2s 180:3

1.3 227:22

1.56 183:4

1.6 232:16

1:15 140:2

10 9:11 20:1 87:1

180:8

10% 216:22

100 60:13 87:25

92:12 95:12

101:24 102:7

104:10 137:11

138:16 177:14

278:14

100% 26:21 29:13

100%--I 29:13

100mg 90:6 90:10

100mg/kg/day

117:8

104 119:22

105 27:1

11 91:2

112 243:19

12 157:2 163:9

163:12

12/12 6:19 6:24

9:13 92:10 95:22

120 26:16

1200 31:10

126 26:20

13 90:10 90:11

90:20 92:15

14 9:11 190:12

255:4 255:8

14/10 6:25 7:1

88:1 95:21

1400 31:10

1410 6:12 6:12

14-10 8:24

145.5 164:20

15 20:10 90:11

90:11 184:18

215:9

150 229:6

1500 175:13

150mg/kg 22:13

15-minute 209:5

15th 34:21

16 2:3

160 124:15

1600 176:11

182:20

16-day 257:8

257:23

17 90:11 90:11

18 61:25

1800 26:22 32:7

175:5 175:7

176:9 182:18

183:7 183:17

183:19

19 16:12 54:6

54:10 69:16

74:12 90:11

90:11 90:20

92:15 109:8

153:18

1987 152:7 154:14

2

2 24:2 24:3

2% 19:25 110:3

2.0 87:5

2.1 87:7 87:8

117:2 117:6

140:15 140:25

149:12 149:21

2.2 117:1 117:4

137:8 140:11

140:15 140:25

2.3 140:12

20 59:24 86:25

107:6 107:7

179:8 180:8

184:16 196:4

210:25

20% 7:25 15:11

222:7 271:5

274:22

20/80 9:18
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 289

200 31:7

2000 27:18

32:11 167:16

176:11 182:20

2002 166:1

2003 34:2 166:1

277:20

2004 70:25

87:11 87:23

117:6 166:2

2005 87:11

89:19 90:4 117:6

270:22 270:23

2006 71:1 261:4

2007 21:19

117:9 171:2

244:17 268:8

2008 153:15

2010 2:3 38:7

67:17 87:11 92:6

117:7 243:8

254:17 263:16

268:8

2011 34:8 35:6

35:17 221:23

228:24 281:6

20-planned 203:12

20's 86:12

21 106:9

215.7 164:20

22 88:25 90:20

24 154:17

154:19 156:12

165:2

24-hour 8:17

25 30:13 90:20

183:22 192:15

193:2

25% 211:4

28th 10:11

29 54:6 55:3

3

3 57:1 63:2

182:10

3.0 58:8

3.1 151:23 151:24

152:5 157:21

157:23 195:8

3.1.c 173:9

3.12 183:8

3.2 195:11 196:12

208:4 209:17

3.2.b 209:18

3.3 63:4 76:24

3.4 181:25

3.7.3.2 231:18

3.b 209:20

3:20 209:5

209:8 209:12

30 153:19

191:19 193:25

30% 211:4 220:8

222:7 271:5

30-40 98:5

31 39:22

32 76:23

324 55:2

33 89:5

350 98:3 98:4

354 40:20

36 76:23

362 42:25

37 61:25

38 54:8

4

4 90:20 280:14

285:14

4.0 65:7

4.1 209:13

4.2 209:14 240:12

4.3 280:17

4.5 182:1

4.b 254:6

40 8:19 260:23

4-0 127:16

40% 211:4 222:7

271:5

40-50 106:10

43 8:21

44 8:21

46 55:18 90:21

47 90:21

5

5 60:14 183:22

192:14 193:2

193:14 193:25

285:14

5% 59:22 60:1

5.3 196:14

5.5 152:13
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 290

156:9 156:15

157:11

5.6 159:25

5.8 173:11 173:22

174:9

5:00 7:18 279:13

5:07 285:16

5:10 209:12

5:15 209:12

50 8:16 94:6

94:21 94:23

95:12 105:22

137:12 239:1

260:23 273:7

50% 164:20 214:13

220:8

502 97:20

50mg/kg 90:9

525 164:5

58 64:9

6

6 27:2 182:1

285:14

6% 19:25

60 105:22

127:18 260:23

67 90:23

68 164:5

68-hour 179:25

7

7.2 231:15 283:11

7.7 243:18

71 174:9

75 30:14

77 26:19

8

8 18:4 30:14

190:11

8.72 121:24

8:30 285:14

80 19:24

80% 15:11 211:2

219:25 249:11

80:20 17:20

84 27:1

85% 27:1 196:4

87 20:12

87% 20:14

9

9:00 228:20

9:15 228:20

90 259:18

90% 249:11

90's 284:23

94 257:4

95% 60:1 179:21

224:23

97% 39:8

9th 15:21

A

A.10 117:14

AAEMP 270:16

abandon 113:21

abdominal 62:20

ability 37:14

40:12 42:18

49:22

able 5:5 12:25

13:10 21:25

22:16 65:23

75:24 89:15

101:4 101:5

108:11 114:3

114:16 132:12

135:23 136:1

168:6 169:19

171:7 208:25

218:23 231:6

232:7 233:6

242:2 246:19

246:20 248:18

251:3 260:1

264:25 275:13

284:23

abortions 43:25

about--when 74:21

absence 81:21

97:15 132:24

153:7 154:4

154:10 169:5

absolute 114:23

176:15 217:12

224:2

absolutely 83:5

135:1 148:17

207:24 280:3

absorb 55:25

absorbed 12:25

75:23 123:7

absorption
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 291

12:18 13:18 14:3

23:16 23:18

23:23 23:24

155:17 156:7

absurd 80:19

Academia 101:11

academic 101:15

170:21

accent 162:7

170:5

accept 179:15

accepted 142:10

access 5:9 125:20

125:21

accomplish 19:6

according

182:16 205:25

214:1

account 28:8 76:9

79:2 143:16

198:18 230:8

accounted 18:20

accounting

46:25 47:2 63:19

197:20

accounts 105:15

accumulation

134:14 163:15

163:18

accuracy 110:20

111:17 114:7

accurate 111:3

247:7 264:22

accurately 117:16

achieve 8:15 8:16

68:16 180:12

180:13

achieved 28:21

33:14 157:8

161:12 173:23

173:24

achieving 81:10

acknowledges

42:19 267:19

acknowledgment

59:1 61:20

acronym 251:13

acronyms 130:18

across 59:2

60:3 98:14 126:2

130:3 131:13

157:16 173:13

173:20 191:18

191:22 193:23

257:16 257:20

act 4:23 135:15

232:17 233:1

250:18

action 66:4 66:15

66:18 66:23

67:20 68:1

75:7 79:12

82:2 120:11

122:9 144:15

147:25 153:11

153:24 185:9

196:22 197:2

203:1 204:10

204:11

actions 122:14

122:15

activate 153:25

activation 207:3

active 27:22

39:25 83:23

109:13 145:23

146:2 146:16

155:5 279:7

activity 42:5

148:8 148:8

150:15 150:19

154:11

actor 271:19

actual 27:14 85:8

142:15 176:7

194:6 212:8

243:14 266:8

266:15 266:16

actually 8:3 10:8

10:12 11:17

18:15 22:23 31:3

49:25 50:18

54:11 66:23 70:9

80:14 106:2

110:1 112:5

116:1 116:9

125:19 130:20

132:22 137:20

149:15 155:7

157:16 158:3

158:15 161:11

169:24 171:1

171:25 172:9

172:18 174:18

176:1 177:21

185:6 185:18

186:16 189:7

191:8 191:21

192:11 193:3

205:24 207:25

209:16 210:24

211:4 221:1
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 292

221:5 221:10

221:15 221:24

222:9 223:6

227:20 227:24

228:2 228:15

234:24 235:9

235:14 240:15

242:9 244:2

245:4 248:9

249:11 250:17

252:12 252:25

258:22 259:5

259:22 259:23

264:18 267:18

272:5 272:6

277:16 278:13

280:16 281:19

283:24

actually--that

30:4

acute 68:5 70:17

adapting 239:17

adaption 239:17

adaptive 239:16

262:12

add 10:2 13:14

41:2 43:17

44:8 44:16

48:8 48:20 48:22

50:5 51:1 63:8

79:6 86:18 102:4

105:16 118:13

168:22 175:22

189:14 195:14

208:6 216:9

236:9 236:25

237:23 242:18

252:5 260:22

274:13 275:25

275:25

added 185:6

adding 225:3

238:10 260:18

262:7

addition 8:22

24:6 44:16

46:4 89:13

141:25 172:17

181:19 215:24

230:3 233:12

additional 5:17

5:18 46:21

47:7 50:2 76:5

78:5 86:21 96:12

133:9 134:6

146:11 175:12

186:8 216:15

229:7 232:13

240:7 268:21

280:14

Additionally

232:21 250:2

additions 63:7

additivity 122:11

145:25 146:1

address 12:22

21:25 22:16

25:24 41:8 66:25

80:7 80:19 91:23

100:4 101:17

110:10 120:24

121:4 142:15

142:22 146:12

190:8 262:2

addressed 40:8

43:11 60:21

91:22 119:5

127:17 134:5

147:12 185:9

198:5 210:4

212:9

addresses 122:1

addressing 101:12

228:15

address--well

45:19

adequate 42:4

50:7 50:15 50:17

262:2

adequately 47:2

80:1

adjusted 40:17

46:24 48:4 63:18

247:11

adjustment

48:25 49:7 163:3

adjustments 45:14

199:9

administer 122:3

administered 7:12

7:19 101:24

123:4 146:23

155:23 157:19

208:22

administering

208:11

administration

9:5 14:4

132:11 161:17

173:17

admit 167:13

ado 38:13

adopt 108:8

adopted 108:23
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 293

adrenal 25:3

33:11

adult 19:5

26:15 27:6 105:7

173:24 189:3

adulthood

137:24 138:5

adults 105:22

148:22

advances 264:13

advantage 42:21

46:1

advantages 197:17

198:1

adverse 29:16

39:1 47:4 49:3

64:3 66:2

66:11 82:3

96:8 96:11 143:9

196:24 197:3

197:5 198:9

199:24 211:15

212:1 214:4

214:11 222:25

adversity 198:3

advice 4:25 5:3

38:11

advise 108:1

Advisory 2:1

2:6 4:23

advocacy 198:20

advocating 272:18

AEEMP 237:4

238:10 246:7

246:21

AEEP 251:13

affect 11:11

13:11 28:24 49:8

70:12 148:9

150:19 157:17

217:13 244:4

264:15

affected 53:8

106:15

affecting 131:9

affects 11:19

110:12

affiliation 2:16

affirm 187:16

aforementioned

142:9 150:21

afternoon 2:11

119:15 123:10

140:7 180:13

183:15 209:6

228:6

afterward 207:20

afterwards

105:7 209:4

against 78:18

157:18 217:2

AgCal 48:20

age 26:19 41:12

42:5 47:23 49:14

80:4 88:18 89:22

107:9 128:1

200:25 217:9

aged 188:7 217:24

agencies 82:6

133:1

agency 35:2 41:18

83:11 131:1

133:9 140:16

140:21 148:6

152:13 158:20

168:23 173:21

174:1 187:2

200:2 200:6

205:21 213:12

223:7 228:14

240:20 254:17

280:8

agency's 5:2

36:13 58:7 87:13

96:4 99:16

117:13 117:16

147:9 155:2

174:5 201:9

240:16

agent 39:22

153:21 155:25

agents 101:21

141:10 141:13

141:22 143:13

201:3

ages 27:5

aggregate 38:1

255:16

aggregates 85:15

aggression

94:17 260:6

aggressive 35:9

164:9 251:9

aggressively

191:2

aging 27:4 141:20

185:18 188:8

189:6 213:2

216:19

ago 33:25 57:7
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 294

202:23 213:22

229:10 285:3

agreed 5:15 33:14

113:16 113:17

135:21

agreement 29:13

53:9 67:13 76:18

152:24 196:20

agrichemicals

45:20

agricultural

38:18 38:23 41:5

41:17 42:14

49:19 63:9

64:6 70:5

70:23 249:9

agriculture 45:19

233:2 249:12

ahead 17:17

23:6 255:6 265:1

aim 26:15

aired 253:1

Akana 4:4 4:4

5:20 5:22 6:5

7:9 7:10 7:21

9:21 9:25 106:20

106:21 107:23

125:3 125:4

126:23 127:3

127:17 137:1

137:16 137:17

148:16 148:17

149:23 150:1

150:2 150:23

150:24 174:7

174:8 176:21

177:24 182:16

204:6 216:5

216:7

Akana's 142:16

Akuna 47:10

al 27:18 28:9

30:2 30:20 87:10

101:23 117:6

117:9 117:15

121:18

Alcott 155:12

ALIC 6:12

allergen 122:4

allow 17:16

159:13 168:14

168:17 184:19

223:4 223:12

256:13 285:7

allowable 266:22

allows 162:23

221:25 267:22

alluded 95:21

163:7 218:16

alone 29:14 46:23

alphabetical

176:24 177:3

248:25

already 41:1 47:7

51:5 95:20

96:1 97:1 135:16

135:25 163:7

166:7 171:8

172:19 173:2

177:24 189:5

189:6 212:23

213:1 220:17

243:9 259:24

Alright 83:5

246:4

also--one 47:11

alterations 148:8

150:18

altered 189:6

alternative

101:18 166:18

168:5 168:7

169:1 204:1

alternatively

241:20

altogether 262:19

am 7:18 29:13

29:13 102:3

111:4 196:20

amended 137:14

American 2:19

ammonia 275:25

ammunition 212:20

among 91:17 146:1

146:3 146:6

165:18 206:22

amongst 53:9

152:25

amount 12:18

12:24 33:25

64:21 109:25

150:17 165:14

175:7 226:12

227:10 233:22

239:7 239:7

239:8 241:4

249:8 256:8

268:11 269:17

277:4 278:3

amounts 233:13

AMP 237:3 260:19
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 295

amplitude

202:24 204:21

212:22 213:4

213:8 214:14

218:17 224:20

analites 159:4

analogy 143:3

analyses 76:17

analysis 26:8

26:22 29:1

30:2 31:1

39:17 48:3

48:8 52:24 56:22

58:11 58:16

58:24 59:22

59:23 60:2 61:22

65:6 71:6

71:18 71:19

71:21 73:7

75:5 78:10 92:16

94:13 97:19

103:13 103:19

104:20 114:3

114:14 114:20

115:5 116:7

116:12 116:13

124:6 133:20

135:7 135:13

140:24 141:6

148:11 152:6

154:16 159:7

168:25 169:16

169:19 169:23

170:10 174:4

176:23 180:15

183:6 187:7

191:14 191:18

191:22 191:24

192:7 192:18

194:14 196:14

197:10 199:7

199:18 199:19

200:20 201:1

201:10 201:21

202:2 206:2

207:25 208:7

208:8 208:11

208:12 209:23

210:12 219:10

223:14 242:21

243:6 246:1

246:5 246:7

267:4 267:23

268:11 272:21

274:14 275:13

275:20 275:22

275:24 276:1

analytical 103:9

analyze 72:8

182:17 202:4

254:14 265:3

271:4

analyzed 20:7

24:9 41:14

69:7 88:14

266:15 267:7

analyzing 112:9

ancillary 260:21

andit's 279:5

anesthetized

107:20

animal 7:3 7:17

10:4 10:9

14:15 21:13

27:13 28:4 28:11

28:12 28:12

28:13 31:16

67:18 77:6

80:6 81:25

86:9 86:11 86:12

97:13 97:20 98:8

104:12 120:1

120:5 129:9

150:18 157:25

179:22 186:2

189:3 189:4

animals 6:16 6:19

6:20 6:23 8:5

8:9 10:13

11:17 11:23

12:13 12:24

18:19 19:5

26:4 26:5

26:15 26:16

26:19 26:24

26:24 27:2 27:22

28:7 28:25

29:6 30:4 31:7

31:8 31:13 31:17

32:9 51:20 59:13

66:3 66:9

66:12 68:5 72:22

79:12 92:10

95:23 96:1

97:2 98:4

105:5 105:11

129:21 180:11

180:24 181:12

181:16 182:13

184:11 184:16

184:18 184:18

184:21 185:15

200:25 206:1

221:19 225:18

253:23

animal's 13:24

Animals 92:12

ANNA 25:17

33:21 86:2
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 296

104:25 117:5

225:25 226:15

228:18 229:12

277:18 278:22

280:7

annually 241:3

answer 11:8

33:9 65:11 65:13

70:20 77:5 82:18

98:1 140:20

175:3 175:6

192:21 210:8

224:17 226:1

226:7 240:8

276:2 277:12

277:13 284:12

answered 62:15

210:4

answering 6:4

152:1

answers 121:15

antagonist 222:5

anticipate 6:2

21:8

anticipated

120:17 142:16

anticipating

22:23 84:16 86:3

anybody 84:8

244:12

anymore 103:19

278:21

anyone 192:4

227:12 244:11

252:8 277:23

anything 34:16

43:1 45:22 48:19

52:15 84:8

84:9 103:3

110:24 129:5

129:25 130:10

153:23 177:11

193:16 215:8

226:6 231:1

253:17 253:20

257:9 257:23

267:12 269:7

279:5 282:4

282:8

anyway 28:12 43:5

51:19 76:12

186:2 222:13

234:14 259:12

278:9

anywhere 177:23

283:3

apical 77:7 78:18

222:3

apologies

175:10 263:11

apologize

230:19 277:4

apparent 52:25

apparently 103:22

112:23 139:20

223:20

appealing 178:13

appear 26:1 26:14

31:12 38:8

67:2 154:23

164:2 164:4

appeared 18:17

18:19

appears 40:18

52:12 106:12

115:1 115:3

197:23 255:12

277:8

appendix 54:21

56:14 117:14

196:15 208:2

apples 29:6

application

156:22 198:19

237:11 243:3

266:9

applications

139:5 254:24

265:17

applied 20:21

45:21 55:12

56:24 113:6

232:22 233:23

234:12 241:4

applies 59:17

59:17 139:18

apply 19:7 26:8

39:16 51:23

56:25 149:25

applying 7:4

274:9

appreciate 130:10

140:2 162:17

164:17 168:23

197:15 253:21

appreciation

59:15

approach 60:1

73:11 73:17

76:20 89:13

90:17 90:22

92:17 104:8

111:18 112:18
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 297

112:25 113:21

114:14 114:21

116:20 116:21

153:3 154:4

158:20 162:12

178:13 178:17

193:21 202:10

206:8 212:13

220:2 220:18

221:13 232:14

233:14 247:2

248:18 255:12

255:15 256:6

260:13 264:1

268:3 268:16

271:12 273:3

approached 114:18

approaches

77:11 94:13

94:14 112:4

112:17 143:6

143:12 240:21

254:23

appropriate 47:18

73:1 80:22

98:1 113:17

121:22 121:25

141:2 153:2

165:4 199:8

200:22 200:24

201:14 202:10

202:12 202:16

227:16 231:24

232:12 258:10

260:15

appropriately

57:14 75:1

262:11

approximate 9:22

approximately

152:17 157:15

257:12

approximation

247:6

April 33:11 34:25

66:20 190:22

227:21 228:13

230:22 236:10

237:13 237:18

243:8 254:17

263:16 268:8

apt 187:20

aq 117:23

Arbuncle 43:15

57:4

area 16:25

18:14 18:17

90:19 96:23

109:11 142:8

152:25 153:11

153:17 154:3

156:18 156:21

156:24 157:18

159:8 159:23

160:4 160:4

160:10 160:12

160:22 160:22

161:5 161:7

161:10 161:12

190:3 211:7

236:12 244:20

251:11 260:15

265:13 265:14

are--and 131:15

areas 75:9

75:11 121:22

155:8 201:21

230:12 232:12

233:16 249:9

251:25 258:23

259:8 259:11

259:11 267:25

272:1 285:11

are--I 106:1

are--it 120:16

arena 229:2

aren't 32:13

44:25 71:3

252:10

are--there 131:10

are--these 120:10

argue 57:8

142:9 144:4

argued 269:2

arguing 28:11

157:7

argument 112:18

116:1

arise 117:2

aroma 89:3 89:5

articulate

147:6 216:13

artifact 145:21

146:13

aside 85:3 178:21

aspect 12:8

168:24 275:23

aspects 102:25

assays 237:24

asses 41:11 41:14

49:8

assess 40:5 48:23

63:15 66:23

88:22 103:9
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 298

105:4 266:19

assessed 49:10

82:8

assessing 144:5

assessment 34:2

38:21 40:6

49:6 55:19 55:19

56:3 56:16

57:7 57:8

58:12 63:23

64:13 64:16

65:24 67:19

69:12 70:1 71:11

72:12 73:8 73:13

73:18 75:1 75:24

80:12 80:24

82:10 109:18

118:10 118:11

118:14 118:20

118:21 119:8

119:13 119:22

120:8 120:23

121:6 128:16

129:14 129:18

133:17 140:18

144:1 144:14

145:2 147:11

147:20 149:5

162:22 168:13

172:24 185:14

197:5 198:17

199:5 201:12

239:21 262:23

263:3 274:24

274:24 277:21

assessments

70:7 80:14 173:5

229:23 244:9

264:9

asset 22:11

assign 111:25

assigned 31:18

73:22

assignment 119:10

assignments 74:2

assimilate 24:18

assisted 197:3

associate 36:8

50:23 62:24

associated

50:16 81:17

145:7 158:1

161:19 164:5

169:8 174:2

177:13 183:1

185:1 199:1

199:6 199:10

associates

26:18 116:24

185:20

association 39:24

40:2 44:13

associations

40:20 43:24 50:9

assume 193:10

assumed 48:9

122:9 134:25

155:19

assumes 119:23

assuming 66:6

76:14 76:15

165:2

assumption 19:4

66:12 66:13

66:18 108:24

155:3 155:16

assumptions 101:6

165:20 168:9

194:7 194:9

assurance 274:2

asthma 50:1

at--because 116:8

ATEMP 237:18

Atlanta 2:20

atrazine 2:7 7:22

7:23 8:3 11:11

12:19 12:24 13:3

14:8 15:6

15:17 16:8

18:6 19:24 20:13

20:20 20:23

21:18 24:13 25:3

34:1 35:5

35:15 35:24 36:4

37:11 39:23 40:6

41:14 43:6 43:25

44:11 45:6 45:17

46:14 46:18

46:20 47:24 49:6

49:8 49:13 49:16

51:13 51:23

52:13 52:19

52:21 53:1 53:11

55:4 55:8

55:11 58:24

59:21 60:23 62:4

66:8 69:8

76:13 77:3 77:12

82:20 84:1

84:4 84:14 85:14

85:17 87:14

87:25 88:8 88:10

88:11 88:12

88:17 88:19

88:25 89:6

89:7 89:8
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 299

89:24 90:9 90:12

91:2 91:6

91:14 92:4 92:12

93:3 93:5 93:7

93:11 93:14

93:20 94:4

94:6 94:19

95:4 95:9

95:24 96:2 96:11

97:10 98:20

101:24 102:7

102:13 104:9

106:5 109:5

109:11 109:25

110:3 110:6

111:12 117:8

117:10 122:25

123:22 124:22

127:14 128:6

129:3 131:14

131:22 131:23

132:4 132:7

132:10 138:6

138:15 138:16

140:18 143:14

145:7 145:10

146:22 147:17

148:1 148:12

150:22 152:8

152:14 153:22

155:7 155:11

155:11 155:24

162:15 163:4

164:19 164:24

165:3 165:3

165:14 165:21

166:4 166:4

170:25 173:13

174:3 176:1

188:21 192:5

203:1 205:2

205:5 205:10

206:1 207:15

209:23 217:21

218:9 218:19

219:2 228:3

228:21 229:6

230:25 231:2

232:1 232:2

232:22 232:24

233:19 233:22

234:1 234:5

234:8 234:9

234:12 245:25

251:11 252:7

252:20 254:15

259:23 262:20

262:21 263:2

263:7 264:10

264:15 264:23

265:17 265:20

266:6 266:12

266:15 266:20

267:21 268:14

270:9 270:12

271:3 272:3

273:4 274:25

277:19 277:21

278:8 278:14

283:22

atrazine-caused

106:15

atrazine's 204:20

atrazinetreated 107:15

ATSDR 3:9

attached 56:1

attacked 51:16

attempt 142:21

164:9

attempted 169:3

178:14

attention 53:16

59:7 61:24 62:11

78:14 126:18

127:23

attentive 125:13

attenuates 66:9

218:1

attenuating 203:1

250:24

attenuation 66:10

67:11 68:8

173:12 173:18

174:3 175:2

176:1 176:5

186:19 203:8

204:4 205:19

209:22 217:8

217:12 218:10

218:20 218:22

attenuations

183:2

at--that 65:16

attitude 119:23

attribute 99:11

attributes 62:22

at--we've 234:17

AUC 18:6 162:12

168:2 170:6

208:18 263:25

AUCs 18:8

augment 100:9

247:10

August 237:13

Austin 52:5

authors 53:3
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 300

127:10 274:19

276:7

automatic 236:4

automatically

100:17

autosamplers

230:15

availability 71:3

123:4 197:12

available 5:6

69:17 77:14

81:16 118:17

143:24 145:11

157:6 168:14

169:4 169:9

175:19 182:20

199:10 247:12

249:22 262:13

265:12

average 19:24

20:13 59:24

116:17 156:16

235:23 236:13

averaging 184:16

avoid 161:19

aware 8:22

14:14 84:7 98:22

99:3 142:7

164:16 192:4

228:23 253:16

253:19

away 121:1 132:14

136:18 193:11

206:20

awful 232:23

249:25

axis 33:12

148:9 148:10

174:16

axis's 17:7

B

backed 250:9

backgrounds

121:11 121:11

backing 247:14

backwards

225:20 234:11

bad 83:2 138:5

138:5 191:10

bag 277:19

Bailar 50:23

50:25 53:18

58:13 58:15

62:24 76:4

76:5 79:4

82:22 263:11

Bailey 4:18

4:19 4:21

balance 13:7 61:2

61:5 191:23

209:6

balances 101:19

ball 273:21

bands 195:6

257:25 259:10

banks 84:21

bar 27:19

Barry 3:17 3:17

96:16 144:10

176:22 199:16

200:15

basal 153:17

203:22 206:12

base 35:24 49:4

62:2 75:4 112:17

112:18 148:20

235:4 235:7

238:4 248:14

249:4 249:5

249:12

based 15:25

17:9 18:7 18:8

19:16 21:19

21:20 21:24

67:25 71:13

73:23 77:25 86:9

97:8 140:24

145:14 152:13

153:6 154:7

165:7 166:13

168:4 168:10

168:25 172:2

186:24 189:6

197:20 197:22

198:10 198:14

203:6 208:8

212:12 233:14

234:15 241:21

242:25 244:9

254:17 261:25

269:10 278:15

281:18 281:22

282:4 283:22

284:22

baseline 31:9

31:11 31:23

179:18

bases 113:12

232:23 232:23

basic 102:10

108:22 236:17

basically 9:9

37:3 66:4 67:7
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 301

74:11 88:13

101:23 104:25

147:5 165:21

188:19

basin 234:12

250:19 252:14

252:19 252:22

basing 230:13

236:20

basins 252:25

basis 21:6

22:25 122:7

131:17 133:19

134:17 157:24

159:16 164:24

196:22 199:2

199:5 199:12

208:22 248:13

262:8 266:5

271:9 271:10

battery 278:19

be--and 120:15

beans 45:7 45:18

bear 5:19 100:11

beautiful 241:16

became 117:24

become 85:8 118:1

167:20 217:10

becomes 72:20

262:13 270:5

becoming 16:23

been--point 26:23

beforehand 108:11

begin 4:16 34:4

87:1 87:4

118:4 162:9

beginning 23:20

85:4 86:6

87:23 112:4

265:12

behave 242:11

262:3

behaves 155:11

behavior 10:8

12:3 125:14

125:17 125:24

126:17 247:24

behaviors 37:15

be--if 71:21

believe 5:22

8:5 12:10

14:13 17:3 25:14

25:18 34:10

34:20 37:22

52:17 55:20

68:21 68:22 73:6

78:12 87:5

98:4 100:7

109:21 111:16

111:23 119:9

140:11 141:8

174:10 186:2

202:9 211:22

215:17 220:11

238:11 261:3

276:16

believed 121:17

believes 174:1

belt 251:16 284:3

benchmark

197:18 197:22

198:4 199:3

199:13 201:14

201:24 202:10

202:16 202:19

209:22 210:7

210:12 219:10

219:15 220:13

222:12 225:5

benchmarked

191:14

benefit 52:1

266:25

best 45:1 65:20

67:19 80:23 83:2

101:20 115:8

153:3 158:6

158:10 195:18

200:3 203:15

256:20 273:8

Bette 4:2 4:2

133:6 168:20

196:17 200:14

better 8:20

9:16 12:22

22:2 28:14 37:20

41:11 41:15

41:16 49:5 49:11

59:12 64:4 64:11

65:19 82:8

108:21 112:19

130:20 134:12

136:21 142:5

151:6 156:20

158:21 166:15

182:5 182:16

182:24 184:1

193:4 197:9

199:12 218:24

242:2 250:17

250:22 251:1

255:19 255:23

267:5 268:17

273:10 283:25
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 302

beyond 47:7

183:23 204:10

bias 38:25

40:11 44:20 61:3

61:7 114:6

157:15 207:12

211:17 211:17

212:4

biased 63:11

253:2

biases 193:8

bias--for 40:11

bias--potentially

114:6

bigger 100:4

100:12

biggest 100:15

billion 20:10

232:17 253:14

bin 149:3

binding 153:21

153:22 164:1

binds 153:24

bio 237:6

bioavailability

163:3 166:22

biochemically

155:25

biologic 81:19

82:9

biological

41:13 64:1

69:7 84:21

109:19 109:19

159:10 163:22

198:3 198:25

199:4 211:15

212:16 212:18

220:12 220:14

242:23 271:17

272:4 273:3

biologically

67:25 188:17

193:15 211:11

biology 179:12

217:14

biomarker 221:18

biomarkers

42:22 43:8

biotransformation

171:24

birth 11:18 44:21

45:11 46:12 47:4

47:21 48:21

48:22 62:1

64:3 80:4 82:4

88:2 106:8

109:13 138:11

150:10

births 104:13

bit 13:24 26:5

53:16 54:1 54:17

74:6 83:3

113:3 156:11

161:22 181:15

185:16 192:11

203:10 204:25

208:13 208:21

234:4 235:1

235:6 236:4

236:16 242:19

243:17 245:7

251:23 258:8

269:11 273:25

280:18 280:20

bits 204:18

207:17

blasted 206:3

blend 64:21

blending 248:2

blinded 114:2

blinders 204:17

block 8:25 201:2

blocking 200:7

blocks 201:1

202:4

blood 9:6 9:8

13:24 18:11

18:25 24:10

84:16 166:25

233:10

blurring 126:20

BMD 196:14

208:8 208:12

208:21 209:23

221:11 221:25

BMDI 206:10

boasted 82:22

Bob 206:23

bode 29:15

bodies 124:16

body 8:11 88:14

88:18 92:21 93:1

93:3 93:4 93:7

93:11 93:13

93:15 94:19

94:24 95:4 95:14

119:25 120:1

120:5 125:16

127:13 143:25

156:5 175:16
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 303

175:20 176:2

176:17 202:11

210:22 244:20

boils 221:15

bolus 13:19

14:1 14:2

books 243:7

boring 259:11

259:11

born 118:7

borne 155:9

borrow 232:18

bottle 235:19

bottom 28:19

bound 154:23

281:5

bounded 97:21

Bradford 52:5

52:6 52:7

brain 24:11

153:16 153:20

204:13 207:4

207:9

branched 100:25

Bransen 243:7

BrDU 93:16

93:21 93:24

break 23:22

65:1 86:24

87:2 140:1 140:4

195:11 201:15

209:5 209:9

221:3 284:2

breast 6:7 6:13

7:7 100:2

100:3 141:23

142:1 143:18

143:20 143:21

150:15

breastfeed 150:10

breastfeeding

142:4 143:18

breathing 35:6

35:16

Breckenridge 5:23

5:25 6:3 6:9 7:9

7:13 8:7 9:24

10:1 10:25 11:14

12:1 12:9

12:15 12:21

13:13 14:9

15:8 16:2

17:23 18:10

22:21 22:22

23:11

brief 36:24 38:15

79:6 79:6

briefly 58:25

285:9

bring 25:9

117:2 149:16

167:19 215:24

277:19 279:13

bringing 6:20

33:24 255:18

277:4 277:6

277:14 277:15

brings 121:18

163:21 241:14

broad 21:10 26:13

26:14 59:14 60:4

62:2

broader 58:20

226:17 263:1

broadest 77:9

broken 50:9 87:18

232:10

brought 26:13

28:17 51:6 62:21

105:3 164:14

181:23 183:8

236:10 237:1

238:12 247:19

261:24 262:23

277:7

Bucher 3:1 3:1

82:16 82:17

110:16 110:17

115:14 115:16

213:21 214:17

215:20

Bucher's 213:18

bud 93:22

buds 109:15 110:4

build 35:1 177:19

244:8 262:12

building 34:2

built 56:21

247:18 261:4

bulk 11:17

bullet 26:12

27:11 28:19

bunch 123:24

124:7

by--help 37:2

C

C14 154:8 164:13

Caco 172:1 172:8
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 304

cages 11:25

calculate 54:7

160:9

calculated 9:10

61:4 180:5

181:22 191:22

calculating

165:22 202:19

calculation

18:7 161:8

221:11

calculations

162:24 166:9

168:11 181:23

calibrate 222:3

222:10

calibrated 247:11

256:11

calibration 223:1

California 3:4

4:1 4:5 4:10

calories 165:14

Canada 3:8 4:3

canary 217:10

218:12

canary's 218:13

cancer 2:19

4:12 54:8

54:12 55:23

68:13 70:7

70:8 70:15 70:18

71:23 83:12

100:3 141:24

143:20 143:22

213:22 214:25

215:10 215:13

cancers 217:20

candidate 146:19

cannibalization

94:17

cannulization

92:20

can--with 98:1

capture 86:20

238:6

captured 122:21

capturing 274:5

carcinoegenicity

278:15

care 4:12 92:3

92:5

careful 278:23

carefully 53:24

63:3 158:17

179:3 198:6

Carey 2:24 2:24

Carol 36:10

58:5 65:3

83:17 83:18

83:22 84:10

84:18 85:5

140:14 187:21

188:2 188:11

189:13 190:17

196:13

Carolina 4:14

Carrie 221:2

carried 220:10

220:20

carries 125:23

carry 178:14

253:5

case 11:5 14:14

20:11 31:3 37:24

42:13 45:25 49:4

51:13 60:1

81:7 81:14

101:22 108:11

111:7 118:19

120:12 122:12

123:18 141:14

145:15 151:15

160:16 167:25

178:21 185:6

185:8 191:20

199:21 200:2

200:21 237:16

242:6 248:8

248:13 248:13

251:24 263:11

265:5 269:4

285:9

cases 46:7 165:23

180:8 248:13

catalysts 143:14

catch 119:16

195:9

catchment 272:1

catecholamine

207:13 207:15

categories 72:17

category 44:24

56:7 75:15 80:10

85:21

caught 208:25

causal 77:4

cause 6:1 50:4

53:7 120:11

135:14 188:17
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 305

207:3 218:10

caused 213:25

213:25 218:22

causes 53:7

124:23

causing 92:4

123:12 217:22

caveat 212:15

caveated 86:7

caveats 262:10

CDC 3:10 62:2

CDC's 124:5

124:14

ceased 101:9

cell 166:25

cells 55:17

161:22 172:1

172:8

cenozine 270:13

Center 3:16

centers 218:4

certain 26:21

31:24 46:5 73:15

79:8 172:18

200:8 218:19

222:11 233:16

242:24 245:13

245:14 245:14

248:14 251:21

269:25 274:5

certainly 27:23

27:24 41:16

62:21 76:1 84:22

99:1 103:22

129:20 129:23

130:2 146:11

153:14 161:18

193:22 196:9

200:1 200:9

204:21 206:16

228:24 232:12

232:20 247:6

249:1 251:22

262:9 272:17

certainty 198:19

cetera 69:9

chair 2:10 2:13

chairing 209:7

209:11

Chairman 6:3

22:23

challenge

108:10 110:25

164:1 268:24

challenges

107:2 134:21

136:10 137:2

142:20

challenging 161:4

Chamber 136:13

Chambers 2:21

12:4 127:3 130:5

271:13 272:11

272:22

Chamber's 138:21

CHAMBERS 2:21

12:5 12:12 12:17

14:6 127:4

271:14 272:24

chance 45:11

46:23 63:17

195:23 239:24

283:19

change 9:16 33:14

33:19 74:20

93:13 122:21

123:13 140:8

161:9 161:9

181:20 183:12

196:3 199:23

204:22 204:23

204:24 205:12

205:13 206:1

206:2 212:2

212:9 212:24

214:10 214:23

214:24 215:3

215:5 215:7

215:9 216:24

219:24 223:21

226:12 227:3

227:4 227:10

229:15 233:3

235:5 261:21

284:10

changed 90:8

changes 29:14

29:17 33:12 34:6

39:20 51:7 84:15

109:22 111:10

123:14 128:15

132:3 148:19

181:20 206:18

206:24 207:9

207:14 210:15

212:22 213:2

213:3 213:8

266:20

changing 206:17

274:6

characteristic

142:23 227:7

characteristics
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 306

42:20 64:2

68:7 71:1

241:2 241:25

250:19 250:20

251:18 253:10

259:23 265:15

characterization

58:9 58:11 64:12

64:22 81:2

144:13 187:12

196:23 197:10

223:12 229:10

235:2

characterizations

264:19

characterize

13:25 17:6 65:17

92:17 143:13

166:16 229:24

231:16 237:19

283:25

characterized

234:5 234:9

characterizing

111:7 145:7

198:24 264:13

269:8 274:5

284:6

charge 5:20 25:13

32:23 36:2 36:11

51:2 87:18 114:1

117:1 117:3

137:10 140:11

186:9 195:10

charging 99:12

CHARIMAN 170:17

Charles 6:3 6:9

6:16 6:18 7:13

8:7 9:24 10:1

10:25 11:14 12:1

12:9 12:15 12:21

13:13 14:9

15:8 16:2

17:23 18:10

22:22

Charlottesville

4:7

cheap 273:11

273:11

check 34:18

35:3 35:7

85:18 105:11

151:25

checking 261:8

checks 101:19

chem 92:9

chemical 118:3

118:14 118:21

119:11 119:19

119:24 123:2

123:12 124:11

132:5 132:6

132:8 132:10

143:14 163:4

266:9 272:2

272:4 279:20

chemicals 50:8

50:17 72:25

75:17 117:25

118:4 118:8

118:25 119:2

119:3 119:18

119:20 120:6

120:20 122:6

122:9 122:10

123:8 123:16

123:25 124:7

124:16 124:22

133:13 135:12

170:11 191:20

278:3

chemist 117:23

chemograph 237:19

240:17 240:22

240:25 241:5

241:13 248:10

253:7 269:22

283:21

chemographs 219:8

244:1 244:18

246:6 259:7

263:19 273:16

chested 138:1

CHESTER 152:4

173:11 195:13

209:20

child 137:22

childhood 137:21

children 84:12

85:3

chilled 14:13

chime 138:21

chlonotriazine

154:7

chlorinated 20:15

20:18

chlorinating 20:7

chloro 15:9

chloroatrazine

275:11

chloronitrotrizin

e 20:4

chlorotriazine

18:23 20:14
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 307

270:12

chlorotriazines

20:11

choice 139:1

159:4 165:6

279:4

choices 165:13

169:4

choline 221:3

221:4 221:22

222:14

chores 200:8

chose 274:21

chosen 199:18

Christensen

36:4 36:10 57:22

58:5 65:3

83:16 83:17

83:18 83:22

84:10 84:11

84:18 85:5

140:14 187:21

188:2 188:11

189:13 190:17

196:13

chronic 70:17

162:22 219:2

278:13 278:15

chronically

218:24

cigarette 71:21

71:22

cigarettes

71:25 72:6

Cincinnati 3:12

circadian 7:24

circle 24:2

circled 86:12

circuitry 148:19

circulation

13:4 13:6

citations 105:14

cited 102:15

cities 73:5

claim 170:21

clarification

5:19 17:19 19:21

25:19 32:19

57:22 82:18 86:4

115:17 167:13

189:12 201:19

215:12

clarifications

25:25

clarified 200:18

clarifies 32:19

clarify 16:2

clarity 44:17

162:18

class 37:24

classification

47:16 63:22

classifications

72:13 72:18

classified 72:2

clause 103:16

267:12

clean 237:8 237:9

cleans 98:4

clear 33:11 34:12

43:2 67:3 99:2

131:1 136:9

144:22 151:20

161:16 164:6

177:12 177:16

189:20 189:22

190:25 199:25

210:21 263:24

268:6

clearance 24:3

163:2 166:20

clearer 82:25

clearing 109:12

clearly 9:19

11:18 29:16

67:16 78:20 83:8

95:8 96:18

111:12 114:16

163:14 163:16

165:20 166:17

258:8

climate 241:24

265:13

clock 13:15

close 25:10 26:24

86:5 92:23

105:22 124:12

126:24 127:2

128:2 233:25

258:16

closely 51:2 84:4

184:25 260:10

266:22

closer 40:1

151:15 231:5

clothing 70:12

clue 154:1

clustered 88:3
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 308

88:9

c-max 18:6

18:20 19:8

co-add 166:2

coalmine 217:11

code 54:8

Coder 11:2

67:17 87:11

87:21 92:7 94:22

95:1 95:2

95:22 95:23 96:9

96:21 98:16

102:24 103:6

104:1 104:8

104:22 112:22

116:1 117:7

145:13 149:3

149:8

Coders 96:19

coding 116:10

coefficient

166:25

coefficients 24:3

159:12

co-engineer

139:11

cohort 37:24

47:12 49:4 68:12

70:16 71:2

276:24

cohorts 68:20

69:23

coincide 265:20

coinvestigators

83:20

collaborate 82:6

collaborating

132:25

collaboration

115:20

collaborative

166:2

colleague

147:12 237:20

colleagues 5:25

37:5 100:11

102:3 102:15

133:3 147:6

247:17

colleague's

162:11

collect 9:8

69:5 107:20

235:11 235:17

265:3 266:4

collected 16:15

17:3 24:11

169:20 173:16

261:25 262:8

274:16

collecting 233:18

235:16 238:16

240:4 245:4

263:4 267:10

283:14

collection 9:6

24:10 59:18 60:5

73:1 169:15

collective 133:23

College 2:22

Colorado 3:14

columns 179:11

combination

92:7 103:23

111:11 256:5

combine 192:18

193:5

combined 159:11

201:4 254:18

combining

191:17 193:23

255:10 255:21

256:7

comes 19:4

97:10 163:16

171:12 212:6

214:15 234:19

238:23 250:14

270:6 282:15

285:11

comforting 264:12

coming 35:13 76:1

116:21 130:10

170:22 199:20

208:13 229:13

249:8 259:17

264:24 270:2

276:25

commend 118:5

127:10

comment 5:13 5:16

25:1 25:10

26:3 36:12

44:3 45:16

53:6 55:3 55:5

55:16 56:4

57:1 57:2 57:4

57:19 58:6 60:23

61:24 64:9

65:4 67:18 71:19
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 309

73:4 87:9

87:13 87:19

99:22 99:23

101:6 105:20

106:17 111:22

113:3 117:15

124:25 126:4

136:3 137:10

137:17 138:21

138:24 140:16

151:5 152:6

152:18 166:8

170:20 174:4

175:23 180:4

185:2 188:3

195:12 196:13

209:21 224:18

230:10 240:16

244:12 245:23

248:25 252:9

254:8 254:22

270:8 274:18

275:16 279:19

commentators

202:20

commented 51:9

commenters 5:15

24:23

comments 10:21

25:21 26:8 33:22

36:20 36:22

36:22 36:25 37:4

37:8 38:6 38:7

38:16 38:20

39:16 41:20

43:13 47:7 50:22

51:1 52:2

53:23 54:16

54:16 54:18

57:15 58:17

58:19 64:24 73:3

74:6 74:10

75:6 75:19 82:14

86:7 86:10 87:12

95:19 105:16

106:19 107:25

117:18 118:9

125:7 129:22

130:8 130:11

130:25 133:15

137:7 140:21

142:9 142:16

144:11 148:15

149:22 162:2

165:5 168:22

171:9 173:7

174:21 174:25

178:10 184:5

186:8 196:20

203:4 203:14

208:4 208:7

209:3 229:7

238:21 240:7

249:16 267:13

268:22 279:16

280:14 281:5

commission 197:13

committee 4:23

33:8 77:8 175:11

common 42:20

51:10 118:11

170:13 179:16

commonly 195:24

communities 48:9

community 48:17

119:5 232:16

232:19 240:18

254:15 256:11

259:24 262:22

263:20 264:7

264:10 264:14

264:19 264:24

265:2 265:23

267:2 267:14

267:20 268:15

270:18 271:25

community--what

118:22

comparable 279:5

comparative 97:25

199:7

comparatively

199:5

compare 74:11

98:14 108:14

230:24

compared 22:18

27:18 65:13

68:12 92:13

103:14 198:13

201:2 248:6

comparing 29:5

comparison 18:8

45:8 45:15 52:21

53:1 59:16 60:25

103:14 104:5

104:20 199:12

comparisons

53:4 60:6 105:18

132:12

compartments 24:7

24:15

competes 205:2

competition 121:8

competitions

135:19

complementary
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 310

134:10

complete 14:3

34:3 34:7 215:14

completed 140:10

completely 206:17

232:6 243:13

complex 51:14

205:22 211:13

218:8 263:12

complexity

118:6 210:5

complicated 210:5

235:14 236:2

compliment

67:22 216:24

230:22

compliments

131:11 231:7

component 8:20

9:4 9:8 18:25

20:4 122:8

138:23 206:22

270:2 274:7

277:21

components 20:7

87:18 109:13

123:11 139:17

270:10 270:20

276:17 280:14

composite 130:14

composition

155:22

compositions

131:9

compost 256:14

compound 9:5

14:22 49:7 128:8

128:17 128:22

154:5 154:11

155:3 155:20

159:9

compounding 48:16

73:8 92:11

compounds 69:8

129:25 132:20

139:20 146:6

154:8 155:25

159:6 170:24

comprehensive

37:9 142:24

comprise 258:15

computer 278:10

concentrated

264:4

concentrating

259:7

concentration

8:14 15:6

20:22 20:23 25:2

62:7 122:6

128:24 129:20

153:1 160:11

160:12 161:6

163:6 163:8

186:14 208:18

208:19 234:13

235:5 235:24

248:4 250:24

250:24 256:1

262:21 262:21

267:21 267:25

concentrations

13:16 17:11

31:11 121:23

154:5 163:9

163:11 163:13

163:23 164:16

230:1 230:4

230:6 233:15

248:2 253:5

253:14 261:6

261:11 264:5

264:15 264:23

265:10 266:7

266:13 266:15

266:16 266:21

266:22 267:19

268:12

concept 54:8

83:18 108:7

124:3 170:7

241:9 247:13

conception 126:6

concern 39:2 42:3

77:17 77:17

91:17 91:21

95:23 164:11

176:6 230:5

230:15 235:8

235:10 236:21

254:13 255:8

259:17 259:25

263:5 267:14

281:14 281:19

282:7 282:10

concerned 7:22

18:4 76:6 132:17

175:15 204:4

222:9 243:17

concerning 7:10

77:2 128:13

concerns 77:24

77:25 131:20

141:9 162:19
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 311

175:1

concise 82:14

conclude 83:2

167:8 255:3

concluded 5:13

89:23 101:14

152:14 173:21

concludes 143:23

conclusion 64:9

77:1 80:19 80:21

87:13 94:7

95:8 149:25

162:11 164:4

168:2 168:21

213:12

conclusions

58:8 58:16

61:9 64:17

65:6 76:18 81:25

89:21 95:7

96:4 132:12

141:4 157:16

160:1 174:5

247:5 252:3

concrete 96:9

concur 56:7 131:1

146:17 148:17

202:9

concurred 23:14

concurrently

133:1

condition 208:20

conditions 6:14

7:17 14:22 62:21

78:2 180:24

266:2

conduct 89:14

conducted 13:7

68:11 68:15

68:20 176:22

confidence

40:18 61:2

61:5 79:23

134:14 177:12

191:23 194:6

195:5 244:9

258:2 268:13

confident 60:2

274:8 282:13

confirm 101:17

103:7 200:14

confirmation

277:11

conflicting

144:14

confounder 45:24

confounding

47:2 50:2 50:18

confuse 284:15

confused 26:4

151:13 167:7

183:3

confusion 73:12

congeners 139:6

congratulate

53:20

conjugate 165:22

conjugates 69:9

conjugation

165:16 166:22

conjunction 136:3

connect 75:25

218:23

connected 16:18

283:13

conscious 164:25

consecutive

173:24 179:24

consensus 67:3

174:11

consequences

143:6 144:19

consequently

147:18

conservatism

165:12

conservative

165:8 169:4

220:2 248:18

consider 41:19

42:2 57:24

71:5 103:17

103:25 112:12

120:6 120:20

124:10 130:2

133:18 148:6

156:10 158:3

164:23 182:21

188:16 196:21

198:8 199:6

213:7 237:24

278:2

considerably

46:13

considerate 63:3

consideration 5:3

55:1 70:10

73:7 78:23

117:18 120:18

122:5 129:13
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 312

132:13 133:21

135:2 137:15

145:9 146:11

163:5 164:3

166:21 197:1

198:12 198:18

199:8 203:5

212:25

considerations

145:18 167:3

considered

37:23 38:2 45:15

46:17 56:6 98:23

117:6 128:7

146:12 146:19

147:2 148:2

149:12 149:21

167:23 169:22

171:24 198:6

198:9 241:8

241:11

considering 18:23

132:9 134:25

150:4

consist 119:11

consistency

133:22 134:18

134:24

consistent

74:19 79:25

91:25 104:18

113:6 121:15

134:1 138:18

168:9

consistently

93:14

constant 10:5

156:3 156:4

157:24 158:16

159:23 160:7

160:11 160:14

161:7 163:6

constituents

146:9

constitute 39:23

constraints 169:8

169:12

construct 83:15

178:12 182:25

199:5

construe 272:18

construed 141:4

Consultant 4:7

consultations

130:19

consume 8:9

consumption

8:18 9:2 47:15

49:9

contact 125:18

contained 65:6

173:15

contaminants

48:10

contamination

73:21

content 19:21

97:17 97:22

97:23 224:23

contest 247:17

context 60:4 78:6

121:19 133:8

133:16 162:22

165:9 169:24

197:2 197:17

199:3 199:9

201:8 254:11

274:25

continual 273:20

continuation

203:8 216:23

continue 35:16

91:15 209:19

228:21 229:3

263:25 264:2

264:16

continued 198:9

continuing

78:11 203:10

contradiction

135:13

contradictory

166:3

contrary 66:16

81:4

contrast 117:8

170:20

contribute 176:25

269:1

contributed 24:23

contributing

145:23 146:24

contributions

25:8 195:8

control 26:4 26:5

27:2 37:14 37:24

42:4 45:25

46:8 48:16

49:4 88:9 88:9

93:6 93:9 97:7

99:3 102:8

127:15 128:7
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 313

128:11 129:4

129:6 129:19

151:8 173:19

195:21 202:1

250:14 252:19

252:20 267:16

controlled 50:9

196:4 200:19

203:12 203:23

207:9 267:1

controlling 50:17

controls 8:10

46:8 50:1 92:5

93:23 93:25 94:4

94:7 94:10

103:18 105:25

129:9 138:17

180:9 210:19

223:11 225:2

conundrum 109:23

conversation

124:25 209:19

226:3 227:13

227:19 279:2

conversations

209:18

converted 13:9

convinced 112:13

cooling 184:15

186:24

Cooper 25:19

25:23 27:18 28:9

29:21 30:2

30:7 30:20 30:24

32:22 33:7 86:11

138:20 175:10

180:23 181:5

182:12 183:4

192:14 192:16

194:19 201:17

201:18 202:7

204:6

cooperate 102:9

cooperative

132:18

Cooper's 25:22

29:25 194:16

copout 192:22

corn 45:6 45:7

45:17 45:21

45:23 97:20

237:12 251:16

260:16 284:3

correct 6:5

6:12 12:16

30:6 40:3

81:25 108:2

144:2 158:20

174:10 225:14

227:1 227:11

270:10

corrected 40:2

53:4 66:7 180:20

181:11

correction 42:25

correctly

126:12 255:20

269:21

correlate 128:2

correlates 195:21

correlation 123:1

257:11 257:16

257:19 257:22

258:7 258:9

258:14 258:17

258:22 259:2

259:3 259:6

259:8

correlations

263:19

corresponding

71:6 93:18

cortex 153:17

corticos 9:23

corticosterone

95:24

cost 68:11

count 34:24 182:9

counting 165:21

182:12

Coupe 3:23 3:23

105:3 126:14

230:17 230:18

231:4 231:7

237:23 238:12

242:14 242:15

248:23 248:24

251:5 252:5

252:13 252:17

253:22 254:1

257:2 259:13

259:14 270:8

270:15 270:24

271:2 272:8

272:25 273:5

273:6 275:21

275:21 284:16

284:17 285:2

couple 4:22

11:5 12:5

25:18 25:20

33:21 33:24

39:19 49:21

65:22 68:4 75:19
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 314

102:4 105:13

106:10 109:4

133:7 133:8

184:5 202:23

204:7 204:22

208:7 250:10

252:13 269:5

276:14 285:7

coupled 254:20

course 20:18

24:15 24:18

24:18 32:5 58:22

70:16 76:19

93:18 98:16

106:15 117:1

134:4 155:21

161:20 194:8

201:6 202:22

209:13 255:19

277:18

covariate

192:17 192:23

192:24 193:12

cover 31:20

60:7 137:12

138:14 226:5

covered 50:22

89:21 96:17 97:1

102:3 103:4

152:22 162:6

226:16 237:22

covered--I'm

103:2

covered--this

99:24

cranking 202:5

crass 187:11

Crawford 261:4

create 12:2

120:15 191:10

242:1

created 74:6

creating 255:18

credibility

127:24

CRH 206:25 207:11

CRISP 279:11

criteria 26:20

28:22 29:7

30:5 54:19 56:13

113:5 113:17

135:5 260:10

critic 37:8

42:4 43:14 139:9

critical 42:13

59:11 60:22

90:12 127:11

133:20 133:24

134:3 134:8

135:6 153:11

160:6 160:8

160:15 167:2

167:4 186:18

196:23 199:2

211:23 211:24

211:25 228:12

230:5 237:2

237:6 240:2

242:12

critically 231:13

criticism 200:4

200:17 200:21

200:24

criticisms

50:10 200:9

200:19

criticize 37:13

200:25 205:8

criticized 200:3

critics 139:15

critique 40:3

46:4 47:6

crop 264:24

265:12 266:9

cropping 265:14

cross 42:13 47:17

50:4 88:3 88:8

88:10 88:12

88:24 89:8 89:13

89:15 89:24 91:3

99:2 121:21

232:22

crossed 85:23

92:18 127:22

180:25

cross-sectional

50:12

cross-sponsored

89:6 89:8

Cruz 4:1

C-study 94:22

95:2

culminating

208:11

cumulative 74:24

curiosity 201:5

currency 170:13

current 33:4

35:18 69:4 97:12

144:25 165:10

169:16 169:18

169:19 175:10
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 315

275:3

currently 21:14

24:12 69:17 99:2

130:25 141:3

232:16

currents 113:11

curve 18:14 18:17

22:14 90:7

109:11 138:4

153:1 153:12

154:3 156:18

157:18 159:8

160:4 160:10

160:13 160:22

160:22 161:5

161:7 161:12

175:6 175:25

176:2 181:14

186:1 190:4

193:14 193:18

194:1 194:6

194:8 194:15

194:16 211:7

221:10 221:25

236:12 244:20

247:2 258:15

curve--at 17:10

curved 185:4

curves 192:20

242:22 242:25

246:25 256:1

269:13

cut 24:25

CWS 265:11 265:15

266:4 266:16

cycle 6:6 6:10

7:7 7:11 7:11

7:25 9:13 10:4

28:24 29:4 29:15

33:5 41:12

64:2 68:7 71:1

86:18 88:2 88:16

92:10 92:10

94:16 95:21 97:6

105:6 105:12

151:10 188:22

189:19 190:2

190:7 190:10

190:11 190:13

204:6 211:24

212:17 213:6

214:10 215:7

215:14 216:24

217:4 218:17

224:6

cycles 68:7 215:5

215:10 215:13

215:15 215:17

216:20

cyclicity 31:17

cyclicityin

217:23

cycling 26:20

185:15 189:3

226:4 226:18

D

DACT 19:25

20:15 117:12

155:4 155:6

155:7 155:14

156:2 163:11

164:16 164:20

165:17 172:5

172:11 270:13

270:24 271:9

daily 9:10 62:8

152:17 161:20

162:12 168:2

173:24 218:17

246:7 246:15

246:17 246:18

246:25 261:5

261:11 261:16

265:5 266:5

283:25

dam 94:17

242:10 247:23

250:8

dammed 93:1

dams 94:24 126:10

138:15

Dan 3:3 221:1

danger 126:7

DANIEL 3:3 170:19

dark 8:1 9:2 9:12

9:19 259:21

dash 194:11

data 8:13 10:3

13:2 17:5

19:17 20:2 21:20

21:24 22:3

22:5 22:24 23:20

23:25 24:8 24:18

26:7 26:22 26:22

28:10 29:2 30:11

30:20 30:25 31:6

32:4 32:17 33:10

33:17 34:21 38:1

41:21 41:24

44:19 51:11

51:12 51:14

51:23 52:14

58:21 60:5 60:13

62:9 62:19

64:1 64:14 64:14

65:24 66:21

69:21 69:25
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 316

70:24 71:4 71:11

73:1 73:6

73:16 73:21

74:18 76:20

76:25 77:14 80:3

80:3 80:8

80:20 80:22

80:23 81:11

81:16 81:22

81:25 82:3 89:25

97:15 97:24

101:18 103:19

105:7 109:12

110:2 110:12

110:14 110:25

111:18 111:19

112:5 112:7

112:9 112:11

113:13 115:10

116:13 116:14

120:3 121:2

124:14 128:9

133:2 134:14

143:17 144:12

144:14 144:15

145:5 145:8

147:16 149:24

150:5 150:13

150:18 150:25

152:7 152:19

154:4 154:13

154:18 155:2

157:5 158:5

158:11 158:21

158:21 159:2

167:9 167:21

167:24 168:4

168:12 168:16

169:4 169:9

169:15 170:16

170:24 172:3

172:17 173:15

174:22 175:18

175:19 177:20

178:12 178:15

178:20 179:4

179:7 179:9

179:12 179:14

179:16 180:3

180:12 180:14

180:21 181:11

181:17 181:17

181:21 182:3

182:5 182:7

182:8 182:10

182:13 182:14

182:15 182:21

182:22 183:5

183:12 183:14

183:21 183:23

183:24 184:11

184:14 184:22

184:24 185:5

185:14 185:17

185:17 187:1

187:8 187:9

188:5 188:24

188:25 189:4

189:25 191:5

192:2 192:5

192:14 192:16

193:6 194:17

195:14 195:18

197:8 197:12

197:19 198:10

198:21 200:19

201:7 201:9

201:11 201:24

201:25 202:5

202:11 203:7

204:18 205:5

205:7 205:7

206:8 206:23

207:17 210:22

213:13 214:13

214:14 216:2

217:25 220:19

221:18 222:11

223:10 223:18

223:18 224:15

229:22 229:24

230:4 230:16

235:11 237:3

237:4 237:17

238:10 238:11

239:7 240:19

242:21 243:6

243:11 244:4

244:6 244:21

245:3 245:9

245:25 246:7

246:7 246:15

246:17 246:18

246:21 247:4

247:5 247:8

247:10 247:12

247:20 248:7

248:8 249:1

249:1 249:10

251:7 251:7

251:21 254:16

255:13 255:16

256:23 260:19

260:21 260:22

261:2 261:7

261:21 261:25

262:1 262:7

262:7 262:13

263:4 263:12

263:12 263:13

263:13 265:21

267:9 269:11

270:18 272:20

277:9 277:23

278:3 279:24

283:8 283:10
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 317

283:13 283:16

284:10

database 54:4

54:11 54:12

58:10 64:19

65:18 65:18

65:25 67:1

67:9 67:13 67:16

67:23 69:15

69:16 69:17

69:19 77:2 85:25

128:11 128:25

165:11 198:1

198:15 271:20

278:2

databases 64:11

67:4 69:11 74:11

79:16 81:1

data-intensive

249:24

date 34:20

39:21 54:9 54:24

88:15 105:12

109:14 126:13

dates 105:23

David 12:23 16:10

16:13 17:1 18:22

21:4 21:9 22:6

22:9 23:7

Davis 4:10

Dawley 125:10

167:10 194:13

Dawleys 27:19

day 2:5 7:11

9:6 10:5 10:6

10:11 10:18

10:23 11:7 25:16

27:14 28:20 31:4

31:18 33:5 71:25

72:6 87:25 88:19

88:25 89:5

90:9 90:10 90:16

90:22 91:1

91:2 92:13 92:14

93:20 94:2

95:9 95:10 95:11

98:6 105:5 106:9

109:8 110:19

126:11 126:16

126:19 137:18

160:19 160:19

167:20 167:22

183:5 183:9

185:3 199:20

200:17 234:24

234:25 235:1

235:4 259:19

277:20 285:13

days 6:20 19:6

26:16 26:20 27:1

27:13 27:21

33:13 33:24

34:19 34:22

65:22 68:4 90:10

90:20 91:13

92:15 105:22

109:13 159:24

160:8 160:8

160:9 163:14

163:19 173:14

189:23 189:23

189:24 190:10

190:11 202:23

204:7 219:6

234:24 235:24

235:25 239:3

243:13 243:21

250:10 255:4

255:8 258:5

259:18 265:17

274:1

DCT 19:15

130:16 172:11

DEA 19:25 20:16

117:11 155:4

165:17 165:17

270:13

deactivate 153:25

deal 25:6 58:20

60:7 118:25

169:7 170:14

179:4 221:17

255:13

dealing 13:20

26:1 137:1

dealt 38:15 39:21

51:7

death 104:14

decades 60:3

decay 17:8 17:9

decide 235:21

deciding 112:16

decision 104:9

183:13

decisions 5:1 5:2

35:8 101:21

145:14 169:18

decrease 89:1

89:5 92:4

94:21 104:11

104:12 202:24

204:20 204:21

205:9 211:14

218:2

decreased 30:5
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 318

88:18 127:15

decrement 215:18

deemed 29:7

103:21

deep 246:5

defect 62:1 76:16

defects 44:21

45:11 45:14

46:12 62:20 82:4

defer 219:18

define 16:25

134:9 166:17

170:12 179:14

179:15 214:22

219:3 223:5

defined 55:10

55:12 154:22

179:25 193:13

defining 223:20

definitely

78:12 162:13

171:7 195:5

233:9

definition 156:20

158:7 180:13

degradant 128:20

degradate 273:4

degradation

269:22

degrade 146:25

degree 117:15

198:12 198:24

199:10 269:25

dehydroxylated

272:2

delay 32:6 32:6

32:8 83:11 96:20

98:18 106:3

108:7 108:24

delayed 71:2

226:9

delays 11:4 117:9

delay--that

108:23

delclos 3:17

96:15 96:16

144:10 176:22

199:16

Delclos 3:17

99:20 144:9

145:4 176:21

199:15 200:12

deliberating

35:20

deliberations

35:11 190:23

delicate 202:18

delightful 10:12

delineations

135:4

delivery 11:16

47:25

delta 223:5

demonstrated

248:10

departure 134:4

145:10 198:13

198:23

depend 121:17

194:9

dependable 155:21

depending

123:11 161:24

247:21 283:17

Depending--its

35:9

depends 229:24

230:4

depicted 40:16

depleted 155:12

deposits 238:11

depression 122:16

depth 107:5

251:19

derive 167:4

204:1

derived 42:15

80:1 199:3 264:9

describe 16:7

23:17 77:22

described 58:8

156:12 180:14

191:9

describing 47:2

description

180:17

deserves 192:10

design 6:22 67:18

67:24 68:9 71:17

80:2 80:8

84:20 89:12 91:5

117:18 121:17

125:4 130:13

133:11 145:22

146:14 160:2

219:2 239:17

239:20
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 319

Designated 4:17

4:21

designed 33:1

41:8 41:11

61:8 66:1

69:23 70:8 70:14

70:21 78:24 81:7

81:12 85:7

91:7 107:3

117:19 121:5

121:19 130:18

137:3 220:7

220:9 255:14

designing

230:11 231:15

237:7

designs 41:23

46:14 49:5 49:14

63:15 64:4 80:10

94:10 238:25

desirable 134:10

desire 174:17

174:18 220:1

despair 101:13

Despite 104:7

destruction

126:17

detail 36:24

112:11 168:15

detailed 50:22

249:18

details 181:24

196:15

detect 50:7 50:16

78:24 268:12

detected 14:18

detection 76:8

determinants

114:11

determination

144:12

determine 49:10

139:2 144:22

180:24 254:14

266:16

determined 159:22

168:13 194:2

determines 99:7

determining

118:17 135:6

280:22

deterministic

255:10 256:2

256:7

detox 66:21

detoxification

155:13 155:17

detrimental 129:7

develop 143:11

261:13 275:18

280:20

developed 56:10

66:2 159:14

159:17 203:7

284:22

developing

34:23 142:10

144:4 197:18

246:15 279:25

development 6:7

6:13 7:7 87:6

87:10 87:21

88:23 91:6 92:17

92:22 96:6

100:17 100:19

102:1 102:11

102:22 103:10

104:15 104:17

105:4 105:24

106:3 108:7

111:10 117:9

121:10 121:21

122:19 123:3

126:18 140:17

140:22 141:12

141:17 141:21

142:2 142:12

145:6 145:12

151:8 205:17

221:6 261:1

261:8 283:14

developmental

88:22 144:24

deviates 238:24

268:2

deviation 180:5

182:1 212:14

219:14 219:21

222:17 223:12

224:8 224:10

224:10

deviations 179:19

179:20 212:15

devices 107:19

DIA 19:25 20:15

117:12 155:4

165:17 165:17

DIAA 270:13

DIADA 280:6

diagnosis 72:1

diagram 256:18
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 320

256:24

diagrams 256:17

diaries 69:5

dichotomous 72:2

Di-D 153:21

diet 8:12 8:14

10:17 14:1

18:9 18:18

97:7 97:9

97:11 97:22

97:23 99:1

99:4 99:11

dietary 9:3

12:8 12:19 13:20

14:5 17:21 17:22

17:22 17:25

156:22 161:16

161:21 162:2

173:17

diethylatrazine

271:4 271:8

272:11

diets 7:22 7:23

8:3 97:15

97:16 97:18

differ 70:2 97:19

174:23

difference

11:22 18:21 27:7

32:1 48:25 90:24

92:24 93:1 94:23

94:25 97:6

97:7 99:8 103:18

104:19 107:13

114:10 114:24

125:11 164:17

164:21 164:22

167:18 177:25

203:18 214:18

224:22 225:2

249:3 249:13

250:6

differences 18:18

27:7 28:8 50:8

65:8 73:14

83:8 91:1

91:19 91:20

93:19 95:14

95:20 96:7

96:7 96:8

96:18 96:24

96:25 98:18

103:20 103:21

113:25 115:2

115:4 143:4

143:7 165:1

166:21 171:15

171:15 172:4

178:21 201:11

different 28:5

31:19 34:11 44:5

46:7 46:7

54:10 55:12

62:21 62:22

65:13 75:9 77:11

79:12 79:13

82:19 84:2 84:23

87:12 93:6 93:17

94:2 94:18 97:10

97:19 98:12

103:8 104:7

104:19 105:25

112:3 112:8

115:4 121:10

121:23 122:15

126:2 126:2

127:6 130:24

131:8 132:2

138:7 147:23

157:2 157:3

159:4 159:8

160:18 161:11

161:13 162:7

162:8 164:11

164:15 173:13

173:16 173:18

175:20 176:16

176:17 178:20

181:21 184:15

185:8 185:10

185:13 185:20

185:23 186:1

186:4 186:6

191:11 192:20

193:1 194:3

195:15 196:5

201:9 201:25

202:4 202:5

206:6 208:21

215:21 219:7

231:17 238:13

240:15 244:2

244:7 244:19

248:2 248:2

250:5 250:25

256:25 257:5

257:6 257:17

258:17 258:22

269:14 269:16

270:1 271:12

271:25 273:21

275:11 278:23

279:20 279:20

279:21 279:25

280:1 280:5

280:18 281:13

282:11 282:17

differential

13:25

differentiation
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 321

165:18

differently

203:12

difficult 13:19

29:9 81:9

89:16 98:14

114:15 125:5

150:12 158:7

160:3 161:4

190:7 212:6

254:3 276:25

difficulties

126:1

difficulty 214:15

220:14 256:4

digestive 161:25

dimethyl 18:23

diminishing 211:4

diminution 216:18

diode 45:15

direct 53:12

104:5 147:17

147:24 148:7

148:12 148:13

direction 29:2

153:8 162:14

162:23 176:3

191:25 240:6

282:9 282:11

directions 77:9

directly 136:23

212:10

Director 2:19

disagree 54:5

81:23

disagreement 83:3

83:4 99:6

disappointing

171:1

discarded 111:5

disciplines 51:15

disclaimer 110:17

disconcerted

113:10

disconcerting

113:20

disconnect 212:19

213:10

discounted 57:6

discover 101:16

discovered 101:23

discrepancies

26:2

discrepancy

104:16

discretion 23:16

discuss 50:23

59:4 65:21 87:11

95:19 112:10

117:22 121:9

245:19

discussant 36:7

62:24 96:15

99:15

discussants

36:8 116:23

discussed 23:11

57:14 59:11 68:4

75:8 103:3

144:17 152:24

201:20 208:17

227:20 243:19

discussion

28:18 59:1

86:7 90:2

95:13 112:6

115:24 116:23

140:11 140:15

152:22 185:2

185:21 192:10

192:11 196:10

203:3 209:17

216:6 226:23

227:23 228:6

228:13 229:9

229:18 262:11

262:25 269:8

277:5

discussions

23:1 58:24 164:6

176:24 200:16

disease 104:9

dismiss 272:12

272:14

dismissiveness

113:11

disorganized

230:23

dispose 120:5

distinct 141:17

141:20 169:24

214:18

distinction 73:10

distribute 239:2

distributed 13:21

153:13 156:5

268:4

distribution

20:10 155:17
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 322

156:7 230:2

distributions

265:18

disturb 7:3

disturbances

62:15

diurnal 8:23 9:22

9:23

dive 38:13

diversity 41:16

63:12

divide 44:4

division 110:18

DMD 204:2

docket 5:6

14:13 16:5 76:24

94:8

doctor 36:18

57:22 85:13

106:19

document 43:3

54:4 65:7 66:5

67:5 69:18 111:7

163:20 200:2

documented

142:7 197:19

documents 21:17

does--and 125:21

dogma 214:1

dollars 240:3

dominates 155:14

done 6:17 11:3

15:9 17:25

18:8 22:12

49:3 51:4

53:23 56:14 63:4

76:25 103:16

107:16 108:3

116:6 116:12

147:8 155:12

158:6 158:14

160:6 161:3

172:1 180:22

185:24 187:7

189:2 191:19

192:5 213:15

218:21 219:9

238:2 238:25

244:25 251:8

252:6 254:4

256:9 259:24

276:1 281:7

283:20

dosage 177:18

225:12

dose 7:16 7:19

8:15 9:1 9:11

11:8 11:11 13:16

13:19 13:21 14:1

14:2 14:4

17:10 19:7 20:10

22:13 27:3

28:4 28:12 28:19

28:24 29:12

30:11 30:12 31:7

32:7 32:9

32:10 50:2 51:20

51:22 55:19

58:11 64:15 69:6

71:9 74:16 75:23

78:18 78:19

78:19 78:23

80:23 88:2

90:7 93:11

104:10 117:8

119:22 122:3

122:20 122:24

123:2 128:14

129:11 131:4

131:16 132:19

133:20 133:24

134:3 134:9

135:6 144:15

146:1 147:17

152:8 155:21

156:4 156:13

156:17 157:19

158:3 159:22

159:25 160:18

162:13 163:23

165:6 165:19

166:10 166:18

167:6 168:5

168:15 168:24

168:25 169:1

174:19 180:10

183:8 184:25

185:10 190:1

190:2 190:24

195:1 195:6

197:19 197:22

199:3 201:14

201:24 202:16

208:9 208:12

208:14 208:22

218:19 221:9

221:25 222:6

225:5 225:5

225:18 230:25

279:21 279:23

dosed 10:19 27:13

87:25 90:6 92:12

92:15

doses 8:9 9:10

19:5 22:15 22:17

31:24 68:16

68:22 78:16
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 323

78:21 92:12

124:2 129:13

130:3 130:17

131:18 131:23

132:18 132:21

132:24 132:25

134:1 136:11

136:12 137:21

138:7 144:25

152:18 160:19

163:16 164:13

167:5 175:21

175:24 182:23

183:25 197:6

197:6 197:18

197:24 199:13

225:4

dosing 8:12 9:7

19:6 22:12 26:14

97:5 109:7 157:2

163:4 166:7

dot 40:17

dots 16:17

double 85:18

158:14 278:19

doubled 183:8

doubtful 78:4

downstream

185:1 210:16

211:6 211:11

211:15 212:21

213:7 215:6

223:2

Dr 2:4 2:12

2:18 2:21 2:24

3:1 3:3 3:5

3:7 3:9 3:11

3:13 3:15 3:17

3:19 3:23 3:25

4:2 4:4 4:6

4:9 4:11 4:13

4:15 4:19 5:11

5:20 5:22 5:23

5:24 5:24 6:3

6:4 6:8 6:9 6:14

7:8 7:9 7:9 7:10

7:13 7:21 8:7

9:21 9:24 9:25

10:1 10:15 10:15

10:16 10:20

10:25 11:3 11:10

11:14 11:21 12:1

12:4 12:4 12:5

12:9 12:12 12:15

12:17 12:21

12:23 13:13 14:6

14:9 15:3 15:3

15:4 15:8

15:12 15:14

15:15 15:18

15:20 15:22

15:24 16:2

16:3 16:4 16:6

16:6 16:7

16:10 16:12

16:13 16:20 17:1

17:13 17:15

17:15 17:18

17:23 18:1

18:1 18:1 18:3

18:10 19:13

19:18 19:19

19:20 19:23 20:3

20:25 21:2

21:2 21:3 21:4

21:9 22:4 22:6

22:7 22:9

22:19 22:21

22:21 22:22 23:4

23:7 23:10 23:10

24:21 24:22

24:24 25:1

25:4 25:14 25:17

25:19 25:22

25:23 26:8 26:12

27:12 28:18

29:11 29:20

29:20 29:22

29:24 29:25

29:25 30:1

30:8 30:10 30:16

30:16 30:19

30:23 30:24 31:1

32:16 32:21

32:22 32:23

32:25 33:7 33:20

33:20 33:21

35:25 36:1

36:4 36:10 36:16

36:19 50:20

50:20 50:23

50:25 52:1 53:17

53:17 53:18

53:19 53:23

54:14 56:5

57:2 57:16 57:16

57:23 58:1

58:3 58:5

58:13 58:13

58:15 58:16

62:23 62:23

62:25 63:1

64:7 64:7 64:8

64:23 64:23 65:3

65:10 65:10

65:12 66:17

67:20 76:3

76:3 76:4 76:5

77:13 79:3

79:3 79:4 79:5

80:7 82:12 82:12

82:16 82:17

82:22 82:22 83:1
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 324

83:5 83:8

83:10 83:16

83:16 83:17

83:17 83:21

83:22 84:7 84:10

84:11 84:11

84:18 84:25

84:25 84:25 85:2

85:5 85:6

85:12 85:14

85:17 85:22

85:24 86:1

86:1 86:2 86:6

86:14 86:16 87:3

87:6 87:8

87:15 87:15

87:17 91:22 92:7

96:14 96:15

96:16 99:19

99:19 99:20

99:21 101:7

101:7 101:8

101:9 104:23

104:23 104:24

104:25 105:3

105:16 106:18

106:20 106:21

107:22 107:22

107:23 107:24

109:1 109:1

109:3 110:16

110:16 110:17

110:18 110:23

111:21 111:21

111:22 113:1

113:1 113:2

113:4 113:22

113:22 113:23

114:8 114:18

114:25 115:3

115:9 115:14

115:14 115:15

115:16 115:21

115:21 115:23

116:22 117:3

117:5 117:21

117:21 117:23

125:2 125:2

125:3 125:4

126:9 126:13

126:22 126:22

127:3 127:3

127:4 127:17

130:4 130:4

130:5 130:6

133:4 133:4

133:5 133:6

135:8 135:8

135:9 135:10

136:2 136:6

136:7 136:13

137:1 137:6

137:6 137:8

137:9 137:16

137:16 137:17

138:9 138:9

138:10 138:20

138:21 139:24

140:5 140:9

140:14 140:19

140:19 140:20

141:14 142:16

144:8 144:8

144:9 144:10

145:3 145:3

145:4 145:5

147:3 147:3

147:4 147:5

147:13 147:14

147:15 148:14

148:14 148:16

148:17 148:18

148:23 148:23

148:24 149:8

149:13 149:18

149:18 149:20

149:22 149:23

149:23 149:25

150:2 150:4

150:23 150:23

150:24 151:2

151:3 151:4

151:12 151:14

151:16 151:22

151:25 152:2

152:4 152:10

152:12 152:20

152:22 152:23

162:3 162:3

162:4 162:5

168:18 168:18

168:19 168:19

168:20 170:2

170:2 170:3

170:4 170:17

170:18 170:19

173:6 173:6

173:10 173:11

174:7 174:7

174:8 176:20

176:20 176:21

176:22 177:2

177:5 177:7

177:24 178:7

178:7 178:8

178:9 182:16

184:2 184:2

184:3 184:4

186:7 186:7

186:9 186:10

186:20 186:20

186:21 186:22

187:15 187:17

187:18 187:21

187:25 187:25

188:1 188:2
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 325

188:4 188:10

188:11 189:11

189:13 189:14

190:15 190:17

190:19 190:20

190:20 191:14

192:9 192:12

192:25 193:18

193:20 194:10

194:16 194:18

194:19 195:7

195:9 195:11

195:13 196:1

196:13 196:16

196:16 196:17

199:14 199:14

199:15 199:16

200:11 200:11

200:12 200:13

201:17 201:17

201:18 201:19

202:6 202:6

202:7 202:8

202:14 202:14

202:15 204:6

204:6 207:22

207:22 207:24

208:1 208:2

208:3 208:5

208:6 209:2

209:3 209:7

209:10 209:20

209:25 209:25

210:2 213:14

213:14 213:16

213:18 213:18

213:20 213:21

214:6 214:17

214:21 215:4

215:11 216:1

216:5 216:5

216:7 216:11

216:11 216:12

216:12 219:12

219:16 219:16

219:18 219:20

219:22 220:22

220:23 220:23

221:1 222:15

222:15 222:18

222:20 222:20

222:22 223:15

223:24 224:1

224:3 224:5

224:7 224:12

224:16 224:17

225:17 225:21

225:23 225:23

225:25 226:8

226:15 226:19

226:22 226:24

227:1 227:3

227:8 227:11

227:12 227:14

227:15 228:4

228:4 228:5

228:9 228:10

228:17 228:17

228:18 229:4

229:8 229:12

229:14 229:21

230:17 230:17

230:18 231:4

231:4 231:7

236:7 236:7

236:8 236:23

236:25 237:23

238:12 238:20

240:10 240:11

240:13 240:24

242:14 242:14

242:15 242:16

242:17 242:18

244:11 245:16

245:17 245:21

245:23 246:3

246:3 246:4

247:19 248:11

248:23 248:23

248:24 251:4

251:5 252:5

252:8 252:13

252:17 253:11

253:22 253:25

254:1 254:5

254:7 254:25

254:25 255:1

257:2 257:3

259:13 259:13

259:14 260:2

261:13 261:23

262:10 262:16

263:11 268:22

268:23 269:19

270:8 270:11

270:15 270:17

270:23 270:24

271:1 271:2

271:13 271:13

271:14 271:22

271:22 271:23

272:8 272:11

272:22 272:22

272:24 272:25

273:5 273:14

273:14 273:15

273:23 274:12

274:17 275:5

275:5 275:6

275:21 277:3

277:17 277:17

277:18 278:11

278:22 279:1

279:9 279:9

279:10 279:11

279:12 279:14
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 326

279:17 279:17

279:18 280:2

280:7 280:11

280:16 280:21

282:21 283:2

283:19 284:13

284:16 284:25

285:2 285:4

Dr.Legan 224:16

draft 51:4

52:10 52:15 59:2

59:10 61:24

61:25 78:10

105:13 152:13

173:12 196:15

207:17

drainage 232:23

draw 160:1 186:25

187:8 246:8

252:3

drawn 166:6 247:5

draws 194:12

drew 191:6 191:6

dribble 187:9

drinking 2:8

48:10 48:15

68:25 73:22

80:14 80:15

139:22 161:24

219:5 229:22

230:1 230:7

231:16 232:8

232:17 233:1

237:9 254:16

264:10 265:4

265:11 276:16

281:12

drive 18:15

279:23

driven 42:22 43:8

driving 18:25

169:25 204:9

227:5 259:1

drop 218:16 224:9

dropped 180:15

drug 9:23

Ds 224:23

ductal 91:10

due 33:14 46:23

72:13 96:19

104:19 134:2

147:17 163:15

277:4

dug 235:4

Dulley 153:15

duplicate 53:23

54:16 135:23

duration 6:10

13:17 19:3 62:13

70:10 71:9 71:17

173:21 179:25

190:4 190:12

211:7 211:23

230:5 230:15

235:8 235:10

236:21 237:23

240:17 244:19

254:12 255:4

259:25 265:19

274:6 281:14

281:16 282:7

282:15 283:2

283:5 283:17

284:9

durations 29:19

173:14 262:5

during 10:19 17:2

88:8 88:11

90:5 93:2 93:5

93:9 93:15 95:14

106:6 106:8

123:8 138:15

140:24 141:18

141:19 141:19

141:20 156:3

208:20 211:24

211:24 221:3

dwell 58:18

dynamic 198:20

199:11

dynamics 169:25

171:21

E

earlier 32:2

38:11 63:24

75:12 98:11

125:6 127:17

127:25 179:6

190:2 197:6

197:8 207:8

215:25 237:16

258:8 261:1

280:21 284:1

early 8:12

43:25 106:7

109:22 126:18

137:18 148:20

196:21 213:24

213:25 237:19

easier 135:25

245:15 256:5

easiest 255:2

easily 206:18
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 327

218:6 266:25

275:14

easy 37:13

51:25 249:18

251:3 251:14

271:3 277:1

eat 7:23 7:24

eating 8:5 9:22

echo 201:14

eco 14:11 34:10

ecologic 37:23

44:23 46:3 46:14

46:19 48:7

49:3 49:14

55:6 56:4

57:12 63:14 74:1

80:8 80:8

80:10 80:12

ecological 73:5

73:10 73:11

73:13 73:17 74:1

118:16 221:16

234:6 246:1

ecologically

48:14

economic 143:5

267:13

ecstatic 195:22

196:3

educated 39:9

61:16 186:23

education 42:8

educational 48:5

effect 8:11 11:20

19:1 29:10

39:4 39:12

47:1 49:22

53:1 53:12

59:6 59:8 59:9

59:21 59:25

60:14 60:20

61:12 61:13

61:18 63:20

68:23 78:20

79:22 94:20 95:4

98:20 99:6 99:11

102:13 104:13

104:14 109:8

109:14 110:6

110:7 110:8

114:4 129:21

137:13 138:14

138:18 141:22

147:19 147:24

188:13 189:22

190:1 190:3

198:3 198:9

199:1 204:20

206:25 211:3

211:6 211:8

211:9 211:10

211:12 211:16

211:18 211:18

212:1 212:18

212:24 213:8

214:5 214:9

214:11 215:3

215:6 220:5

220:5 220:21

222:25 279:20

effective 196:8

effectively 9:6

effects 2:7 2:8

13:10 33:11

49:18 50:11

58:24 60:20 61:2

62:13 64:4

66:3 66:11

67:4 68:3 73:1

77:3 77:7

77:16 77:21

77:25 78:9 78:19

88:17 88:24 90:1

91:7 91:14 94:16

94:19 95:8 95:10

95:11 96:2

96:3 96:6 96:8

102:11 104:17

109:4 117:7

123:13 129:7

130:14 131:17

133:12 133:25

135:1 136:11

137:11 143:13

144:17 144:18

144:21 144:23

144:23 144:24

148:11 150:21

196:25 197:5

197:18 197:24

210:16 210:20

215:10 220:9

221:6 223:2

228:1 271:17

effect--the 95:7

efficacy 102:14

effort 77:10

102:5 105:4

239:1 239:5

239:13 261:10

264:3 266:25

267:16 268:2

268:3 274:20

280:9

efforts 35:4

81:21 113:7

260:5 260:8

260:13 263:2
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 328

EGFR 89:7

egg 83:13 83:14

eggs 216:25

eight 60:16 83:25

either 12:19 14:8

18:6 29:18

77:6 78:21

101:24 108:2

112:1 112:16

120:14 120:21

141:5 168:12

172:1 185:8

222:4

elaborate

108:11 222:16

elapsed 154:6

elected 6:23

elevated 17:11

47:21

elicit 215:3

eliminate 130:8

elimination

154:25 155:18

156:8 157:24

158:16 158:18

Ellen 4:9 4:9

36:19 58:1

63:1 79:5 83:1

84:7 85:2 85:6

85:17 85:24

Ellsworth 276:15

eloquence 177:2

eloquent 174:21

177:1

else 12:22 84:8

84:9 88:1 177:11

192:4 202:13

216:4 217:18

218:13 225:8

227:12 244:11

252:8 267:12

276:12

elsewhere 51:8

207:3 249:15

emerge 194:14

emergence 101:20

emerges 176:2

emphasis 122:19

230:1

emphasize 43:24

emphasized 136:17

empirical

256:16 256:18

256:24 261:25

employed 42:22

169:13 172:18

en 51:22

enables 133:12

enclosed 131:2

encourage 38:10

63:25 79:15

80:17 82:5

encouraged 35:4

115:10

encouraging 34:22

41:18 75:20

end-bud 93:21

94:1 94:3 95:9

endocrine 53:13

endocrinologist

205:13

endogenous 188:21

endpoint 64:15

75:5 81:6 122:20

122:22 131:13

endpoints 98:9

106:22 136:22

136:22 137:1

energy 207:14

enhance 184:8

enhanced 124:14

146:3 147:1

enhancement

187:11

enhancing 236:15

enlargement

121:20

enlightening

257:4

Enoch 102:7 111:9

111:14 117:8

117:15 118:12

121:18 145:16

147:1

enroll 199:22

ensure 263:7

266:20

enter 13:3 121:12

entered 10:5

enterprise 233:3

enters 213:4

entire 8:17 9:7

10:11 100:15

147:25 189:21

251:15

entirely 42:16
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 329

62:20 185:5

entities 113:16

environment

12:2 123:21

124:3 124:18

263:2 263:7

environmental

128:19 130:18

139:19 141:9

141:13 143:13

146:25 263:18

environmentally

129:24 130:15

269:3

enzymes 120:16

EPA 4:25 14:13

23:13 25:11 37:8

40:3 43:14 43:17

47:5 47:18

51:3 51:4

51:24 52:10

52:11 52:15 53:4

53:20 54:3 54:20

57:5 58:16

59:1 59:4

59:10 60:22

61:24 61:25 63:1

64:10 67:5

67:7 73:9 77:9

78:10 79:7

82:5 84:12 97:25

98:24 103:25

118:3 118:5

122:22 127:12

135:19 151:18

162:11 162:16

197:15 228:23

264:12 264:16

EPA's 21:17

64:9 79:2 144:11

epic 77:22

epidemiologic

36:3 38:12 41:10

51:3 55:17

56:1 56:9

58:10 65:9 65:18

65:24 67:23 68:2

69:21 71:11

71:16 76:25 77:2

77:11 77:16 79:9

80:20 80:23

80:25 81:5 81:12

82:3 82:13 86:18

86:22

epidemiologist

65:20

epidemiologists

56:21 74:22

86:24

epidemiology

36:13 37:4 37:10

61:15 64:10

64:19 67:8 67:13

69:14 69:16

71:20 74:8 74:12

75:15 75:25 77:6

77:19 78:2 82:20

83:12 142:3

epidermal 89:4

epithelial

90:18 91:11

epithelium 90:19

equal 9:16 50:7

59:18 170:12

equally 160:24

equation 284:22

equipment 70:13

equivalence

164:24 165:8

166:5 166:11

166:14 168:4

168:8

equivalent 8:17

108:15 162:1

167:5 208:15

equivalents 18:7

equivocal 78:3

error 61:3 61:3

errors 105:13

224:14

especially 9:4

33:4 37:18

41:9 55:23 59:13

76:11 165:1

188:16 252:7

essential 84:17

122:16

essentially

17:5 130:14

131:24 162:6

162:25 163:2

163:9 163:19

163:25 165:10

192:24 238:13

245:18 247:22

274:24

establish

160:19 210:11

211:14

established 77:25

110:1 123:1

210:18

establishing
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 330

209:22 210:7

212:8

esterase 221:4

221:5 221:22

222:14

estimate 152:8

182:5 230:3

230:5 236:11

236:12 245:13

246:12 246:20

257:24 259:1

259:5 267:23

281:4 281:16

283:25

estimated 44:5

258:10 263:22

estimates 69:6

154:25 157:23

158:10 159:12

169:6 193:8

254:16 257:1

257:21 261:17

282:13

estimating 231:22

256:19 258:4

281:23

estimation 256:14

257:10 281:3

281:12 283:9

estimations

229:23

estradiol 33:18

estrocycle 189:21

189:23

estrogen 10:7

186:3 203:23

214:1 218:3

estrous 105:6

105:12

estrus 10:6 27:14

27:14

et 27:18 28:9

30:2 30:20

69:9 87:10

101:23 117:6

117:8 117:15

121:18

etcetera 42:1

ethnicity 39:12

42:1

evaluate 41:21

67:24 68:10

102:6 114:2

114:16 119:21

131:5 200:8

evaluated 50:8

60:4 64:2

82:10 89:12

90:10 93:16

130:21 130:22

144:20 263:23

277:22

evaluates

114:24 114:25

evaluating 44:7

130:23 131:12

282:2

evaluation 34:4

35:5 38:12 131:1

evaluations 95:16

113:6 113:13

113:14 127:20

evalutation 275:3

Evans 11:1

27:19 27:20

27:24 27:25

27:25 28:7

125:10 125:13

evening 285:8

event 5:16

20:20 188:17

203:20 206:25

214:24 214:25

219:13

events 78:18

190:21 196:22

197:6 197:9

210:11 215:13

242:23 265:19

265:20 266:24

eventually

264:6 264:22

everybody 5:6 5:8

25:7 126:23

everyday 235:17

everyone 2:5 4:20

135:21 140:6

175:11 180:23

202:9

everything 26:9

105:1 119:17

162:6 167:17

171:18 202:13

225:8 255:6

everywhere 154:2

evidence 8:5

11:17 52:25 59:8

59:9 66:16 77:16

125:22 125:25

132:15 133:17

133:18 133:23

134:1 134:23

135:5 135:25

140:22 143:25
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 331

144:3 147:23

148:3 155:5

155:6 156:6

166:3 203:11

204:3 214:7

evidenced 197:1

evidences 135:16

evolved 136:21

exact 153:10

265:17

exactly 40:21

86:15 97:24

195:2 201:8

263:12 265:16

267:6 279:1

281:10

examination 41:25

examine 41:7

42:18 46:18

49:15

examined 90:14

examining 101:5

example 26:17

26:18 38:22 47:9

51:20 53:11

62:17 70:4 96:23

163:1 191:14

198:19 204:19

243:6 243:18

248:11 253:15

254:20

examples 76:22

exceed 281:19

exceedance 265:8

exceedances

265:25 265:25

266:3

exceeded 124:13

exceeding 20:10

258:4 263:8

282:10 282:14

282:14

excellent 86:25

168:3

except 44:8

51:1 78:25 188:8

exception 131:25

excess 40:17

excited 159:15

exciting 159:18

exclude 55:21

81:8

excluded 28:25

54:25 150:16

exclusion 54:23

56:13

exclusively 62:18

excrete 107:10

excuse 175:7

209:6 237:9

exemplified 94:17

exercise 16:19

263:1 265:22

exercises 262:20

exhibit 247:24

exist 91:20 104:9

129:15

existed 103:18

existence

132:14 146:16

existing 21:14

69:23 79:16

101:18 145:8

254:15 281:18

exists 75:2

exogenously

188:20

expand 103:15

expansive 268:10

expect 60:14

70:19 77:19

103:11 130:7

132:23 161:23

177:9 239:11

239:18 271:24

expectation

265:24

expectations

268:11

expected 60:11

69:25 97:12

233:14 247:6

255:25 265:10

265:14 265:18

265:19 266:2

expecting 98:9

expensive

268:16 268:18

271:11 273:2

273:12 275:19

experience

146:5 265:8

experienced 236:5

experiment

24:10 115:12

117:19 161:3

187:9 200:7

219:2 219:9
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 332

experimental

64:14 65:9 65:17

67:8 67:9

67:15 69:10

69:14 69:15

71:14 74:7

74:8 80:22 81:11

81:16 81:22 88:5

89:11 91:4 121:8

128:8 143:16

160:2

experimentalist

128:6

experimentally

19:10 81:18

experiments

14:2 18:13 21:25

22:10 34:25

89:14 91:7 92:19

159:21 160:5

178:16 205:8

274:2

expert 174:24

275:23

expertise 66:23

66:25 86:8 89:14

explain 47:1

63:20 79:14 83:8

126:1 128:23

131:19 145:20

204:21 206:17

explained 207:5

233:20

explaining 131:16

explains 31:24

explanation

83:3 133:20

271:17

explanations

10:21

explanatory

263:18

explicit 59:1

61:20

explicitly 11:2

14:11 148:25

explore 32:4

264:16

explored 81:18

171:23

exploring 67:20

107:18

exponential 16:23

16:23 158:14

exposed 71:22

72:10 121:4

123:17 123:18

123:23 139:22

188:8 225:6

225:13 225:19

exposure 27:21

33:14 37:12

37:20 38:2 39:23

40:6 40:7 41:6

41:15 43:6 43:25

44:19 45:1

46:3 46:20 47:16

49:6 49:9

49:13 52:13 55:8

55:11 55:19

55:25 57:7

57:8 68:6 70:7

70:11 71:4 71:17

72:1 72:8 73:8

73:12 73:18

75:16 75:23 77:3

77:17 78:14

80:12 82:10

85:20 89:22

89:24 90:6 90:12

117:10 119:9

119:18 122:4

144:25 145:8

148:12 153:2

153:4 153:5

160:15 160:24

161:23 162:2

162:15 165:2

173:13 173:21

174:3 185:17

185:23 186:5

186:17 186:18

188:12 188:14

189:17 189:18

189:21 189:22

190:4 190:9

190:11 194:20

195:1 198:10

214:9 214:19

228:3 229:23

230:2 230:9

230:14 231:17

231:22 235:7

235:11 236:21

237:23 246:1

246:9 246:14

248:3 253:15

254:13 254:16

262:2 263:21

264:8 266:17

274:6 281:2

281:12 282:7

283:2 283:5

283:17 284:8

exposures 37:15

37:25 39:3

40:6 42:23

43:8 46:14 46:18
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 333

46:25 48:15

48:24 49:11

51:22 52:18

57:10 62:12

63:15 63:19 66:9

67:12 68:16

68:17 68:24

68:24 68:25 69:4

70:12 71:15

72:21 72:22

72:24 72:25

73:19 74:2 74:14

74:15 74:16

74:18 76:12

78:17 79:17

81:18 84:3

120:21 121:20

123:14 152:17

173:25 281:4

281:23

expound 176:19

expressed 42:3

62:13

expression 89:3

extend 59:2

102:25 103:7

extension 29:3

extensive 52:2

94:12 94:12

197:25 199:21

extent 83:6

152:18 156:23

164:1 165:11

228:25 267:23

external 113:19

extinction 133:12

extract 38:19

extraction 273:9

extracts 118:3

extraordinary

131:17

extrapolating

153:5 159:16

extrapolation

21:14 163:1

166:13 173:4

extrapolations

198:21

extremely 71:17

169:23 271:16

276:25

eyeball 175:17

175:17

eyes 207:6 209:1

F

F1 90:13

F2 90:13 90:25

91:8 144:18

faces 188:3

facilitate 265:7

facilities 235:4

facility 238:14

fact 7:4 8:8 8:10

8:23 10:2

14:19 14:20

14:25 14:25

18:13 18:16

52:14 53:7 59:24

62:2 76:8

76:15 86:17

103:23 111:2

132:19 136:10

141:16 157:1

158:12 158:18

163:12 178:14

181:17 184:12

188:22 191:9

191:12 193:13

197:1 200:16

203:8 213:23

214:4 214:6

225:3 227:9

251:10 258:24

267:19 275:6

factor 89:4

96:1 167:20

167:21 278:14

281:5

factors 13:11

39:13 42:1

42:7 70:11 87:12

95:19 125:8

126:20 198:19

209:21 241:1

241:3

facts 121:5

failing 59:5

fails 255:24

failure 144:18

faint 194:11

fair 93:7 155:15

fair-fed 93:5

93:10 93:13

93:23 94:3 94:10

95:15

fairly 16:15 23:5

35:13 39:9

57:6 69:19

111:16 191:1

194:7 251:3

261:9

fall 13:16 97:4
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 334

98:17 158:2

242:4

false 157:19

familiar 27:20

84:19 284:19

284:25

families 42:16

family 43:15

farm 43:15 123:23

farms 68:21

fascinated 101:10

fascinating

221:21

fashion 7:24

69:25

fast 23:18

fat 97:19 97:20

father's 53:14

favorite 125:20

FDA 3:17

feasible 264:21

features 141:11

141:16

February 18:4

38:7 39:6 51:9

feces 15:11

fed 12:13 93:8

federal 4:17 4:21

4:23 82:6

feedback 191:25

192:7 195:17

203:23 218:3

283:12

feeding 92:3 92:5

feel 28:23 32:8

44:23 90:14

117:2 138:11

153:2 153:8

178:18

feeling 98:24

fell 8:18 158:4

felt 25:25 29:1

56:18 158:13

female 25:1 26:15

27:6 27:13 38:15

40:23 42:10 64:3

69:22 75:10

83:24 149:7

149:10 151:21

226:4 270:22

females 28:21

107:20 228:1

FENNER 279:11

Fenner-Crisp

4:6 4:7 83:9

83:10 83:21 85:1

85:13 85:14

113:1 113:2

148:23 148:24

149:8 149:13

200:12 200:13

278:11 279:1

279:10 280:2

Fenton 10:20

91:22 95:21

100:8 110:18

113:4 114:9

114:14 114:18

115:3 126:9

141:14

Fenton's 6:15

11:3 114:25

Fenton--should

115:9

FEPA 36:17

fertility 52:13

185:1 210:17

210:21 211:16

211:19 214:24

217:4 222:8

223:23 226:4

fertilizer 45:20

fertilizers

123:24

fetal 43:12 44:11

fetus 53:12

fiber 97:16 97:20

field 186:18

256:10 256:12

265:21

fields 45:6

45:7 45:18 45:21

45:23 55:8 73:2

FIFRA 2:1 2:5

fighting 203:20

figure 15:16 26:6

27:20 139:7

152:13 156:9

156:15 157:10

157:11 159:25

164:5 164:14

173:11 173:15

173:22 174:8

176:16 194:11

216:22 224:13

243:18 263:14

figured 183:11

206:3

figures 15:25
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 335

15:25 156:19

163:13

fill 32:14 245:5

filled 31:23

final 16:9

24:14 48:20 57:2

105:20 274:15

279:15

finalizing 285:14

finally 9:1

38:5 42:3

49:17 92:2

finding 41:19

41:21 49:1 50:12

133:23 152:19

205:3

findings 39:10

41:23 45:12

47:20 49:12

60:23 60:24

61:11 61:12

63:14 65:9 65:21

76:9 78:1 78:3

78:6 79:8 79:9

79:14 79:24 80:6

81:6 84:4

87:13 95:20

131:7 131:7

131:12 134:19

173:22 174:6

finds 205:2

fine 24:6 46:4

83:7 130:13

164:4 168:10

217:6 225:8

225:15 255:3

fingerprint 241:8

finis 276:23

finish 115:12

250:16 254:6

271:2

finished 231:20

231:23 232:2

232:3 232:9

finishing 280:13

first 4:22 9:6

9:8 11:19 36:2

36:11 37:8 38:15

38:16 38:17 45:5

47:25 49:18

50:23 51:12 52:6

53:20 54:3 54:20

59:5 62:24 65:23

76:14 80:21

87:19 101:6

110:17 114:9

152:23 154:9

155:19 158:13

162:9 178:11

178:11 179:1

179:14 179:18

184:5 184:6

185:24 189:23

190:10 192:17

210:10 230:18

234:19 234:25

241:10 241:21

246:4 252:25

260:5 277:3

282:2 283:10

fit 125:5

157:17 182:6

204:18 221:24

256:23

fits 208:12

221:14 245:1

fitting 16:18

five 6:21 13:3

33:5 34:25 35:10

44:23 49:2 116:6

126:13 171:11

185:3 190:10

285:5

five's 30:13

fixed 160:14

161:6

flag 204:25

flashy 234:18

246:16

flaw 14:19 145:22

146:13

flexibility 233:6

233:17

flipside 139:12

flow 234:16

234:21 238:11

flowing 237:5

241:22 242:10

246:2 247:25

248:6 248:7

248:17 248:19

255:13 273:17

273:19

fluid 12:13

focus 52:17 53:25

54:17 62:17

70:18 78:12

171:20 197:4

252:16 261:21

262:20 267:17

282:23

focused 76:11

107:2 253:12

268:17
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 336

focusing 11:16

49:14 130:15

254:18

folks 71:19

75:3 251:22

followup 10:17

follow-up 12:6

32:25 228:12

252:13 253:12

272:23

food 7:25 8:9

8:18 8:23 8:25

10:3 10:6 10:8

10:10 12:7

161:25 175:16

175:19 176:2

278:2

force 51:23 59:18

91:19 99:3

foreign 113:18

foresight 241:6

241:8

forget 25:3 184:6

217:12

forgot 24:24

forgotten 280:10

for--I 99:16

form 75:16

172:6 172:11

266:6 272:3

formal 178:12

formed 61:21

172:5 172:12

forth 51:17

99:8 105:9 164:1

167:16 167:18

178:22 180:5

180:11 223:11

251:7 269:3

forward 6:1 25:16

34:5 169:16

171:25 259:20

foster 89:15 91:3

fostered 92:19

fostering 89:13

found--they 88:22

four-day 10:4

189:17 235:21

283:9

four-days 188:15

fourth 51:19

235:6

fraction 19:15

215:15 215:16

fragmentation

275:10

frame 35:11 125:7

188:9 188:12

221:24 231:10

281:6 283:15

framework

230:11 274:3

Francisco 4:5

frankly 31:25

200:5

free 117:2

freezer 235:20

frequency 2:9

70:10 71:9

101:10 119:8

204:24 205:16

226:11 230:14

235:7 241:17

241:18 241:18

265:5 268:10

frequent 62:5

62:9 141:23

159:2 240:23

253:21 280:23

frequently 106:13

242:5 247:8

274:6

freshly 20:21

front 190:22

232:19

fruit 284:21

frustrating 238:3

FSH 204:23

fully 64:19

fun 257:2 259:15

function 7:16

122:20 150:9

150:15 150:20

217:5 233:20

233:21 238:6

244:15 245:12

254:19

functional 106:23

107:18 108:3

122:7 142:22

143:8 143:17

144:6 150:14

242:21 243:6

functions 141:9

252:21

fundamental 99:25

100:14 101:6

funded 82:7
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 337

fungicides 72:14

funny 29:8 191:9

furthermore

41:4 95:6 109:24

114:23 130:17

future 41:20

42:15 43:11

55:22 81:8

105:10 136:19

138:23 144:7

145:17 146:17

161:16 172:23

266:21

G

gain 107:13 155:6

Galian 258:9

game 273:22

gap 52:14 52:15

154:19

garnered 22:10

gastric 62:19

gather 21:11

64:11 112:5

112:7 128:9

gathering 143:16

gavage 12:13

12:19 12:25

13:14 17:20

17:24 18:8 18:18

88:1 92:13

155:24 156:2

156:22 156:23

159:24 161:20

162:1 173:18

gavages 7:12

gears 229:15

general 21:7

36:21 37:8 54:18

59:5 68:25 99:23

113:6 118:15

126:10 174:10

196:19 196:20

204:20 228:1

228:2 231:15

232:5 243:20

244:1 246:18

generalizability

39:10 40:12 41:3

42:17 49:22

63:14

generalizable

41:23

generalized 48:13

generally 8:1

26:15 51:18 52:2

75:14 204:25

231:25 248:1

248:20

generate 119:17

256:23 261:16

generated 104:1

104:21 111:10

269:3 269:15

272:4

generates 193:4

generating

69:24 80:11

206:9 216:14

generation 38:4

64:22 71:8

91:8 144:18

generic 58:19

77:8 135:4

206:24 226:11

genesis 259:16

genetic 121:1

Gentner 28:9

genuine 122:6

geographic 62:4

241:25 251:11

Geological 3:22

3:24

geostatistical

263:18

GERALD 4:13 101:9

145:5

gestalt 114:4

195:4 195:4

gestation 6:20

10:19 62:18 88:8

88:11 90:11 93:2

93:6 100:23

106:7 109:8

126:11 126:13

141:19

gestational 11:20

47:22 49:13 80:4

90:5 91:5

92:15 100:22

110:7 128:1

138:11 148:20

gets 182:1

190:3 193:22

238:2 261:3

280:8

getting 8:11

13:22 29:3 51:13

157:22 163:19

219:10 238:21

242:3 258:15

272:20 283:5
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 338

given 12:24 19:16

23:8 26:18 63:11

73:6 79:11

105:12 109:6

122:5 136:10

164:1 166:21

171:14 172:10

190:2 202:10

215:2 224:19

225:6 228:13

233:4 240:22

246:5 250:23

250:25 264:18

271:25 275:6

275:9 279:19

280:21 281:22

282:2 282:13

gives 5:3

134:14 153:20

195:4 195:23

231:19

giving 90:8

125:16 131:23

150:10 158:15

247:1 247:2

262:16 282:12

283:23

glad 192:12 240:5

gland 87:6

87:10 87:20

88:16 88:21

88:23 89:20

91:16 91:17

92:17 92:22 96:6

96:10 96:22 99:2

100:9 100:23

100:25 101:5

102:1 102:11

102:22 103:9

104:15 104:17

105:4 105:24

106:3 107:19

108:7 108:14

110:12 110:13

117:9 121:20

122:18 123:3

128:15 140:17

140:22 141:5

141:8 141:12

141:17 142:12

143:14 143:24

144:6 145:6

145:12 147:16

147:19 148:9

149:9 150:9

150:19 150:20

151:8 151:8

179:1 205:17

279:25

glands 90:16

98:10 141:8

glitches 5:7

GnRH 19:1

204:24 205:16

207:11

goal 34:3 34:15

119:6 121:12

142:19

goals 118:10

118:14 143:1

188:12 262:24

Gold 4:9 4:9

36:18 36:19

50:21 54:14 56:5

57:3 57:23

58:1 62:25

63:1 79:4 79:5

82:13 83:1

84:7 84:25

85:2 85:6

85:17 85:24

Gold's 52:2 53:23

58:17 82:22

gonadotropin

222:5

gone 56:12 132:14

goodness 157:17

gota 234:17

gotten 198:16

276:7 276:7

276:12 280:11

Gould 284:20

government 101:11

101:15

grade 267:9

gram 92:4

graph 40:17

51:5 182:14

183:20 187:9

202:2

graphic 174:12

graphically 40:16

graphs 188:6

great 47:5 169:10

173:1 179:4

179:8 193:2

194:17 196:18

238:9 244:22

249:1

greater 36:24

39:23 60:23

68:24 93:22 94:3

94:6 114:7

170:12 189:8

249:4 253:14

283:4
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 339

greatly 275:11

Greenwood 3:5 3:6

15:14 15:15

15:16 15:20

15:24 16:4 16:20

17:13 151:25

152:22 152:23

162:4 177:5

177:7 178:8

186:9 186:10

189:15 227:14

227:15

Greg 276:15

grossly 246:13

group 8:16

30:14 34:11 45:8

72:13 73:6 73:15

82:13 82:18 88:6

88:7 88:11 88:14

88:20 90:20

91:18 92:9 93:10

93:11 93:13

93:14 93:20

93:23 93:23

93:24 94:3

94:4 94:6

95:21 95:22

110:25 114:25

115:2 119:5

125:24 129:9

133:25 174:9

174:14 174:21

174:24 175:3

179:8 179:9

180:9 202:1

grouped 107:6

grouping 116:10

groups 30:11

30:12 31:10

31:19 31:23 32:9

32:10 32:11

72:18 88:5 90:10

90:25 91:3

93:5 93:8

95:12 95:15

107:15 183:6

183:12 242:1

growing 237:11

239:2

grows 229:2

growth 89:4 91:10

205:3 205:3

205:10 205:13

205:15 205:16

205:16 205:24

206:4 206:19

207:11 227:19

227:23 228:15

GSH 165:16 165:21

166:22

guarantee 229:5

guess 7:14 9:17

10:1 11:15 29:22

36:4 38:14 79:11

89:16 110:25

127:6 128:12

152:5 171:9

183:3 183:11

185:12 188:18

189:10 194:13

195:8 203:2

203:3 205:23

226:11 229:20

246:16 277:5

guessing 256:15

guidance 212:12

220:16 220:17

220:20

guide 260:8

guideline 277:25

guiding 260:12

gun 77:15

gut 23:16 23:16

23:17 23:18

23:22 23:23

guy 99:24

guys 129:11

238:22 275:16

H

habits 80:14

Halberd 246:25

half 25:16 88:2

88:3 140:8 245:2

245:5

half-day 9:11

halfway 255:16

hand 48:3

248:16 255:7

255:21 272:12

272:15

Handa 28:10

Handa's 27:12

29:11

handle 54:24

handled 43:19

43:22 44:18

112:21 112:23

272:1

handling 51:8

76:6 100:23

112:11 115:7

141:3 144:3

147:9
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 340

handout 23:7

hanging 284:21

happen 14:21

101:13 122:13

132:7 239:12

happened 105:25

109:20 201:3

happens 10:13

123:7 170:10

180:7 192:14

216:18 242:23

happy 120:25

264:1

hard 13:25

30:14 32:17

51:24 60:7 77:20

108:20 180:18

216:8 238:5

275:17 277:13

harder 114:20

272:5

hardly 93:12

257:8

Harris 4:11

4:11 53:18 53:19

57:17 64:7

64:8 64:24 65:10

65:12 76:4

80:7 86:6 86:16

Harvey 23:10

has--I 50:14

hate 203:22 277:6

haven't 24:9

32:24 52:14

105:2 116:8

141:25 149:1

149:14 154:1

165:18 198:16

207:15 208:24

218:7 218:21

253:12 269:7

280:10 283:19

having 49:5

67:6 108:21

110:7 113:15

116:1 147:21

154:13 158:9

167:24 169:13

178:20 181:25

182:8 183:12

203:9 211:15

233:17 238:2

238:9 246:18

251:21 265:18

269:5 283:16

hazard 39:22

58:11 64:12

64:12 81:1

119:12 140:18

145:7 149:4

hdyroxyatrazine

275:10

head 17:4 25:12

225:7 250:8

277:23

headed 192:1

heading 231:14

health 2:7

34:10 39:13 41:5

41:9 41:17

42:1 42:14 49:19

59:21 63:10 64:6

68:3 69:22

70:5 70:23 75:10

75:11 77:2 77:16

78:9 81:17 83:13

83:14 83:24

83:24 97:13

100:10 118:16

118:17 124:4

229:23 240:1

263:8

healthy 142:2

142:4

hear 23:1

166:23 189:12

226:22 231:6

269:21

heard 14:10 34:18

34:21 35:21

36:17 53:5 66:16

71:13 79:25

82:19 95:24

109:6 112:5

130:9 139:14

149:24 164:18

169:11 180:23

190:22 190:25

203:14 226:3

267:13 269:7

280:19

hearing 136:8

225:14 272:25

heavier 137:18

137:23 138:7

138:16 138:16

138:18

heavily 253:13

heavy 128:4

137:22 265:19

he'd 182:20

HEDs 167:5

heeded 38:8

Heeringa 2:4 2:10
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 341

4:15 4:20 5:11

5:24 6:8 7:8

10:15 12:4

15:3 15:12 15:18

15:22 16:7 16:12

17:15 18:1 19:18

21:2 22:21

23:4 24:21

25:4 29:20 29:24

30:8 30:16 30:23

32:21 33:20

35:25 36:16

50:20 53:17

57:16 58:3 58:13

62:23 64:7 64:23

65:10 76:3

79:3 82:12

83:5 83:16 84:11

84:25 85:12

85:22 86:1 86:14

87:3 87:15 96:14

99:19 101:7

104:23 106:18

107:22 109:1

110:16 111:21

113:1 113:22

115:14 115:21

116:22 117:21

125:2 126:22

130:4 133:4

135:8 136:2

137:6 137:16

138:9 139:24

140:5 140:19

144:8 145:3

147:3 147:13

148:14 148:23

149:18 149:22

150:23 151:2

151:12 151:16

151:22 152:10

152:20 162:3

168:18 170:2

173:6 174:7

176:20 177:2

178:7 184:2

186:7 186:20

187:25 192:9

192:9 194:10

195:7 196:1

196:1 196:16

199:14 200:11

201:17 202:6

202:14 207:22

208:1 208:3

209:2

Heidelberg 245:25

246:7 249:1

249:10 251:6

251:7 251:25

252:3 261:7

height 32:2

Hello 174:8

help 22:8 30:15

69:9 124:2 125:7

162:24 166:2

187:14 212:10

218:14 238:9

245:4 262:8

263:5

helped 77:12

105:10 125:17

helpful 25:25

61:20 135:3

198:22

helping 134:22

helps 120:19

Hence 194:6

Hendley 19:19

19:20 20:3

Henley 236:25

248:11 261:24

Henry 247:19

Herbert 3:25 3:25

193:20 236:8

242:18 245:17

246:4 251:4

255:1 257:3

259:15 273:15

herbicide 44:19

124:8

herbicides

72:14 123:24

here's 218:19

281:3 281:4

he's 207:1

Hey 120:10

Hi 36:10

hierarchical

193:22

high 8:9 8:15

11:7 17:10

19:5 20:9

20:19 20:23

22:13 27:3 28:12

41:7 47:24 50:22

51:20 52:18

78:18 78:19

93:11 97:16

160:18 164:13

171:6 190:2

197:6 233:8

238:3 238:17

241:17 241:17

241:17 253:15

265:25 266:12

267:14 267:25

268:10 282:3
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 342

high-dose 93:23

93:24 95:15

high-dosed 93:4

93:7 93:20 94:4

higher 20:21

30:13 32:10

93:15 108:17

129:19 164:16

174:19 175:24

177:18 182:24

183:25 184:24

185:5 185:10

218:4 222:3

256:21 282:18

highest 76:12

174:18 176:11

182:17 267:18

highlighte 251:12

highlighted

251:17

highlights 63:4

257:9

highly 51:14

172:20 224:19

228:25 256:17

Hill 52:5 52:6

52:7

hint 137:20 205:9

207:4

histologist 104:4

histopathological

278:16

historical 33:8

47:12 49:4 115:2

115:10 115:18

203:6

historically 70:6

81:15

history 113:15

hit 33:23

175:23 175:23

211:19 211:22

Hobie 92:7 94:5

hold 58:21 126:24

178:25 190:15

235:20 241:23

holding 37:16

holistic 102:21

Hom's 206:23

honest 31:22

honestly 216:8

hop 101:10

hope 34:13

38:10 171:10

190:15

hopefully 5:8

54:8 57:17

101:20 118:12

166:14 177:19

284:3

hoping 5:5 209:13

Hoppen 49:18

hormonal 62:15

105:8

hormonally

39:25 279:6

279:7

hormone 43:6

205:3 205:3

205:4 205:4

205:10 205:14

205:15 205:16

205:24 206:19

207:11 207:11

227:19 227:24

228:15

hormones 29:8

32:10 206:4

horrible 232:12

Horseman 3:11

3:11 99:20 99:21

101:8 107:23

107:24 111:21

111:22 136:6

136:7 137:7

140:19 140:20

144:9 149:18

149:20 151:3

151:4 151:14

186:20 186:22

187:18 190:20

215:11 228:9

228:10 268:22

268:23

hot 120:4 219:6

hour 140:1 285:5

hourly 62:8

hours 9:11 9:12

154:18 154:19

156:1 156:13

157:2 165:2

175:5 175:8

176:9 176:11

176:11 178:1

183:19

housed 11:23

11:23 11:24 88:1

Howder 94:11

HPA 33:10 33:10

33:15 125:20

148:9
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 343

HPG 33:13 148:10

huge 210:21

223:19 260:22

human 2:7 21:13

22:18 22:18

34:10 37:14

37:21 42:21

51:22 52:13 54:5

54:25 55:25

67:23 69:10

74:18 77:6

78:8 78:15 78:17

80:3 81:16

82:3 82:7

86:11 100:10

119:25 120:5

120:7 143:17

151:9 153:5

161:23 163:1

166:20 167:5

186:5 196:25

203:13 219:5

225:13 225:18

229:23 264:9

humans 59:13

66:15 66:24

68:10 68:15

68:23 71:15

72:23 75:22

78:13 79:13

79:16 80:15 82:1

82:2 119:24

159:16 163:5

166:23 190:9

210:19 214:3

225:6

hundred 26:24

hundredfold

122:21 122:24

hundreds 67:16

74:12 118:25

hung 186:11

hydoxyatrazine

138:22

hydrologic 241:23

hydrology

209:15 233:24

252:22

hydrolysis 14:20

hydrolyze 14:23

hydroxy 14:25

20:1 131:3

131:25 146:20

274:18 275:3

276:12 278:6

278:7 279:3

280:9

hydroxyatrazine

14:12 128:19

128:25 136:15

146:18 146:23

269:4 270:14

271:15 271:18

271:24 272:10

275:7 275:25

276:17 276:21

277:22 278:1

278:12

hydroxyatrazine 117:12

hyphenated 52:8

hypothalamic

150:15 150:19

151:7 218:4

hypothalamus 24:7

24:12 148:4

148:18 149:24

150:8 153:17

207:10

hypotheses 66:2

83:25 173:23

hypothesis 38:3

38:4 42:22

43:8 64:22 66:14

69:24 70:3

71:8 80:11

120:19 193:4

216:3 220:6

hypothesize 189:8

216:2

hypothesized

122:11

hypothetically

38:25 38:25

hyrdoxyatrazine

271:10

I

i.e 146:4 189:9

id 279:5

I'd 25:9 25:10

41:2 41:4

53:20 54:11

57:17 57:19

80:19 86:23 87:5

102:4 114:14

116:25 139:25

171:14 178:11

206:20 235:23

254:7 280:16

idea 12:18

128:1 138:13

138:24 153:10

153:21 172:16

184:8 193:2
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 344

194:17 212:24

233:9 235:9

235:12 236:17

236:20 236:22

242:3 242:19

243:2 244:13

250:17 250:23

255:10 271:7

273:22 284:5

ideally 69:3

104:3 153:6

ideas 174:13

192:1 228:24

identification

64:12 91:19

119:12 149:4

identified

31:17 75:11

173:3 218:7

254:13

identifier 241:9

identify 75:3

90:12 91:5

123:21 124:2

124:17 266:11

identifying 36:14

145:9 159:3

ignore 272:13

ignored 57:5

280:10

I'll 2:13 7:14

17:16 21:25 23:8

23:20 23:21

25:13 25:22 36:7

37:3 37:5 38:6

38:13 54:17

58:25 66:7 74:10

75:18 76:2

79:6 83:1 108:10

111:22 116:23

140:8 144:7

149:16 151:18

162:2 164:6

199:13 209:11

259:14 261:3

268:19 277:1

284:17

I'm 2:10 2:10

2:18 2:21 2:22

4:20 7:21

17:23 24:25

26:21 27:23 33:5

36:20 36:23 38:9

38:14 38:19

41:18 44:7 44:22

47:6 52:3

52:16 53:22

58:18 58:19 63:5

63:6 66:7 66:7

66:12 66:13

66:14 66:18

66:22 67:12

67:15 73:3

74:5 75:2 75:8

76:6 77:5 78:4

78:18 79:5

95:1 97:3

99:21 103:4

106:12 106:23

107:25 111:19

117:23 118:2

120:22 120:25

124:24 128:21

129:18 130:9

130:10 136:7

142:17 143:1

148:4 148:10

159:14 170:19

170:21 185:5

187:11 187:21

188:24 189:13

190:12 192:11

194:23 194:23

195:12 206:12

206:19 209:10

209:13 211:8

213:11 213:21

215:20 216:5

219:18 219:20

220:22 226:1

226:6 228:23

235:22 240:5

240:14 243:17

244:14 249:16

251:4 251:5

251:14 251:18

252:15 260:3

264:25 267:8

268:7 268:18

269:1 269:9

272:17 272:24

273:5 273:6

274:14 275:22

275:23 276:6

284:25 285:6

285:6 285:7

image 92:16 94:13

114:3 114:14

114:20 176:2

Imagine 54:12

187:1

immature 229:1

immediate 35:13

35:22

immediately

192:19

immuno 10:12

immunologic 53:21
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 345

impact 9:20 97:12

101:25 153:22

188:21 231:10

impacts 10:14

117:19 227:25

impairment 50:14

imparted 10:6

imperative 78:8

implement 256:5

266:1

implementing

256:4

implicated 275:7

implication 81:2

implications 21:4

110:15

implies 156:6

224:7

implying 188:7

importance 83:6

142:1 142:4

143:16 212:8

important 26:9

27:12 37:17

39:14 39:15

39:20 41:25 42:2

42:7 43:7 48:2

48:16 60:3 61:22

71:10 71:16

71:17 72:20 75:4

76:18 99:12

102:19 108:9

111:9 112:7

119:2 119:6

133:17 135:1

135:24 136:6

138:23 139:3

139:8 139:8

139:11 139:17

141:9 142:8

142:13 142:14

143:22 144:6

144:22 155:8

162:10 169:2

172:13 177:22

178:2 186:12

186:13 189:15

190:13 196:21

198:8 203:19

204:8 209:21

210:6 210:24

217:3 217:7

242:7 246:14

246:19 251:16

257:21 280:7

impossible

37:18 256:10

impoundment 242:9

imprecision 63:22

179:5

improve 40:19

119:7 283:15

improved 121:6

121:7

improvement

262:14

improvements

35:23 68:2

improving 51:4

118:18 254:24

impurities 235:11

I'm--well 110:12

inadequate 50:15

incidence 197:12

incisive 58:17

include 54:20

69:7 74:5

74:10 75:18

79:24 117:17

122:2 141:16

175:8 199:7

268:19 269:17

274:25

included 26:6

26:16 26:21 38:9

41:5 46:21

55:6 55:7

55:13 63:7 85:18

85:24 97:4

115:18 127:16

144:12 174:20

175:8 177:17

184:24 200:20

201:21 202:1

207:25 232:15

232:25 270:14

includes 54:21

62:19 133:21

270:12

including 50:14

53:21 63:9

64:6 68:6 73:5

81:21 84:24

113:7 133:22

136:22 136:25

141:18 163:24

198:10 232:18

283:13

inclusion 54:19

56:5 56:12

91:7 99:16

136:14 232:13

232:13
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 346

inclusively 133:2

income 46:10

inconsistency

172:11

inconsistent 80:5

138:1

incorporate 38:10

65:24 249:9

incorporated

72:11 255:17

incorporating

262:13 263:17

incorporation

71:10

incorrect 39:18

52:23 182:8

incorrectly 7:6

increase 89:7

93:14 115:20

127:20 127:20

128:3 129:2

146:8 163:18

167:14 177:3

214:1 226:11

increased 28:20

102:13 127:14

163:15

increases 61:4

95:25

increasing 206:10

incredibly 128:14

independence

43:20 44:17

independent

49:8 59:19

independently

55:4 56:21

index 89:1

indicate 45:17

163:14 189:5

211:5 223:19

239:15 268:12

indicated 25:12

99:15 110:19

140:6 205:2

212:24

indication 153:20

indicator 121:21

indirect 121:21

148:11

individual

24:11 38:14 45:1

45:13 46:2 46:15

46:18 46:20

47:14 48:15

48:17 48:24 49:6

49:9 49:10

52:3 56:8 57:3

58:17 63:15

72:21 72:25 74:4

100:18 102:23

131:3 132:19

133:12 133:22

134:7 134:24

139:17 154:5

155:4 156:20

159:6 179:10

217:7 257:17

individually 9:10

131:23 139:7

270:20

individuals 37:21

37:25 73:19

73:24 80:12

112:5 116:16

induce 109:21

induced 110:3

inducing 188:20

induction 221:19

indulge 37:5

industry 101:16

infant 107:9

107:9

infants 48:23

48:23

infected 41:13

108:16

infer 121:1

inference 77:4

246:8

infertility

217:20

influence

125:20 126:16

134:2

influenced

241:1 241:2

influences 177:24

influencing 56:16

influential 48:4

inform 64:11 81:1

142:11 169:15

198:21 263:1

272:20

information 19:11

22:10 24:4

58:7 65:5 69:3

69:6 69:11

71:8 72:11 72:16
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 347

72:20 74:25

75:21 76:9 81:20

101:16 102:10

109:25 131:4

131:10 144:16

154:10 166:15

166:20 166:20

169:5 169:20

180:19 185:22

186:5 193:24

194:4 199:11

201:16 211:5

213:1 215:2

219:23 231:11

241:23 255:15

255:19 255:25

257:19 266:10

266:18 266:24

269:2 269:2

272:7 272:9

273:4 276:21

informative 104:6

145:6

informed 136:21

ingestion 51:21

ingredient 97:16

inherent 145:22

224:14

inherit 108:13

108:15

inhibit 220:8

222:7

inhibition

220:4 220:11

221:5

initial 19:3

21:20 140:10

243:23

Initially 130:12

injected 97:9

input 207:10

248:1 252:20

263:19

inputs 24:17

248:2

inquiry 145:17

insecticides

72:14

insight 253:9

insights 119:7

instance 7:2 8:16

148:3 155:20

225:8

instant 257:7

instantaneous

228:11

instead 58:18

64:19 177:8

181:25 184:15

223:7 256:7

265:2

institute 99:10

insufficient

59:15 145:8

intact 7:17 27:13

28:4 28:11 28:13

intake 8:23

8:25 10:3 10:7

10:8 10:10

13:5 13:8 276:22

integrate 67:7

86:19

integrated

58:21 65:25

102:21 143:19

integrating 79:8

integration 74:9

integrative 65:6

intended 90:4

261:17

intends 233:7

intense 16:16

intensity 238:3

intensive

230:13 251:9

260:8 260:12

268:9 282:22

282:22

intensively

240:19 245:24

intent 70:15

260:6 261:10

261:15

intention 113:24

interaction

124:21 131:18

132:21 146:3

146:6 146:7

148:4 172:21

interactions

39:15 42:18

119:24 120:2

120:3 120:7

120:12 120:18

139:10

intercept 208:10

interest 54:10

76:11 78:25

83:23 175:15

255:3 255:4
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 348

260:16 263:22

268:5 270:5

interested

14:11 33:5 51:21

240:20

interesting

10:2 55:5 70:5

98:8 115:24

116:11 125:22

136:14 142:20

147:22 170:9

172:7 185:11

201:10 270:6

272:6 277:8

Interestingly

94:5

intermediate

198:21

internal 112:12

113:18 123:2

129:2 131:16

132:21 133:22

152:8 153:2

162:12 163:22

165:6 167:6

168:15 168:25

190:24 208:8

208:11 208:14

208:22

interpolation

247:8 255:24

281:24

interpolations

255:23

interpret

127:21 129:8

160:1 175:17

187:10 199:3

interpretation

58:20 59:3

117:20 124:19

127:5 155:2

155:10 155:15

161:1 199:17

interpretations

101:18 126:21

interpreted 114:1

157:9

interpreting

154:15 159:20

interspecies

199:9

inter-species

166:13

interstudy 103:14

interval 246:9

246:10 255:5

255:8 257:7

intervals 257:6

258:21

interventions

266:1

intestinal 166:24

171:24 172:9

intraspecies

199:8

intrinsic 108:8

intrinsically

112:1

introduced 227:19

introducing 27:9

introduction

205:25

introductory

36:21

invariance 143:2

investigate 96:10

158:15

investigated 7:16

11:1 197:25

investigating

160:17

investigation

84:1

investigator 52:8

investigators

52:7 77:20

84:5 91:24

134:25 228:20

investments 85:9

invite 25:11

In-Vivo 64:14

involve 191:22

238:15

involved 51:15

118:18 120:14

129:21 152:25

173:16 197:12

206:9 229:17

254:2

involves 155:3

involving

118:22 199:11

Iraq 64:15

ironed 200:9

irrelevant

66:24 71:4

isn't 19:8 28:5

136:9 146:20
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 349

170:21 178:19

217:3 218:18

278:18

isoflavones 98:5

isolating 72:25

isolation

102:23 176:5

issue 26:11

38:9 39:5 40:9

40:15 46:5

46:9 47:19

48:6 49:24

63:8 63:25 80:21

81:5 81:24 99:25

100:9 100:11

118:5 118:20

121:7 134:11

140:23 142:2

143:23 169:6

170:9 170:13

174:9 182:11

189:16 190:14

193:21 196:15

202:21 202:23

214:22 215:17

216:15 228:12

228:15 231:18

237:22 243:19

251:20 267:13

277:7

issues 22:11

28:16 51:10

58:20 67:17 73:8

73:8 74:9

74:21 77:15

86:17 91:23

91:23 110:10

112:15 117:1

118:11 120:22

120:24 122:1

137:4 147:12

171:21 190:5

206:21 254:2

260:20 274:14

it'd 56:23

iterations 252:25

iterative 262:12

iteratively 24:19

it's 11:4 13:19

13:24 14:17

14:19 16:18

16:21 17:8

17:8 17:9

19:10 21:9 22:25

25:4 26:9

27:12 30:14

30:24 32:17

34:14 39:11

40:19 40:20 42:6

51:25 53:22 61:8

67:11 69:19

79:13 80:13 81:7

83:2 84:19 97:18

98:22 98:24 99:1

99:4 99:12 100:8

102:19 103:22

106:3 108:9

108:18 109:5

109:16 109:20

110:6 112:7

114:15 114:19

114:22 114:23

115:23 119:2

119:12 124:12

124:12 125:24

130:13 132:6

132:7 133:17

134:9 134:12

134:13 135:14

135:21 136:9

137:11 138:11

138:23 139:24

142:20 149:11

150:7 150:11

153:2 153:9

153:23 154:2

154:23 155:15

156:3 156:11

157:9 157:10

157:12 157:15

157:25 158:5

158:7 158:16

159:5 159:18

160:2 160:17

161:17 162:9

162:13 162:17

162:23 163:2

164:13 164:20

165:10 168:3

168:10 169:2

169:2 173:3

174:10 174:12

174:15 174:16

176:10 178:2

178:19 179:7

180:21 185:7

185:16 189:20

190:6 190:8

191:8 192:3

192:19 192:22

193:2 193:13

193:14 194:17

195:5 195:19

196:2 196:8

196:21 199:25

200:3 200:18

201:9 201:16

201:25 201:25

203:16 203:19

204:11 204:12

204:13 204:14

204:16 205:22
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 350

206:11 206:21

208:13 208:13

208:15 209:12

210:18 211:9

211:10 215:7

216:14 216:15

217:14 217:14

218:15 218:24

220:12 220:21

221:7 221:13

221:14 222:25

224:6 224:8

224:23 225:12

226:2 227:17

228:2 228:24

231:23 232:11

232:15 234:8

234:18 237:2

237:5 237:18

238:5 240:2

241:7 242:6

242:10 242:22

244:15 245:2

246:5 247:4

249:1 249:2

249:18 249:18

250:7 250:7

251:9 251:13

253:8 254:3

257:14 258:16

258:17 259:1

259:4 259:18

260:7 260:7

260:13 260:17

260:20 261:2

261:4 261:17

262:10 263:12

263:13 263:24

264:12 264:21

265:16 267:14

268:1 270:17

272:2 273:12

273:12 274:9

274:24 275:7

276:20 277:1

278:1 278:14

279:2 279:7

283:22 283:23

284:9 284:9

I've 24:25 37:1

43:9 49:20

52:1 52:9

63:24 71:13

79:17 86:4 95:20

101:14 103:11

109:24 111:19

165:5 171:18

178:5 194:19

212:23 226:3

231:25 237:3

237:17 238:22

241:7 251:8

251:8 253:25

262:16 262:18

269:25 280:18

284:18

J

Jabrowski 48:20

JAMES 3:13

10:16 11:10

11:21 15:4 17:18

87:17 109:3

113:23 137:9

138:10 147:15

150:4 271:23

275:6 277:3

Jan 2:21

JANICE 2:21

12:5 12:12 12:17

14:6 127:4

271:14 272:24

Jerry 4:13

Jim 3:13 99:23

108:11

job 43:14 44:7

47:6 51:4 63:2

86:25 147:8

231:13 244:22

250:22 267:8

Joe 4:18 4:21

5:11

John 3:1 3:1

50:25 58:15 76:5

82:17 110:17

115:16 119:14

213:21 214:17

215:20

John's 113:3

joining 16:1

261:20

joint 120:7

257:10

JOSEPH 4:19

judgment 61:16

61:18 61:21

juices 62:19

July 34:21

jump 184:4 225:24

227:13

jumped 206:5

jumping 252:10

June 34:16

just--if 114:16

justified 55:14

67:25

justify 54:24
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 351

just--really

126:25

just--to 118:19

K

Kannan 3:7 3:7

18:3 19:13 19:23

20:25 130:6

152:12 162:5

170:4 208:6

219:19 219:20

222:18 222:22

223:24 224:3

274:17

Kansas 3:16

KATHERIN 227:11

KATHERINE 3:15

32:25 184:4

187:15 188:10

189:11 190:15

190:19 202:8

210:2 213:16

213:20 214:6

214:21 216:1

223:15 224:1

224:5 224:12

226:8 226:19

226:24 227:3

Kathy 3:15

Ken 2:18 209:10

280:11

Kenneth 2:18

96:16 115:23

194:18 209:10

209:25 213:14

213:18 215:4

216:5 216:11

219:16 220:22

222:15 222:20

224:7 224:16

225:17 225:23

226:22 227:12

228:4 228:17

229:4 229:14

230:17 231:4

236:7 236:23

238:20 240:11

240:24 242:14

242:16 244:11

245:21 246:3

248:23 252:8

254:5 254:25

259:13 260:2

262:16 262:17

269:19 271:13

271:22 272:22

273:14 273:23

275:5 277:17

279:9 279:12

279:17 285:4

Kentucky 3:20

Kevin 151:1

key 20:18 42:7

45:15 46:24

47:18 60:17

60:19 61:2

61:5 63:18 150:8

163:10 163:20

167:22 196:21

197:9 210:3

210:12 210:14

210:21 211:15

243:10 261:12

261:23 262:9

284:7

keying 260:14

keys 23:19 261:9

kg 8:17 8:20 8:21

90:6 192:15

192:15 193:2

193:14 196:4

kidney 278:17

279:22

kilogram 98:6

121:24 183:5

183:9 185:4

Kim 12:23 16:6

16:10 16:13 17:1

21:4 21:9 22:6

22:9 23:7

24:22 25:1

Kimjinski 50:10

Kim's 16:3

kinds 39:15

42:9 70:14 114:8

131:13 132:9

142:22 207:12

219:7 244:20

250:20 275:16

275:18

kinetic 12:6

22:17 198:20

199:11

kinetics 9:4

171:22

KM 172:17

knew 205:6

235:9 251:23

knowledge 35:24

60:4 122:13

165:11 277:24

known 52:12 122:4

132:20 132:21

132:22 150:8

kriging 254:21

255:23 256:6
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 352

257:21

Krishnan 3:7

3:7 18:1 18:3

19:13 19:23

20:25 130:5

130:6 133:5

152:2 152:12

162:4 162:5

168:19 208:5

208:6 209:3

219:19 219:20

220:23 222:15

222:18 222:22

223:24 224:3

274:17

L

lab 6:25 14:11

22:7 44:19

101:12 273:6

273:7 274:1

label 15:11

labeled 59:25

60:10

labeling 89:1

93:17 93:21

93:24

laboratories

98:15 114:19

laboratory 7:6

61:8 61:14

78:1 78:13

275:23

labs 6:17 6:18

101:11 101:15

101:17 126:2

173:16 173:20

271:4

lack 28:13

40:12 43:20

44:17 49:22 53:8

59:6 59:7 59:9

63:11 63:13

63:13 63:20

75:17 94:19

131:3 147:17

158:7 184:13

lactation 90:1

93:2 93:9

95:14 106:8

107:2 107:7

138:15 141:20

142:15 142:25

143:5

lactational 110:8

lactations 93:12

ladder 49:5

Lake 248:11

253:13

lakes 246:2

Lamb's 31:1 67:20

land 252:15

252:20

language 108:8

large 20:8

47:13 49:6 51:22

60:8 69:19

116:11 156:21

156:23 156:24

178:4 180:8

203:20 215:14

215:16 223:22

243:24 250:10

250:11

largely 13:16

19:4 38:1

38:20 44:12 45:6

45:17 46:19 57:5

77:25 80:9 81:25

253:1

larger 48:12

59:13 138:13

198:15 234:20

241:15 247:25

248:5 248:22

249:4 249:12

larger--it 138:12

largest 68:12

Lars 166:1

last 14:6 19:3

25:16 28:17

29:12 30:3 34:18

34:21 52:6

52:8 53:6

62:18 65:21 68:4

76:22 76:23 92:2

96:18 99:14

113:3 146:15

157:24 158:3

166:8 171:3

189:24 190:16

204:7 209:11

209:17 238:25

253:11 258:6

270:21 276:14

late 137:22

148:21 150:19

226:5 247:20

latent 70:9 116:9

later 16:23

54:9 54:23 56:15

66:7 106:10

108:4 109:9

119:15 120:23

123:13 181:9

181:24 186:5
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 353

195:9 235:21

247:18

latest 7:15

latter 193:10

Laughter 177:4

187:4 187:13

187:24 194:21

216:10

law 142:23

layer 133:16

232:22 251:19

layers 112:15

LE 185:21 185:24

lead 15:2 36:7

58:13 87:12

95:20 114:6

114:11 119:14

134:1 197:5

210:1 259:22

268:9 282:8

282:10

leading 215:13

leads 114:7

lean 114:13

learn 257:11

learned 6:18

103:11 143:4

227:14 246:21

257:19

learning 39:14

149:11

least 32:9

34:25 37:20

43:18 52:9 52:10

53:5 56:17

57:9 76:13

103:11 118:23

120:9 137:13

137:14 140:10

165:22 167:2

171:3 171:9

171:10 171:13

172:17 172:19

172:24 179:23

189:4 195:16

215:18 229:12

236:13 270:20

278:5 280:22

281:20 283:6

leave 5:15 123:10

162:2 199:13

251:4 259:14

leaving 275:2

leblanc 4:13 4:13

101:9 110:23

145:4 145:5

147:4

Leblanc 101:8

104:24 105:17

led 190:21

Lee 3:25 3:25

188:1 193:18

193:20 236:7

236:8 242:17

242:18 245:16

245:17 246:3

246:4 254:25

255:1 257:3

262:10 273:14

273:15 283:2

leeching 269:15

Legan 3:19 3:19

28:18 29:25 30:1

30:10 30:16

30:19 32:16

147:4 147:5

178:8 178:9

184:3 192:12

192:25 201:19

202:14 202:15

207:22 207:24

208:2 216:11

216:12 219:22

224:17 225:21

227:1 227:8

228:5 279:17

279:18

legitimate

100:7 112:12

127:19 141:5

143:3

legitimately

134:5 143:9

legs 175:16

lend 116:10 159:6

197:9

length 60:24

90:18 227:20

241:5

lengthy 36:20

lesions 278:17

less 8:9 16:24

27:25 40:1 46:13

93:8 100:19

107:15 112:2

112:25 114:6

114:6 121:17

162:25 164:21

170:24 177:15

196:3 234:23

240:23 241:18

242:5 247:8

248:5 259:18

260:8 268:18
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 354

279:3

lessen 93:25

lesson 143:4

let's 23:6

23:22 27:17 40:3

57:20 80:16

86:25 87:3 131:8

140:1 140:5

173:8 195:10

209:4 226:10

240:11 252:11

257:4

letter 88:4

94:8 165:10

level 14:5 18:5

18:25 19:24 42:8

46:15 48:6 48:17

48:18 59:22

71:15 73:16

94:21 100:15

108:22 134:21

144:6 156:24

158:2 159:23

160:7 160:20

168:15 168:15

176:11 179:18

182:17 182:22

183:4 183:16

183:18 199:23

201:15 220:3

223:3 223:5

224:2 237:25

238:4 258:23

258:25 281:19

282:10

leveled 153:18

levels 24:13

77:17 78:15

97:17 98:2 98:25

111:9 111:13

118:17 119:1

121:3 121:24

122:14 128:14

131:13 135:25

152:14 152:16

156:9 179:23

180:10 181:7

181:10 183:22

184:25 185:10

194:3 195:1

197:13 198:8

204:5 223:2

258:16 258:16

258:18 263:9

264:9 279:21

LH 9:15 18:5

18:12 18:15 27:8

28:11 29:3 29:14

31:2 31:6 31:9

66:9 67:11

68:8 86:12 145:1

167:12 171:16

173:12 173:18

174:3 174:22

175:1 175:4

175:5 175:7

176:1 176:4

176:8 176:9

178:1 178:10

179:4 179:5

179:7 179:11

179:12 179:15

179:18 179:22

179:25 180:12

180:12 180:13

181:25 183:2

183:14 191:5

192:2 192:5

196:4 197:22

202:11 202:24

203:2 203:2

203:7 203:12

203:14 203:22

204:4 204:22

205:16 205:18

206:12 206:20

207:7 209:23

210:8 210:13

210:15 210:18

210:20 210:24

211:1 211:2

211:6 211:14

211:19 211:20

211:22 211:25

212:9 212:20

213:1 213:4

213:8 213:23

213:23 214:5

214:10 214:12

214:13 214:14

215:18 215:24

216:18 216:22

217:5 217:9

217:13 218:1

218:10 224:20

224:20 224:24

225:11 227:4

lies 60:11

97:24 100:16

life 6:6 6:10 7:6

7:10 7:11

51:20 68:6 72:24

74:17 94:16 97:6

141:18 185:13

186:6 215:23

226:20

life-arc 88:1

lifestyle 39:12

42:1

lifetime 71:22
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 355

74:19 212:1

212:25 214:5

214:8

light 7:25 9:2

9:11 9:19

78:19 132:22

140:14 203:11

223:8

lighter 138:8

lights 7:18 7:19

likelihood

45:11 59:11

146:5 265:19

265:25 267:18

likely 39:2

41:6 49:10 63:12

82:1 148:19

163:10 256:4

265:8

limit 18:5

68:20 76:7 76:14

76:20 79:23

150:2 150:6

179:21 224:23

limitation

40:22 46:16

46:21 47:14 92:2

95:6 120:13

159:19 252:2

limitations 36:14

40:25 43:9 44:14

47:19 49:20

50:14 58:9

63:5 79:20

87:9 87:20 89:18

89:21 90:3 91:15

94:15 117:17

133:10 154:14

161:15 251:10

limited 35:7

39:10 59:3

71:3 90:7

106:6 109:25

120:4 122:2

124:16 124:19

144:14 160:2

161:2 169:3

171:19 278:2

limiting 42:17

120:15 167:21

251:19

Lin 26:12

line 53:24

53:24 61:25

163:21 186:25

187:8 189:1

194:11 194:15

242:25 278:25

linear 16:22 17:6

17:9 17:10

174:16 208:23

247:7 255:24

281:24

linearity 164:4

164:8

lines 145:16

lining 242:22

link 33:12 195:23

244:25

linked 156:10

Lin's 26:8

list 54:21 85:16

listed 176:24

243:8 276:10

lists 46:6

liter 107:8

literally 35:14

literature

35:17 36:3 37:10

42:6 53:21 54:22

54:25 60:12 63:5

68:2 75:25

99:6 99:9 125:11

125:16 142:7

142:23 143:11

179:17 207:18

211:5 212:5

212:10 220:6

242:20 243:6

247:9 247:15

liters 107:5

107:14

litter 116:16

little 13:24 26:5

29:18 29:19

52:12 53:16 54:1

54:17 57:8 60:19

63:11 82:18 83:3

83:4 84:22 87:24

110:11 113:3

113:10 113:20

127:6 129:18

130:1 140:1

140:22 171:1

183:3 185:16

186:4 192:11

204:18 204:25

207:17 226:17

227:3 227:17

231:5 231:14

231:17 233:6

234:4 234:16

235:1 236:4

236:5 237:16

242:19 243:17
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 356

245:7 245:8

249:14 258:8

268:14 269:11

271:11 273:25

280:17 280:20

282:22

live 104:13 106:7

170:8

lives 217:6

living 35:6 35:16

load 249:5

loading 31:15

localization

206:24

location 73:23

244:23 245:13

locations 241:25

lodge 123:5

LOEL 223:7

LOELs 181:21

log 177:17 182:4

logic 221:12

logical 261:19

long 11:1 27:19

27:20 27:24

27:25 27:25 28:7

35:23 42:12

68:13 70:9

113:15 125:10

125:13 125:25

138:4 162:24

186:17 212:17

213:22 218:21

218:25 220:10

222:2 233:23

234:21 234:22

238:4 261:12

261:15 265:12

284:14

longer 10:19

29:19 62:14 68:7

85:12 136:4

136:5 190:9

253:5 255:4

Long-Evans

87:24 92:8

95:2 167:11

167:23 171:15

171:20 192:17

194:2 195:2

long-term

144:19 188:14

188:15 188:23

214:19 220:8

220:9 220:21

220:21

loose 19:19

loosely 283:22

lose 99:11 219:13

losing 126:7

loss 16:22

16:23 211:6

217:23 240:22

lost 210:20 280:8

lot 13:9 17:7

17:8 17:10 21:15

23:15 33:22

34:21 50:25

59:20 59:21

59:22 73:11 76:8

77:10 77:11

77:20 79:6

79:7 92:3

97:21 105:6

112:3 113:13

118:20 120:3

127:25 142:21

150:13 167:24

168:21 171:7

174:18 177:23

180:2 182:24

188:19 192:6

194:4 201:15

203:2 205:15

205:17 216:14

223:18 226:3

227:14 232:23

234:7 234:22

236:1 240:2

242:23 244:17

247:14 248:5

249:25 254:1

254:4 257:11

257:18 258:2

258:24 261:22

263:4 264:3

274:4 275:25

276:1 278:3

281:13

lots 22:2

160:25 273:9

loud 190:25

low 38:22 40:10

42:19 47:21

48:22 51:21

68:11 78:19

78:23 80:4

111:11 111:13

117:10 119:1

122:14 128:1

128:14 128:23

129:6 129:10

129:13 131:17

131:18 131:23

132:17 132:25
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 357

136:11 136:12

137:21 138:11

160:19 160:20

176:13 181:14

222:6 234:3

242:10 250:8

258:16 258:18

258:23 258:24

271:16 284:21

low-dose 173:4

lower 22:15 22:17

30:10 30:12

37:24 71:14

78:21 93:11 98:3

129:14 135:25

144:25 178:3

204:5 225:18

250:23 278:13

lowered 231:9

lowest 225:5

Lowit 25:14 25:17

29:22 33:20

33:21 36:1 77:13

86:1 86:2 87:7

87:8 104:24

104:25 117:3

117:5 225:23

225:25 226:15

228:17 228:18

229:8 229:12

277:17 277:18

278:22 279:10

280:7 280:21

Lucciani 191:14

lucky 245:3

246:11

lump 167:6

lunch 140:1 140:4

141:1

M

magnitude 44:1

44:14 50:19 71:9

104:18 146:8

225:6 240:18

241:5 242:4

254:12 264:5

281:17

main 88:7 89:17

101:6 179:19

184:1 243:22

244:19

mainly 128:21

maintain 160:7

maintained 6:18

93:15

maintaining

186:13

major 36:14

45:2 46:16 51:10

61:8 78:23

104:16 143:5

146:10 155:13

majority 20:24

male 42:11 64:3

75:10 79:19

149:12 226:6

226:14 226:23

226:25 226:25

males 228:1

mammalian 275:1

mammary 87:6

87:10 87:20

88:16 88:21

88:23 89:20

90:16 91:16

91:16 92:17

92:22 96:6 96:10

96:22 98:10 99:2

100:9 100:23

100:25 101:5

102:1 102:11

102:22 103:9

104:15 104:17

105:4 105:24

106:3 107:19

108:7 108:13

110:12 110:13

111:10 117:9

121:20 122:18

123:3 128:14

131:5 140:17

140:22 141:5

141:8 141:8

141:12 141:17

142:12 143:14

143:24 144:5

144:12 145:5

145:12 147:16

147:19 148:9

149:9 150:8

150:19 150:20

151:8 179:1

205:17 214:2

214:2 279:24

man 37:21 234:7

manage 25:5

240:18 249:11

management 266:20

managerial 84:15

managers 34:4

267:15

manifestations

79:9

manner 112:23
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 358

140:16 147:6

147:9 184:15

manners 8:2

map 151:15 195:4

mapping 143:17

151:7

March 34:16

marital 46:10

marker 204:1

mass 13:6 159:3

275:11 275:12

275:15

massive 118:2

match 8:14 114:15

129:4 241:9

241:16 243:16

244:3 244:5

244:8 244:10

247:11 249:14

matched 156:8

matching 11:2

240:21 241:21

242:19 244:14

244:22 244:24

material 7:4 13:8

15:7 19:14

153:13

maternal 45:1

46:18 47:14 48:5

48:15 49:9 90:14

98:13 104:12

125:8 125:12

125:14 125:17

125:22 126:17

matrix 170:13

matter 58:23 78:9

149:4 162:12

190:10 200:5

202:5 211:20

211:21 215:23

272:15 279:22

matters 59:7

158:23

mature 77:4

185:15 189:3

228:25

max 172:17

maximum 163:8

169:3 266:21

may 4:16 6:10

17:11 27:8

28:8 28:13 29:13

35:17 40:21

51:20 53:2 54:15

56:19 57:13

60:20 62:12 66:6

68:19 78:6 84:23

85:8 85:9

91:19 103:25

104:19 127:23

127:23 139:16

145:25 146:24

148:1 151:14

155:19 163:5

164:15 164:21

167:13 169:1

169:17 171:21

172:9 181:15

181:20 186:12

186:16 187:11

187:18 198:22

203:18 204:8

204:9 205:7

207:9 212:1

212:16 215:10

218:9 218:23

220:4 223:8

238:1 238:14

242:11 243:13

243:15 244:4

246:16 247:19

248:9 248:14

251:25 253:4

254:10 256:8

259:5 268:13

270:2 279:19

281:19 282:9

maybe 7:25

43:18 48:12 53:9

57:21 60:16

60:20 68:8 69:22

72:19 77:21

78:17 84:3

99:8 100:11

101:4 106:14

112:18 115:6

135:2 151:17

158:14 161:15

162:7 164:14

171:11 171:11

171:12 171:22

172:21 173:5

177:9 177:16

177:25 178:18

180:16 182:4

182:7 182:24

183:23 184:14

184:17 185:22

186:22 186:23

187:5 187:6

187:14 190:13

192:10 192:22

193:10 203:10

203:15 204:12

205:9 206:9

207:2 207:10

207:18 208:24
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 359

210:3 217:7

217:22 219:1

238:14 240:1

241:11 248:12

261:21 282:2

mcg/g 98:3

McManaman 3:13

3:13 10:15 10:16

11:10 11:21 15:3

15:4 17:15 17:18

87:15 87:17

109:2 109:3

113:22 113:23

137:8 137:9

138:9 138:10

147:14 147:15

148:15 148:18

149:23 150:4

271:22 271:23

275:5 275:6

277:3 279:14

McMullen 166:1

171:3 175:9

175:19 180:21

182:10

McMullin 22:24

23:12

McMullins 21:19

meal 8:6

mean 30:12

39:19 42:24

61:11 61:13

73:22 78:20

79:20 81:7

106:12 124:12

128:15 134:4

146:7 165:14

175:16 179:20

180:5 183:25

191:1 193:6

199:23 201:24

204:5 212:14

212:16 213:12

216:19 221:4

221:20 222:11

223:15 223:16

224:3 224:10

225:18 228:7

251:24 252:18

252:18 258:1

272:3 274:11

275:15 282:19

meaning 125:16

247:11

meaningful

50:16 202:25

meaningfully

143:19

means 4:24

115:5 116:2

154:19 154:21

184:14 201:8

211:1 213:6

224:8 239:9

239:17 282:21

meant 21:23

225:22

measurable 267:18

measure 15:5

37:19 46:3 46:19

46:20 55:4

61:2 71:23

102:15 102:22

129:5 131:16

153:2 153:12

160:21 193:9

202:16 208:14

211:18 218:6

219:14 272:5

272:10

measured 13:9

31:6 90:18 90:19

90:24 92:21

142:25 156:25

173:19 224:25

269:11 270:20

272:19

measurement 55:11

55:11 72:2 75:16

105:8 112:17

112:24 115:25

224:20

measurements 55:7

68:17 91:10

105:8 105:9

112:22 116:6

164:2 218:21

243:12

measures 55:9

61:7 61:19 63:21

64:5 71:10

74:2 85:20 96:22

97:1 134:10

165:7 167:6

167:12

measuring 31:9

80:15 107:4

108:19 134:17

178:10 179:5

179:11 203:21

210:14 216:22

217:16 218:13

225:11 269:18

272:13

mechanical 61:17

mechanism 90:2

110:21 110:22
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 360

113:18 144:22

204:9 217:17

283:12

mechanisms

53:10 53:14

78:13 81:19

81:22 206:15

207:20

medial 153:17

medical 3:16

129:23

Medicine 2:22

Meek 4:2 4:2

133:5 133:6

135:9 168:19

168:20 170:3

195:9 196:16

196:17 199:15

meeting 2:2 2:6

4:22 4:23 5:13

33:11 51:9 55:23

65:20 66:20

67:11 68:17 75:9

121:14 140:24

167:1 209:7

228:5 228:14

230:22 236:10

285:16

meetings 4:17

55:22

members 4:16 5:17

57:18 64:24

82:15 106:19

118:13 124:25

137:7 146:18

148:15 173:7

174:23 176:19

177:1 187:20

memory 33:8

men 54:14

menopause 39:22

40:5 41:12

42:5 71:2

212:2 212:3

213:4 226:20

menstrual 41:12

64:2 68:7 71:1

190:11

mention 28:18

40:9 43:10 54:15

97:3 127:1

135:16 147:7

149:21 178:11

mentioned 40:13

43:22 45:12

46:11 49:20 56:6

73:4 79:18 96:23

97:6 105:2

107:14 122:18

124:7 176:7

178:22 181:13

182:16 202:20

207:19 208:9

233:2

mentioning 18:6

40:19

merge 69:9 73:2

merged 194:14

mesh 11:25

message 82:25

met 26:20 30:4

174:10

metabolic 138:3

138:17

metabolism 14:8

142:5 150:21

165:13 165:16

166:24 172:4

205:17

metabolite

13:16 16:11

128:20 128:21

131:25 132:6

132:10 146:20

172:12 269:9

269:14 272:19

274:18 275:3

metabolites 13:10

15:6 15:17 21:18

24:13 66:8

69:8 109:11

122:5 122:6

123:15 123:23

131:3 131:15

132:1 135:17

145:23 146:2

146:2 146:3

146:16 152:9

152:15 154:6

154:12 155:4

155:21 159:8

163:11 164:12

166:4 167:3

269:4 270:19

271:3 275:1

metabolites/

degradates

117:11

method 105:19

159:4 230:3

232:13 255:22

268:1

methodological

104:7 143:11

methodologies
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 361

142:10

methodology 14:20

54:18 91:9 91:18

94:15 103:9

116:15 118:18

246:15 246:21

methods 51:16

54:1 54:17

55:2 56:13 56:22

70:7 96:19 104:6

104:19 104:21

142:14 143:15

144:5 248:20

254:14 255:17

255:24 256:4

281:3

metric 103:19

163:22 168:25

169:1 171:25

190:24

metrics 165:19

166:18 168:5

168:7 168:10

264:8

mg 30:13 193:2

mg/kg 87:25 92:13

94:6 94:21 94:23

95:12 101:24

102:7 104:10

138:16

mg/kg/day

117:10 137:11

mgs 8:21

mic 126:24

127:2 222:21

231:5

mice 27:22

107:9 108:15

Michael 196:17

micro 49:21

125:16

microbial 14:7

14:19

microsomal

172:4 172:10

microsomes

172:2 172:6

middle 177:15

217:24 235:3

midpoint 183:22

midrange 182:23

Midwest 249:24

milk 100:3 107:12

107:16 107:20

110:1 110:4

127:18 142:1

151:8

milked 106:25

mill 260:16

milligram 8:17

milligrams 8:19

98:5 165:3 183:5

183:8 185:4

192:14 192:15

196:4

million 98:4

mind 28:16

35:12 35:22

78:22 85:23

106:4 127:22

139:23 154:15

155:9 166:11

176:4 182:9

198:4 206:11

206:14 218:5

243:10 243:15

244:3 250:4

277:5

mind-blowing

147:10

mindful 8:8

mine 182:3

mineral 237:24

minima 156:15

175:9 175:18

178:23 182:10

minimal 163:8

208:15 208:19

223:22

minimum 156:16

186:13 186:18

ministered 169:1

minor 39:19

46:5 52:5

125:6 125:6

126:4 165:24

208:7 267:12

minus 34:11

165:16 183:19

minute 153:10

161:14 226:5

229:10 254:9

minutes 13:3

86:25 87:1 107:7

107:7 227:21

mirror 176:1

misleading 156:11

miss 239:25 242:5

243:13 243:23

246:13 249:13
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 362

missed 43:18

52:14 145:2

238:6 241:19

missing 135:11

139:10 139:16

241:15 243:11

243:22 244:4

267:24

Mississippi

2:23 237:20

Missouri 251:23

misspelled 52:9

mistakes 157:3

misunderstood

11:15

mitigate 266:2

mix 30:14

mixed 101:21

mixture 12:10

12:24 20:5 20:24

66:8 111:12

117:11 117:13

118:20 120:7

120:23 121:22

122:2 122:8

123:4 123:15

123:20 123:20

123:22 124:2

124:17 124:20

127:8 127:13

128:17 129:3

129:8 129:12

129:24 130:15

130:16 130:20

132:1 132:6

132:10 132:11

133:11 136:12

138:22 138:25

139:1 139:18

144:21 145:16

145:19 156:4

275:8 276:4

276:10

mixtures 72:23

72:24 117:19

118:4 118:6

118:7 118:19

118:22 119:3

119:11 119:19

119:21 121:2

128:12 131:8

138:7 139:14

170:8 172:16

276:8 276:11

mode 66:4 66:15

66:18 66:23

67:10 67:20 68:1

120:11 122:9

122:14 122:15

132:20 144:15

147:25 153:23

185:9 197:2

203:1 204:9

204:11

model 7:17 9:15

13:1 13:12 16:18

21:5 21:8

21:12 21:13

21:14 21:17

21:20 22:2 22:24

23:2 23:21 23:22

23:23 24:2

24:6 24:16 24:17

24:20 28:14 35:5

75:7 125:4

137:21 153:7

155:12 155:24

159:14 166:19

168:7 168:13

169:11 169:17

171:2 171:3

171:13 171:17

171:25 172:18

172:21 174:11

193:22 194:5

215:12 232:25

239:19 245:19

247:4 247:19

249:18 249:18

249:20 249:23

250:3 251:2

254:20 254:21

254:24 255:10

255:11 255:21

256:2 256:7

261:5 261:8

261:15 262:3

266:7 266:11

282:18 284:10

284:11

modelers 121:9

modeling 21:4

35:10 209:15

236:15 239:8

244:25 245:1

246:24 254:18

254:19 255:6

255:9 262:21

264:4 270:11

281:1

models 22:12

34:23 35:1 138:2

172:14 208:21

208:25 232:19

247:10 255:14

255:20 261:14

261:20 261:24

261:25 262:1

262:5 262:6

263:17 264:18
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 363

264:21 265:1

265:6 267:24

281:10 283:6

283:14 284:8

model's 262:14

modes 75:7

79:12 82:2

173:17 196:22

modest 44:9 44:14

50:18

modification

39:12 40:13

41:25 49:23

modified 23:2

172:22 264:7

modifies 10:8

Moiz 3:9 3:9 21:3

22:4 22:7

22:19 117:23

127:8 135:10

170:4 219:18

molecular

164:17 164:19

164:24 164:25

206:8

mom 107:2

moment 23:2 57:23

79:14 110:14

227:17

moms 88:25 89:7

107:11

Monday 65:13

66:14

money 77:10

monitor 143:7

263:6

monitored

240:19 274:20

monitoring 2:9

19:17 20:6

34:6 78:7

85:16 229:22

229:24 230:4

230:8 230:11

230:16 231:16

231:21 232:11

232:14 233:5

245:4 246:1

253:16 254:15

263:1 264:17

264:20 270:14

280:23 280:24

281:19 282:3

282:5 282:22

month 72:6

monthly 62:8

months 34:17

34:24 34:25

35:10 35:14

41:21 106:10

173:15 250:11

Montreal 3:8

morning 2:4

2:13 4:20 5:10

5:16 6:1 7:18

23:8 24:24 25:17

33:23 34:24

36:11 36:19

53:19 136:4

139:25 158:11

228:19 282:25

284:18 285:9

285:13

morphological

106:22

morphology 108:19

112:17

Morseth 175:9

178:23 182:10

mostly 18:19

76:10 249:20

mother 62:16

116:17 142:5

mothers 125:18

mother's 53:13

141:19

mouse 106:25

move 25:16 34:4

36:2 57:20 72:17

72:21 72:23 86:9

87:5 95:18 99:10

115:22 116:25

140:12 151:22

169:16 233:6

233:23 234:10

237:15 237:21

245:22 260:1

263:2 263:6

284:3 284:24

moved 202:1

movement 103:13

270:1

moves 234:10

moving 65:15

74:17 120:25

190:24 229:1

234:1 239:15

240:5 273:25

283:20

Mr.Thurman 280:15

MTV 108:13
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 364

MTV's 113:12

multi-center

42:15

multiple 44:18

45:13 45:14

46:22 46:24 49:7

53:9 53:14 59:15

60:6 60:25 63:16

63:18 67:12

71:15 72:22

74:15 89:11

89:12 91:11

91:24 118:15

123:16 191:17

237:25 248:1

multiply 160:10

160:14

multivariate

116:7

Mumtaz 3:9 3:9

18:2 21:2 21:3

22:4 22:7

22:19 117:21

117:23 125:3

135:9 135:10

168:19 170:3

170:4 170:17

219:16 219:18

muster 19:9

myself 103:5

112:12 150:2

168:11 186:1

209:6 213:11

N

na 170:21

namely 70:16

narrow 61:6

NAS 223:9

natal 88:25 89:5

national 3:1 62:1

84:12 85:3 260:7

260:13

nationwide 260:7

natural 97:15

naturally

216:18 218:11

218:11

nature 6:22

68:5 102:17

136:12 176:7

217:14 224:19

naysayer 259:16

NCTR 3:18

nearly 152:15

173:23 256:10

necessarily 29:15

97:14 111:23

116:2 137:25

198:5 247:4

257:14 275:2

281:24

necessary 6:21

32:14 68:22

226:12

need--the 54:5

negative 59:11

61:11 78:20

100:6 100:13

108:24 121:25

203:23 208:10

213:7

negatively 101:25

neither 68:20

100:2

Nelson 3:11

3:11 34:11 99:21

107:24 111:22

136:7 140:20

149:20 151:4

151:14 186:22

187:18 215:11

228:10 229:20

229:21 240:8

240:10 240:13

245:23 253:11

253:25 254:7

268:23 270:9

270:11 270:17

270:23 271:1

274:12 274:12

280:16 282:21

284:13 284:25

neonatal 138:2

nervous 117:25

net 133:16

networks 255:18

neural 151:7

255:18

neuro 89:24

neurons 148:4

207:3 207:10

242:23

neurotoxic 221:6

nevertheless

196:8

next--in 35:13

NHANES 85:16

85:18

nice 53:22

56:23 56:24
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 365

105:17 174:12

178:17 191:3

194:24 194:25

196:2 221:9

221:10 221:25

231:13 249:20

nicely 246:8

NIEHS 83:20

night 9:5 9:7

NIH 2:12

ninety 26:16

nitrates 124:8

No.1.1 36:11

NOAEL 145:12

nobody 272:17

nods 67:12

no-effect 95:7

NOEL 181:20 183:4

183:7 183:11

202:17 223:6

278:12 278:13

non 43:3 49:14

163:18 166:2

non-cancer 2:8

36:13 37:10

37:18 58:10

69:16 77:2 83:14

83:18

non-destructive

217:22

non-detectable

13:17

none 43:1 48:3

69:13 77:21 78:7

155:16 278:7

279:7

nonetheless

112:10 150:7

non-gestational

11:16

non-parametric

245:9

non-significant

44:13 103:21

non-standard 40:4

non-stationary

258:8 258:12

non-target 153:14

non-use 133:2

normal 8:5 100:22

137:23 177:9

223:13 223:16

223:17 223:20

223:22

normalized 106:11

188:19

normalizing 184:7

223:25

normally 7:24

12:1 38:2

134:8 134:19

177:8 224:21

271:8

North 4:14

Northeast 251:23

Northwest 251:22

notable 63:10

notably 173:20

note 13:15 38:5

39:19 45:9 47:20

79:21 126:23

138:14 231:14

238:5 256:16

noted 10:3

38:22 44:15 46:3

46:9 47:18 49:24

52:15 63:24

79:20 133:9

138:19 247:3

notes 64:5 277:20

noteworthy 11:11

nothing 84:6

109:21 154:1

216:8 239:11

not--I 106:12

Notice 209:23

noticed 10:17

44:1 46:15 98:11

176:23 253:25

noting 79:8

notion 101:3

108:10 108:23

136:16 141:7

215:12

nuance 126:20

number--So 48:8

numerical 280:5

numerous 56:22

75:13 178:10

nutrients 45:20

O

obesity 137:22

objective 61:19

90:17 90:21

91:10 92:16
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 366

94:12 94:13

112:16 114:5

114:17 114:20

130:13

objective--they

111:24

obligation 2:12

obliged 187:22

observation

108:18 136:11

observational

66:1 69:15 73:18

73:25

observations

50:18 76:10

133:1 133:9

134:18 165:25

193:1 256:16

obtain 69:3

154:24

obtained 6:16

47:15 55:18

69:22 152:7

obtaining 266:24

obvious 142:20

283:1

obviously 8:13

69:11 85:20

86:19 112:21

137:4 143:22

171:20 216:13

269:10 283:16

occasion 129:6

129:10

occupational

68:20 68:24

occur 14:16

62:7 68:5

68:24 74:18

120:2 120:4

120:21 183:19

197:5

occurred 63:23

occurrence

20:19 57:10

68:18 233:8

occurrences 73:15

occurring 197:24

occurs 20:19

109:5 141:17

217:9 240:23

Ochoa 47:9

Ochoa-Akuna 47:1

o'clock 140:9

odd 71:7 204:23

odds 47:21 238:17

offer 58:19

141:21

offered 198:13

200:17

office 201:22

Official 4:17

4:21

offspring 90:13

offstream

250:12 250:13

of--once 132:5

of--over 97:21

of--there 99:8

of--well 107:7

oh 15:14 15:22

176:7 213:20

256:3 279:3

279:11

okay 6:9 7:13 8:7

11:14 15:12 17:1

17:3 17:16 19:21

25:7 31:24 32:23

40:2 42:10

57:2 57:19

58:4 58:5 65:3

65:23 74:5

87:3 87:8

137:8 140:5

151:18 152:20

157:17 173:7

175:14 177:5

184:4 189:13

196:18 202:8

208:1 209:10

209:25 210:2

213:14 216:11

217:6 222:6

229:14 229:21

231:7 240:8

240:13 242:17

252:11 254:10

257:3 282:24

Oklahoma 2:24

old 57:6 201:15

216:25 232:22

279:8

older 175:8

175:12 176:8

oncology 161:21

one--each 115:12

ones 7:15 31:11

39:20 41:1

46:9 60:18

104:14 148:12
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 367

174:19 183:10

232:18 238:1

249:13

ongoing 83:14

84:8 84:14

110:19

onset 32:2 205:14

211:23 212:2

212:2 214:2

214:2 226:13

226:20 278:6

Ontario 4:12

43:15

onward 11:18

126:6

OP 221:3 222:14

open 5:16 36:9

145:16 195:20

205:23 207:6

opening 10:18

10:23 11:4

11:7 11:12 88:15

88:16 88:18

90:15 90:15

91:13 92:22

92:25 94:2 95:11

96:20 98:10

98:12 98:17

98:18 99:7

102:14 105:5

105:11 109:15

222:8

operated 62:2

operating 61:13

142:11 143:12

275:18

operator 235:16

239:3

opinion 188:14

200:14 224:21

225:1

opportunities

141:21

opportunity

153:12 236:3

251:1

opposed 27:9

70:22 202:17

215:17

opposite 27:4

214:7

optimal 46:14

138:4

optimizing 22:1

optimum 24:19

options 199:6

oral 13:8 13:14

152:17 161:20

173:17 173:17

173:25

oranges 29:6

or--because 138:2

ORD 278:5

order 23:18 126:5

146:8 152:21

155:23 158:13

170:8 176:24

177:3 177:19

248:25 261:12

ordered 126:10

131:8

orders 225:5

organ 90:24

organism 223:3

organization

116:24

organized 37:2

82:14

organizing 268:2

organophosphates

191:20

original 53:3

151:6 283:2

originally 33:1

74:15 88:4

178:24

others 45:17 62:8

129:20 133:10

151:17 162:16

163:12 164:21

272:25

others--it's

136:5

otherwise 51:1

Ottawa 4:3

ought 44:24 46:11

137:4

ourselves 29:9

187:1 191:13

283:1

outcome 29:16

37:18 38:2

40:7 42:8 47:5

63:23 64:5

70:5 70:15 71:23

72:5 97:13 100:5

100:7 100:13

100:19 108:3

108:21 108:24

132:23 155:25
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 368

197:3

outcomes 37:11

37:15 37:19

37:25 38:24 39:1

41:11 41:12

42:23 43:9 43:13

44:11 46:15 48:5

49:3 53:15 54:10

63:16 63:21

67:25 68:10

68:13 69:22 70:2

70:9 70:17 70:21

73:15 78:25

79:17 81:15

81:17 82:7 96:11

138:5 143:5

143:9 143:15

143:20 143:22

148:21

outlier 277:8

outlook 107:18

output 106:24

199:24 262:22

264:10 265:4

266:16 267:21

268:15

outraces 109:19

outside 26:17

27:3 60:11

outstanding 84:6

ovarian 151:10

ovaries 33:16

overall 37:8

60:10 62:10

176:15 184:22

199:13 248:10

overconfident

252:4

overlap 47:24

47:25

overlapping

192:24

overlooked 237:3

overruled 225:16

overstated 42:5

overtime 84:21

217:19

ovulated 216:25

ovulation 213:6

216:25

owing 197:11

P

p.m 285:16

package 149:11

260:25 273:7

packages 223:18

packed 280:12

packet 94:22

pad 202:2

page 16:12

22:25 39:22

40:20 42:25 55:2

61:24 64:9 76:23

76:23 164:5

174:9 174:9

283:21

page--but 54:20

pages 175:13

178:23 180:18

pair 138:16 139:2

palatable 8:4

panel 2:1 2:6

2:11 2:16 4:16

5:3 5:17 10:5

15:13 23:5

36:9 57:18 64:25

82:15 106:19

111:16 118:13

121:10 124:24

126:23 137:7

140:15 146:18

148:15 151:25

170:23 173:7

176:19 187:20

190:23 197:17

208:5 263:16

263:25 268:7

panelist 56:2

Panel's 4:24

paper 16:5

30:17 38:9 39:18

39:21 40:9 40:15

40:15 43:15 44:6

46:6 46:9 47:8

47:20 48:3 48:20

48:21 49:18

49:24 49:25

54:15 54:15

56:10 59:18 63:8

81:5 81:24 89:10

89:19 89:19 94:8

94:11 102:2

105:14 111:10

140:23 143:23

152:13 157:11

163:10 163:21

167:2 167:16

173:12 196:15

201:20 205:1

205:24 207:18

207:25 231:18

243:19 274:15
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papers 35:1 36:23

39:18 40:24

43:23 49:18 54:6

54:7 54:8

54:10 55:3 56:20

94:5 105:14

108:6 166:1

204:22 205:8

229:6 251:24

276:9

paradigm 115:13

paradoxes 100:12

paradoxically

210:3

paragraph 76:22

parameter 10:14

parameterization

245:12

parameterized

244:16 247:1

parameters 22:1

102:23 131:16

137:13 245:3

245:5 245:13

parent 13:8 14:22

48:6 69:8 154:11

155:3 155:20

159:9 269:23

278:8 280:6

parity 42:8

partial 244:6

247:12

partially 203:15

participant 63:10

participate 39:2

participation

38:23 38:24 39:8

40:10 40:11

42:19 44:9 44:20

49:21 63:11

particular 7:2

10:22 26:25 42:7

64:25 82:15

84:13 94:20

99:24 100:7

100:18 112:21

124:20 141:11

148:16 168:24

170:14 171:17

186:8 209:3

222:23 240:20

243:4 243:21

246:23 264:21

270:6

particularly

34:23 64:14 71:7

72:12 72:15

76:25 97:16

100:21 131:2

131:22 133:10

134:8 134:16

144:23 145:16

153:9 154:9

162:22 167:12

172:5 172:9

172:10 172:14

173:2 191:18

198:20 221:22

223:2

partition 24:3

159:12 166:25

partitioned

192:23

partly 195:22

Pasqual 16:6

pass 144:7

past 13:7 13:10

15:10 72:6

266:12

Patagalan

284:19 284:20

pathological

113:13

pathologist

114:24 115:1

115:5

pathology 113:7

134:18

patients 123:17

pattern 8:6

8:23 9:18 10:7

60:10 176:23

244:14 244:22

244:24 275:10

patterns 219:7

230:9 266:9

paucity 121:6

Paul 19:20 20:3

231:8

pay 127:23

paying 126:18

PBPK 21:4 21:12

21:12 22:2

24:1 166:19

168:6 169:11

169:17 170:23

171:2 171:12

171:17

PBPKs 170:23

PC 274:1

PCB 139:11
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PCB's 139:5 139:6

PD 195:24

peak 15:6 31:21

32:2 62:6

163:7 175:4

175:7 176:9

176:10 176:15

179:5 181:25

182:21 183:15

183:18 184:7

203:2 203:15

211:7 216:23

225:11 230:6

234:21 235:5

239:25 243:13

243:22 243:23

243:23 243:24

244:19 246:13

248:19 250:24

253:4 253:8

258:11 258:13

259:7 267:24

peaks 224:24

240:18 240:23

258:21 258:23

259:3 259:4

pee 107:10

peer 113:19

Penelope 4:6

83:10 83:21

85:14 113:2

148:24 149:8

149:13 200:12

200:13 278:11

279:1 279:11

280:2

Penny 4:6 279:19

people 21:8 34:11

99:10 112:6

121:10 125:16

128:8 170:14

192:3 209:15

231:5 243:11

249:22 252:6

256:21 277:6

280:19

per 8:17 8:19

8:21 20:10

98:4 98:5 98:6

107:14 121:24

136:9 183:5

183:5 183:9

183:9 185:4

186:12 192:14

192:15 195:20

196:4 228:24

236:14 236:18

246:10 253:14

255:5

perceive 282:19

percent 9:18 13:8

19:24 20:1

173:19 210:25

211:14 212:8

214:23 215:3

215:5 215:7

215:9 218:19

219:15 223:21

229:11 234:4

249:5 274:21

percentage 20:14

percentages 177:8

177:10

percentile 177:14

177:14

percentiles 178:3

253:1

percolate 285:8

perfect 239:19

perfection 81:10

perform 162:24

performance

266:19

performed

163:19 261:9

perhaps 22:25

56:6 69:24

80:4 127:8 135:2

135:3 147:25

169:21 172:21

260:14

perimenopausal

189:9 213:5

217:10

perimenopause

40:5

perinatal 43:13

44:11

period 5:12

5:15 7:4 8:18

17:2 25:10 26:25

47:24 48:11

93:15 106:6

113:7 123:9

137:23 154:24

158:8 160:15

161:10 188:22

189:18 211:25

213:5 218:25

233:8 235:22

236:13 236:14

236:18 237:11

238:4 253:6

255:3 258:18

266:14 267:7

268:4
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 371

periodic 266:5

periods 48:1

62:14 68:6

70:9 74:16 90:12

91:5 236:11

265:7 266:12

267:17 267:24

perisenescent

185:18 186:2

189:4 189:9

permit 14:15

198:12

Perry 248:11

253:13

persisted 10:11

persistent 43:1

43:2 43:3 43:4

person 56:20

139:21 221:16

275:23

personal 174:25

175:15 211:17

212:4 273:15

personally

84:19 114:8

114:13 210:9

223:6 240:5

personnel 236:5

perspective 68:9

perspectives

82:19 82:22

127:7

pertinent 222:24

pervasive 60:7

pesticide 39:3

40:5 48:24

53:8 229:22

229:25 241:4

241:24 248:4

261:5 261:16

pesticides

39:25 41:6

43:1 45:22 49:15

50:13 52:20

52:22 53:2 72:10

72:13 72:18

123:24 124:8

233:10 248:3

250:18 251:9

252:6 260:6

273:7

pharmacodynamic

154:10

pharmacodynamics

75:22

pharmacokinetic

152:6 152:15

153:6 154:16

155:16 158:24

159:5 159:14

159:21 162:15

167:9

pharmacokinetics

75:22 109:7

129:21 162:21

195:22

pharmacy 13:6

phase 10:12 13:19

17:12 22:14

23:19 79:19

141:18 141:21

262:15 273:8

phases 158:18

270:1

phenol 154:5

phenomenon 19:6

106:14 113:8

phenoxies 72:19

phone 100:3

physical 42:4

247:10

physiologic 10:14

14:15 81:22

physiological

136:25 143:8

physiologically

153:6 202:25

203:19 204:8

216:19 216:23

217:3 218:18

physiologicallybased 159:13

physiologist

106:23

physiologists

252:9 279:16

physiology 142:13

151:7 280:12

phytoestrogen

97:17 97:23 98:3

98:7 98:25

phytoestrogens

98:20

pick 275:13

284:23

picture 157:13

196:2 196:3

pictures 20:22

picturing 54:14
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 372

piece 84:23

pieces 30:9

147:23 204:3

207:17

pituitary 24:7

24:12 153:16

153:19

PK 8:20 9:8

13:1 13:12

18:3 24:10

166:20 167:21

167:24 168:7

168:12 168:13

185:10 195:23

places 52:9

204:12

plain 279:7

plan 140:5 209:11

237:7 237:10

265:8 274:10

plane 195:9

planned 84:8

267:1

planning 53:25

84:9 92:18

228:14 267:5

plans 140:8

plant 66:17

118:21 232:4

235:15 266:22

plants 264:17

plasma 14:12

14:18 14:25

16:10 16:15

21:18 21:22

22:14 22:14

23:20 23:25

152:9 152:14

152:16 153:1

154:5 154:17

155:14 159:12

160:12 161:6

161:10 163:21

163:24 164:2

166:25 168:9

plastic 11:24

12:2 12:3

plateau 157:5

157:9 157:16

161:18 185:4

191:4

plausibility

60:24

play 82:2

plays 61:17 150:8

please 6:8

17:17 25:16

31:20 36:12 58:6

65:4 65:10

87:8 87:11 87:13

117:14 117:17

140:13 140:15

152:6 152:18

174:4 195:12

196:13 209:21

230:10 240:16

245:23 254:8

254:22 256:16

pleasure 162:16

plot 173:12

177:20 188:4

191:6 191:6

191:7 197:14

plots 15:17

167:16 191:3

284:10

plotted 157:18

195:20

plotting 195:14

plus 34:17 183:19

225:3

podocyte 172:3

172:6 172:14

point 11:13 15:13

24:22 25:9 26:23

27:4 27:16 28:17

29:10 30:3 32:18

32:22 36:1 36:18

39:8 52:4

55:24 56:15

57:17 61:1

73:2 80:7

81:23 86:23

86:25 87:4 89:17

116:25 117:3

127:17 133:14

134:3 136:1

141:22 143:8

145:9 147:7

151:18 167:7

168:17 170:8

176:6 179:18

182:18 183:7

184:1 184:20

185:1 185:12

189:12 191:2

191:15 192:8

193:18 196:11

198:13 199:18

199:22 199:22

200:1 202:3

202:12 202:12

202:19 203:8

203:15 204:15

204:16 207:7
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 373

209:13 210:14

222:3 222:4

222:9 222:12

222:24 225:24

229:15 235:13

236:6 236:25

237:16 238:12

238:21 240:1

245:14 246:10

251:24 263:6

263:8 263:10

271:20 271:21

272:15 278:16

278:24 279:2

280:8 285:12

pointed 18:22

23:13 40:25

86:16 113:4

127:12 129:11

136:13 137:1

177:24 192:13

193:24 283:3

pointing 40:4

100:8

points 16:1 16:17

16:24 17:5 21:16

23:14 23:14

31:16 50:23

59:22 61:10

61:21 67:3

76:6 80:18 89:11

89:12 91:11

97:24 102:4

106:13 123:19

127:7 127:12

133:8 138:25

142:12 142:16

142:22 142:25

143:17 154:20

154:20 154:23

154:24 156:14

157:5 157:12

157:25 164:12

176:13 176:16

176:18 177:11

177:13 179:24

184:24 185:5

194:12 197:23

198:7 198:23

198:23 236:14

258:24 263:22

280:24

policy 5:1

politics 170:22

pool 20:8

178:20 193:6

Poole 23:10

poop 107:10

poor 51:18

Pope 2:24 2:24

populated 154:18

population 39:7

41:7 42:21 48:11

49:4 68:25 119:6

130:3 196:25

232:8 247:12

populations 78:15

Portier 2:12 2:18

2:18 115:15

115:21 115:23

140:9 187:25

194:18 209:7

209:10 209:11

209:25 213:14

213:18 215:4

216:5 216:11

219:16 220:22

222:15 222:20

224:7 224:16

225:17 225:23

226:22 227:12

228:4 228:17

229:4 229:14

230:17 231:4

236:7 236:23

238:20 240:11

240:24 242:14

242:16 244:11

245:21 246:3

248:23 252:8

254:5 254:25

259:13 260:2

262:16 262:17

269:19 271:13

271:22 272:22

273:14 273:23

275:5 277:17

279:9 279:12

279:17 280:11

285:4

Portsmouth 3:6

POS 206:23

position 167:4

277:7

positive 44:12

61:12 100:19

102:8 121:25

127:15 128:7

128:10 129:4

129:19 218:2

221:8

possibilities

107:21 145:20

possibility 53:14

81:9 81:20 107:4

107:17 146:12

146:15 269:12

269:17 272:13
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 374

possible 10:21

11:22 32:3 33:16

39:11 61:19

92:23 94:16 95:8

96:8 103:17

103:22 132:13

143:10 161:17

169:6 180:22

206:15 207:21

224:6 265:6

281:2

possibly 5:24

120:12 131:4

131:18 217:20

217:20 281:3

post 11:16

88:25 89:5

235:23

postnatal 90:20

90:21 90:22 91:1

91:2 93:20

95:9 95:10

105:22 106:7

106:7 106:9

137:18 148:20

postnatally

101:25

post-natally

92:18

post-op 181:9

posts 100:2

post-season

266:11

potency 119:22

170:11

potent 278:8

potential 37:11

38:24 40:11

44:20 63:10

79:11 81:19

85:20 97:2

100:10 135:14

138:17 139:10

165:19 166:8

198:22 250:20

potential--and

99:4

potentially 25:15

41:7 41:25 57:10

84:22 92:11

95:25 138:5

139:21 141:13

144:21 152:1

252:1

pour 235:18

power 50:7

50:16 59:7 78:22

78:24

powerful 125:19

173:3

PPBK 34:23 35:4

PPM 8:14

practical 115:6

256:8 273:3

practice 6:25

practices 265:14

preamble 254:10

precedent 191:16

precipitation

241:3 241:24

precise 142:24

precision

176:13 181:13

181:15

precocious 100:9

preconception

43:24

precursor 29:17

196:21 197:6

197:8 223:1

223:1 223:5

precursors 68:8

68:10

predefined 265:4

predict 29:17

233:25 248:19

261:10 265:1

predicted 256:1

265:24 266:13

predicting

21:22 57:9 153:5

prediction 265:10

predictions 24:17

264:23 265:13

predicts 21:17

prefer 174:23

preference 112:16

preferentially

45:21

preferred 9:12

pregnancies 43:19

44:18

pregnancy 47:23

49:3 126:8

141:19

pregnant 85:8

126:5 126:10

126:16

pre-judges 159:17
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 375

preliminary

16:8 121:20

158:25 174:5

prematurity 80:5

premise 15:2

18:13 48:2

prenatal 141:18

prenatally 101:25

preoptic 153:17

prepared 22:24

207:1

prepubertal

185:17 185:23

presence 138:22

161:25

present 60:20

120:11 124:7

124:11 124:22

128:18 129:12

142:19 155:24

163:23 172:19

presentation

15:18 16:3 29:25

66:17 67:21

82:14 83:12

100:8 116:24

162:18 173:8

196:9 231:9

243:18 257:5

presentations 5:4

5:14 65:14

67:4 75:20

167:10 168:1

263:24 268:7

presented 4:25

19:14 24:5 33:10

52:21 99:17

108:6 108:6

140:23 156:9

165:7 170:6

172:3 172:19

175:2 193:3

presenters 5:18

presenting 86:21

presents 39:7

136:10

preserve 266:6

pressures 61:18

67:20

presumably 127:14

146:1 146:20

presume 19:2

presumed 13:18

103:6

presupposition

100:16

pretend 34:16

preterm 47:25

48:23

pretty 36:24 43:2

51:5 55:5 79:1

79:22 96:17

107:25 108:1

116:19 151:19

188:21 226:1

231:9 233:25

234:8 236:18

238:17 238:21

275:13 283:4

pretty--try 36:23

prevent 96:9

prevention 196:24

preventive 81:21

previous 22:12

92:14 97:9 97:11

156:13 197:16

224:13 227:18

primarily 23:15

89:23 107:2

primary 70:2 70:5

70:18 155:13

primate 149:24

primates 150:5

150:25

primers 24:1

print 37:4

prior 60:4 72:1

prioritize 169:15

priority 83:25

135:4 171:6

Private 4:7

pro 27:14

probabilities

239:22

probability 177:9

178:5 239:24

260:9 282:19

probable 97:10

probably 9:22

48:2 54:8 116:19

129:22 147:18

151:6 153:3

157:4 170:24

172:13 172:15

178:19 206:21

206:22 217:4

218:20 225:12

228:5 231:24

232:9 238:25
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 376

252:23 258:4

259:18 265:21

280:5 280:22

282:1 284:13

probe 141:21

problem 59:14

59:16 59:16 60:6

124:3 124:17

138:3 138:6

138:8 153:3

157:22 164:9

164:18 164:19

180:16 181:4

215:1 217:1

219:1 219:23

223:20 246:8

problematic

108:25 208:13

problems 42:20

51:10 51:15

51:24 59:4 61:23

154:16 155:1

157:3 161:20

205:7 253:18

procedures

74:23 96:7

142:11 142:15

143:12 263:23

275:18

proceed 6:4

proceeded 191:1

proceedings

5:20 25:15

process 14:15

23:9 33:25 53:22

55:17 56:5 56:17

56:24 65:25

113:5 113:16

119:12 119:25

120:7 129:14

129:18 131:5

158:14 181:4

191:10 217:15

218:8 245:10

264:8 264:20

processes 15:1

71:12 155:16

156:7 264:14

272:4 272:5

produce 78:2

110:25 124:23

211:25 215:6

215:10 240:4

produced 121:25

146:20 165:14

produces 95:24

producing 138:7

product 135:18

155:13 269:18

269:22 269:23

production 132:9

productive 60:2

83:24 139:25

pro-esterase

190:1

pro-estrus

28:21 29:7

30:4 31:5 33:2

professor 2:22

profile 22:14

23:20 23:25

24:15 124:21

154:21 155:14

156:14 157:3

185:10 278:18

280:6

profiles 16:11

16:11 21:18

21:22 22:17

159:5 269:12

269:13

profound 109:5

109:18 109:19

110:15 230:20

profounder 99:12

progesterone

33:17 181:6

181:7 181:10

program 3:2 85:18

138:3 191:16

201:22 228:11

228:14 231:22

233:5 246:1

260:19 270:16

programming

125:20

programs 169:13

progress 140:3

260:17 264:2

project 110:19

127:2

projects 139:5

promise 261:22

promising 255:12

properly 247:11

264:4

properties

88:16 88:21

154:25 263:24

proportion

12:18 30:3

proportional
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 377

159:24 208:23

proportions

121:22 123:8

123:13 130:19

155:20 164:12

164:15

proposal 132:1

240:16

proposals 139:5

139:12

propose 68:19

proposed 140:16

140:21 198:13

217:24 254:9

254:23 263:22

proposing

139:13 238:24

protected

197:23 199:22

protecting 118:16

protection 198:12

198:24 199:10

231:20

protective 70:12

proteins 153:18

153:25

protocols 84:20

90:8 266:23

prove 41:24 121:7

provide 50:15

71:8 81:20

102:10 103:14

104:5 153:12

159:1 159:15

195:5 196:19

207:23 254:23

264:22 268:13

268:17 276:20

provided 15:17

50:6 117:14

178:24 196:15

201:3

provides 102:12

102:21

providing

246:25 253:20

257:19

provocative 45:5

proxies 55:7

proximity 55:8

55:9

PRZM 249:17

249:17 249:22

255:11 255:11

PRZM/EXAMS

246:2 246:24

247:3 247:14

247:18 249:17

pseudo 156:5

160:3 174:1

pseudo-steady

152:16 157:8

159:20 159:23

160:20 173:23

178:15 182:6

183:1 186:12

186:15 192:1

puberty 141:18

205:14 211:24

226:6 226:9

226:10 226:13

278:6

public 5:12

5:14 5:15 5:18

24:23 25:10

51:10 94:8

118:16 124:4

162:10 240:1

263:7 267:13

publication 21:19

published 57:6

70:25 153:15

171:4 276:20

pull 203:17 231:5

pulling 223:25

pulsatility

204:20

pulse 205:16

242:24 243:4

pulses 226:12

pump 250:13

pup 104:13 125:23

125:23 125:24

128:3 137:18

138:2

pups 88:3 88:8

88:9 88:10 88:10

88:12 88:12

88:19 88:24 89:6

89:7 89:9 91:1

107:15 127:13

138:8 138:18

pure 193:9 247:10

purely 20:17

255:17 255:22

purpose 130:22

130:24 231:18

231:21

purposes 38:3

38:3 64:13 67:11

69:24 70:22
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 378

pursuance 171:2

pursue 32:8

35:4 264:1

pursued 145:17

146:17 203:9

pursuing 168:24

228:23

push 29:2 171:13

putting 160:21

168:23 171:10

244:23

Q

QC 274:1 274:10

qualifier 66:19

qualifiers 67:6

qualitative

103:12 115:24

116:14

qualitatively

142:18 221:13

quality 43:6

51:19 56:11

56:15 58:9

70:1 74:22 74:23

87:9 87:19

133:21 159:2

174:22 269:2

274:2

quantify 13:1

114:12 196:22

quantifying 24:13

quantitative

51:11 58:12

64:13 96:19

96:22 96:25

103:12 110:21

111:1 111:17

111:24 111:25

115:25 118:2

145:1 149:5

201:12 221:13

quantitatively

142:18

quantities 111:25

146:21

quasi 60:22

query 15:16

question 6:4 6:10

7:21 10:17

11:8 12:22 13:20

14:7 16:21 17:16

17:21 19:13 21:3

21:10 21:10 30:1

36:5 36:11 36:11

36:17 40:4 50:19

57:19 57:19

57:20 58:5

60:8 64:18 64:25

65:1 65:2 65:4

65:16 71:24 72:7

82:15 82:23

83:10 86:5

87:5 87:7 87:8

95:3 95:18

97:2 101:13

106:20 109:19

109:20 117:1

117:4 117:6

119:14 127:25

128:11 137:8

140:11 140:12

148:16 151:6

151:23 151:23

151:24 152:5

152:21 157:21

173:9 175:3

179:1 179:2

186:9 190:16

190:21 191:5

195:8 195:11

196:11 196:18

196:19 199:17

202:13 202:18

202:21 208:4

209:18 209:19

209:20 210:3

210:6 211:12

212:7 213:19

214:22 216:17

219:10 219:21

220:14 220:24

220:25 221:7

221:15 224:18

226:1 226:3

226:17 227:9

227:22 232:10

240:8 240:12

241:10 241:14

252:11 254:6

254:11 258:11

276:2 280:14

280:17 283:2

284:12 285:6

285:10 285:14

questioned 158:11

questioning

188:25

questionnaires

69:4

questions 5:17

5:19 5:20 12:6

15:14 22:23

24:25 25:13

29:23 29:24

32:23 32:24

33:22 36:2
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 379

36:6 39:7 51:2

70:14 70:20

84:23 84:24

86:10 87:19

110:5 110:9

110:10 128:5

134:5 140:15

140:25 151:20

160:25 176:25

190:8 191:13

195:10 241:15

245:20 252:13

280:13 285:14

quick 15:16

25:1 27:17 29:24

151:4

quickly 2:15 23:6

103:2 202:8

231:9 233:3

233:4 242:11

276:5

quite 9:18 17:2

18:24 19:8 25:18

32:22 42:19

45:10 54:11

74:20 109:5

113:6 150:11

156:21 159:14

161:4 161:4

162:13 174:18

177:12 181:15

235:6 236:19

237:19 244:2

251:23 258:22

269:14 269:16

quits 285:12

quote 242:16

R

race 39:12 46:10

radical 84:15

radiolabel

15:10 155:22

raging 120:10

rainfall 243:3

249:24 265:17

265:19 266:8

raise 28:23 134:5

176:18

raised 39:5 152:2

171:8 202:23

raises 38:24

110:5

Raleigh 6:17

Ralph 25:23 30:24

33:7 138:20

280:4

random 61:3

61:3 62:7 250:19

254:19 267:22

randomly 157:4

range 26:13 26:17

26:19 27:3

27:5 27:6 49:3

60:11 76:16

129:7 152:18

173:14 197:24

200:25 223:19

234:13 244:7

ranges 164:7

244:9

rank 80:16 198:22

ranking 48:9

rapid 13:5 158:2

rapidly 155:12

rare 266:24

rat 11:1 21:13

31:18 97:5

125:13 126:16

152:9 152:16

153:16 163:1

167:23 173:18

178:21 182:17

192:17 192:19

193:12 193:15

193:17 193:20

194:2 195:2

203:13 213:24

215:23 216:18

217:4 217:24

218:11 278:16

278:24

rate 13:25

23:24 38:23 39:8

40:10 44:9

80:4 81:2

98:17 135:16

218:14

rates 73:14

rather 6:24 16:22

36:20 46:19 51:8

61:7 62:10 94:13

102:22 129:6

158:13 160:12

161:6 163:12

164:7 165:10

165:12 166:16

176:24 182:18

197:25 199:2

201:14 220:24

222:23 272:4

ratification

208:24

ratio 17:20 39:22

40:17 92:20
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 380

163:2

rationale 62:5

231:19

ratios 71:7

163:23

rats 7:23 10:23

27:15 66:24

87:24 89:16 92:8

92:25 107:10

125:9 126:5

153:4 153:19

166:22 167:10

167:12 167:24

167:25 171:16

173:24 174:4

174:6 179:10

179:12 179:13

180:8 181:3

182:20 193:1

193:25 194:13

196:6 217:5

217:9 231:1

rat's 215:23

rattle 130:14

Rauner 93:18

raw 20:11 20:12

264:5 265:11

Rayner 87:10

87:21 87:23 90:4

92:14 92:23

94:14 96:3 96:20

96:21 97:5 98:17

101:22 101:23

102:5 102:25

103:7 103:15

103:24 104:2

104:4 104:8

107:1 116:5

117:6

RBC 163:25

RBCs 163:24

re 171:11

reach 152:15

152:15 234:21

reached 156:7

reaching 285:5

reaction 14:16

reactions 14:21

reading 52:1

268:20

reads 36:12

ready 87:5

99:15 99:17

140:12 149:5

real 60:14

61:17 97:8 124:3

129:13 142:22

216:8 217:25

222:24 247:12

271:17 284:9

realistic 22:18

161:22 189:1

206:13 223:13

reality 34:17

35:3

realize 206:10

realized 118:4

realizing 264:6

really 10:2

32:7 33:18

37:9 39:4

39:11 39:13 40:8

41:8 43:9

43:10 43:10 44:4

44:22 44:25

44:25 45:18

45:22 50:6 51:21

53:22 63:2 66:24

69:25 70:19 71:3

75:21 84:19

91:20 99:12

102:5 102:21

105:17 110:6

111:6 111:8

120:25 122:23

122:24 123:6

123:9 123:12

123:16 124:2

125:22 127:7

128:10 128:22

129:16 133:8

134:13 138:23

139:2 150:5

153:10 154:18

154:24 159:13

160:1 161:2

161:5 161:8

169:2 169:2

169:2 169:7

174:15 178:12

178:13 178:17

178:18 179:3

184:20 185:14

186:10 188:13

188:23 190:18

191:9 192:13

194:24 194:25

197:3 197:11

200:10 202:25

210:4 210:4

210:8 210:23

211:5 211:7

211:13 212:7

212:19 216:14

218:7 220:19

221:15 221:20

223:5 226:9
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 381

227:18 230:21

231:3 231:11

231:12 234:18

235:6 235:12

236:13 236:15

236:21 238:24

239:4 239:10

241:7 241:10

243:22 244:14

244:22 244:24

246:14 246:19

250:2 251:19

252:15 253:13

253:15 253:23

253:23 255:1

257:9 257:21

257:25 259:12

268:21 271:3

271:7 271:14

271:21 271:24

272:6 273:1

273:2 273:4

273:11 275:17

276:3 284:9

reason 16:14

21:12 54:23

86:16 100:6

112:20 126:25

158:15 167:19

218:1 220:15

269:17 271:14

277:6 277:14

277:19

reasonable

69:12 136:8

154:9 162:13

163:22 164:3

258:3

reasonably 75:14

reasoning 225:15

reasons 28:1 28:3

54:7 55:1 62:3

70:22 101:14

141:7 208:16

253:12

reassess 85:10

reassuring 175:22

rebound 138:14

recall 190:20

receipt 7:5

receive 88:11

126:7

received 149:14

223:18

receiving 206:5

recent 223:9

recently 100:10

142:5 261:14

276:14

receptor 89:4

205:4 205:24

206:5

recessed 285:16

recognition

122:12

recognize 27:12

77:24 102:20

119:18 262:20

recognized

59:16 62:14 92:4

102:20 142:6

142:6 268:1

recollection

190:21

recommend 149:2

171:5 171:23

172:20 243:5

269:10 281:8

recommendation

96:9 98:2 119:17

136:8 141:6

197:16 280:24

recommendations

4:24 4:25 77:8

136:5 240:21

254:17 254:24

280:18 281:7

recommended

194:19 230:11

263:17 275:9

recommending

239:14

recommend--that

120:13

reconcile 82:24

113:25

reconsider 174:15

reconvene 209:5

record 30:17 36:5

36:7 36:18 57:23

117:4 140:13

151:24 152:3

173:10 196:12

208:9 226:2

226:7 227:18

recorded 11:6

11:20 30:6

132:17 163:9

records 69:5 78:1

recoveries 271:5

recruit 85:7

rectified 83:7
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 382

red 166:24 204:25

redesign 84:15

85:10

reduce 21:5

43:6 52:25

54:6 169:2 171:7

reduced 93:2

reducing 21:23

184:12

reduction

214:14 232:1

249:5 249:7

249:8

reevaluated 283:7

reevaluation

35:18

re-evaluation

34:1 34:7

Reevaluation 2:7

reexamined 148:1

refer 7:15

reference 30:17

131:10 167:15

207:23 276:19

277:2

referenced 278:9

references 30:6

54:21 151:1

243:9 261:3

276:6 276:8

referring 20:9

20:17 64:18 95:1

262:10

refers 20:3

refers--just 51:3

refined 24:16

172:22

refinement

168:4 283:24

reflect 13:18

reflecting 15:1

reflection 113:9

reflective 152:8

reflects 117:16

163:22

refrigerator

235:19

refuse 126:5

167:14

refute 101:17

103:7

regard 9:3

21:21 22:12 33:4

48:10 106:20

184:6 184:10

210:8 210:13

281:25

regarded 100:21

regarding 10:17

38:11 42:4

51:2 51:11 58:17

67:3 87:14 95:19

96:4 96:10 109:4

133:2 150:5

regardless 73:19

regards 13:14

40:14 272:7

regime 233:17

234:25

registrant 104:1

registration

171:12 242:20

242:22 278:20

registry 62:1

62:3

regression 284:22

regressionbased 254:18

255:11

regular 6:25

271:9 271:10

regularly 108:2

regulate 270:9

regulating 135:15

regulation 78:7

151:10

regulatory 5:1

35:12 145:14

158:23 169:13

229:2

reiterate

228:19 236:9

reiterates 162:14

reiterating

219:23

rejoinder 25:15

relate 36:3

118:12 130:18

133:15 153:4

159:9 170:1

214:10 237:8

related 41:9 51:2

52:13 55:16

59:20 86:10

89:24 133:10

136:23 142:12

143:18 160:23
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 383

171:21 188:13

190:9 207:3

210:16 214:24

relates 9:4 19:15

34:23 166:8

174:5 189:17

226:13 227:8

242:20 252:22

relating 22:23

relation 43:6

44:11 49:13

107:11 134:20

189:18 198:6

198:25

relationship

6:6 18:11

20:13 37:11 50:3

50:13 134:9

188:16

relationships

133:24 134:3

150:14 159:7

196:24

relative 40:7

48:9 110:20

114:22 123:21

154:10 178:10

193:9

relatively

47:13 68:6 68:11

256:6

releasing 158:5

205:3 205:4

207:11

relevance 51:23

78:15 86:11

86:12 221:8

relevant 54:13

54:24 66:8 66:15

66:19 67:24

68:17 69:8 69:21

70:8 71:23

72:1 72:7

75:15 78:8 78:17

82:1 84:24

100:12 102:25

124:23 127:9

129:24 130:16

133:11 133:19

139:19 162:14

162:23 163:3

190:8 196:24

197:7 206:7

247:20 269:5

reliable 177:15

reliance 61:17

remain 48:10

remainder 8:1

20:15

remaining 143:21

remains 21:21

141:11 198:4

remark 52:5

remarkable 195:19

remarkably 173:19

remarks 52:4

remember 8:15

157:25 177:22

178:2 207:16

228:7

remembered 228:8

remind 5:12

115:17 186:11

reminded 189:15

213:21

reminders 4:22

reminds 139:4

221:2 221:3

remove 55:3

192:16

repeat 41:1

52:4 63:6 103:4

repeated 66:9

68:18

repeating 130:9

185:25 189:2

213:11

replace 61:18

177:10

replaced 24:4

61:17

replacing 22:2

replicate 6:14

102:24

replicates 274:9

replicating

132:25 136:20

replication

129:17 130:2

130:8 135:23

144:19

re-plotted 182:3

report 9:1

14:11 16:9

17:4 18:4

24:14 30:21

30:22 53:3 53:25

60:23 73:9 74:10

75:18 76:19

86:19 97:8

103:20 104:12

104:13 104:14
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 384

117:9 122:22

135:20 159:1

170:6 183:4

201:21 243:8

253:20 261:2

274:15 274:15

280:15

reported 11:4

60:17 100:10

104:11 144:23

144:25 184:17

reportedly 107:13

reporting 73:5

98:25

reports 14:10

14:25 52:19 60:8

60:13 84:13 99:5

99:9 145:12

represent 16:17

43:20 54:10

73:20 123:16

143:24 163:25

185:8 251:25

representation

9:17 185:19

representative

63:13 237:7

248:9

representativenes

s 39:6 41:2

41:15

representing 24:7

246:22

represents

102:5 163:11

165:23 251:11

268:16

reproduce

114:19 114:20

reproducibility

91:12 131:11

reproducity 94:19

reproductive

38:16 39:1 40:24

41:9 42:11 42:12

64:3 66:11

68:3 69:22 70:20

72:4 75:10 75:10

79:19 82:4 222:8

request 175:12

requesting 103:25

require 98:25

101:5 112:3

required 104:2

118:1 221:24

265:16 266:1

266:4

requiring 265:2

reread 65:15

rerun 208:25

research 41:8

77:6 77:9

78:11 78:23

83:23 131:24

228:21

reservations

145:13

reserve 284:17

reserving 192:6

reservoir

247:21 248:6

248:14 248:22

250:8 250:10

250:15 250:17

250:18 250:19

250:23 251:2

251:5 254:2

reservoirs

242:7 246:2

247:18 247:21

247:22 247:25

249:23 250:4

250:5 250:7

250:12 250:25

252:7 253:1

253:17 253:22

273:21 284:20

reservoir's

250:25

Reservoirs 253:4

residence 45:5

73:23 242:12

253:3

residential 55:9

residing 68:21

residual 12:7

169:7 208:15

208:19

residue 19:15

20:22

resolution 18:24

resolve 19:12

51:24 168:14

168:16

resolving 14:24

resort 127:25

resource 143:11

resources 37:19

41:19 41:21

respect 36:13
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 385

58:8 65:7 102:10

104:18 111:9

111:11 145:6

145:19

Respectively

93:22

respiratory 49:17

50:11 50:13 64:4

83:24

respond 184:5

187:16

responding 28:2

response 7:16

8:11 27:9 28:5

28:8 29:11

31:5 58:11 64:16

80:24 81:15

86:17 90:7 94:10

119:13 119:23

122:20 122:24

131:4 131:10

132:19 133:20

133:24 134:3

134:9 135:6

140:25 144:15

147:18 151:20

153:4 161:11

161:13 168:24

171:16 184:13

186:1 186:3

187:17 187:19

188:13 190:19

195:6 196:23

197:10 197:19

208:4 209:22

210:7 210:12

216:17 218:2

220:12 221:9

221:25 222:1

222:3 226:16

227:21 228:11

272:11

responses 32:7

129:11 159:10

160:23

responsible

145:24 146:25

267:3 267:10

responsiveness

189:5

rest 20:1

118:12 123:17

restrictive

251:19

result 16:18

48:12 52:24 53:9

60:9 60:25 63:17

66:11 115:18

127:12 128:24

131:24 134:24

160:18 163:5

205:10 210:16

217:19 223:22

272:12 272:15

resulted 45:12

46:22 47:16

109:12

resulting 132:5

146:3 146:8

results 16:8 33:3

40:15 52:20

52:22 59:12

59:19 60:3 60:10

63:12 71:6

76:7 77:22

92:1 96:7 101:13

101:19 104:5

104:9 117:20

125:14 128:4

134:15 134:20

145:20 145:21

146:13 159:1

185:23 188:25

194:9 201:9

240:5 247:7

248:21 255:19

257:7 268:17

274:19 274:20

278:15 278:19

resume 140:6

retention 242:8

250:9 250:11

rethink 128:16

retrieval 125:18

retrieved 266:14

retrovirus 108:14

108:16

return 4:16

85:8 197:14

reversed 59:12

reverted 105:24

review 2:8

24:16 24:20

34:20 35:2 35:18

54:2 54:19 54:22

55:13 55:20

55:24 56:8 58:25

60:22 66:21

75:23 87:6 87:24

107:6 113:19

116:13 117:13

117:16 139:4

149:1 149:2

reviewed 54:15

56:13 57:3 60:13

64:5 77:13

82:9 149:1
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 386

149:14

reviewing 37:10

reviews 35:15

36:13 60:7 60:8

revisions 51:6

revisit 261:20

285:8

RFD 278:12

279:4 279:24

280:1

rhythm 9:22 9:23

rich 143:11

170:24

Richard 3:5 3:5

3:23 3:23

15:15 15:15

15:19 15:20

15:24 16:4 16:20

17:13 152:23

177:7 186:10

208:16 227:15

230:18 231:7

242:15 248:24

252:5 252:17

253:22 254:1

257:2 259:14

270:8 270:15

270:24 271:2

272:8 273:5

273:6 275:21

275:21 284:16

284:16 285:2

Richmond 123:12

rigging 256:13

257:6 258:7

rigorous 112:23

risen 158:4

risk 8:16 34:2

34:4 41:7 56:3

56:16 58:12

64:13 67:19

69:12 70:1 72:11

75:1 99:17

109:17 118:14

118:20 118:21

119:8 119:21

120:8 120:23

121:6 128:16

129:14 129:17

143:25 144:13

145:2 147:10

147:20 149:5

197:4 199:4

229:23 239:21

252:3 262:23

263:3 264:9

277:21

risks 44:4

river 6:17 6:18

250:7 250:13

276:22

Riverside 3:4

road 5:2

robots 238:16

robust 77:3

112:14 197:20

robustly 198:5

robustness 27:8

Roby 3:15 3:15

32:23 32:25

184:3 184:4

186:8 186:21

187:15 188:4

188:10 189:11

190:15 190:19

202:7 202:8

209:25 210:2

213:14 213:16

213:20 214:6

214:21 216:1

216:12 219:12

222:20 223:15

224:1 224:5

224:12 226:8

226:19 226:24

227:3 227:11

228:4

rodent 14:12 75:7

146:22 149:25

150:3 150:6

151:9 190:7

rodents 210:19

213:2 223:23

Rodriguez 152:4

173:10 173:11

195:11 195:13

209:20

Rodriguez's

190:20

role 61:22 74:9

82:2 120:6 150:8

151:11 166:24

269:6

room 127:1 151:18

277:24

rough 76:13 76:17

roughly 60:12

93:18 245:2

round 86:4

route 14:8

routinely

142:25 143:7

Rowe 151:1
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 387

RU 105:8

rule 62:15 97:22

ruled 206:16

rules 61:17

run 20:5 29:18

252:3 266:8

266:11 272:18

282:18

running 217:2

265:11

runoff 20:20

runs 100:1

S

sacked 181:3

sacrifice 180:25

safe 234:8

safety 281:5

Saldata 40:14

Saltanga 39:18

sample 13:24

16:17 20:6

20:6 41:5

41:16 42:14 45:9

47:13 49:7

50:5 50:15 63:12

63:13 79:22

124:9 154:23

197:21 231:23

233:18 234:18

234:22 234:25

235:2 235:3

235:17 235:20

235:25 239:3

241:17 242:5

254:3 254:3

257:23 267:4

269:11 272:19

274:8

sampled 232:6

246:9 247:8

samplers 236:4

238:8

samples 9:8 14:18

16:16 17:2

20:5 20:8

20:12 20:12

41:13 41:21 62:7

64:1 64:1 69:7

84:17 84:17

114:25 115:1

154:17 156:12

157:1 181:1

224:6 233:14

235:16 235:22

236:3 236:18

236:20 237:25

238:16 239:10

240:4 245:25

246:6 255:5

265:3 266:5

266:14 267:11

267:23

sampling 16:16

62:5 159:3

229:19 230:13

230:14 231:2

231:19 233:8

233:10 233:17

234:15 234:25

235:7 237:10

238:3 238:25

239:1 239:5

239:16 239:18

239:20 239:23

240:3 240:7

240:23 243:11

243:20 248:9

253:17 254:2

255:2 257:6

257:8 258:19

259:21 260:8

263:23 264:17

265:7 265:8

266:23 266:25

267:16 267:17

268:2 268:3

268:10 268:18

274:4 274:6

276:22 280:13

281:23

samplings

235:11 276:24

San 4:5

SANDRA 3:19

30:1 30:10 30:19

32:16 147:5

178:9 202:15

207:24 208:2

216:12 219:22

224:17 225:21

227:1 227:8

279:18

Sandy 3:19

Santa 4:1

SAP 5:13 34:25

38:7 55:22 66:20

143:21 190:22

236:10 243:8

254:17 263:16

268:8 268:8

268:8

saturated 155:18

saturation 164:7

Savitz 44:6
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 388

saw 28:10 33:11

96:3 110:2 180:2

185:22 195:23

231:14 244:17

256:18

say--where 106:23

scale 17:7

68:12 177:10

177:17 177:20

178:5 182:4

249:3 249:13

250:3 260:15

scaled 174:15

scaling 174:22

177:19

scalloping 10:6

10:10

scan 175:17

175:18

scattered 154:2

scenario 255:14

schedule 6:19

6:24 6:24 7:1

8:24 16:16

35:9 209:14

scheme 34:6 104:2

211:20

Schlenk 3:3 3:3

170:18 170:19

173:7 220:23

221:1

science 2:6

34:3 34:7 35:5

35:18 60:3

101:20 195:25

223:4 225:9

229:1 272:16

scientific 2:1

25:11 58:6 59:23

61:16 65:4 73:24

78:5 121:11

122:7 209:21

212:8

scientist 121:8

168:1

scientists 25:5

195:16

scope 143:21

score 56:20 74:23

88:22 108:17

116:2 116:17

scored 56:10

56:15 104:2

104:3 104:4

143:9

scores 22:18

74:24 116:16

scoring 56:9

89:20 90:16

90:23 91:16

91:17 91:19

91:25 102:15

102:17 103:12

103:23 105:18

110:21 110:22

111:1 112:1

112:11 112:17

115:25 116:14

116:14 131:4

SCR 170:10

screen 234:17

234:19 234:20

234:21 234:23

251:2

screening

233:15 234:2

screens 102:2

SD 167:24

167:25 185:21

220:15

se 136:9 186:12

195:21 228:24

search 68:9

131:22

season 237:11

239:2 264:24

266:7 266:12

267:11

seasonal 70:10

230:13

seats 87:4

SEAWAVE 245:19

255:11 256:11

260:4

SEAWAVE-Q

254:21 261:14

283:20

second 22:20

37:13 47:10

51:14 59:14

96:15 126:4

176:6 202:3

210:10 226:14

233:7 233:24

235:1 243:23

secondary 185:9

215:3 227:6

secondly 185:2

185:25

section 30:21

37:2 38:15 40:24

40:25 42:13
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 389

43:12 44:21 57:1

58:8 63:2 63:2

63:4 65:7 117:14

150:24 162:15

164:5 196:14

231:15 231:18

sectional 47:18

50:5

sections 76:24

207:1

security 157:20

seeding 260:10

seeing 13:3

54:9 68:22 138:6

145:25 194:16

238:19 239:4

267:18 282:4

seek 114:12

seeking 101:16

101:17

seem 23:24 30:8

81:4 141:2

168:10 211:11

239:15 275:8

275:12

seemed 14:3 187:7

seemingly 101:13

seems 17:9 57:5

79:1 80:1

130:6 142:19

143:23 144:2

167:2 167:17

168:3 172:11

172:12 179:6

186:23 198:8

221:24 222:13

222:13 236:21

258:7 264:3

seen 28:9 39:3

42:20 52:9 52:11

110:24 111:19

130:21 147:1

148:10 148:11

157:4 170:23

171:18 195:24

197:14 201:6

206:18 213:24

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251:11

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29:8 41:6

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111:25 112:24

114:9 136:21

157:19 168:3

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214:16 214:20

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273:12

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154:22

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15:21

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189:9

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214:19 217:1

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185:16

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FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 391

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282:18

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216:20

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63:17 198:3

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47:22 48:1

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60:9 60:15 60:18

63:5 66:10

68:1 79:21 88:17

88:23 89:25

94:11 94:21

95:14 96:6 98:18

103:22 125:15

137:12 144:24

146:6 146:7

146:21 176:12

180:7 182:19

184:20 203:18

210:15 210:16

210:20 211:2

211:6 212:22

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74:20 90:25

92:18 92:24 93:2

93:8 93:10

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86:14 93:5 106:2

118:8 121:9

122:9 122:15

173:19 195:20

222:14 242:25

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273:18

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261:5

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39:20 43:5

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282:18

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148:7 237:8

237:14 237:18

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281:18 281:22

282:3 282:4

282:23

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170:23 196:5

230:19

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FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 392

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239:7 245:14

situations

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194:3 243:13

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189:17 194:20

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250:20 252:14

252:15 252:19

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135:22

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19:22 25:22

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104:4 104:21

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116:2

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69:18 79:23 80:4

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103:17 137:9

138:1 138:2

143:4 143:7

202:24 203:18

204:4 204:12

211:8 212:21

217:5 220:4

223:21 234:17

234:19 249:4

249:12 250:7

250:9 256:14

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100:5 100:6

182:2 234:23

269:24

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77:15

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258:17

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259:2

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270:1

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273:8

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12:10

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136:20 222:4

223:12 224:4

280:12

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25:21 72:5 206:9

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26:25 27:24

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209:24 215:20

248:25 268:20
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 393

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34:17 41:3 42:13

44:17 45:23 69:6

80:15 83:7 83:23

83:25 84:6 101:2

115:19 129:5

136:4 151:3

162:1 170:19

171:19 178:25

193:17 194:13

195:17 202:18

207:14 215:21

221:4 233:20

236:12 236:13

255:9 258:14

259:3 259:10

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110:10

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154:25

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197:2 231:19

232:1 232:6

232:17 232:25

233:21 237:1

241:22 248:19

252:21 262:22

264:5 264:6

264:23 269:15

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248:1 248:17

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237:21

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97:17 97:22

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192:23 192:24

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229:25 230:8

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126:24

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60:12 214:3

214:4 214:18

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275:12 275:15

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78:14 197:22

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171:15 171:17

171:20 198:21

217:15

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15:9 22:5 24:1

36:22 44:19

49:15 50:8 50:17

53:15 57:11

61:24 62:9 70:20

91:5 95:5 108:11

111:2 111:16

118:7 122:2

124:19 131:15

143:1 171:21

174:21 178:9

210:13 225:8

specifically 20:4

40:6 64:9

67:24 84:1 210:7

251:18

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266:2

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84:21

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227:6

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213:1

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248:5

splattery 188:4

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88:24 89:25

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spot 175:21

spotted 174:19

Sprague 27:18

125:10 167:9

194:12

Sprague-Dawley

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95:3 167:11

171:16 193:25

213:24 215:23

Sprague-Dawleys

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27:23

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251:15

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34:13 34:15

34:16 219:6

281:6 283:16

staff 25:11 31:20

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100:18 189:18

193:5

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74:17 74:19

74:20 100:18

185:13 186:6

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169:6

standard 9:14

10:4 63:21 92:19

116:20 124:13

142:10 143:12

179:19 179:20

180:5 182:1

212:14 212:15

219:11 219:14

219:21 222:16

224:8 224:9

224:10 224:14

224:18 224:23

224:25 225:7

275:22

standardize 10:13

223:16

standardized

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standpoint

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start 13:2

23:12 25:22 37:7

54:6 87:16 153:7

158:22 174:11

174:12 190:8

192:10 196:20

203:25 229:18

237:14

started 9:5 9:7

31:9 31:12 33:24

46:15 85:10

92:19 122:19

179:1 179:3

191:5 209:16

213:22 217:19

223:7 228:19

starting 27:14

33:23 43:14

243:1 285:13

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stat 277:10

state 2:23 2:25

4:14 51:1

82:21 100:22

152:16 156:6

157:8 159:20

159:23 160:4

160:20 162:20

162:21 163:6

163:20 164:3

165:20 174:2

178:15 182:6

183:1 186:12

186:15 191:19

192:1 208:20

stated 66:5

81:5 97:8

147:6 231:17

statement 6:5

6:11 14:5 21:7

21:12 70:24

96:18 220:24

279:15

states 3:22

54:3 54:4 55:6

64:10 69:18 94:5

222:25

static 233:3

237:5 241:22

242:6

stating 7:6

120:22

statistical

26:7 27:7

43:21 45:13

46:22 46:23 50:7

50:15 59:7 59:23

61:1 61:21 63:16

63:17 78:22

112:8 112:11

112:14 112:24

116:12 193:4
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211:10 247:15

255:17 255:22

256:6 263:17

263:23

statistically

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44:1 44:13 47:22

48:1 59:25

60:9 60:15 60:18

187:20 193:16

211:9 232:1

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112:13 244:14

statisticians

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200:6 239:16

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58:23 201:20

201:23

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stay 35:16

118:7 249:17

281:7

steady 156:6

160:4 162:20

162:21 163:6

163:20 164:3

174:2 191:18

208:20

step 2:12 24:2

154:9 187:23

247:8 261:19

Stephen 196:1

stepping 274:23

steps 23:9

121:3 217:16

steroidogenesis

31:4 33:2

steroids 33:16

Steve 192:9

Steven 2:4 2:10

4:15 5:11 5:24

6:8 7:8 10:15

12:4 15:3

15:12 15:18

15:22 16:7 16:12

17:15 18:1 19:18

21:2 22:21

23:4 24:21

25:4 29:20 29:24

30:8 30:16 30:23

32:21 33:20

35:25 36:16

50:20 53:17

57:16 58:3 58:13

62:23 64:7 64:23

65:10 76:3

79:3 82:12

83:5 83:16 84:11

84:25 85:12

85:22 86:1 86:14

87:3 87:15 96:14

99:19 101:7

104:23 106:18

107:22 109:1

110:16 111:21

113:1 113:22

115:14 115:21

116:22 117:21

125:2 126:22

130:4 133:4

135:8 136:2

137:6 137:16

138:9 139:24

140:5 140:19

144:8 145:3

147:3 147:13

148:14 148:23

149:18 149:22

150:23 151:2

151:12 151:16

151:22 152:10

152:20 162:3

168:18 170:2

173:6 174:7

176:20 177:2

178:7 184:2

186:7 186:20

187:25 192:9

194:10 195:7

196:1 196:16

199:14 200:11

201:17 202:6

202:14 207:22

208:1 208:3

209:2

stimulated 196:9

stimulation 214:2

Stone 3:21 3:21

236:23 236:24

240:24 240:25

251:4 252:12

252:24 252:24

260:2 260:3

269:19 269:20

273:23 273:24

276:13 277:15

281:9 281:11

282:16 282:17

282:24 284:14

stop 35:17

109:7 124:24

204:6 205:20

251:5

stopped 106:8

Stopper 166:1

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storage 14:22

250:12 250:14

store 266:5

stored 41:14

82:10 266:13

267:23

storing 267:10

story 135:22

206:19 221:4

straight 194:12

194:15

strain 10:23

28:15 89:16

92:25 97:5 97:11

99:8 125:23

125:25 185:24

193:12 193:17

193:21

strains 11:5

99:24 125:9

125:12 126:3

153:19 173:18

173:20 178:21

185:21 193:23

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231:15

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80:16

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242:11 248:6

248:8 253:6

262:1 265:4

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251:1 259:24

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91:9 91:12 94:11

95:5 99:22

102:20 126:19

139:23 187:10

233:21 252:22

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166:6 184:21

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131:7

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42:23 43:7 47:11

47:12 47:19 49:5

87:9 87:20 89:10

91:4 94:9

95:16 96:17

103:1 117:17

127:5 187:6

245:24 254:8

254:22

stress 78:16

94:16 96:8

161:19

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96:1 97:2

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strictly 5:2

114:17

strong 9:18

51:5 54:4 67:9

67:15 69:4 77:16

79:22 184:20

191:19 194:7

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59:10 59:12 78:9

183:21

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169:21 173:22

174:2 182:25

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252:13

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257:22 258:7

258:9 258:14

258:17 258:22

259:2 259:4

259:6 259:8

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structures

118:9 122:10

257:12 257:16

257:20

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253:19

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271:18

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11:5 11:12 11:15

11:22 12:2 12:20

13:7 15:9

15:10 16:8 16:21

17:24 28:20

37:14 37:16

37:21 37:24

37:25 38:12

38:14 38:17

39:17 41:10

41:22 41:24

43:11 44:10

44:23 45:4 46:13

46:16 47:5

49:2 49:19 49:19

50:12 50:12

51:21 52:3 52:16
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53:9 54:22 54:25

55:6 55:13 55:17

55:18 55:19

55:24 55:25 56:4

56:9 56:25

57:4 57:11 57:12

57:14 58:18 59:2

59:4 59:6

59:13 59:17

59:20 61:8 61:14

62:4 63:9

63:21 64:20 66:1

67:16 67:23 68:9

68:12 68:14

68:19 69:2 69:16

70:21 70:25

71:16 73:6 73:11

73:13 73:18 74:4

74:12 74:13

74:25 75:4 75:14

75:24 76:6

77:7 77:20

78:5 78:8

78:14 78:18

78:19 78:24 79:1

79:10 79:22 80:2

81:7 81:9

81:17 82:7

82:8 82:20 83:14

86:10 87:6 87:11

87:14 87:23

92:24 95:1

96:3 96:5 96:5

96:13 96:21 97:9

98:1 98:8

98:13 99:2 99:13

99:15 100:4

100:16 100:22

101:3 101:23

102:6 102:7

102:9 102:18

102:24 103:8

103:15 103:24

104:8 104:11

105:10 105:18

105:21 106:2

107:1 108:6

108:9 109:14

111:12 111:14

112:22 113:14

114:8 116:5

117:5 117:14

121:1 121:2

121:17 121:21

124:5 125:6

126:12 126:15

127:24 128:18

129:8 129:15

130:21 131:12

131:22 132:24

133:19 133:22

134:7 134:20

135:6 138:1

138:24 138:25

140:17 140:22

141:3 142:3

142:21 143:24

144:3 144:4

145:15 146:17

146:22 147:10

147:10 150:3

150:7 153:15

161:16 172:15

173:20 175:8

175:9 175:12

176:8 176:10

178:20 178:23

179:9 182:10

185:20 186:25

188:19 189:2

191:17 195:20

197:14 198:11

206:6 220:7

220:9 220:10

220:20 223:11

234:5 234:7

237:17 238:2

271:19 277:25

278:7 279:4

studies--the 74:7

studying 124:16

271:21

stuff 98:11

127:18 206:13

227:10 242:24

249:15 259:15

subject 110:21

114:17 269:17

273:25

subjected 134:16

subjective

89:20 90:23

91:16 91:25

94:14 97:1

102:17 105:18

111:5 112:1

sublinear 194:16

submissions 15:23

15:24

submit 9:1 35:1

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199:20 278:1

subparts 57:21

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subsequence

214:24

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49:25 89:19

163:15 282:20

subset 139:2
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 398

200:19

substantial

76:7 247:9

subtract 135:12

Sue 115:17

suffer 75:16

suffers 49:20

sufficiency 36:12

sufficient

31:13 32:9 37:19

59:6 70:1

81:20 143:25

248:15 265:21

sufficiently 77:3

suggest 14:10

14:13 32:5

56:8 57:20

72:9 111:1

111:19 113:24

141:4 169:21

169:22 170:15

212:5 271:20

282:5

suggested 77:23

81:17 89:25

141:12 152:1

182:4 203:24

suggesting 138:17

268:18

suggestion

68:14 206:13

234:16

suggestions

149:23 231:12

suggestive 38:3

43:7 44:12

45:2 46:17 47:20

49:12 80:5

82:3 144:3

suggests 42:6

110:2 147:25

suit 65:19

suited 67:19

67:19 80:23

Sulken 201:19

sum 116:15 165:16

165:17

summarize 37:3

40:23 43:13

44:22 63:6

summarized

46:12 61:10

140:23 196:14

summarizing 13:2

summary 9:9 22:25

30:21 44:10 49:2

50:11 101:2

201:7

summer 34:15

summing 166:16

summit 37:23

superior 111:18

supplement 230:16

supplemented

58:16

supplies 237:9

252:1 260:24

supply 73:23

support 58:7

64:21 65:5

77:4 78:5 110:13

110:14 112:6

131:21 135:20

152:19 170:16

173:22 178:19

182:6 202:17

216:3 220:6

262:23 283:8

284:11

supported

101:12 220:19

supporting

78:13 102:14

126:2

suppose 100:17

220:7

supposed 19:16

100:5

supposedly 208:23

supposing 255:7

suppress 204:24

suppression

18:5 18:12 18:15

28:11 29:3 29:14

213:23 213:24

214:5 215:12

215:14

sure 26:10

31:13 31:20 44:8

52:16 65:3

66:7 66:14

75:2 75:8 77:5

79:5 86:8

86:20 111:5

111:19 126:24

127:2 139:16

181:11 185:5

187:21 206:12

226:1 226:2

226:6 226:16

232:24 261:3
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 399

262:10 267:8

274:14 276:3

276:6

surface 271:9

surge 9:15 31:2

31:12 66:9 145:1

174:3 175:2

176:4 178:1

179:12 179:13

179:16 180:1

180:13 181:12

184:11 184:13

184:16 184:18

184:19 184:21

186:3 186:19

188:20 188:21

197:23 202:24

203:2 203:7

203:12 203:15

204:5 204:22

205:19 206:20

207:7 210:15

210:18 210:20

210:24 211:1

211:2 211:7

211:14 211:19

211:20 211:22

211:25 212:9

212:20 212:22

213:2 213:4

213:9 214:5

214:10 214:12

214:14 215:18

215:24 216:18

217:5 217:9

217:13 218:10

218:17 223:16

223:17 223:21

224:20 224:21

224:24

surged 181:16

surges 178:11

183:2 214:13

226:11

surgical 67:12

surprised 14:4

18:16 60:15

276:3

surprising

103:8 208:21

surrogate 166:10

survey 3:22

3:24 274:19

survival 218:14

Susan 4:4 4:4

5:22 7:10 7:21

9:21 9:25 106:21

125:4 137:17

148:17 150:2

150:24 150:25

166:1 174:8

216:7

susceptibility

167:15

susceptible

160:24

suspect 14:17

78:1

suspected 32:1

SWAT 249:19

249:20 249:20

249:22

switch 251:6

switching 6:24

sympathy 175:11

symptoms 42:9

syndromes 228:2

synergy 146:4

146:10

Syngenta 5:23

15:21 35:4

55:2 66:17

92:6 149:9

158:11 158:25

172:3 172:19

201:7 231:8

231:10 234:16

263:25 264:13

264:16 274:13

Syngenta's 156:19

synthesis 63:4

76:24

system 53:13 56:9

56:17 89:20

90:16 90:23

91:17 91:19

91:25 100:25

111:1 111:5

119:24 120:1

120:4 123:7

131:5 172:9

173:1 208:16

232:16 232:20

239:18 254:15

256:12 257:12

262:22 263:21

264:7 264:10

264:19 264:24

265:3 265:14

266:20 267:14

268:15 270:18

271:25 273:19

275:1

systemic 13:4
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 400

13:6

systems 117:25

121:9 248:14

259:24 263:20

265:23 267:2

267:20 269:8

269:24 273:17

273:17 280:4

system's 264:14

266:19

Systems 240:18

T

table 2:15

25:12 30:5 30:22

32:18 74:6

229:16 229:16

268:24 276:9

tables 31:18

179:10

tails 258:14

tainted 233:10

taking 5:3

84:20 118:5

162:11 165:1

191:17 249:25

280:20

talk 45:19

73:13 73:14

108:9 167:7

187:6 219:8

236:15 249:19

251:6 260:3

283:19 284:14

talked 41:19 79:7

141:25 142:3

164:9 168:5

179:5 202:22

219:3 234:4

238:22 260:16

261:14 283:21

talking 108:17

139:6 179:24

188:18 218:16

221:2 223:6

224:2 224:13

229:18 243:14

251:18 252:14

259:21 268:7

269:13 273:6

274:1 280:25

282:25 283:10

283:15

talks 56:25 275:2

tallying 131:6

Tami 21:19 22:13

tap 47:14 235:18

tapped 98:3

target 65:15

94:16 141:5

141:13 141:24

147:17 147:24

153:14 173:2

230:24 233:7

233:13 233:16

238:1 270:7

targeted 8:15

171:17 265:7

targeting

230:12 230:12

232:11 233:16

targets 148:2

148:7 148:13

task 222:23

taste 89:4 89:5

TCT 20:19 20:24

team 34:10

86:22 156:25

159:21

teamwork 121:7

tease 13:11

technical 5:7

26:3 31:20 89:14

technique 48:24

113:12

techniques 73:1

112:14 273:9

technology 118:18

238:15 273:1

tee 25:21

temporal 35:22

131:9 132:2

229:25 230:9

temporalities

104:18

temporary 106:4

106:5 106:14

ten 17:5 60:17

71:25 71:25

71:25 72:5 97:24

256:25 256:25

285:5

tend 8:9 51:14

58:15 245:11

248:3 248:4

tendencies 50:16

tendency 139:9

139:15

tends 250:23

tenfold 18:21
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 401

Tennessee 237:18

tenth 103:6

126:11

term 28:14

35:23 39:24 44:4

68:13 136:4

136:4 150:20

212:18 218:18

218:21 220:10

222:2 261:12

261:16 265:13

terminology 73:12

terms 21:7 23:4

71:20 79:25

85:13 93:21

94:15 105:14

106:2 109:17

112:4 121:11

130:19 131:2

135:5 135:15

136:5 151:10

152:21 156:4

158:22 159:20

161:22 164:8

165:15 170:22

170:22 170:25

172:17 178:5

199:24 202:18

202:25 206:24

214:3 214:4

221:12 221:14

221:23 223:11

233:25 237:12

241:21 242:7

242:8 245:11

253:7 253:17

260:12 260:14

264:25 277:9

279:6 281:1

281:11 281:23

282:2 283:6

284:3

test 7:4 42:13

43:21 66:1 74:24

76:13 84:2

91:7 174:13

246:20

tested 102:13

111:13 121:23

225:4 280:3

testimonies 80:1

testing 46:22

46:25 63:16

63:19 120:19

124:1 133:11

testosterone

227:5

tests 45:13 59:24

60:13 108:17

textbook 76:21

thalasemia 62:19

thank 4:15 4:19

5:11 7:8 8:7

9:25 15:13 17:13

20:25 24:21

24:22 25:7 29:20

30:23 32:16

32:20 32:21

35:25 36:18

50:20 53:16

53:17 57:16

62:22 62:23

64:23 76:3

79:3 82:11 82:12

82:13 85:22

86:24 96:14

99:19 101:7

104:23 104:25

106:18 107:22

109:1 115:9

116:23 125:2

126:22 127:3

130:4 133:4

135:8 136:1

137:6 137:15

138:19 140:3

144:8 145:3

147:3 147:13

148:14 151:2

151:12 151:21

152:4 162:3

168:18 170:2

170:17 173:6

173:8 176:20

178:7 184:2

186:7 189:11

195:13 196:10

199:14 200:11

202:6 208:3

209:2 285:15

thanks 5:9 83:1

127:4 151:16

that--I 111:23

that--if 114:2

that's 11:13

12:15 18:25

21:10 23:24

29:19 30:11 32:3

32:12 32:12

32:18 36:16 41:6

42:10 42:17

44:12 45:2 50:19

55:12 66:12

66:15 72:6 73:12

73:24 73:25 76:1

84:5 85:12 89:17

95:2 99:17 100:5

101:6 106:14

106:16 106:16
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 402

107:7 111:7

115:4 116:19

119:9 122:16

123:9 124:10

128:10 128:19

129:22 132:4

135:7 135:24

136:2 137:20

144:7 145:12

149:1 151:17

154:8 154:25

159:16 159:24

160:16 160:17

160:23 161:18

163:3 163:16

163:19 164:7

165:11 165:22

169:20 171:3

172:13 172:18

176:3 176:12

178:17 179:22

180:3 182:13

189:10 189:22

189:22 190:12

191:10 193:13

195:24 196:7

197:11 199:23

200:5 200:10

200:21 204:5

204:23 206:13

208:12 208:18

208:25 209:24

210:12 210:21

210:21 211:2

211:12 212:4

212:5 212:5

212:21 214:15

215:1 215:18

217:7 217:13

218:3 218:5

218:11 218:12

218:20 220:13

222:9 223:13

223:20 223:23

224:14 224:14

224:15 225:15

225:21 227:1

228:12 228:15

228:18 231:17

232:8 232:17

233:9 233:20

233:22 235:9

235:12 236:6

237:6 238:18

238:19 239:8

240:2 241:11

242:12 243:19

244:25 245:18

247:1 249:2

249:8 251:16

251:20 251:24

252:2 252:21

253:18 256:19

257:2 257:20

260:16 261:12

262:9 262:15

262:23 269:14

272:2 272:8

272:15 272:21

274:15 275:18

277:14 279:25

282:12

that--well 115:6

the--because

133:13

Thede 152:7

154:14 167:25

191:3

Thede's 158:21

the--in 98:11

the--mentioned

136:3

themselves 197:9

then--will 24:16

theoretical

157:10 157:13

theoretically

193:7

theory 217:24

221:14

therefore 19:8

107:16 143:6

172:8

there'll 84:21

166:5

thereof 195:6

there's 9:22 13:5

14:7 16:22

17:9 20:20

23:9 54:7

59:14 61:12

73:11 91:17 95:3

95:13 95:25

98:15 99:5

99:5 99:25 103:3

105:6 106:13

106:15 114:4

116:8 116:11

116:20 120:2

120:13 124:6

125:11 127:24

127:25 128:3

128:25 130:7

130:23 132:2

133:6 135:10

135:18 135:23

138:13 139:9

143:3 144:2

144:14 144:22

145:21 146:2
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 403

147:24 150:5

150:13 151:19

154:9 154:19

155:5 156:6

156:22 156:23

160:6 163:7

163:15 164:6

164:11 167:17

168:21 171:19

176:1 183:23

183:23 193:16

193:24 194:11

197:20 198:2

204:22 205:5

205:9 205:9

205:12 205:13

206:8 206:21

207:3 211:4

211:6 212:10

212:19 212:25

213:10 214:4

214:13 214:18

216:2 217:18

217:25 217:25

220:14 223:1

223:17 223:19

223:24 227:9

227:9 229:5

232:2 238:13

239:24 241:22

243:2 247:23

249:7 249:14

249:19 254:4

256:3 262:25

271:19 273:20

277:11 278:5

the--thinking

116:4

the--well 107:4

they'll 14:5

260:1

the--you've 16:24

they're 5:6 5:7

30:14 39:20

39:24 45:1 51:13

51:18 51:19

56:16 62:8 62:20

68:23 74:19 78:8

85:7 98:9

99:16 106:9

106:12 107:1

125:7 139:8

144:24 156:11

156:15 156:16

156:16 187:22

188:7 190:17

220:10 224:24

231:6 232:24

238:14 241:2

244:19 245:8

249:24 249:25

257:15 267:9

271:11 273:22

276:17 278:24

they've 47:7

85:10 88:17

103:4 147:8

156:19 158:12

231:9 237:22

261:4

third 2:5 37:22

47:23 51:18

64:24 92:10

201:2 202:1

235:6

this--and 101:1

thoughtful 37:9

thoughts 21:11

86:21 109:4

190:16 193:19

194:17 216:16

thousands

178:23 180:18

threat 61:8

threshold 18:5

18:14 229:11

258:4

thresholds

219:3 263:5

thriving 13:16

throughout

48:11 159:22

185:13 189:21

212:24 251:15

284:10

throw 205:19

206:20

throwing 184:10

Thurman 229:21

240:10 240:13

245:23 253:11

253:25 254:7

270:11 270:17

270:23 271:1

274:12 274:13

280:16 282:21

284:13 284:25

thus 136:16

tick 26:3

tie 161:5

tied 190:18

tiered 233:14

tight 159:5 284:9

tighten 182:7

182:22
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 404

tightened 181:15

tightly 259:7

timetable 161:10

tiring 240:3

tissues 24:8 25:3

153:13 153:14

163:24 279:21

TK0040016 159:1

to--at 135:25

today 2:11 5:8

62:24 65:14 66:6

74:6 121:13

148:10 209:14

today's 268:8

to--hypothesis

120:9

Tom 149:2

tomorrow 2:13

119:14 284:17

285:13

ton 204:14

Tony 66:17 203:24

tool 173:3

top 17:4 27:19

118:9

topic 2:6 49:25

279:13 280:12

topics 228:22

Toronto 4:12

total 15:11 20:11

20:14 90:9 98:12

154:7 155:22

164:13 165:13

165:14 165:16

165:22 166:10

168:4

totality 166:14

198:10

totally 31:22

96:19 138:10

153:1 206:18

279:20 279:25

280:1

to--that's 138:3

touch 274:3

touched 107:3

175:20

touchstone 136:24

tough 25:4 216:15

216:15

toward 121:3

239:15

towards 65:19

72:17 114:13

121:6 126:6

171:2 171:13

tox 10:12 56:2

278:18

toxic 39:25

59:6 279:3

toxicity 8:19

121:1 127:20

145:24 146:4

146:8 146:24

147:1 173:5

279:23

toxicological

66:4 67:1

67:10 81:1

197:25 230:14

235:7 254:13

toxicologically

155:5 277:8

toxicologist

66:22 117:24

118:1 120:25

121:8

toxicology 3:2

64:10 64:14 65:9

65:18 65:21

65:25 67:8

67:9 67:16 67:22

69:10 71:14 74:8

74:12 74:14 75:3

75:14 75:25

80:20 80:22

86:10 135:13

135:19 197:4

259:17 259:22

284:1

toxin 198:14

tracer 17:19

110:2

tracking 13:15

tract 161:25

traction 193:23

trade-off 256:3

traditional

198:11 198:14

199:4 260:23

268:3 268:9

traditionally

197:11

trained 112:6

training 117:24

transcending

223:8

transcript 206:3
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 405

transgenerational

125:24

transitioning

197:4

transparency

135:3

transport 234:8

238:2

treat 125:12

treated 11:18

18:19 88:6

88:7 88:9 91:3

206:1 262:22

276:15

treatment 17:21

88:2 90:1

91:14 93:3 96:23

106:5 107:13

230:25 232:4

235:15 264:7

264:14

treatments 102:14

103:18

tree 90:18

tremendous 64:21

trends 261:12

261:16 261:17

triage 281:21

trial 163:8

triazine 144:18

165:7 166:10

166:14 168:4

168:8 208:15

triazines 20:18

55:4 55:7

72:19 85:15

85:19

tricky 236:5

tried 77:20 92:23

165:18 167:12

195:14 216:7

276:23

tries 273:7

trigger 220:12

232:16

triggered 161:8

180:4 205:24

triggering 238:10

282:6

trimester 47:23

47:25 62:18

trio 285:11

troops 242:4

trouble 29:18

229:13 271:5

true 14:11 15:1

61:14 97:18

128:10 128:15

134:17 141:11

181:17 212:5

223:23 246:15

246:18 247:4

272:9

truly 35:19

231:22

truth 156:15

try 8:13 21:11

25:5 26:10

29:6 38:14

79:6 79:14 79:16

86:18 86:19

86:20 91:23

114:10 114:11

114:12 114:15

114:19 115:12

121:1 139:7

192:18 193:5

260:19 263:5

276:19 283:11

trying 6:14

26:6 33:12 72:23

73:3 85:7 100:11

121:12 124:17

128:9 130:8

130:22 150:9

153:3 159:9

160:3 170:7

178:3 203:17

236:11 236:11

241:9 243:16

245:12 246:8

246:20 260:20

281:16

tumor 108:21

108:21 109:21

turn 25:13

25:13 36:8

37:5 151:18

154:20 173:9

195:10 222:21

turned 7:18 7:20

turning 237:14

turnout 159:18

turnover 242:8

273:20

turns 140:8 283:3

twelve 13:15

twice 90:9

92:14 234:25

234:25 255:2

twofold 18:23
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 406

two's 30:13

type 17:25

29:11 50:1

56:9 68:19

69:1 69:11

75:5 127:16

159:6 192:19

193:15 196:9

212:16 277:25

281:5 283:8

284:8 284:11

types 45:10 56:20

56:22 56:23

66:11 68:14 69:2

74:4 82:7

125:6 135:1

183:2 193:1

196:5 246:23

typical 15:10

238:24

typically 11:6

11:16 20:19

101:11 101:15

237:12 245:8

266:23

U

U.S 62:1

ugly 253:23

UK 3:6

ultimate 166:12

172:12 211:18

229:21 264:8

ultimately 56:3

74:15 78:17

121:15 220:4

unattainable

81:10

uncertainties

21:15 22:1 23:13

23:15 65:8 65:17

66:25 169:8

171:7 199:6

uncertainty

21:6 21:21 21:23

25:6 61:4

158:1 162:25

177:23 178:4

187:1 199:1

199:7 244:7

278:14

unclear 34:14

43:18 199:23

uncomfortable

129:18

uncommented 45:24

uncontrolled

45:24

underdeveloped

100:24 100:24

underestimate

246:14 259:4

underestimated

156:18

underlying 215:10

undermine 141:7

underpredicting

253:2

underscore 27:8

understand 10:7

17:24 50:21

61:18 81:19

84:20 109:10

126:9 126:11

149:13 151:5

188:12 228:8

246:19 251:14

251:17

understanding

21:22 66:3 67:10

83:19 84:14

119:9 169:12

215:19 239:7

242:2 247:16

undertake 81:21

undertaken 52:17

underway 2:17

158:25

undoubting 95:25

unexpected 79:13

unfinished 83:13

unfortunate 158:5

unfortunately

54:13 69:13

252:18

unfurled 175:2

unhinged 225:12

uniformly 239:2

268:4

unique 6:13

174:25 176:18

192:3 193:15

249:2

United 3:21

University 2:23

2:25 3:4 3:6 3:8

3:12 3:14 3:16

3:20 4:1 4:2 4:5

4:9 4:12 4:14

unknown 227:9

227:9
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 407

unknowns 284:7

unless 32:1 68:23

161:20 220:20

236:14

unlikely 215:8

228:25

unlucky 246:13

unnecessary

268:16

unraveling 33:13

unreplicated

145:14

unresolved 19:11

untreated 276:16

unusual 33:17

update 16:9

updated 35:16

upon 34:2

102:25 103:7

113:16 113:17

135:15 140:24

166:6 172:2

225:13 269:10

278:15

upper 76:15

177:13 177:14

178:3 230:2

uptake 13:11

23:18

urge 60:22 148:6

urine 14:18 15:11

USCS 252:6

useful 70:1

71:7 72:10 72:15

76:8 80:11 80:13

109:17 136:9

142:11 144:4

153:9 165:19

170:15 172:23

177:20 246:25

256:15 277:16

usefully 77:13

USEPA 230:22

254:13

USEPA's 230:10

USGS 124:6 253:16

254:21 276:4

276:8 276:11

276:15

usual 59:22

Usually 131:14

236:4 249:3

uterine 181:8

utero 89:22

utility 58:10

229:22

V

vaginal 10:18

10:23 11:4

11:7 11:12

31:5 88:15 88:15

88:18 90:15

90:15 91:13

92:22 92:25 94:2

95:11 96:20

98:10 98:12

98:16 98:18 99:7

102:13 105:5

105:11 109:15

181:6 181:8

222:7

valid 23:14

57:9 112:25

130:3 141:12

200:24 204:16

validity 129:9

validly 269:15

valuable 112:2

151:17 169:23

value 41:19 73:22

78:4 78:6

82:19 181:25

196:5 198:4

229:13

values 15:1

24:4 46:24 60:17

60:19 61:2

61:5 63:18 76:14

93:17 115:2

181:14 247:1

variability 10:22

10:24 11:9 27:10

105:6 203:20

225:10 229:25

232:25 256:20

variable 9:16

10:14 72:3 92:11

97:7 97:17 129:5

203:16 203:19

224:19 224:25

256:17

variables 46:6

47:3 48:4

48:17 49:8

260:23 263:18

284:23

variances 193:7

variants 12:10

variation 92:9

98:15 217:7
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 408

varied 122:23

variety 93:17

147:23 228:22

variogram

256:23 257:1

variograms 256:21

257:10

various 11:4 54:7

54:25 55:1 71:11

100:2 135:12

135:17 198:23

199:6 210:15

vary 155:22

163:14 164:12

241:3

varying 175:6

241:6

vast 20:23

51:12 263:11

268:11

vaster 51:13

Vecchia 261:13

283:19

vehicle 88:1 88:6

88:7 88:10

vendors 126:5

vents 256:14

verbally 49:15

verification

130:1

verified 169:5

181:10

verify 12:11

versus 9:2 14:1

18:9 18:11 22:24

27:14 28:9 41:20

80:8 80:20

97:1 110:21

112:17 132:11

149:25 166:22

173:13 173:17

185:21 189:17

189:24 190:4

190:11 208:22

231:19 272:1

275:10

vertebrates

146:21

Veterinary 2:22

via 12:24 68:25

90:15

viable 114:11

view 39:8

106:21 114:17

116:21 198:23

222:24 240:2

260:4 263:8

viewed 102:17

viewpoint 19:4

views 82:24

83:6 104:20

vigilant 143:21

Villanueva 47:17

violates 43:21

Virginia 4:8

virtually 156:1

virtue 108:5

virus 108:21

108:22

vision 204:14

visual 174:12

vitro 21:24 22:11

24:4 24:17

172:15 205:6

vivo 21:20

21:25 24:18

volatile 120:4

vulnerable 230:12

233:16 282:4

W

wait 216:6 254:9

waiting 56:17

walk 23:8

wall 62:20

Waller 45:25 47:3

want--suggested

116:1

warn 172:2

warning 209:15

WARP 254:20

255:12 255:22

256:5 256:7

256:11 260:4

260:6 262:5

283:6

warrant 145:9

warranted 165:9

172:10

wash 251:8

washout 16:21

17:2 17:6

17:12 154:24

was--if 113:24

wasn't 19:14 21:1

39:4 111:12

114:16 140:7
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 409

207:19 237:18

276:3

wasting 240:2

watch 229:3 260:5

watch-out 158:8

water 2:8 34:6

47:15 48:10

48:15 49:9

62:6 68:25 73:21

73:22 73:24 78:5

78:8 79:1

80:14 80:15

124:6 124:9

139:20 139:22

161:24 219:5

229:19 229:22

230:1 230:7

231:2 231:16

231:19 231:23

232:2 232:2

232:3 232:6

232:9 232:9

232:16 232:17

232:19 232:25

233:20 233:23

234:10 235:4

235:15 235:18

237:1 237:8

237:9 237:9

240:18 241:22

241:24 242:10

242:11 246:2

247:25 248:17

248:19 250:8

250:14 252:1

252:19 254:15

254:16 255:13

256:11 257:12

259:24 260:24

262:22 262:23

263:20 264:7

264:10 264:14

264:14 264:19

264:24 265:2

265:3 265:4

265:11 265:13

265:23 266:5

266:14 266:19

267:2 267:3

267:11 267:14

267:20 267:23

268:15 270:1

270:18 271:25

272:1 273:17

273:17 273:19

273:20 276:16

276:24 281:12

waters 242:6

263:19 264:5

264:6 264:11

264:23 266:16

267:21 268:15

271:9

watershed

251:17 260:21

watersheds 246:23

257:17 257:20

watrershed 241:2

wave 181:8 259:21

ways 76:20

129:2 141:15

161:13 191:11

195:15 273:13

281:13 281:21

weak 54:11

weakens 182:14

weakness 96:17

100:15 102:16

102:18 111:6

114:23 114:23

weaknesses

95:17 99:22

103:1 127:5

187:6 245:24

254:9 254:23

261:23

weaned 106:9

we'd 68:14

193:8 253:21

281:20

week 121:23

137:19 230:19

243:12 259:16

weekly 246:6

248:9 248:14

253:16 256:25

262:8 265:5

281:18 281:23

282:5

weeks 109:9 283:4

weigh 74:25

weighed 107:5

107:8

weighing 167:5

weight 8:11 47:21

48:21 48:22 74:3

88:15 88:15

90:15 90:15 92:5

92:21 93:11

93:13 93:15

94:19 94:24 95:4

95:14 97:10

104:12 104:13

107:13 122:22

127:13 128:3

132:15 133:18

133:23 134:23
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 410

135:5 135:24

137:18 137:23

137:23 164:17

164:19 164:25

175:16 175:20

176:2

weight--and 98:7

weights 88:19

90:24 91:2

93:1 93:3 93:4

93:7 132:16

138:12

welcome 2:5

4:20 5:9 140:6

269:1

we'll 2:16 5:8

12:25 13:10

24:19 26:10

34:11 34:15 36:6

36:8 54:9

56:25 57:20

86:19 86:20 87:4

119:16 125:3

135:9 142:17

159:1 159:17

168:19 209:13

209:19 219:9

222:17 229:2

245:19 260:19

285:8

well-designed

230:7

well-known 138:11

we're 8:11 13:3

13:15 14:4 14:11

14:14 18:23 20:9

22:15 26:4

26:6 26:7

28:23 29:9 33:12

33:25 33:25 35:3

35:7 39:14 51:21

58:1 70:16

72:4 74:4 87:4

98:21 108:17

121:14 123:22

127:6 134:15

135:24 139:13

139:19 140:11

145:1 152:24

157:7 170:7

170:16 179:24

185:12 186:24

192:4 196:7

202:2 204:13

214:23 217:21

218:13 219:8

219:22 223:6

225:11 229:15

229:18 230:24

238:19 239:4

239:9 239:22

245:17 248:17

253:16 255:9

259:21 262:1

262:4 263:4

263:8 264:1

269:10 269:12

269:20 271:15

273:8 274:5

277:22 280:13

280:22 280:23

281:16 282:8

283:4 283:9

285:4 285:12

285:13

were--or 131:7

Wes 3:21 252:24

281:9 282:17

WESLEY 3:21

236:24 240:25

252:12 252:24

260:3 269:20

273:24 276:13

277:15 281:9

281:11 282:16

282:24 284:14

we've 14:9

33:10 34:18

34:21 56:21

65:16 66:16

67:14 79:7 79:20

95:24 140:3

148:10 148:11

170:23 191:12

191:19 191:25

197:14 201:6

203:2 209:16

217:16 219:3

223:18 234:4

238:25 248:7

252:14 253:18

255:5 261:14

274:14 277:9

278:2 280:11

whatever 31:7

72:19 81:23

113:8 182:20

216:23 217:14

231:1 241:23

248:20 279:23

282:14

whenever 172:24

179:11 238:15

whereas 74:18

89:24 93:13

94:21 108:2

164:20 247:25

273:21

WHEREUPON 87:2

140:4 209:9
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 411

285:16

whether 14:24

26:21 34:5 59:13

60:9 60:10 60:13

62:8 66:23

75:7 95:4

98:20 114:6

128:6 153:21

160:6 161:9

161:10 168:10

176:25 180:22

180:24 181:3

199:17 200:7

201:25 202:3

202:23 204:5

228:13 237:2

237:5 250:6

250:9 272:16

274:9 275:17

277:12 285:10

which--it's 16:5

White 39:8

42:16 48:12

whole 59:2 78:9

113:13 125:15

156:15 169:17

174:14 180:4

182:24 183:15

185:14 194:6

213:22 217:6

228:6 259:9

268:20 273:21

who's 47:23

127:10 150:9

219:5 233:18

235:16

whose 180:11

wide 152:17

197:24 200:25

widely 59:16

62:14 154:22

wider 253:8 258:2

259:10

willing 139:16

187:22

window 90:5 161:9

wire 11:25

wise 107:25

wish 139:8 194:25

Wistar 11:5

witching 285:5

withheld 191:24

with--made 136:21

woman 43:20 44:18

150:9 213:4

women 38:25 38:25

46:2 54:14 57:11

85:8 190:9 213:2

217:9 218:11

223:23

wonder 65:19

169:14 185:19

187:2

wondered 55:10

wondering 10:20

148:5 252:15

269:9

work 6:15 11:3

33:15 37:1 53:13

83:13 83:19

168:23 174:11

200:6 223:9

227:10 231:25

248:20 249:25

251:8 252:6

253:24 254:4

261:1 274:4

276:15 285:1

worked 133:3

234:14 260:17

workforce 240:4

working 28:5 84:4

84:19 139:11

150:11 174:9

239:21 239:23

workloads 206:10

works 84:6 110:18

191:10

world 278:4

worldwide 228:21

worried 259:12

worry 255:6

worth 40:19

160:17 271:21

worthy 129:13

would--that

127:19

wrap 17:16

25:14 57:14 65:1

write 35:1

writing 171:10

written 36:24

51:8 52:2 63:3

105:15 162:17

wrong 53:3 66:7

178:6 238:18

238:18 274:20

wrote 251:23

X
FIFRA SCIENTIFIC ADVISORY PANEL 09/16/10 CCR#15732-7 412

xeroxes 181:23

Y

Y-axis 175:1

176:7 177:10

Y-competence

257:25

yesterday 5:5

5:14 5:18 6:6

7:7 18:22

19:16 23:11 24:5

24:9 24:24 25:12

25:24 65:14

66:17 67:5 67:18

73:21 75:20

79:25 97:3 105:3

107:3 107:14

109:6 113:4

149:24 166:23

185:3 208:10

237:1 261:24

yet 5:7 14:24

18:22 18:24

19:11 34:15

77:25 81:16

109:13 110:24

111:20 147:24

149:1 198:17

212:21 218:6

you'll 9:1 70:6

101:3 156:21

161:11 171:6

256:25 258:1

young 19:5

26:15 27:6

125:12 142:2

you've 15:17

35:21 50:22

54:25 56:14

114:17 155:23

157:18 158:6

158:6 158:10

158:14 159:22

169:3 169:4

178:4 204:14

212:13 239:25

252:17 281:7

Z

zero 23:18

76:15 258:16

258:16 283:24

zeros 180:3