EPA-HQ-OPP-2010-0481-0048 ATRAZINE INTRO AND STATUS SEPT 14-17, 2010

8/8/2019 EPA-HQ-OPP-2010-0481-0048 ATRAZINE INTRO AND STATUS SEPT 14-17, 2010

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Atrazine Re-Evaluation:

Introduction & Status



Presentation to the FIFRA Scientific Advisory Panel

September 14-17, 2010

Anna Lowit, Ph.D.

Health Effects Division

USEPA Office of Pesticide Programs


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Atrazine Human Health Re-EvaluationAtrazine Human Health Re-Evaluation

Risk Management Goal Protect PublicHealth

Hazard Assessment: Sensitive lifestages andendpoints, associated durations of exposure, and

dose-response relationships Exposure Assessment: Residue levels in drinking

water with adequate confidence

Goals of the Re-Evaluation

Determine if the risk assessment for atrazineshould be revised

Determine whether the drinking water monitoringfrequency is adequate to accurately assesspotential exposure .


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Peer ReviewPeer Review

1988 SAP: Evaluation of rat mammary glandtumors

2000 SAP: Evaluation of MOA

Mammary gland tumors, reproductive anddevelopmental findings in rats

Human relevance

2003 SAP: Evaluation of prostate cancer

Epidemiology study on relationshipbetween atrazine exposure and prostatecancer in workers at atrazinemanufacturing plant


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February 2010 April 2010 September 2010

Draft Framework:

Incorporating

epidemiologystudies and human

health incident

data in risk

assessment

Two atrazine case

studies

Preliminary

evaluation ofin vitro

&in vivo laboratory

studies

(non-cancer)

Epidemiology studies:

non-cancer

Integration of

epidemiology &

experimental toxicology

into hazard

characterization for non-

cancer

Frequency of atrazinemonitoring in drinking

water sources

(proposed

approaches)

Frequency of atrazinemonitoring in drinking

water sources (updated

approaches and

preliminary analyses)

Atrazine 2010 SAPMeetings: Human Health


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Atrazine Human Hazard Re-EvaluationAtrazine Human Hazard Re-Evaluation

April SAP, 2010 Focus on toxic effects & mode of action

Re-affirmed key events related to the hypothalamic-pituitary-gonadal (HPG) axis

Hypothalamic effects resulting in changes in

catecholamine function and regulation of the pulsatilerelease of GnRH.

Attenuation of the Luteinizing hormone (LH) surge anddisruption of ovarian cycles

Cessation of ovulation with the ensuing persistent releaseof estrogen

Appearance of mammary gland tumors in rats isassociated with LH suppression & dependent on a specificpattern of reproductive senescence

Human relevance of HPG MOA to developmental andreproductive effects


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April SAP,2010, contd

Focus on toxic effects & mode of action

New data on effects on the hypothalamic-pituitary-

adrenal (HPA) axis Plausible hypothesis but not yet fully supportable

Single day effects of corticosterone & ACTH not relevant

for risk assessment

Variety of toxic effects (e.g., immunotoxicity,

neurotoxicity, steroidogenesis) None shown at doses lower than those eliciting

attenuation of LH


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April SAP, contd

Drinking Water Monitoring Data

Demonstrated examples for evaluating drinking

water monitoring data Proposed other modeling approaches for

supplementing monitoring data and sought

feedback from SAP


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September SAP, 2010

Non-cancer epidemiology

Mammary gland development

Agency reviews in Appendix A Discrepancy in findings between the Rayner et

al (2004, 2005) & Coder (2010) studies

Results only shown at high doses, not relevant for

risk assessment

Mixture study (Enoch et al, 2007):

Includes low doses

Concerns about study design & interpretation to

apply to risk assessment


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September SAP, 2010, contd

New experimental toxicology studies

Available from January 30-July 15, 2010

Reviews in Appendix A Proposals for dose-response assessment

Emphasis on LH attenuation as a key event in

the neuroendocrine MOA & protective of other

toxicities Includes internal dosimetry calculations &

benchmark dose analysis recommended at

April SAP


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September SAP, 2010, contd Drinking water exposure

Proposed monitoring study framework

Updated approaches and example analyses

Analysis for the FQPA 10X Safety Factor Analysis is still ongoing at this time

Key toxicology studies & drinking water analysis

Soliciting comment from the Panel on important scientificfactors to consider for both drinking water exposure &hazard analyses

Implications of toxicity & MOA on the criticalduration of exposure

Key component of evaluating the drinking watermonitoring frequency


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Non-Cancer Cancer

Review of new experimental

toxicology or epidemiology

studies (after July 15, 2010)

Epidemiology studies:

cancer

Duration of exposure

Lifestage sensitivity analysis Integrated weight of the

evidence with

epidemiology&

experimental toxicology for

cancerAssessment of potential

atrazine exposure from

drinking water sources

Atrazine 2011 SAP: Human Health


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PresentationsPresentations


Non-cancer Epidemiology (Carol Christensen)

Proposals for Dose-Response Assessment(Chester Rodriguez)

Approaches to Evaluating Water Sampling Strategiesand Frequency of Monitoring (Nelson Thurman)

Scientific Considerations in Potential Sensitivity ofInfants & Children/ Implications of MOA on WaterMonitoring Strategy (Anna Lowit)

Summary &Next Steps (Anna Lowit)


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Evaluation of Atrazine Non-Cancer

Epidemiology Literature

Evaluation of Atrazine Non-Cancer

Epidemiology Literature

Presentation to the FIFRA ScientificAdvisory Panel


Carol H. Christensen, Ph.D., M.P.H.




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OutlineOutline

Background and Purpose

Literature Review Methodology

Brief Summary of Studies

Causal Inference

OtherNon-Causal Explanations

Synthesis and Integration of Toxicology andEpidemiology Databases

Conclusions and Next Steps

14


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BackgroundBackground

In Feb 2010, EPA presented framework forincorporating epidemiologic research intopesticide risk assessments:

Explicitly considering observationalepidemiological research in problem formulationusing this Framework

Several studies related to fetal, perinataloutcomes also reviewed Feb 2010

Only non-cancer epidemiology included herein Atrazine cancer effects evaluated 2011


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PurposePurpose

EPA reviewed non-cancer epidemiologystudies to inform risk assessment

Identified studies evaluating atrazine risk in humanpopulation

Strengths and limitations each study evaluated(Appendix B.2)

Synthesis and integration across the epidemiologydatabase (Section 3.0)

Results of non-cancer epidemiology studiesintegratedwith experimental database toformulate overall conclusions (Section 4.0)

Using modified Bradford-Hill criteria and scientific

judgment (i.e., Frameworkdocument)


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Previous SAPGuidancePrevious SAPGuidance

Among several aspects to consider, Panelrecommended (Feb 2010):

Hypothesis generating versus testing

Valid and reliable exposure assessment andoutcome ascertainment

Potentially confounding variables

Similar between comparison groups

Statistical power Including sensitivity analysis, e.g.,effect modification


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MethodologyMethodology

Identify epidemiologic investigation of non-canceradverse health outcomes in published literature

Searched major biomedical databases

Applied Exclusion Criteria:

Did not measure association, or atrazine or

triazine exposure specifically; did not reflect

chronic health effect (case report), no full textavailable, or editorial

Nineteen studies of non-cancer health effects of

atrazine included in this review


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Overview of StudiesOverview of Studies

Health Effects: Female

Reproductive Health

Male ReproductiveHealth

Fetal and PerinatalOutcomes

Respiratory Health

Other

Several differentstudy designs

Several studieswithin long-terminvestigations

Exposure

assessment limitedin many instances

Particularly for riskassessment


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Female Reproductive HealthFemale Reproductive Health

Within the AHS, self-reported (lifetime) use atrazine(questionnaire) and:

Menstrual cycle characteristics* (increased oddslong- and missed menstrual cycles, s.s.) (Farr

2004)

Delayed timing of menopause (5 month delay,s.s.) (Farr 2006)

Gestational Diabetes- 2-fold increase (s.s.) amongdirect applicators (Saldena 2007)

Elevated odds for atrazine users (graphic only)

20*Atrazine or Lindane users


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Female Reproductive HealthFemale Reproductive Health

Strengths: Individual level exposure assessment

Large, highly exposed sample to investigate question

Limitations:

Lack of atrazine specific measurement (menstrual cycle),or measurement of timing of exposure

Frequency of assessment of menopause

Possible residual confounding (physical activity)

Conclusions:

Supportive of hypothesis that atrazine may affecthormonal milieu, and possibly reproductive healthoutcomes


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Male Reproductive HealthMale Reproductive Health

Within the Study for Future Families, semenparameters in association with urinaryatrazine mercapturate (AZM) (Swan 2003)

Elevated odds of poor semen qualityamong men with AZM greater than limit ofdetection (LOD)

OR 11.3 (95% CI 1.3-98.9)

Men with more pesticide analytes in urine,more likely to have poor quality semenparameters


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Male Reproductive HealthMale Reproductive Health

Strengths: Use of biomarkers of exposure and outcome

Analytic techniques maximize information gained

Limitations: Small sample size for some comparisons

AZM likely under-estimates total atrazine exposure

Additional metabolite measures useful (Barr 2007)

Other environmental exposure

Possibly associated with atrazine as well as semen

parameters

Conclusions Suggestive of possible association, replication needed


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Fetal andPerinatalOutcomes:

Miscarriage


Miscarriage

Within the Ontario Farm Family Study,

Self-reported crop herbicide/atrazine use

by male partner and miscarriage (Savitz 1997)

OR 1.4 (95%CI 0.9-2.4)

Self-reported crop herbicide/atrazine use

during pre- and peri-conception period andspontaneous abortion (SA) (Arbuckle 2001)

OR 1.2 (95%CI 0.9-1.7)


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Miscarriage


Miscarriage

Strengths: Highly exposed population (live and work on farm)

Nested case-control study, recall bias limited

Limitations:

Probabilistic method of exposure assessment

Difficulty isolating male and female exposure inrelation to fetal outcome

Period of recall lengthy

Conclusions: Reflects initial investigations

Several major uncertainties


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Birth Defects


Birth Defects

Abdominal wall defects (AWD) Rates 3.75IN v. 2.75U.S per 10,000 (Mattix

2007)

Correlation observed with surface water

concentration and AWD rates in IN (s.s.)

Gastroschisis (AWD):

Statistically significant 40-60% increasedodds of gastroschisis among infants ofmothers residing


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Birth Defects


Birth Defects

MajorBirth Defects: 11/22 (s.s.) birth defects elevated with Spring

conception (Winchester 2009)

Spinal cord, circulatory system, cleft lip, limbdefects (adactyly), muscular problems, Downssyndrome, other congenital birth defects,tracheo-larengyl, gastrointestinal

Omphalocele (AWD), no significant difference

20% increased odds (s.s.) of limb abnormalities

among women residing closer to corn, soybeanfields (Ochoa-Acuna 2009)

Abdominal cavity defects 50% increased odds(n.s.)


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Birth Defects


Birth Defects

Strengths: Several hypothesis generating studies suggest

associations in similar direction

Limitations:

Ecologic or exposure surrogate, not validated Under-reporting of birth defects

Conclusion: Several hypothesis-generating studies suggest

atrazine may play a role in developmental effects Several major uncertainties


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Adverse Birth Outcomes



Small-for-Gestational Age (SGA) No association entire pregnancy period

(Villanueva 2005; Savitz 1997)

Approximate 20% increased odds (s.s.) if 3rd

trimester overlaps atrazine use period (Ochoa-Acuna 2009)

50% increased odds (s.s.) if 3rd trimester overlapsatrazine use period (Villanueva 2005)

Statistically significant exposure-response trend

Correlation residence high atrazine use area(southern Iowa) and intra-uterine growthretardation (Munger 1997)


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Pre-Term Delivery No significant association (Ochoa-Acuna 2009;

Villanueva 2005; Dabrowski 2003)

2- to 4-fold increased odds (s.s.) male partnerexposure pre-conception (Savitz 1997)

30% increased odds (n.s.) if 1st trimester overlapsperiod of atrazine use (Villanueva 2005)

Low Birth Weight (LBW)

No association (Villanueva 2005; Dabrowski 2003;Sathuanarayana 2010)

20% (n.s.) increase (if 3rd trimester overlapsatrazine use) (Villanueva 2005)


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Strengths: Studies with refined exposure measurement,

consistent for SGA

Hints to critical windows of exposure

Limitations: Exposure measurement error

Possible unmeasured confounding

Conclusions: Suggestion of possible association with SGA,

evidence for pre-term delivery and low birth weightlimited


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Respiratory HealthRespiratory Health

In AHS, ever-use of atrazine in associationwith wheeze: OR 1.20 (95% CI 1.07-1.34)

Strengths:

Pesticide use and wheeze episodes measuredsimilar, recent period

Control for corn and grain dust

Limitations: Atrazine not among a priorihypotheses

No biological mechanism proposed at this time Conclusions:

Reflects initial investigation


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Causal InferenceCausal Inference

EPA cannot conclude that the associations identified in theepidemiologic database are causal in nature

Other non-causal explanations cannot be ruled out at this time,

Exposure measurement error leading to misclassification

Likely non-differential misclassification

Potential confounder measurement error, e.g.,

Physical activity in female reproductive health (residual)

Other pesticide or environmental chemical exposure,male reproductive health and birth outcomes

(unmeasured)

Small sample size leading to reduced statistical power


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Synthesis and IntegrationSynthesis and Integration

Among comparatively stronger studies in the epidemiologicaldatabase, some support within the toxicological database:

Menstrual cycle functioning, timing of menopause (Farr et al.2004 and 2006)

Apriorihypothesis regarding hormonally activepesticides; self-report menstrual cycle reliable; accuracyof exposure measure

Semen parameters (Swan et al. 2003)

Biomarkers of exposure and outcome, statistical and

analytic methods

Small-for-gestational age (Villanueva et al. 2005; Ochoa-Acuna et al. 2009)

Measurement in finished water, timing of exposure (sub-

analyses), reporting of birth characteristics reliable


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Synthesis and IntegrationSynthesis and Integration

Similarity with experimental observations: Female reproductive effects Semen parameters Small for gestational age i.e., reduced pup

weight

Epidemiologic data not sufficient quality toinclude in quantitative risk assessment:

Lack exposure-response Lack of individual level measurement Use of surrogate for individual exposure not

validated Biomarker, AZM, under-estimates total exposure Under-reporting of birth defects Lack of precision in some estimates


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Conclusion and Next StepsConclusion and Next Steps

Use of non-cancer epidemiology resultssupport and inform hazard characterization

Qualitative, not quantitative use

Provides some support for human relevance

of critical effects found in rats

Although plausible can not link influence of

atrazine on HPG axis to reported outcomes

EPA will continue to rely upon toxicology datain quantitative risk assessment


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Atrazine: Proposed Updates to the

Dose-Response Assessment

Atrazine: Proposed Updates to the

Dose-Response Assessment



Chester Rodriguez, Ph.D.

John Liccione, Ph.D.




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Human Health Risk AssessmentHuman Health Risk Assessment

Previous risk assessment to support theRED

Based on a NOAEL/LOAEL approach

Critical study: 6 month dietary study

Key event: Luteinizing Hormone (LH)Attenuation

Current re-evaluation examining new

science and more sophisticated approaches Internal dosimetry

Benchmark dose modeling


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OutlineOutline

Support for an internal dose responseassessment

Temporal aspects of plasma triazines

Comparison of LH attenuation studies

of different repeated dosing durations

A daily steady state area under thecurve (AUC) as internal dose metric

Benchmark Dose Modeling (BMD)


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Internal Dose in Dose Response

Assessment

Internal Dose in Dose Response

Assessment

The dose at the target site, the internal dose,is the ultimate determinant of risk.National Research Council,1994.

MOA key eventsMechanisms of Toxicity

PharmacokineticsInternal Dosimetry

External Dose

Observed toxicity


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Metabolic Scheme for AtrazineMetabolic Scheme for Atrazine

Atrazine

GST

Glutathione conjugation

DealkylationDealkylation

DealkylationDealkylation

CYP450

CYP450

Deethylatrazine (DEA)Deisopropylatrazine (DIA)

Diaminochlorotriazine (DACT)


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Temporal Aspectsof Atrazine ExposureTemporal Aspectsof Atrazine Exposure

A very short in vivo half-life, primarily due tometabolism

Pharmacokinetic/metabolism studies indicate a very

short in vivo half-life

No direct half-life measurements available

For example, following dosing of rats via oral gavage

with 90 mg/kg atrazine, McMullin et al. (2003) reported:

Atrazine ~ 4% of total plasma chlorotriazines

DACT > 50% 30 min post-dose

Atrazine barely detectable in plasma

DACT > 98% of total plasma chlorotriazines24 hrs post-dose


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Chlorinated metabolites and LH attenuation Intrinsic activity of DACT at equimolar levels as

atrazine (McMullin, 2004)

DEA and DIA presumed active based on intact

chlorinated structure

Glutathione conjugates presumed inactive for

LH attentuation

However, only core radiolabel dispositionavailable.

Thus, glutathione conjugates included in internal

dosimetry

Activity of MetabolitesActivity of Metabolites


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Internal dose metric based onall triazine species

Proposed Dosimetry ApproachProposed Dosimetry Approach

A very short in vivo half-life, primarily due tometabolism

Chlorinated metabolites are active or assumedto be active in attenuating LH

Conservative in case not all the metabolitesare active

Convenient since only pharmacokinetic dataavailable are based on disposition of a coreradiolabel (i.e., do not distinguish betweenparent chemical and metabolites)


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OutlineOutline








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Temporal Pattern of LH Attenuation in the

Rat

Temporal Pattern of LH Attenuation in the

Rat

ime ( )

D e (mg/kg)

ime ( )

D e (mg/kg d)

Single oral gavage

dose of atrazine

Repeated daily oralgavage dosing

(once per day for 3 days)

No NOELNo dose responseComplete attenuation

- Effect not governed by peak levels (i.e., Cmax); Repeating dosingeffect

* From Cooper et al. (2000)


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Proposed Internal Dosimetry ApproachProposed Internal Dosimetry Approach

AUC = concentration * durationHow much

internal dose * How long

- Total triazines- Not a single dose effect

Internal dose metric

The area under the plasma concentration-time

curve (AUC) that includes all triazine species

0 100 200 300 4 00 00 00 7 00 00 000

10

20

30

4 0

t i m e

erum

lev

els

Hypothetical


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Plasma Concentration-Time Profile forTriazines

from Repeated Daily Dosing with Atrazine

Plasma Concentration-Time Profile forTriazines

from Repeated Daily Dosing with Atrazine

Thede 1987 Pharmacokinetic Study Based on 14C-radiolabeled triazine ring:

N

*

*

*

Atrazine

Female rats were dosed daily for 10 days with a wide range

of doses (1, 3, 7, 10, 50, and 100 mg/kg atrazine)

Plasma levels monitored frequently including elimination phase

Provides the most thorough plasma concentration-time profile for

triazines available from repeated dosing with atrazine


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M id f d t d t tM id f d t d t t


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More evidence ofpseudo steady state

plasma levels

More evidence ofpseudo steady state

plasma levels

Stoker et al. (2010) dosed pregnant Wistar rats daily with

5 or 25 mg/kg-d atrazine for 3 or 7 days

Plasma samples were analyzed for chlorotriazines at the

end of the dosing period

No difference in plasma levels of chlorotriazines between

the 2 exposure groups

ATRA dose (mg/ g-d) Da ly Dos ng ATRA (ng/ml) DACT (ng/ml) DIA (ng/ml) DEA (ng/ml) TotalCl-tr az ne (ng/ml)

5.0 GD 18-20 2.9 0.8 980.0 88.6 108.3 27.0 24.7 6.2 1115.9

25.0 GD 18-20 37.3 9.5 3333.3 540.1 953.3 155.2 190 25.5 4513.9

5.0 GD 14-20 1.5 0.6 883.4 108.0 108.3 27.0 21.0 5.0 1014.225.0 GD 14-20 8.0 3.5 3100.0 717.7 573.3 290.2 88.7 40.9 3770.0

Wh t i d t d t t lWh t i d t d t t l


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What ispseudo steady state plasma

levels?

What ispseudo steady state plasma

levels?

5 1 15 5 5 4 45 5 55

4

Pseudo St St t

H th tic lEli i ti Ph

{

t (hr)

S

ru

L

v

l

Chemical levels increase and oscillate about a mean value determined

by the dose rate and clearance

Oscillations depend on the dosing interval

Once pseudo steady is reached, plasma levels will remain fairly

constant regardless of how long repeated dosing continues


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OutlineOutline








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Temporal Aspect of LH AttenuationTemporal Aspect of LH Attenuation

Hypothesis:The level of H attenuationwould be similar forstudies that achieve thesame level ofpseudo steady state plasmalevelsof triazines.

In order to investigate this hypothesis: Compiled studies where daily dosing with atrazine

was performed for at least 4 days

Expressed LH attenuation as % control to account

for strain differences and interlaboratory variability Examined only doses 30 mg/kg-day


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4-Day LH Attenuation Critical Study4-Day LH Attenuation Critical Study

Aimed at identifying the NOEL or LOEL for LHattenuation following repeated daily atrazine

exposure

Used intact, regularly cycling rats

Evaluated effect over the course of one full

estrous cycle (i.e., 4 consecutive days)

Dosing was performed via oral gavage once

per day beginning at 0900 hr on the day ofvaginal estrus, continuing on the days of

diestrus I and II, and ending on the day of

proestrous.


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4-Day LH Attenuation Critical Study4-Day LH Attenuation Critical Study

Vehicle 1.56 3.12 6.25 12.5 25

0

5

10

15

20

25

Atrazine (mg/kg-d)

LeutinizingH

ormone

Serum(

ng

/ml)

1800 h: Luteinizing Hormone Serum Levels

NOEL = 3.12 mg/kg-day

LOEL = 6.25 mg/kg-day

* From Cooper et al. 2010 unpublished data

LH Attenuation Studies of DifferentLH Attenuation Studies of Different


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LH Attenuation Studies of Different

Repeated Daily Dosing Durations

LH Attenuation Studies of Different

Repeated Daily Dosing Durations

0 5 10 15 20 25 300

25

50

75

100

125

OAE

NOAE s

Morseth 1996 - 6 onth study (Sprague a ley)

ooper 2010 - 4-day study (Long Evans)

McMullin 2004 - 5-day study (Sprague a ley)Minne a 2001 - 1 onth study (Sprague a ley)

{

{

A RA se ( k ay)

HS

reAtte

ati

(%c

trl)


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OutlineOutline





A daily steady state area under the

curve (AUC) as internal dose metric



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Summary of Internal DosimetrySummary of Internal Dosimetry

Internal dosimetry based on total triazines Short in vivo half-life

Active metabolites

AUC

LH attenuation not a single dose (Cmax) effect

Steady state condition strongly associates with

LH attenuation

Repeated daily exposures result in pseudo steadystate plasma levels by the fourth day in the rat

Proposed Internal Dose Metric: Daily Steady State AUC

Internal Dose Metric: Daily Steady StateInternal Dose Metric: Daily Steady State


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Internal Dose Metric: Daily Steady State

AUC

Internal Dose Metric: Daily Steady State

AUC

Pseudo

steady state

Thede 1987

0 24 48 72 96 120 144 168 192 216 240 264 2880

2

4

6

20

40

60

1 mg/kg bw ATRA

3 mg/kg bw ATRA

7 mg/kg bw ATRA

10 mg/kg bw ATRA

50 mg/kg bw ATRA

100 mg/kg bw ATRA

Daily dosing stopped

t me (hrs)Plasmar

adiolabeledeq

ialents

(mg/L)

Estimating AUC:Estimating AUC:


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Estimating AUC:

Non-Compartmental Analysis

Estimating AUC:

Non-Compartmental Analysis

Used trapezoidal rule linear elimination phase assumption

0 24 4 2 96 120 144 168 192 216 240 264 2880.0

0.2

0.4

0.6

0.81 / - ay

ti (hrs)

Plas

ara

iolab

l

quival

ts(

/L)

1 / : Eli ination Phas Analysis

220 240 260 280 300-2.5

-2.0

-1.5

-1.0

ti (hrs)

Ln(Cp)

AUC = 110.3 mg/L*h0p288h

AUC = Cp288h288hp g

____

kel= 2.26 / *h+

AUC = 122.56 mg/L*h0pg

Relationship Between Atrazine Dose and AUCRelationship Between Atrazine Dose and AUC


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Relationship Between Atrazine Dose and AUC

forTotalTriazines

Relationship Between Atrazine Dose and AUC

forTotalTriazines

0 20 40 60 80 100

0

2500

5000

7500

10000

12500y 116 4x 1 67

R2

0 999

ATRA dose mg kg day

AUC

mg

Lh

R l ti hi B t At i D d St dR l ti hi B t At i D d St d


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Relationship Between Atrazine Dose and Steady

State Serum Levels forTotalTriazines

Relationship Between Atrazine Dose and Steady

State Serum Levels forTotalTriazines

0 20 40 60 80 1000

10

20

30

40

50

60 y= 0.541x - 0.357

R2 = 0.994

ATRA dose (mg kg day)

pseudo-steadystate

serum

levels(mg

)

0 20 40 60 80 1000

250

500

750

1000

1250

1500y= 13.0x -8.57

R2 = 0.994

ATRA dose (mg kg day)

pseudo-daily

steadystateA

UC

(mg

-h)

Linear Pharmacokinetics

No dose-dependent changes


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OutlineOutline





A daily steady state area under the

curve (AUC) as internal dose metric


BMD Modeling of LH Attenuation asBMD Modeling of LH Attenuation as


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BMD Modeling of LH Attenuation as

Most Sensitive Endpoint

BMD Modeling of LH Attenuation as

Most Sensitive Endpoint

Rat LH attenuation studies for

BMD modeling

Cooper et al. 2010 4 day, oral gavage

Minnema 2001 1 month, oral gavage

Morseth 1996a 1 month, oral gavage

Morseth 1996b 6 month, dietary

Used EPAs Benchmark Dose Software(BMDS 2.1.2)

Models for continuous data were evaluated

Exponential, Hill, Power, Polynomial, Linear

Details of analysis including best-fit criteria in

Appendix C

Focus of Presentation will be on the Cooper et

al. (2010) 4-day LH attenuation study in rats

BMD Modeling of LH Attenuation MostBMD Modeling of LH Attenuation Most


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BMD Modeling of LH Attenuation Most

Reliable Dataset

BMD Modeling of LH Attenuation Most

Reliable Dataset

Cooper et al. (2010) 4 day oral gavage rat study

Most Robust Dataset

Well-defined dose response relationship

Less data variability compared to other datasets

Vehicle 1.56 3.12 6.25 12.5 25

0

5

10

15

20

25

At zine ( - )

Leutiniz

ingHormone

Seru

m(

ng/ml)

* From Cooper et al. 2010 unpublished data

1800 h: LH

Selection of Benchmark ResponseSelection of Benchmark Response


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Selection ofBenchmark Response

(BMR)

Selection ofBenchmark Response

(BMR)

Generally, the BMR is selected on the basis

of biological and/or statistical considerations

In the absence of information regarding the

level of LH attenuation associated with anadverse effect, the Agency used:

BMR based onone SD from Control Mean

BMD Modeling of 4-Day LH AttenuationBMD Modeling of 4-Day LH Attenuation


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BMD Modeling of4-Day LH Attenuation

Critical Study

BMD Modeling of4-Day LH Attenuation

Critical Study

BMD Modeling based on administered

atrazine dose

Best-fit model Exponential

BMD modeling based on steady state triazine

levels (from linear regression analysis)

Best-fit model Hill

Details of analysis in Appendix C

BMD Modeling Results of 4-Day LHBMD Modeling Results of 4-Day LH


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Dose metric BMD BMD

Atrazine dose4.23

mg/kg-day

1.96

mg/kg-day

Steady statetriazine levels

2.08

mg/ L

0.65

mg/L

Daily steady

state AUC fortotal triazines

49.98

mg/L*h

15.56

mg/L*h

BMD Modeling Results of4-Day LH

Attenuation Critical Study

BMD Modeling Results of4-Day LH

Attenuation Critical Study

Steady state based BMDLs correspond to 1.86 mg/kg-d atrazine

These dose metrics may be used to establish a point of

departure (POD)

Proposed Uses of BMD Modeling Results Proposed Uses of BMD Modeling Results


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Proposed Uses ofBMD Modeling Results

Hypothetical Example



Hypothetical Water Chemograph

0 24 48 72 96 1200

10

20

30

40

50

Time

TriazineLee

ls



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Approaches for analysis:

A drinking water rolling average value can be

compared to a POD that is based on

administered dose of atrazine

Average daily values of triazines can be

compared to a POD based on

steady state levels of triazines

a daily steady state AUC



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Given the linear relationship between steady

state triazine levels and administered triazine

dose,

potential levels of concern may be related to an

atrazine exposure.

0 20 40 60 80 1000

250

500

750

1000

1250

1500 y= 13.0x - 8.57

R2= 0.994

ATRA dose ( g/ g/day)

pseudo-daily

steadystateAUC

(

g/L-h)

Summary of Proposed Updates to DoseSummary of Proposed Updates to Dose


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Summary ofProposed Updates to Dose

Response Assessment

Summary ofProposed Updates to Dose

Response Assessment

Internal dose response assessmentbased on total plasma triazines

Temporal aspects of plasma triazinesand LH attenuation support the use of adaily steady state AUC

Benchmark dose modeling based onsteady state internal dose metrics of total

triazines Valuable perspectives for refining water

monitoring frequency


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Approaches to Evaluating Water

Sampling Strategies and

Frequency of Monitoring

Approaches to Evaluating Water

Sampling Strategies and

Frequency of Monitoring


September 14, 2010

Nelson Thurman & Mary FrankenberryEnvironmental Fate and Effects Division


P i SAP At i M it iP i SAP At i M it i


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Previous SAPs on Atrazine MonitoringPrevious SAPs on Atrazine Monitoring

April 2010: Re- Evaluation of Human HealthEffects of Atrazine: Review of Experimental

Animal and In Vitro Studies and DrinkingWater Monitoring Frequency

December 2007: Interpretation of theEcological Significance of Atrazine Stream-Water Concentrations Using a Statistically-Designed Monitoring Program

May 2009: The Ecological Significance ofAtrazine Effects on Primary Producers inSurface Water Streams in the Corn andSorghum Growing Region of the UnitedStates

Quick Recap of the Monitoring IssuesQuick Recap of the Monitoring Issues


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Quick Recap of the Monitoring Issues

Raised in the April 2010 SAP

Quick Recap of the Monitoring Issues

Raised in the April 2010 SAP

Given a possible change in the exposure

duration of the human health level of concern,

the USEPA is focusing on the monitoring design

for the existing CWS monitoring study. How well does the existing monitoring design

characterize shorter durations of exposure?

Does the existing monitoring program need to include

more frequent sampling or is it sufficient for a shorter

duration of exposure?

What the SAP Said RegardingWhat the SAP Said Regarding


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What the SAPSaid Regarding

Monitoring in April

What the SAPSaid Regarding

Monitoring in April

To evaluate proposed monitoring strategies or

evaluate the utility of exposure estimation

methods, the USEPA needs:

The toxicological exposure duration of concern to

define the importance of peaks in exposure estimates

Intensive empirical data covering a representative

range of sites to evaluate sample frequencies and

estimation methods

Methods that can predict values that may be greaterthan those sampled, particularly where short exposure

durations are important

Focus for Drinking Water MonitoringFocus for Drinking Water Monitoring


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Focus for Drinking Water Monitoring

Strategies for this SAP

Focus for Drinking Water Monitoring

Strategies for this SAP

1) General framework for designing a

monitoring study to characterize drinking

water exposures Question 5.1

2) A set of intensively sampled datasets toevaluate sampling strategies and exposure

estimation methods Question 5.2

3) Proposed approaches for analyzing/

interpreting monitoring data depending ontoxicological endpoint duration of concern

Question 5.3

1) General Framework For Designing A1) General Framework For Designing A


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1) GeneralFrameworkFor Designing A

Monitoring Study

1) GeneralFrameworkFor Designing A

Monitoring Study

To provide reliable exposure estimates that will

not underestimate exposure and can be used in

human health assessments, a drinking water

monitoring study should: Target the most vulnerable areas

Sample intensively during the likely high occurrence

period

Base sampling frequency on toxicological exposureduration of concern

Use autosamplers to collect data for exposure periods

of interest

T t d M it i I N t NT t d M it i I N t N


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Targeted Monitoring Is Not NewTargeted Monitoring Is Not New

* WARP used to identify

most vulnerable

watersheds for atrazine

ecological exposure

monitoring program.

* The CWS identified asmost vulnerable (based

on SDWA monitoring)

fell within the same

high-vulnerability

watersheds.

* Both studies sampled

more intensively during

the high occurrence

period.

2) S t Of I t i l S l d D t t2) S t Of I t i l S l d D t t


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2) SetOf Intensively Sampled Datasets2) SetOf Intensively Sampled Datasets

SAP recommended that sampling strategies andexposure estimation methods be evaluated

against intensive empirical data covering a

representative range of sites

Such datasets are critical for determiningconfidence bounds on monitoring estimates or

evaluating exposure estimation methods

USEPA proposes to use:

Available ambient monitoring from Heidelberg

Monitoring Data, AEEMP for streams, rivers

PRZM/EXAMS to generate chemographs for

reservoirs

S T t d W t ?S T t d W t ?


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Source orTreated Water?Source orTreated Water?

Analyzed 44 CWS with TCT detects >15

ug/L, comparing paired source and treated

water samples

No difference between source and treated (no

treatment effect) in 16 of 44 CWS

TCT concentrations reduced, but not

completely removed in 21 of 44 CWS Most/all TCT removed in treated samples in 7

of 44 CWS

Chemograph Illustrating No DrinkingChemograph Illustrating No Drinking


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IL-24 2004-05

0

5

10

15

20

25

30

35

40

45

50

J-04 M-04 M-04 J-04 S-04 N-04 J-05 M-05 M-05 J-05 S-05 N-05Date

TCTug/L(ppb

)

C Sou e

C reated

g p g g

WaterTreatment Effects

g p g g

WaterTreatment Effects

Continual pattern ofsimilar TCTconcentrations insource (magenta) and

treated (blue) watersamples over multipleyears

Concept of Matching ChemographsConcept of Matching Chemographs


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p g g p

Based on Shape

p g g p

Based on Shape

CWS Chemograph Intensively Sampled Chemograph

~

~

CWS #

Illinois,2008

0

1

2

3

4

5

6

1/1 1/31 3/1 3/31 4/30 5/30 6/29 7/29 8/28 9/27 10/27 11/26 12/26Date

Atrazine(T

T),u

/L

ampli i t

Li ar I t rpolation

W

#25

hi

,2005

0

1

2

3

4

5

6

7

8

9

10

1/1 1/31 3/2 4/1 5/1 5/31 6/30 7/30 8/29 9/28 10/28 11/27 12/27Date

Atrazine(T

T),u

/L

ampling oint

Linear Interpolation

Mau

ee

i

er,200

0

10

20

30

40

50

60

4/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date

Atrazine,u

/L

easured (! / infill" avg4-dayavg

Mau ee i

er,200 $

0

5

10

15

20

25

30

35

40

45

50

4/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19Date

Atrazine,u

/L

easured (! / infill"

max

easured (! / infill"

avg4-dayavg

Evaluating 7-day Sampling Intervals fromEvaluating 7-day Sampling Intervals from


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Evaluating 7 day Sampling Intervals from

Intensive Sampled Datasets

Evaluating 7 day Sampling Intervals from

Intensive Sampled Datasets

Focused on 4/1 8/31 (intensive sampling period) Sampled chemographs at 7-day intervals

Fixed intervals with 7 separate sampling intervals per

chemograph year

Further analysis is planned with bootstrapping methodsfor variable dates within weekly sampling windows

Compared number of spikes, maximum detection,

maximum 4-day average, number of days in

which 4-day averages exceeded 20 and 40 ug/L Ultimately will focus on the exposure magnitude and

duration of toxicological concern

Maumee River 2008 actualMaumee River 2008 actual


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M v 8

0

10

20

30

40

50

60

4/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19

L

Measured (w/ infill), avg

4-day avg

Maumee River, 2008, actualMaumee River, 2008, actual

Actual:

Max: 52.2 ug/L

4-da: 49.6 ug/L

# spikes: 9

Days 4-da avg

>40: 2

Relatively simple pattern driven by 1 high-concentrationspike

M Ri 2008 I t l #6M Ri 2008 I t l #6


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8 7 S p #

0

10

20

30

40

50

60

4/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19

Measured (w/ infill)

Measured 4-day avg

7-da interval 6

linear 7-da interval 6

4-day avg, linear 7-da int 6

Maumee River, 2008, Interval #6Maumee River, 2008, Interval #6

Actual:

Max: 52.2 ug/L

4-da: 49.6 ug/L

# spikes: 9

Days 4-da avg

>40: 2

Sampled:

Max: 52.2 ug/L

4-da: 46.6 ug/L

# spikes: 3

Days 4-da avg

>40: 4

Maumee River 2008 Interval #2Maumee River 2008 Interval #2


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8

0

10

20

30

40

50

60

4/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19

Measured (w/ i i ll

Measured 4-day avg

7-da i terval 2

li ear 7-da i terval 2

4-day avg, li ear 7-da i t 2

Maumee River, 2008, Interval #2Maumee River, 2008, Interval #2

Actual:

Max: 52.2 ug/L

4-da: 49.6 ug/L

# spikes: 9

Days 4-da avg

>40: 2

Sampled:

Max: 19.2 ug/L

4-da: 17.6 ug/L

# spikes: 2

Days 4-da avg

>40: 0

MO 02 2009 7 da Sample Interval #3MO 02 2009 7 da Sample Interval #3


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O , 9 - l I l

0

20

40

60

80

100

4/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19

i

,

MO-02Measured

4-dayavg

7-da terval3

linear7-dainterval3

4-dayavg, linear7-daint 3

MO-02, 2009, 7-da Sample Interval #3MO-02, 2009, 7-da Sample Interval #3

Actual:

Max: 155 ug/L

4-da: 68.9 ug/L

# spikes: 14

Days 4-da avg

>40: 8 (2x4)

Sampled:

Max: 66.0 ug/L

4-da: 58.2 ug/L

# spikes: 5

Days 4-da avg

>40: 7

155 (max)109 (max)

Complex chemograph 2 majorspikes, numerousshort-durationspikes

MO 02 2009 7 da Sample Interval #7MO 02 2009 7 da Sample Interval #7


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O-02,2009 7-Day Sample I terval #7

0

20

40

60

80

100

4/1 4/15 4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/ 8/19

Date

Atrazie,

/

-02 Measured

4-day vg

7-dainterv l7

linear7-dainterv l7

4-day vg linear7-daint 7

MO-02, 2009, 7-da Sample Interval #7MO-02, 2009, 7-da Sample Interval #7

Actual:

Max: 155 ug/L

4-da: 68.9 ug/L

# spikes: 14

Days 4-da avg

>40: 8 (2x4)

Sampled:

Max: 17.8 ug/L

4-da: 17.2 ug/L

# spikes: 3

Days 4-da avg

>40: 0

155 (max)109 (max)

Preliminary Analysis Using IntensivePreliminary Analysis Using Intensive


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Monitoring DataMonitoring Data

Chemograph shape, particularly the duration andfrequency of peaks, is critical to sampling analysis

For simple chemographs, weekly sampling may capture

the number but underestimate the magnitude of peaks

For complex chemographs with overlapping peaks,weekly sampling underestimates the number of peaks,

especially those of short-duration

Effect of sample intervals on data smoothing is

evident in 7-day fixed sampling analysis Intensive monitoring, with daily sampling during

the expected exposure period, is critical to

evaluate less frequent sampling strategies

(3) Approaches For Analyzing/(3) Approaches For Analyzing/


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Interpreting Monitoring DataInterpreting Monitoring Data

Common interpolation methods are likely tounderestimate peaks, short-duration exposures

Artificial neural networks may be too complicatedfor easy use

Extreme value theory has been used best wheremeasurements exist over long periods

Kriging methods to account for temporalautocorrelation assume stationary mean and

variance, and may require estimating correlationstructures across pooled systems

SAP recommended using kriging in combinationwith regression modeling

Kriging Analysis with ConditionalKriging Analysis with Conditional


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SimulationSimulation

Variogram (Atrazine,2005)

Time Lag (Days)

0 20 40 60 80 100 120 140

gamma(h)

0.0

0.5

1.0

1.5

2.0

2.5

Sill=1.60

Range=83 days

Nugget=0.25

Nugget: 0.025-0.5Sill: 0.9-10

Range: 35-83 days

(across all years)

Kriging for eachyear using

GEOEASE

Conditional Simulation of Atrazine 2005 Time Series

X Data

80 100 120 140 160 180 200 220 240

Y

Data

0

5

10

15

20

25

30

Maximum

75th percentile

50th percentileCol 9 vs Col 11

Used Gaussian

se uentialsimulations to

assess uncertainty

Limitations With Less FrequentLimitations With Less Frequent


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Sampling Intervals Loss of DataSampling Intervals Loss of Data

4 day sam

%

li&

'

J(

lian Days

80 100 120 140 160 180 200

A

traz

ineConc(ug/L)

0

5

10

15

20

25

30

8 day sam

)

ling

Julian Day

100 120 140 160 180 200

Atraz

ineCon

c(ug/L)

0

5

10

15

20

25

30

16 day sam

0

ling

Julian Day100 120 140 160 180 200

Atraz

ineConc(ug/L)

0

5

10

15

20

25

30

Exposure Estimation Modeling OptionsExposure Estimation Modeling Options


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Exposure Estimation ModelingOptionsExposure Estimation ModelingOptions

Co-kriging measured concentrations with dailystreamflow

Conditional simulations based on data structure(mean, median, sd, percentiles, variogram or

temporal correlation) Updates to WARP (Watershed Regression on

Pesticides) 2009 SAP on atrazine ecological exposure

recommended developing a corn-belt version of WARP

Use WARP percentile estimates in combination withkriging or conditional simulations

Combine WARP with seasonal variability (SEAWAVE-Q) model

Focus for Drinking Water MonitoringFocus for Drinking Water Monitoring


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Strategies for this SAPStrategies for this SAP

1) General framework for designing a

monitoring study to characterize drinking

water exposures Question 5.1

2) A set of intensively sampled datasets toevaluate sampling strategies and exposure

estimation methods Question 5.2

3) Proposed approaches for analyzing/

interpreting monitoring data depending ontoxicological endpoint duration of concern

Question 5.3


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Atrazine Re-Evaluation:Scientific Considerations in Potential

Sensitivity of Infants & Children

Implications of MOA onWater Monitoring Strategy

Atrazine Re-Evaluation:Scientific Considerations in Potential

Sensitivity of Infants & Children




Anna Lowit, Ph.D.



Scientific Considerations in PotentialScientific Considerations in Potential


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Sensitivity of Infants & ChildrenSensitivity of Infants & Children

FFDCA, as amended by the FQPA (1996), requiresthe Agency to give special attention to infants andchildren by placing emphasis on the availability oftoxicology and exposure information to estimate thepotential risk to these age groups.

Scientific analysis for the FQPA 10X Safety Factor TheAgency has not yet proposed any updates to the SafetyFactor used in the RED.

Charge question 5.0

Soliciting comment on important scientific factorsfor the Agency to consider in its analysis

Both hazard & drinking water exposure

Scientific Considerations in Potential

S i i i f I f & Child


S i i i f I f & Child


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HazardConsiderations Important Issue: availability of data to

assess critical lifestages

Key studies still on-going

Consider all the relevant information

Mode of action

Animal database of toxicology studies

Dose-response relationships Human relevance of animal findings

Epidemiology findings


S iti it f I f t & Child




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HazardConsiderations, contd Consider all the relevant information

Neuroendocrine MOA is relevant for

developmental&

reproductive effects

Epidemiology findings: Provide

qualitative information on the human

relevance of some animal findings.






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Animal studies:

Studies on specific lifestages (gestation, lactation, early

post-natal, peri-pubertal) Tissue dosimetry studies in fetuses and lactating pups

LH attenuation:

Well documented for its dose and temporal response

Most sensitive endpoint

--No other high quality study provides endpoints lowerthan studies on LH attenuation

Biological plausibility in humans

Associated with reproductive and developmental processes






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Animal studies:

Key on-going studies not yet available: Potential hormonal changes and outcomes from

gestational exposure to male and female rats

Behavioral changes in male rats (e.g., rough and

tumble play) following gestational exposure (GD

14-21) Possible latent effects manifested in adults from

gestational and lactational exposure.






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Sensitivity of Infants & ChildrenSensitivity of Infants & Children On-going multi-life stage study by Syngenta:

Oral gavage dosing of 0, 6.25, 25 or 50 mg/kg/day.

Recently submitted to the Agency: Cohort I, Subset A & Cohort II, Subset D

Lack of effect on LH surge at 50 mg/kg/day in OVX-estrogenprimed animals contradicts the results in other studies (Foradoriet al. 2009, Cooper et al. 2007 and Morseth et al. 1996).






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Drinking Water Exposure Considerations Important Issue: Sufficiency of drinking water

monitoring data to confidently estimate exposure

i.e., the data do not underestimate exposure

Charge questions 4.1-4.3 address drinking water

monitoring issues

Additional approaches proposed for analyzing/

interpreting monitoring data depending on the duration of

concern

Point of departure & critical duration of exposure are

important components of evaluating the drinking water

monitoring data






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Charge question 5.0 In the coming months, the Agency will

work towards completing the scientific

analysis Soliciting comment on important

scientific factors for the Agency to

consider in its analysis Both hazard & drinking water exposure


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Implications of MOA on

W t M it i St t


W t M it i St t


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Water Monitoring StrategyWater Monitoring Strategy

Current drinking water monitoring program calls for Weekly monitoring during the application and growing

season in a given area (usually April thru July/August)

Biweekly monitoring during the remainder of the year.

When monitoring data are used, the Agency derivesan average for the exposure duration period ofconcern.

In the last risk assessment, the consecutive [rolling] 90-dayaverages were derived by interpolating between the weekly

monitoring points. This distribution of averages was then compared against the

level of concern for the 90-day period to see if computedrolling average values exceeded the LOC and, if so, howoften.


W t M it i St t


W t M it i St t


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Should the critical duration of

exposure be revised? Ifso,

how?


Water Monitoring Strategy




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Database is lacking in human specific

information on the effects of atrazine which

could be used to quantitatively extrapolate

between rats & humans.

Thus, must infer generic knowledge on critical

durations of exposure from a variety of sources

Empirical effects data from animal studies

Toxicodynamic and toxicokinetic information






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Delayed puberty onset Greater than 4 days of exposure in rat

In humans, puberty occurs over a long period of time

Prostatitis

In rats, exposure to the dam results in inhibition of prolactintransmission in milk by atrazine during a early critical time forTIDA neurons activation in offspring (first postnatal week)

Indicates hypothalamic-pituitary gonadal axis is affected in rat

In humans, prolactin plays a role in development andmaintenance of prostate but critical periods for developmentalexposures and hormonal involvement in induction of prostatitisremain unknown






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LH attenuation in female rat: Single day of dosing not sufficient

Reach maximal effect by 4 days ofexposure

Pseudo steady-state tissue levels by 4days of daily exposures

Extrapolation of rat findings to humans?






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Preferred approach for interspecies extrapolation--Physiologically based pharmacokinetic (PBPK) model

McMullin et al. 2007 has shortcomings

Does not capture the rapid kinetics of atrazine

Underpredicts plasma levels of chlorotriazines

Pharmacokinetic analysis is based entirely on non-compartmental analysis

Empirical in nature

At steady state, plasma and brain levels will likely be atequilibrium making plasma levels a good surrogate

Human inference possible through allometric scalingin the absence of more specific information






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Dose groups were analyzed assuming linearbehavior (i.e., lnCp vs. time)

1 - 10 mg/kg-day dose groups: comparable elimination rateconstants of 0.010 0.015 hr-1

Allometric scaling for an average human female BW

of 60 kg Converted allometrically scaled human female eliminationrate constants to plasma half-lives

Assume that it would take 3 - 5 half-lives to steady state

Human time estimates to steady state are between

2.5 - 4 weeksATRA dose (mg/kg/day) Rat kel (hr-1) Allometrically scaled human female kel (hr-1) t1/2 (days) time to steady state (days)1 0.010 0.00343 .42 25.3 - 42.1

3 0.015 0.0052 5.4 16.4 - 2 .4

0.013 0.004 1 6.14 1 .4 - 30.

10 0.014 0.004 5 5. 4 1 .5 - 2 .2






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What is known about LH surge in

human menstrual cycle that informs

critical duration of exposure?

Infer from pharmaceutical literature on invitro fertilization (IVF); possible windows:

Follicular phase (lasting 12 days)

Or the late follicular phase [days 8-12 (i.e., 4-5

days) of the follicular phase].

Uncertainty IVF drugs extremely potent






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Charge question 6.0

Soliciting comment on the analysis and

preliminary conclusions as it relates to the

potential critical windows of exposure

Possible additional or alternative

approaches or data that may inform thisissue.


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Summary & Next Steps


Summary & Next Steps



Anna Lowit, Ph.D.Health Effects Division


SummarySummary


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yy

Non-cancer epidemiology Mammary gland development

Proposals for dose-responseassessment

Emphasis on LH attenuation as a keybiologically plausible event in humans

Use of a sensitive & early precursor eventconsidered protective of reproductive &

developmental outcomes, and other toxicities Includes internal dosimetry calculations &

benchmark dose analysis recommended atApril SAP

SummarySummary


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Drinking water exposure: Updatedapproaches and example analyses

Analysis for the FQPA 10X Safety

Factor is still ongoing solicitingcomment from the Panel on important

scientific factors to consider for both

drinking water exposure & hazard

Implications of MOA & adverse

outcomes on the critical duration of

exposure

Next StepsNext Steps


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Non-cancer assessment

Pending the outcome of the September,

2010 SAP meeting, the Agency anticipates

in the coming months: Selecting critical duration(s) of exposure

Developing detailed drinking water monitoring

analysis

Completing FQPA Safety Factor analysis

Follow-up SAP on these issues in 2011

Next StepsNext Steps


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Cancer epidemiology

AHS epidemiology study on atrazine and

human cancer publication expected in

2011

Integrate experimental carcinogenesis data

and epidemiology data (AHS and other

cancer epidemiology studies)

SAP meeting on cancer effects of atrazine

in 2011

EPA-HQ-OPP-2010-0481-0048 ATRAZINE INTRO AND STATUS SEPT 14-17, 2010

Documents

Transcript of EPA-HQ-OPP-2010-0481-0048 ATRAZINE INTRO AND STATUS SEPT 14-17, 2010