1. business and industrial
2. agriculture and forestry
3. crops and seed

Re-Evaluation of Human Health Effects of Atrazine:
Review of Cancer Epidemiology, Non-cancer Experimental Animal and
In vitro Studies and Drinking Water Monitoring Frequency
Presented Jointly To The FIFRA Scientific Advisory Panel By:
U.S. Environmental Protection Agency
Office of Pesticide Programs
Health Effects Division
and
Environmental Fate and Effects Division
in collaboration with the
Office of Research and Development
Presented On:
July 26-29, 2011
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TABLE OF CONTENTS
1. INTRODUCTION ...............................................................................................................7
2. MODE OF ACTION (MOA) & EXPERIMENTAL TOXICOLOGY ....................................10
2.1 HISTORICAL BACKGROUND ............................................................................................................................................ 10
2.2 2010 SCIENTIFIC RE-EVALUATION .................................................................................................................................. 11
2.3 MODE OF ACTION (MOA) AND ADVERSE HEALTH OUTCOMES............................................................................................. 12
2.3.1 Background .................................................................................................................................................... 12
2.3.2 Reproductive Senescence and Mammary tumors in rats: Well established MOA ......................................... 14
2.3.3 Adverse Health Outcomes – LH Changes as a Sentinel Effect ......................................................................... 16
2.3.3.1 Factors influencing GnRH Modulation of LH following atrazine exposure .................................................................. 17
2.3.3.1.2 GnRH in primates ............................................................................................................................................. 17
2.3.3.2 Females of Reproductive Age: Role of LH, Progesterone and Corticosterone in Atrazine Induced Changes in
Ovarian Cyclicity ...................................................................................................................................................................... 18
2.3.3.2.1 Estrogen and progesterone regulation of LH secretion ..................................................................................... 19
2.3.3.2.2 Differential effects of single versus multiple atrazine exposures on the LH surge ........................................... 21
2.3.3.2.3 LH Attenuation in Humans – Pharmaceutical Experience .................................................................................. 24
2.3.3.3 Prepubertal Individuals: Atrazine-induced delays in sexual maturation .................................................................... 25
2.3.3.3.1 Delays in Female Sexual Maturation .................................................................................................................. 26
2.3.3.3.2 Delays in Male Sexual Maturation .................................................................................................................... 27
2.3.3.4 Atrazine-associated decrease in prolactin and prostatitis in young rats ................................................................... 29
2.3.4 Mammary gland whole mounts ..................................................................................................................... 30
2.3.5 Summary ........................................................................................................................................................ 31
3. EPIDEMIOLOGIC EVIDENCE OF THE CARCINOGENIC POTENTIAL OF ATRAZINE
............................................................................................................................................33
3.1 INTRODUCTION ........................................................................................................................................................... 33
3.1.1 Background.................................................................................................................................................... 33
3.1.2 Identification of Epidemiology Studies included in Current Review ............................................................... 34
3.2 EPIDEMIOLOGY LITERATURE OF THE CARCINOGENIC POTENTIAL OF ATRAZINE ......................................................................... 35
3.2.1 Overview of Epidemiologic Database ............................................................................................................ 36
3.2.2 Agricultural Health Study (AHS) Atrazine Cancer Point Estimates ................................................................ 37
3.2.2.1 Study Design and Methods....................................................................................................................................... 37
3.2.2.2 AHS Strengths and Weaknesses ............................................................................................................................... 39
3.2.3 Reproductive and Endocrine System Cancers ................................................................................................ 39
3.2.3.1
3.2.3.2
3.2.3.3
3.2.3.4
Prostate Cancer ........................................................................................................................................................ 40
Breast Cancer ........................................................................................................................................................... 43
Ovarian Cancer ......................................................................................................................................................... 48
Thyroid Cancer ......................................................................................................................................................... 52
3.2.4 Lymphohematopoietic Cancers ..................................................................................................................... 53
3.2.4.1
3.2.4.2
3.2.4.3
3.2.4.4
Population-Based Case-Control Studies: Midwestern U.S........................................................................................ 54
Hospital-Based Case-Control Studies (France) ......................................................................................................... 56
AHS Estimates of Atrazine and Lymphohematopoietic Cancers............................................................................... 58
Conclusions: Lymphohematopoietic Cancers ........................................................................................................... 59
3.2.5 Other Cancer Sites ......................................................................................................................................... 60
3.2.5.1 Brain/Glioma ............................................................................................................................................................ 60
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3.2.5.2 Pediatric Cancers ...................................................................................................................................................... 61
3.2.5.3 Colon Cancer ............................................................................................................................................................ 63
3.2.5.4 Other AHS ................................................................................................................................................................. 64
3.3 SUMMARY OF EVIDENCE CONCERNING THE CARCINOGENIC POTENTIAL OF ATRAZINE ............................................................... 65
3.3.1 Synthesis and Integration of Toxicology and Epidemiology Literature.......................................................... 65
3.3.2 EPA Conclusion ................................................................................................................................................. 71
4. PROPOSED UPDATES TO THE DOSE-RESPONSE ASSESSMENT ........................72
4.1 BENCHMARK DOSE ANALYSIS ........................................................................................................................................ 72
4.1.1 Identification of Critical Studies ...................................................................................................................... 73
4.1.2 Methods & Results ......................................................................................................................................... 74
4.1.3 Proposed Point of Departure ......................................................................................................................... 78
4.2 PHARMACOKINETIC ANALYSIS .......................................................................................................................................... 79
4.2.1 Background ...................................................................................................................................................... 79
4.2.2 Updates on the Pharmacokinetics of Atrazine and Its Metabolites in the Rat ............................................... 80
4.2.3 Oral Gavage Dosing of Sprague Dawley Rats with Atrazine .......................................................................... 81
4.2.4 Dietary Exposures of Sprague Dawley Rats to Atrazine ................................................................................. 87
4.2.5 Plasma Clearance Discrepancy Based on Studies with Radiolabeled Atrazine ............................................... 88
4.2.6 Additional Rat Radiolabeled Atrazine Studies Evaluated for Plasma Clearance ............................................ 90
4.2.7 Pharmacokinetic Studies with Radiolabeled Atrazine in Monkeys ................................................................. 93
4.2.8 Pharmacokinetic Studies with Atrazine in Humans ........................................................................................ 94
4.2.8.1 Human Blood Pharmacokinetic Information on Atrazine and its Chloro-s-triazine Metabolites .............................. 95
4.2.8.2 Human Urine Pharmacokinetic Information on Atrazine and its Chloro-s-triazine Metabolites .............................. 95
4.2.9 Updates on the Metabolism of Atrazine ........................................................................................................ 97
4.2.10 Summary of Pharmacokinetic Information .................................................................................................. 98
4.2.11 Progress on Physiologically Based Pharmacokinetic Modeling Efforts ........................................................ 99
4.2.12 Proposed Interim Pharmacokinetic Modeling Approach ............................................................................ 100
4.2.13 Implications for Water Monitoring Frequency ........................................................................................... 103
4.2.13.1 Rat Forward Internal Dosimetry for LH Attenuation ............................................................................................. 104
4.2.13.2 Human Reverse Dosimetry from Rat Internal Dosimetry for LH Attenuation ....................................................... 105
4.2.14 Calculating Average Chlorotriazine Levels in Drinking Water for Human Plasma Triazine AUC Estimates 107
5. SCIENTIFIC CONSIDERATIONS IN POTENTIAL SENSITIVITY OF INFANTS &
CHILDREN........................................................................................................................109
5.1 BACKGROUND .......................................................................................................................................................... 109
5.2 EXPERIMENTAL TOXICOLOGY ....................................................................................................................................... 110
5.2.1 Gestational Exposure ................................................................................................................................... 110
5.2.2 Multi-Lifestage Exposure .............................................................................................................................. 111
5.3. SUMMARY............................................................................................................................................................... 112
6.EVALUATING ATRAZINE DRINKING WATER MONITORING DATA FOR USE IN
HUMAN HEALTH ASSESSMENTS .................................................................................114
6.1 INTRODUCTION ......................................................................................................................................................... 114
6.2 KEY QUESTIONS TO ADDRESS IN ANALYZING MONITORING DATA........................................................................................ 115
6.3 SHORT-TERM TEMPORAL VARIABILITY: DAILY VARIATIONS WITHIN A GIVEN YEAR .................................................................. 117
6.3.1 General Approach for Assessing Uncertainties in Monitoring Related to Short-term Temporal Variability 118
6.3.2 Comparative Analysis of Monitoring Frequency Performance ..................................................................... 120
6.3.3 Geostatistical Analysis and Stochastic Application ...................................................................................... 125
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6.3.4 Mechanistic Approach: PRZM Hybrid Model ................................................................................................ 132
6.3.5 Watershed Atrazine Analysis Model............................................................................................................. 133
6.3.6 Generating Atrazine Time Series from Weekly CWS Monitoring for Use in Dietary Exposure Assessments 134
6.4 YEAR-TO-YEAR TEMPORAL VARIABILITY ......................................................................................................................... 137
6.5 SPATIAL VARIABILITY .................................................................................................................................................. 139
6.6 SUMMARY................................................................................................................................................................ 140
7. CASE STUDY: IMPLICATIONS OF MOA & TOXICITY PROFILE ON WATER
MONITORING ...................................................................................................................142
7.1 INTRODUCTION ......................................................................................................................................................... 142
7.2 ATRAZINE TIME SERIES ESTIMATES FOR USE IN DIETARY EXPOSURE ASSESSMENTS ................................................................. 143
7.3 ESTIMATING HUMAN PLASMA AVERAGE DAILY AUC FROM ROLLING AVERAGE ATRAZINE CONCENTRATIONS ................................. 145
8. REFERENCES ............................................................................................................149
LIST OF FIGURES
FIGURE 1: LH SUPPRESSION AND ADVERSE OUTCOMES OBSERVED IN RATS ....................................................................................... 14
FIGURE 2: AUGMENTATION BY A SUBCUTANEOUS INJECTION OF PROGESTERONE ON AN LH SURGE IN OVARIECTOMIZED RATS IMPLANTED WITH
AN ESTRADIOL CAPSULE AT THE TIME OF SURGERY. PROGESTERONE WAS ADMINISTERED JUST PRIOR TO THE SAMPLING OF SMALL
ALIQUOTS OF BLOOD BY TAIL NICK OVER THE AFTERNOON OF THE THIRD DAY AFTER OVARIECTOMY. CONCENTRATIONS ARE INDICATED IN
NG/ML ± SEM .............................................................................................................................................................. 21
FIGURE 3: THE LH SURGE (NG/ML + SEM) AND AREAS UNDER THE CURVE IN RESPONSE TO 1 AND 4 DAYS OF ATRAZINE TREATMENT IN
OVARIECTOMIZED / ESTRADIOL-PRIMED LONG-EVANS RATS. THE NUMBERS IN PARENTHESES INDICATE THE GROUP SIZES FOR EACH OF
THE TREATMENTS. NUMBERS WITHIN THE 100 MG/KG COLUMNS AT THE RIGHT INDICATE THE PERCENT CHANGE FROM THE 0 MG/KG
GROUP. *P<0.05. ......................................................................................................................................................... 23
FIGURE 4: EPA BMDS EXPONENTIAL MODEL (CONSTANT VARIANCE; BMR = 1 SD FROM CONTROL MEAN) RESULTS FOR ATTENUATION OF
LH SURGE IN FEMALE RATS ADMINISTERED ATRAZINE BY GAVAGE - NHEERL DATA. ................................................................. 76
FIGURE 5: EPA BMDS HILL MODEL (CONSTANT VARIANCE; BMR = 1 SD FROM CONTROL MEAN) RESULTS FOR “AREA UNDER THE TRIAZINE
EQUIVALENT PLASMA-CONCENTRATION TIME CURVE” AS THE DOSE METRIC - NHEERL DATA (ATTENUATION OF LH SURGE IN FEMALE
RATS ADMINISTERED ATRAZINE BY GAVAGE). ...................................................................................................................... 77
FIGURE 6: EPA BMDS HILL MODEL (CONSTANT VARIANCE; BMR = 1 SD FROM CONTROL MEAN) RESULTS FOR “DAILY STEADY STATE AUC
FOR TRIAZINE EQUIVALENTS” AS THE DOSE METRIC - NHEERL DATA (ATTENUATION OF LH SURGE IN FEMALE RATS ADMINISTERED
ATRAZINE BY GAVAGE). ................................................................................................................................................... 78
FIGURE 7: METABOLIC SCHEME FOR ATRAZINE ............................................................................................................................. 80
FIGURE 8: PLASMA PROFILE OF ATRAZINE FOR 24 HOURS FOLLOWING ORAL GAVAGE DOSING OF RATS WITH 3, 10, OR 50 MG/KG BW ATRAZINE.
EACH TIME POINT REPRESENTS AVERAGE OF SIX MEASUREMENTS ± SD. DATA IS FROM CODER 2011. ............................................ 82
FIGURE 9: THE PARALLEL PLASMA PROFILES OF ATRAZINE VERSUS DEA OR DIA FOLLOWING ORAL GAVAGE DOSING OF RATS WITH 3 MG/KG BW
ATRAZINE. DATA IS FROM CODER 2011. ............................................................................................................................. 84
FIGURE 10: 24-H PLASMA PROFILE OF ATRAZINE AND ITS CHLORINATED METABOLITES DEA, DIA, AND DACT FOLLOWING ORAL GAVAGE
DOSING OF RATS WITH 3 MG/KG BW ATRAZINE. EACH TIME POINT REPRESENTS AVERAGE OF SIX MEASUREMENTS ± SD. DATA IS FROM
CODER 2011. ............................................................................................................................................................... 85
FIGURE 11: PLASMA PROFILE OF ATRAZINE, DEA, DIA, AND DACT FROM REPEATED ONCE A DAY ORAL GAVAGE DOSING OF RATS WITH 3 MG/KG
BW ATRAZINE FOR 4 DAYS. VALUES REPRESENT THE MEAN OF SIX REPLICATES ± SD. DATA IS FROM CODER 2011. ............................ 86
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FIGURE 12: PLASMA PROFILE OF ATRAZINE, DEA, DIA, AND DACT FROM DIETARY DOSING OF RATS WITH ATRAZINE AT A NOMINAL DOSE OF 3
MG/KG BW/DAY ATRAZINE FOR FOUR DAYS. DATA AT EACH TIME POINT REPRESENTS MEASUREMENTS IN 6 ANIMALS. DATA POINTS ARE
NOT CONNECTED TO HELP ELUCIDATE PATTERN. DATA IS FROM CODER 2011. ............................................................................ 87
FIGURE 13: PLASMA PROFILES OF RADIOLABELED TRIAZINE EQUIVALENTS RESULTING FROM REPEATED ONCE DAILY DOSING OF RATS WITH
RADIOLABELED ATRAZINE AT 1, 3, 7, 10, 50 AND 100 MG/KG BW ATRAZINE. DATA IS FROM THEDE 1987. .................................... 90
FIGURE 14: PLASMA PROFILE AND ANALYSIS OF ELIMINATION OF A SINGLE ORAL GAVAGE DOSE OF 1 AND 100 MG/KG BW RADIOLABELED
ATRAZINE IN RATS. DATA IS FROM PAUL ET AL. 1993. ........................................................................................................... 92
FIGURE 15: PLASMA PROFILE AND ANALYSIS OF ELIMINATION OF REPEATED ORAL GAVAGE DOSING WITH EITHER 0.4 OR 4 MG/KG BW 14CATRAZINE IN RATS FOR SEVEN DAYS. DATA IS FROM SIMONEAUX 1985. .................................................................................... 93
FIGURE 16: UPDATED METABOLIC PROFILE FOR ATRAZINE. NEWLY IDENTIFIED METABOLITES ARE ABOVE THE DASHED LINE FOR COMPARISON TO
FIGURE 7. NEW METABOLITES ARE FROM LEBLANC ET AL. 2011 AND JOO ET AL. 2011. .............................................................. 98
FIGURE 17: PROPOSED INTERIM PHARMACOKINETIC MODEL FOR ATRAZINE AND ITS METABOLITES ........................................................ 101
FIGURE 18: PROPOSED LINKAGE BETWEEN ATRAZINE EXPOSURE AND OPTIMAL LH ATTENUATION IN RAT STUDIES..................................... 104
FIGURE 19: PROPOSED LINKAGES BETWEEN ATRAZINE EXPOSURE AND LH ATTENUATION IN ADULT HUMANS. THE ASTERISKS INDICATE THE
PARAMETERS THAT WERE ALLOMETRICALLY SCALED FROM ADULT RATS. .................................................................................. 106
FIGURE 20: HYPOTHETICAL WATER CHEMOGRAPH SHOWING THE TYPICAL VARIATION IN CHLOROTRIAZINE LEVELS AS A FUNCTION OF TIME. ... 106
FIGURE 21: THE TRAPEZOID RULE FOR ESTIMATING THE AREA UNDER A WATER CHEMOGRAPH FOR A GIVEN DURATION OF EXPOSURE. .......... 107
FIGURE 22: ANALYSIS STRATEGY FOR MODELING PESTICIDE TIME SERIES CONCENTRATIONS FROM MONITORING DATA. .......................... 126
FIGURE 23: VARIOGRAMS FOR 4-DAY AND 7-DAY SAMPLING FREQUENCY FOR THE MAUMEE AND MO-01 2007 DATA: A.) 4-DAY 1995
MAUMEE; B.) 7-DAY 1995 MAUMEE; C.) 4-DAY GRAB MO-01 2007; AND D.) 7-DAY GRAB MO-01 2007................................ 128
FIGURE 24: REPRESENTATIVE REALIZATIONS OF KRIGED ATRAZINE TIME SERIES FROM (A) 4-DAY, (B) 7-DAY, (C) 14-DAY, AND (D) 28-DAY
SAMPLING OF THE NCWQR MAUMEE RIVER 1995 DATA.................................................................................................... 129
FIGURE 25: REPRESENTATIVE REALIZATIONS OF KRIGED ATRAZINE TIME SERIES FROM (A) 4-DAY, (B) 7-DAY, (C) 14-DAY, AND (D) 28-DAY
SAMPLING OF THE MO-01 2007 DATA. ........................................................................................................................... 130
FIGURE 26: ESTIMATES OF DAILY AND ROLLING-AVERAGE ATRAZINE CONCENTRATIONS FROM THE ACTUAL TIME SERIES (BLACK LINE) AND 5TH
AND 95TH PERCENTILES (DASHED RED LINES) OF REALIZATIONS OF KRIGED TIME SERIES FROM 7-DAY SAMPLING OF THE MAUMEE RIVER
1995 DATA. ............................................................................................................................................................... 135
FIGURE 27: ESTIMATES OF DAILY AND ROLLING-AVERAGE ATRAZINE CONCENTRATIONS FROM FROM THE ACTUAL TIME SERIES (BLACK LINE) AND
5TH AND 95TH PERCENTILES (DASHED RED LINES) OF REALIZATIONS OF KRIGED TIME SERIES FROM 7-DAY SAMPLING OF THE MO-01
2007 DATA. ............................................................................................................................................................... 136
FIGURE 28: LOCATION OF CWS IN THE AMP IN RELATION TO VULNERABLE WATERSHEDS IDENTIFIED BY WARP BASED ON ATRAZINE USE ON
CORN AND SORGHUM.................................................................................................................................................... 140
FIGURE 29: ESTIMATES OF DAILY AND ROLLING-AVERAGE ATRAZINE CONCENTRATIONS FROM THE ACTUAL TIME SERIES (BLACK LINE) AND 5TH
AND 95TH PERCENTILES (DASHED RED LINES) OF REALIZATIONS OF KRIGED TIME SERIES FROM 7-DAY SAMPLING OF THE MAUMEE RIVER
1995 DATA. ............................................................................................................................................................... 144
FIGURE 30: ESTIMATES OF DAILY AND ROLLING-AVERAGE ATRAZINE CONCENTRATIONS FROM THE ACTUAL TIME SERIES (BLACK LINE) AND 5TH
AND 95TH PERCENTILES (DASHED RED LINES) OF REALIZATIONS OF KRIGED TIME SERIES FROM 7-DAY SAMPLING OF THE MO-01 2007
DATA. ........................................................................................................................................................................ 145
FIGURE 31: ESTIMATES OF ROLLING AVERAGE DAILY HUMAN PLASMA AUC VALUES CORRESPONDING TO THE ACTUAL TIME SERIES (BLACK LINE)
AND 5TH AND 95TH PERCENTILES (DASHED RED LINES) FOR THE MAUMEE RIVER 1995 DATASET ACCORDING TO FIGURE 29. THE DASHED
LINE IS FOR COMPARISON TO THE RAT PLASMA POD AUC WITH A 300X APPLIED UNCERTAINTY FACTOR. ...................................... 147
FIGURE 32: ESTIMATES OF DAILY ROLLING-AVERAGE ATRAZINE CONCENTRATIONS FROM THE ACTUAL TIME SERIES (BLACK LINE) AND 5TH AND
95TH PERCENTILES (DASHED RED LINES) OF REALIZATIONS OF KRIGED TIME SERIES FROM 7-DAY SAMPLING OF THE MO-01 2007 DATA.
................................................................................................................................................................................ 148
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LIST OF TABLES
TABLE 1: SUMMARY OF ADMINISTERED DOSE BMD ANALYSES........................................................................................................ 75
TABLE 2: BMD MODELING RESULTS WITH AVERAGE STEADY STATE PLASMA TRIAZINES AND AUC ESTIMATES............................................ 76
TABLE 3: PHARMACOKINETIC PARAMETERS ESTIMATED FOR ATRAZINE AND ITS CHLORINATED METABOLITES DEA, DIA, AND DACT FOLLOWING
ORAL GAVAGE DOSING OF RATS WITH 3, 10, AND 50 MG/KG BW ATRAZINE. DATA ANALYZED IS FROM CODER 2011. ........................ 83
TABLE 4: PHARMACOKINETIC ELIMINATION PARAMETERS ESTIMATED FOR ATRAZINE AND ITS CHLORINATED METABOLITES DEA, DIA, AND
DACT FOLLOWING DOSING OF RATS WITH ATRAZINE THROUGH THE DIET AT NOMINAL DOSES OF 3, 10, AND 50 MG/KG BW. DATA IS
FROM CODER 2011........................................................................................................................................................ 88
TABLE 5: FIRST ORDER ELIMINATION RATE CONSTANTS ESTIMATED FROM PLASMA DATA IN THE THEDE 1987 STUDY INVOLVING REPEATED ONCE
DAILY DOSING OF RATS WITH RADIOLABELED ATRAZINE (THEDE 1987). ..................................................................................... 90
TABLE 6: ESTIMATED PLASMA PHARMACOKINETIC PARAMETERS IN MONKEYS FOLLOWING ORAL GAVAGE DOSING WITH 1, 10, AND 100 MG
14
[ C]-ATRAZINE. DATA IS FROM HUI ET AL. 2011................................................................................................................. 94
TABLE 7: ESTIMATED PHARMACOKINETIC PARAMETERS FROM HUMAN BLOOD AND URINE .................................................................... 96
TABLE 8: EVALUATION OF THE ONE-COMPARTMENT LINEAR MODEL FOR RELATING AN ATRAZINE DOSE TO STEADY STATE PLASMA TRIAZINES.. 102
TABLE 9: SUMMARY OF AVAILABLE ATRAZINE EXPERIMENTAL TOXICOLOGY STUDIES FROM THE GESTATIONAL PERIOD................................ 111
TABLE 10: DOSING REGIMEN MULTI-LIFESTAGE STUDY................................................................................................................ 112
TABLE 11: SUMMARY OF 10,000 SIMULATIONS OF MONITORING ESTIMATES AT DIFFERENT SAMPLING INTERVALS FOR THE AEEMP MO-01
SITE IN 2007. .............................................................................................................................................................. 122
TABLE 12: SUMMARY OF 10,000 SIMULATIONS OF MONITORING ESTIMATES AT DIFFERENT SAMPLING INTERVALS FOR THE NCWQR MAUMEE
RIVER SITE IN 1995. ..................................................................................................................................................... 123
TABLE 13: VARIOGRAM MODELS USING THE NCWQR MAUMEE RIVER 1995 AND AEEMP MO-01 2007 TIME SERIES WITH DIFFERENT
SAMPLING FREQUENCIES. .............................................................................................................................................. 127
TABLE 14: COMPARISON OF LOWEST AND HIGHEST YEARLY MAXIMUM DETECTIONS FOR CWS IN THE AMP BASED ON NUMBER OF YEARS
SAMPLED. ................................................................................................................................................................... 138
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1. INTRODUCTION
Atrazine – a chlorotriazine herbicide – is currently one of the most widely used agricultural
herbicides in the United States, with approximately 70 million pounds of active ingredient
applied domestically per year. The Interim Reregistration Eligibility Document (IRED) for
atrazine was finalized in January 2003 with an addendum added in October 2003 (US EPA
2003). The Agency completed the reregistration eligibility document (RED) for atrazine in
April 2006. In recent years, numerous experimental toxicology and epidemiologic studies
evaluating the toxicity profile and/or mode of action (MOA) of atrazine have become
available. To consider the extent to which these new studies may impact the Agency’s
human health risk characterization for atrazine, the Office of Pesticide Programs (OPP) in
collaboration with the Office of Research and Development (ORD) is re-evaluating atrazine
and its chloro-s-triazines metabolites [deethyl-atrazine (DEA), deisopropyl-atrazine (DIA),
and diamino-s-chlorotriazine (DACT)]. 1
As part of this re-evaluation, the Agency held three meetings of the FIFRA SAP in 2010
which involved atrazine. The July 2011 meeting includes a variety of challenging scientific
topics initially discussed at these three 2010 meetings; the July meeting will cover the most
recent analyses on each. Specifically, the July SAP meeting will cover issues related to
non-cancer effects, particularly related to effects on Luteinizing Hormone (LH) to several
different lifestages and considering the temporal component of these health outcomes and
how such temporality impacts the confidence/uncertainty in water monitoring data. In
addition, the Agency will discuss cancer epidemiology studies and the integration of such
cancer epidemiology studies with animal toxicology studies and mode of action information
(MOA).
The February, 2010 SAP meeting focused on a number of broad scientific issues related to
the use of epidemiology and human incident data in risk assessment. One of the case
studies presented at the February meeting involved a collaborative project between the
Agency and investigators at the Agricultural Health Study (AHS)
(http://aghealth.nci.nih.gov). The goal of this collaborative effort is to compare exposure
assessment approaches used for pesticide applicator exposure assessment and is part of a
broad goal of OPP to better use epidemiology data in risk assessment. In this broad
context, the collaborative project is considered an on-going methods development effort;
the Agency will not solicit comments from the Panel on the status of this effort. However, a
short summary of the on-going analyses are provided simply as an update to the Panel and
public on that effort.
Two of the major goals of the atrazine human health re-evaluation are to consider the
degree to which the current drinking water monitoring program conducted by Syngenta
1
Henceforth mention of atrazine in this document also infers its chloro-s-triazine metabolites (DEA, DIA, and
DACT) unless otherwise specified.
Page 7 of 184
(and as required by EPA) is sufficient to protect human health and whether or not a new
human health risk assessment needs to be developed. As part of the re-evaluation of the
human health effects of atrazine, the Agency has evaluated the hazard potential of atrazine
and the drinking water monitoring strategy together. Specifically, the frequency of drinking
water monitoring is related to the temporal pattern of the toxicological endpoint of concern
used for the risk assessment. Generally, longer durations of toxicological concern (e.g., a
long-term chronic effect) require a less frequent drinking water sampling design to
approximate longer term exposures. However, as the duration of concern shortens, the
frequency and timing of sampling become more important in determining how well the
sample data capture short-duration exposures. The current drinking water monitoring
frequency involves weekly sampling during the growing season and bi-weekly (i.e., every
other week) sampling for the remainder of the year. This frequency is considered adequate
for the 90-day rolling average duration used in the previous risk assessment.
Relying on atrazine’s established neuroendocrine mode of action (MOA), this draft issue
paper will discuss experimental toxicology findings in the context of adverse health
outcomes and lifestages/populations that may be affected by atrazine exposure.
Attenuation of the LH surge continues to be the most sensitive effect (i.e., occurs at the
lowest dose) identified to date in the atrazine database. Perturbations of LH signal a
disruption of the hormonal environment in the individual and serve as a sentinel effect used
to establish a point of departure (PoD) for the risk assessment that would be health
protective for the other effects noted in the database. These other effects occur at higher
doses than the LH surge attenuation and include delays in puberty onset, disruption of
estrous cycles, and and reduced prolactin from milk early in life and prostatitis in young
adult rats; they provide insight into the temporal relationship between atrazine exposure
and adverse health outcomes. At the September 2010 SAP, the Agency proposed a range
of exposure durations relevant for the toxic effects of 4-28 days. A single value was not
proposed due to uncertainties in the duration required to elicit toxic effects, particularly in
humans. The SAP commented in the December 2010 report that “the imprecision in the
Agency’s proposed sampling frequency seems justified. This may be about as precise an
estimate as can be obtained when starting with the experimental animal data and the
exposure requirements for LH surge suppression…” At the July 2011 meeting, the Agency
will consider internal dosimetry of atrazine and its chlorinated metabolites in the context of
potential adverse health outcomes relevant to specific lifestages and subpopulations, will
update the benchmark dose analysis and internal dosimetry calculations based on new and
additional pharmacokinetic data available for atrazine, and review the current status of
efforts to develop a physiologically-based pharmacokinetic (PBPK) model.
The Food Quality Protection Act (FQPA) requires the Agency to give special attention to
the potential risk to infants and children. Specifically, FQPA instructs EPA, in making its
“reasonable certainty of no harm” finding, that in “the case of threshold effects, an
additional tenfold margin of safety for the pesticide chemical residue and other sources of
exposure shall be applied for infants and children to take into account potential pre- and
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post-natal toxicity and completeness of data with respect to exposure and toxicity to infants
and children.” At the September 2010 SAP, the Agency considered the experimental
toxicology and epidemiology studies considering the potential for increased susceptibility of
infants and children; this literature review will be updated as part of the July 2011 SAP.
Although the new science supports a duration of exposure shorter than 90-days like used in
the previous risk assessment, this shortening of the duration of exposure may not
necessarily translate to increased water monitoring. At the July 2011, the Agency has
evaluated the confidence/uncertainty in existing water monitoring data; this analysis has
been informed using advice from the Panel at the April and September 2010 meetings
along with preliminary conclusions about the completeness of the data on exposure and
toxicity to infants and children.
The potential cancer effects of atrazine have been considered by the SAP on multiple
occasions beginning in 1988 with an evaluation of rat mammary gland tumor response
(FIFRA SAP, 1988). Subsequent to this meeting, substantial research was conducted on
atrazine's hormonal or neuroendocrine mode of action (MOA) and the Agency returned to
the SAP in 2000 (FIFRA SAP, 2000a) for advice on atrazine’s MOA leading to mammary
gland tumors, reproductive and developmental effects in rats, as well as the human
relevance of these findings. The SAP agreed with the Agency’s proposal for atrazine’s
neuroendocrine mode of action, and they further concluded that it is unlikely that the
mechanism by which atrazine induces mammary tumors in female Sprague Dawley rats
could be operational in humans. At the April, 2010 SAP, the Agency reviewed new
experimental toxicology studies on cancer published since the last atrazine risk
assessment in 2003. The Agency concluded, and the SAP concurred, that the new
experimental toxicology studies on cancer did not alter or contradict the major key events in
the neuroendocrine MOA leading to mammary gland tumors in the rat or the conclusion
that MOA leading to mammary gland tumors in the rat is not relevant to humans (USEPA,
2010b; FIFRA SAP, 2010).
In 2003, the Agency presented its evaluation on prostate cancer and exposure to atrazine
to the FIFRA SAP. At that meeting, the SAP evaluated the available epidemiology studies
suggesting an association between atrazine and prostate cancer in workers at an atrazine
manufacturing plant in St. Gabriel Louisiana. The SAP concurred with EPA’s conclusion
that the data did not suggest an association between atrazine exposure and prostate
cancer, but that the increase in incidence of prostate cancer in workers at the plant could
likely be explained by the increase in Prostate Serum Antigen (PSA) screening at the plant
(FIFRA SAP, 2003a). At the 2003 meeting, the SAP recommended a more thorough and
systematic review of the epidemiologic literature on the topic of the potential or observed
carcinogenic effects of atrazine exposure. The Agency developed such a comprehensive
review of the cancer epidemiologic literature; this analysis will be reviewed as part of the
July 2011 SAP. In addition, the Agency has developed an integrative analysis of cancer
epidemiology studies and experimental toxicology studies to consider the cancer potential
of atrazine.
Page 9 of 184
As summarized above, this draft issue discussed a wide array of scientific issues related to
atrazine. The document is organized by the following:
•
•
•
•
•
•
Section 2 of this paper briefly describes the linkage between atrazine-induced
hormonal perturbations and adverse health outcomes in females of reproductive
age, prepubescent children, and newborns.
Section 3 discusses the Agency’s evaluation of the cancer epidemiology data (the
non-cancer epidemiology data was reviewed during the September 2010 SAP
meeting) and includes a brief summary of the Agency’s review of the pre-2003 data
as well as a review of the epidemiology studies published since the 2003 IRED.
Section 4 describes the Agency’s proposed updates to the dose response
assessment incorporating recommendations received during the September SAP
meeting.
Section 5 discusses the scientific considerations in potential sensitivity of infants and
children.
Section 6 discusses the Agency’s proposed approaches to evaluating water
sampling strategies and frequency of monitoring.
Section 7 presents a case study integrating all the concepts discussed in the
previous Sections and connects them to the water chemographs to get information
on lifestage/temporal/outcome specific risks using an internal dose metric. This
Section will also illustrate how all these factors intersect to inform the FQPA Safety
Factor determination.
2. MODE OF ACTION (MOA) & EXPERIMENTAL TOXICOLOGY
2.1 Historical Background
Over the past one and a half years, the Agency has conducted a comprehensive and
extensive evaluation of atrazine. This review encompassing experimental toxicity data,
pharmacokinetic data, epidemiological data, and water monitoring data was undertaken to
determine if new data collected since the 2003 human health risk assessment needed to be
updated. The 2003 human health risk assessment for atrazine is complex and
incorporated significant data development, regulatory evaluation, and FIFRA SAP review.
Prior to the 2010 re-evaluation, the Agency sought advice from the SAP on human health
issues during three previous meetings (FIFRA SAP, 1988, 2000, 2003). Atrazine was first
taken to the SAP for evaluation of rat mammary gland tumor response seen in adult cancer
studies in 1988 (FIFRA SAP, 1988). At that time, the SAP noted that a “hormonal
influence” might be an important consideration in the development of these mammary
gland tumors in adult rats. The Agency returned to the SAP in 2000 (FIFRA SAP, 2000) for
advice on atrazine’s MOA leading to mammary gland tumors, reproductive and
developmental effects in rats, as well as the human relevance of these findings.
Page 10 of 184
The SAP agreed with the Agency’s proposal for atrazine’s neuroendocrine MOA, and they
further concluded that it is unlikely that the mechanism by which atrazine induces
mammary tumors in adult female Sprague Dawley rats could be operational in humans.
Based on the 2000 SAP guidance, EPA changed its position on atrazine and reclassified it
from a “possible carcinogen” to “not likely to be carcinogenic in humans”. Nevertheless, the
SAP further concluded that it is not unreasonable to expect that atrazine might cause
adverse effects on hypothalamic-pituitary-gonadal (HPG) function in humans if exposures
were sufficiently high and that the MOA was relevant for developmental and reproductive
effects (FIFRA SAP, 2000). It is these developmental and reproductive effects of atrazine
which have been the focus of significant research in the last few years. The Agency
concluded, and the 2010 SAP concurred, that new research conducted in the last seven
years supports the neuroendocrine MOA identified in the 2003 IRED and the conclusions
regarding the human relevance of the mode of action for rat mammary gland tumors. As
described in this draft issue paper, the critical and most sensitive adverse effects continue
to be related to the reproductive/developmental consequences of perturbations to the HPG
axis.
In 2003, the Agency presented its evaluation on prostate cancer to the FIFRA SAP. At that
meeting, the SAP evaluated the available epidemiology studies suggesting an association
between atrazine and prostate cancer in workers at an atrazine manufacturing plant in St.
Gabriel, Louisiana. The SAP concurred with EPA’s conclusion that the data did not
suggest an association between atrazine exposure and prostate cancer, but that the
increase in incidence of prostate cancer in workers at the plant could likely be explained by
the increase in Prostate Specific Antigen (PSA) screening at the plant (FIFRA SAP, 2003).
The SAP also stated that a more thorough and systematic review of the biologic and
epidemiologic literature on the topic of the potential or observed carcinogenic effects of
atrazine exposure was required before concluding that atrazine exposure is an unlikely
explanation for at least part of the excess of prostate cancer in the St. Gabriel
manufacturing plant. Review of the cancer epidemiologic studies with atrazine is now
complete and the Agency is seeking the SAP’s comments on its evaluation and
conclusions as part of this SAP meeting.
2.2 2010 Scientific Re-Evaluation
In preparation for the 2010 SAP meetings, the Agency reviewed more than 100 new
experimental studies on a variety of topics including reproductive/developmental toxicity in
males and females, neurotoxicity, immunotoxicity, mammary gland development, mixtures
effects, andrology, aromatase and insulin resistance and mitochondrial function
impairment. Additional studies have been reviewed since the September 2010 SAP. The
Agency’s reviews on all these topics are provided in Appendix A. In addition summaries of
the new literature are provided on each of the scientific areas in Appendix A.
Page 11 of 184
In the 2003 IRED, the Agency identified perturbations of the neuroendocrine system
(particularly LH regulation) leading to reproductive toxicity as the most biologically plausible
and sensitive effects attributable to atrazine exposure. These adverse outcomes include
disruption of estrous cyclicity and delays in puberty onset (males and females) occurring as
a consequence of disruptions to the HPG axis in rats. An additional effect – not directly
linked to LH disruption – is the finding of decreased suckling-induced prolactin release in
milk early in life (perinatally) leads to increased incidence of prostatitis in young adult ratsis
an atrazine-induced increase in prostatitis in animals exposed perinatally to atrazine.
During the September 2010 SAP meeting, the Agency addressed the hypothesis that
atrazine exposure during critical developmental stages may result in delays in mammary
gland development (MGD). The SAP evaluated and agreed with the Agency’s review of
the publicly available data that the significant limitations of these studies preclude definitive
conclusions. The Panel, however, recommended that additional research be conducted
including a comparison of the subjective scoring methodology used to assess MGD with an
objective approach such as morphometric analysis. Agency scientists in the Office of
Research and Development (ORD) have since conducted these studies and the results of
this research are discussed in Appendix A.
2.3 Mode of Action (MOA) and Adverse Health Outcomes
2.3.1 Background
The Agency defines a MOA as “a sequence of key events and processes, starting with
interaction of an agent with a cell, proceeding through operational and anatomical changes,
and resulting in the adverse effect. The current evaluation of the post-2003 data supports
the neuroendocrine MOA and key events originally identified in the 2003 IRED. In addition,
new research has become available that extends our understanding of the neuroendocrine
events that occur following atrazine exposure and that are germane to our understanding of
the processes responsible for the adverse outcomes identified in different rodent models.
Thus, this issue paper will briefly discuss atrazine’s established neuroendocrine MOA and
then proceed to discuss how this MOA informs our understanding of the reproductive and
developmental effects observed after atrazine exposure.
For most pesticides, there is little information on the mode of action and even fewer with
epidemiology studies that can be used in the risk assessment process. As such, the
Agency makes assumptions about the relevance of animal findings to humans and
quantitative animal to human extrapolation. In the case of atrazine, the wealth of data
across many scientific disciplines allows for a highly refined assessment for atrazine using
MOA understanding, human relevance of animal studies informed qualitatively by
epidemiology studies, and refined analysis of critical durations of exposure. These
refinements will continue to improve as the PBPK model currently being developed is
refined.
Page 12 of 184
The 2003 IRED concluded that the most probable explanation for many of the cancer and
non-cancer effects observed in laboratory species following atrazine exposure could be a
disruption of LH release. In addition to the premature reproductive senescence and
development of mammary gland tumors in the Sprague Dawley rat, the MOA also provides
an explanation for the broader range of adverse outcomes attributed to atrazine exposure
including altered ovarian function in the adult female, delays in the onset of puberty in both
the male and female, impaired pregnancy maintenance (i.e., full litter resorptions) and
premature reproductive senescence. Components of this MOA also provide an explanation
for the decrease in serum testosterone observed at high experimental doses in studies of
the male rat. In the sections below, the MOA for atrazine for the development of mammary
tumors in the female rats is described followed by an evaluation of the role of LH level
disruption in eliciting other adverse outcomes.
In the course of the current evaluation, the Agency identified an effect of atrazine on
reproductive function in both male and female rats. These effects provide insight into
evaluating the vulnerability of specific lifestages such as sexual development, puberty, and
the perturbation of adult reproductive performance (including premature aging). These
effects can be linked to the atrazine-induced changes in LH secretion. Consequently, the
Agency will continue to use changes in LH secretion as the basis of the atrazine risk
assessment. As such, any of the identified adverse outcomes would be protected since
they occur at doses higher than those eliciting changes in LH (Figure 1).
Page 13 of 184
Figure 1: LH Suppression and Adverse Outcomes Observed in Rats
Atrazine-induced changes in the hormonal milieu lead to a cascade of effects on reproductive
function in male and female rats. The decrease in LH is a precursor event to reproductive effects
both on a quantitative (i.e., occurs at lower doses) and temporal basis (occurs after 4 days of
exposure). An atrazine related suppression of suckling-induced prolactin release in the lactating
dams, is another hormonal change leading to an adverse effect (prostatitis) in the rat animal model.
2.3.2 Reproductive Senescence and Mammary tumors in rats: Well
established MOA
The key events initially postulated leading to the development of mammary gland tumors
were described in the 2003 IRED. Briefly, this pathway begins with a perturbation of the
brain’s ability to regulate LH secretion, a disruption of ovarian function and eventually the
development of an endocrine environment that is favorable to the initiation of mammary
gland tumors. This MOA is described below:
Page 14 of 184
•
Atrazine alters the secretion of LH through gonadotropin releasing hormone
(GnRH) modulation: It is clear that atrazine impairs the secretion of LH from the
pituitary. In the female rat, a number of reports describe the dose and time
response for the suppression of the ovulatory surge of LH that typically occurs on
the afternoon of vaginal proestrus (Cooper et al., 2007). It remains to be determined
whether or not the changes in GnRH release are the result of a direct effect of
atrazine (or its metabolites) on the GnRH neurons themselves, an indirect action of
the chemical in other neurons that regulate GnRH activity, or both. Nevertheless,
changes in GnRH neuronal activity represent a change in the key signal emanating
from the central nervous system (CNS) and regulating gonadotropin (LH & FSH
[follicular stimulating hormone]) secretion. This effect on the LH surge in the female
rat is considered to be the primary reason that dietary atrazine leads to a premature
reproductive senescence in this species.
•
A loss of the LH surge is responsible for age-dependent disruption of ovarian
cycles in the rat: The disruption of the positive feedback processes regulating the
LH surge in the female rat is considered to be the primary cause of reproductive
senescence in this species. In the young adult female, the LH surge is a key event
in the ovarian cycle leading to ovulation. In the aging female (beginning
approximately 1 year of age), the regular 4-5 day estrous cycles typical of the youngadult female gives way to a pattern of persistent or constant estrus. The transition
from regular cycles to persistent estrus is the result of a loss of the ability of the
female to sustain appropriate LH secretion. The preponderance of evidence
indicates that the age-related impairment of LH secretion reflects the loss of the
positive feedback mechanisms controlling the generation of the surge. This includes
altered neurotransmitter and neuropeptide control of gonadotropin releasing
hormone (GnRH) the neuropeptide that represents the final common signal from the
brain regulating LH release. Thus, aging in the female rat is the result of an agerelated change in the hypothalamic (GnRH/LH) control of the reproductive cycle.
•
Atrazine accelerates the rate of reproductive aging in rats: Similar to aging, the
atrazine-induced changes in GnRH pulses lead to an attenuation of the proestrous,
ovulatory LH surge (lower amplitude and reduced area under the curve). Therefore,
it is not surprising that chronic exposure to dietary atrazine in young-adult females
results in premature reproductive senescence. Similar to normal aging, these
prematurely senescent females do not ovulate but develop persistent ovarian
follicles which continue to secrete estradiol (i.e., polyfollicular ovaries). Since there
is no ovulation, there are no corpora lutea in the polyfollicular ovary and thus
progesterone secretion from the ovary is nil. At the same time there is increased
prolactin secretion from the pituitary. It is this increase serum estradiol and prolactin
and low serum progesterone concentrations that creates the endocrine milieu
conducive to the development of mammary gland tumors.
Page 15 of 184
•
Reproductive aging (menopause) in humans initiated differently than in rats.
Unlike rats, reproductive senescence in humans (menopause) is caused by the
depletion of follicles and a concomitant decrease in estrogen instead of changes in
the LH surge (which remains normal during menopause). As described above,
reproductive senescence in rodents is driven by changes in the regulation of LH at
the level of the brain. Thus, the altered hormonal environment observed in the postreproductive female rat is not present in the human. In the absence of elevated
estrogen levels in humans, prolactin levels remain constant or even decrease
slightly. Consequently, there is no sustained prolactin stimulation of the proliferative
processes in the mammary gland and thus no tumor development. Although there
are similarities in hypothalamic/pituitary function, there is a fundamental difference
between rats and humans, and thus, the key events in the MOA leading to
mammary gland tumors in rats are not relevant for breast tumorigenesis in humans.
2.3.3 Adverse Health Outcomes – LH Changes as a Sentinel Effect
Although tumor development following chronic atrazine exposure is an adverse outcome
mediated by a disruption of the neuroendocrine axis controlling reproduction in the female
rat, this MOA for mammary tumor development is considered highly unlikely to occur in
humans due to the different pattern of aging between the two species. However, it is
plausible that the same initial atrazine-induced endocrine perturbations that induce tumors
in rats (i.e., altered GnRH) could occur in humans. In fact, the Agency considered the
atrazine-induced disruption of the LH surge, in rats, as the key event of the cascade of
changes leading to the adverse reproductive outcomes following atrazine exposure.
Relevant to this MOA, a number of recent studies have further characterized the cellular
and neuroendocrine changes responsible for how atrazine interferes with the regulation of
LH secretion. The preponderance of evidence provides added support for the hypothesis
that atrazine modifies the hypothalamic (GnRH) control of pituitary function. The ovulatory
surge of LH depends on the activity of GnRH neurons (Kalra and Kalra, 1983) with the
afternoon rise in LH the result of modifications in GnRH pulsatile secretion into the
hypothalamic portal system (Fox and Smith, 1985; Bergendahl et al., 1996; Veldhuis et al.,
2008). Research from two laboratories has shown that atrazine interferes with the pattern
and amplitude of GnRH release by slowing the frequency of the pulses and lowering the
overall amount of GnRH released into the portal system (Cooper et al ., 2007; Foradori et al
., 2009). It is important to note that the modulation of GnRH/LH during the peripubertal
period is not limited to rodents but is seen across several species including primates, in
which Terasawa and coworkers have demonstrated that all aspects of GnRH pulsatility
(pulse amplitude, frequency, and mean GnRH release) are markedly increased during the
peripubertal period (Terasawa et al., 1984).
Testing the hypothesis that atrazine-induced changes in the regulation of LH ultimately alter
gonadal function in rodents, several studies reported adverse effects on reproductive
Page 16 of 184
development and adult function including delayed puberty in both sexes (Stoker et al.,
2000; Laws et al ., 2000), disruption of regular ovarian cycles in the adult female (Cooper et
al., 1996, 2000), inability to maintain pregnancy (Narotsky et al., 2001) and reduced
testicular hormone secretion in the male (Stoker et al., 2000; Trentacoste et al., 2001;
Rosenberg et al., 2007) after atrazine exposure. Of these potential adverse outcomes,
the two that appear to be the most sensitive (i.e. occurring at the lowest dose levels) and/or
occurring after the shorter duration of exposure are the disruption of the ovarian cycles and
the delays in puberty onset.
2.3.3.1 Factors influencing GnRH Modulation of LH following atrazine
exposure
As indicated, the accepted mode of action (MOA) for the atrazine-induced impairment of LH
secretion is a disruption in the central (CNS) mechanisms controlling LH release [(i.e.,
impaired gonadotropin releasing hormone (GnRH) release] (Cooper et al., 2007; Foradori
et al., 2009). This MOA was reviewed and endorsed by the 2003 atrazine Scientific
Advisory Panel (SAP). Since then, additional research from several laboratories (McMullen
et al., 2004; Foradori et al., 2009) has provided further evidence supporting this MOA. Still,
little is known about the exact molecular or neuronal targets responsible for an atrazineinduced change in GnRH/LH release. It is assumed that the changes in GnRH occur as a
result of either (i) a direct effect of the atrazine and/or its metabolites on the GnRH neurons
themselves, (ii) an effect on the neurons known to regulate GnRH (and thus affect GnRH
cellular activity indirectly) or (iii) an effect on both the GnRH neurons and those neurons
regulating GnRH activity. These three alternatives would imply that the CNS is the primary
target for atrazine. Preliminary data suggest that atrazine exposure may lead to: (i) a
decrease in c-fos in GnRH neurons (Foradori et al, 2009) or (ii) a disruption of
catecholaminergic activity after in vitro exposure of undifferentiated PC12 cells [dopamine
and norepinephrine have both been implicated in the generation of the LH surge] (Das et
al., 2000). However, detailed studies examining any of the putative CNS mechanism
controlling GnRH pulsatility are not available.
2.3.3.1.2
GnRH in primates
The integral role of GnRH secretion in the function of the HPG axis is widely accepted in
humans and non-human primates. Hypothalamic amenorrhea (abnormal absence of
menstruation) is a prime example that impairment of pulsatile GnRH secretion can have an
adverse functional impact on menstrual cycles. With our increasing understanding of the
brain’s role in reproduction, the neuroendocrine similarities and disparities among
mammalian species have become more apparent. Although there are anatomical
differences in the distribution of the GnRH neurons, however the functional role of these
neurons are similar in rodents and primates. In terms of intercellular connectivity, it
appears that the regulatory input to human and nonhuman primate GnRH neurons is at
Page 17 of 184
least comparable to those in rodents. With a few noted exceptions, the response of GnRH
neuronal activity to the positive and negative feedback to endogenous and/or exogenous
estrogen and progesterone is the same. It is noteworthy, that impairments in the activity of
one or more of the factors regulating GnRH release (e.g., kisspeptin, galanin, and
corticotrophin releasing hormone [CRH]) have been implicated with functional hypothalamic
amenorrhea in women (Meczekalski et al., 2008). These observations support the use of
the rat as a surrogate species for the evaluation of environmental toxicants on the
regulation of the LH surge.
2.3.3.2 Females of Reproductive Age: Role of LH, Progesterone and
Corticosterone in Atrazine Induced Changes in Ovarian
Cyclicity
In the previous sections, the evidence indicating that atrazine has a clear effect on the
hypothalamic control of LH through regulation of the GnRH pulse generator was discussed.
In this section these observations are discussed within the context of atrazine-induced
changes in progesterone and corticosterone that adversely impact the steroid hormone
feedback regulation of LH secretion. The significance of these atrazine-mediated changes
stems from the observation that they occur both in vitro and in vivo through possibly
different events in the toxicity pathway. In vitro work by Sanderson et al. (2000) and
Hecker et al. using H295R cells demonstrated an atrazine-mediated increase in
estradiol/estrone leading researchers to hypothesize that atrazine exerted its effect on
steroidogenesis through alterations in aromatase activity. However, atrazine has also been
shown to increase both progesterone (Tinfo et al., 2010) and testosterone (Pogrmic-Majkic
et al., 2010) production in rat granulosa cells and primary rat Leydig cell cultures,
respectively. In vivo, an immediate increase in the levels of circulating adrenocorticotropic
hormone (ACTH) occurs in response to atrazine exposure. This in turn stimulates the
production and release of progesterone and corticosterone from the adrenal cortex.
Considered together, these data not only suggest that the effects of atrazine may involve
several levels of biological organization beyond the HPG axis but that brief atrazine
exposures (as brief as 4 days) elicit changes in the adrenal hormonal milieu that may have
the potential to modify LH release. Whether these brief changes may result in adverse
health outcomes, however, is not clear at this time. Nonetheless, it is important to note that
the change in LH surge that serves as the basis for the atrazine risk assessment is
considered a precursor event to the reproductive and developmental effects noted after
atrazine exposure.
A study recently submitted to the Agency was designed to evaluate the impact on fertility or
reproductive performance following a brief (4-5 days) atrazine exposure at doses up to 100
mg/kg/day (Coder 2011). In the study, Sprague Dawley rats were exposed either by
gavage (≤ 100 mg/kg/day) or diet (≤51 mg/kg/day). The animals were then mated on the
day of proestrous. No effects on reproductive performance (mating, fertility, or conception
Page 18 of 184
indices) were observed at any dose level regardless of strain or method of administration
(dietary or gavage). Moreover, no effects on pre-implantation loss, implantation sites, fetal
development and/or offspring survival were reported. Based on these observations, the
author concluded that “the magnitude of the atrazine-induced preovulatory surge
suppression shown in previous studies was not sufficient to influence ovulation or
reproductive performance of SD or LE female rats as evaluated in this study.” However,
the interpretation of these data is confounded. LH measurements were not conducted as
part of this study; although it is reasonable to assume the LH surge would be attenuated at
the dose levels administered. The absence of these data, however, does introduce a
confounding factor to the study’s interpretation since it is known that mating rats on the day
of proestrous causes an LH surge due to coital stimulation causing the rats to become
reflex ovulators and resulting in normal pregnancies. Furthermore, it has been previously
shown with other chemicals affecting the LH surge (e.g. pentobarbital) that steroid hormone
regulation of mating behavior in rats is not affected by this treatment.
In a study intended to replicate the work by Cooper et al. (2007) and to assess the effects
of brief atrazine exposure (4-14 days) on the LH surge, ovariectomized Sprague Dawley
rats received atrazine via gavage at 0, 6.5, 50, and 100 mg/kg/day for 4, 8, or 14 days
(Coder, 2011). A statistically significant decrease in LH was observed after 4 days of
atrazine exposure at doses ≥ 50 mg/kg/day. Though a statistically significant decrease in
LH was also noted at the 6.5 mg/kg/day dose level, the authors discounted it because there
was no effect in the AUC for this measure. However, when a BMD analysis is conducted
on these data, the BMDL is 3.4 mg/kg/day. Thus, the Agency considered the decrease in
LH at 6.5 mg/kg/day to be treatment-related. A longer atrazine exposure (8 or 14 days)
also resulted in a statistically significant decrease in LH. Furthermore, after 14 days of
exposure the number of animals exhibiting a lengthened estrous cycle (6-7 days vs 4 days
control) increased (16-20%) at doses ≥ 50 mg/kg/day.
2.3.3.2.1 Estrogen and progesterone regulation of LH secretion
Estradiol and progesterone have long been known to play coordinate roles in the
generation of the LH surge (Mann & Barraclough, 1973). In a female rat exhibiting a
normal 4 day estrous cycle, estradiol begins to rise on diestrus day 2, reaching a midday
apex on the following day of proestrus. This increase serves to increase the
responsiveness of the hypothalamic mechanisms that promote the secretion of GnRH into
the portal vessels and the subsequent surge of LH on the afternoon of proestrus. One
outcome of such an increase in estrogen is the increase in progesterone receptors within
the positive feedback sites of the hypothalamus. Serum progesterone concentrations
increase beginning early on the afternoon of vaginal proestrous within this sequence, a rise
in estrogen followed by a rise in progesterone which act together to generate a surge
(Brown-Grant and Naftolin, 1972). Importantly, it should be noted that in addition to ovarian
derived progesterone, the adrenal cortex contributes a significant portion of the serum
progesterone at this time (Feder and Ruf, 1968; Shaikh, 1974). As atrazine was found to
Page 19 of 184
stimulate adrenal progesterone and corticosterone release, the possibility that these brief
changes in the adrenal steroid milieu could be a part of the atrazine MOA for LH
suppression was examined in a rat study conducted by EPA’s Office of Research and
Development (ORD) . The background and rationale for evaluating the adrenal effects are
presented below followed by a summary of the study.
Estradiol alone can initiate a surge, as demonstrated in ovariectomized controls implanted
with silastic capsules containing this steroid (see blue Figure 2). The time course for this
induction is several days. Over this period, estradiol stimulates an increase in
hypothalamic and pituitary progesterone receptors (Camp and Barraclough, 1985; Brown et
al., 1987; Turgeon and Waring, 2000), which is essential for any subsequent effect of
progesterone on the LH surge. In contrast to the effect of estradiol, progesterone alone is
unable to induce a surge (e.g., Caligaris et al., 1971). However, when combined with
estradiol, progesterone can either enhance or suppress LH secretion depending on the
timing and sequence of exposure to these two hormones. The time course for an
influence of progesterone under estrogen priming is different. In ovariectomized female
rats, primed with estradiol for 72 h, progesterone exposure around 10 AM in the morning of
the surge, will act synergistically with the estrogen to cause a large augmentation in LH
(see red line Figure 2). This synergistic effect of progesterone is dependent on the
estrogen-induced increase in progesterone receptors mentioned previously. This effect is
well-known in rodents (e.g., Caligaris et al., 1968; Brown-Grant and Naftolin, 1972; Banks
and Freeman, 1978; 1980) and non-human primates (e.g., Terasawa et al., 1984) and has
been identified in women. In fact, Odell and Swerdloff (1968) found that a progestin
injection in post-menopausal women previously treated with ethinyl estradiol could induce a
surge-like elevation in circulating LH in samples taken 24 hours later.
In contrast to the augmentation in the LH surge by a progesterone exposure that
immediately precedes an estradiol-induced rise in LH, longer exposure is essentially
inhibitory to the surge (e.g., Caligaris et al., 1971; Banks & Freeman, 1978; 1980; Prilusky
et al., 1984). Such extended treatment will cause a down-regulation by progesterone of its
own receptors which appears to be a factor underlying the suppressive effect on LH
(Turgeon and Waring, 2000). This influence of progesterone is the basis for the action of
many oral contraceptives. Furthermore, increased levels of progesterone during a
woman’s pregnancy, secreted first from the ovaries and subsequently from the placenta,
prevent the appearance of a surge and ovulation over that time. Finally, it is noteworthy
that treatment with progestins also lowers serum gonadotropins and testosterone
production in men (e.g., Amory, 2004).
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OVX- Estradiol Implant / sc Progesterone
40
30
E + P
20
10
E only
0
-6
-4
-2 Peak
+2
+4
+6
Hours from Peak
Figure 2: Augmentation by a subcutaneous injection of progesterone on an LH surge in
ovariectomized rats implanted with an estradiol capsule at the time of surgery. Progesterone was
administered just prior to the sampling of small aliquots of blood by tail nick over the afternoon of the
third day after ovariectomy. Concentrations are indicated in ng/ml ± SEM
2.3.3.2.2 Differential effects of single versus multiple atrazine
exposures on the LH surge
During the September 2010 SAP, the Panel raised the issue of single vs multiple atrazine
exposure effects on the LH surge. In their report to the Agency the SAP commented:
“Data are clear in identifying that a greater-than-one pulse of exposure to atrazine is
necessary for attenuation of the LH surge. For example, single high doses (over 100
mg/kg) administered on the morning of proestrus did not alter characteristics of the
LH surge occurring later the same day. Additional data clearly demonstrate a once
daily dose for 4 days and beginning on estrus can induce significant inhibition of the
LH surge peak. In this instance, a dose response is observed. However, what is not
clear is if less than 4, but greater than 1 days’ exposure is sufficient to alter the LH
surge. Further complicating the matter, it is not clear if a 4-day exposure, beginning
on a different day of the cycle, will result in changes in the LH surge similar to those
when dosing begins on the morning of proestrus. Understanding of the relationship
between duration of exposure and phase of the cycle will be key in translating rodent
data to humans for risk assessment purposes.”
In response to the Panel’s comments, EPA scientists in the Office of Research and
Development have undertaken a series of experiments to try to elucidate the nexus
between phase of the cycle and duration of exposure. This research, is in the early stages.
However, initial results suggest that, unlike previous assumptions, a single high dose of
atrazine (100 mg/kg bw) can affect the LH surge. Notably, the single atrazine exposure
leads to an increase in LH instead of the decrease that is seen after 4 days of exposure.
Page 21 of 184
As noted above the biphasic effect of progesterone and the suppressive effect of
corticosterone on the LH surge suggested that these two hormones may also play a role in
the suppression of the LH surge following a 4-day exposure to atrazine. That is, the
demonstrated atrazine-induced increase in these two adrenal hormones could modulate
the secretion of LH by feeding back onto both the positive and negative control of the
GnRH pulse generator. Indeed, if this were the case, it would be expected that a single
dose of atrazine administered to the ovariectomized, estrogen-primed rat would first
increase the LH surge because of the immediate increase in adrenal progesterone induced
by the atrazine treatment. In contrast, repeated administration of atrazine (i.e., 4 days) to
the same ovariectomized, estrogen primed female would result in a decrease in the surge
because of the repeated increases in adrenal progesterone and corticosterone levels
immediately following each exposure to this herbicide. Figure 3 demonstrates that this is
what happens in the ovariectomized model. In response to a single exposure to atrazine
the amplitude of the LH surge and the area under the curve were both significantly
increased. It should be noted that these preliminary data are only available at high doses
(100 mg/kg/day) far exceeding the dose levels currently being used for risk assessment
purposes. Research to further elucidate the dose-response curve for this acute effect is
ongoing.
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1 Day Dosing Atrz (1300h)
Ovariectomized / E2 prime - Atrazine 1 Day
sampling 1400-1600-1800-2000h
40
10
*
mg/kg
*
(11)
8
Serum LH (ng/ml)
OVX / E2-implant
0
Serum LH (ng/ml)
Area Under the Curve
9
100
7
(10)
6
*
5
4
3
30
181%
20
10
2
1
0
0
-6
-4
-2
Peak
+2
0
+4
100
A t r azine (m g/kg)
Time Relative to Peak
Ovariectomized / E2 prime - Atrazine 4 Days
40
10
OVX / E2-implant
mg/kg
9
35
(10)
100
7
(10)
6
p= 0.06
5
4
*
3
Serum LH (ng/ml)
Area Under the Curve
0
8
Serum LH (ng/ml)
4 Days Dosing Atrz (1300h)
sampling 1400-1600-1800-2000h
30
25
20
15
*
10
2
5
1
0
0
-4
-2
Peak
+2
+4
54%
0
100
A t r azine (m g/kg)
Time Relative to Peak
Figure 3: The LH surge (ng/ml + SEM) and areas under the curve in response to 1 and 4 days of
atrazine treatment in ovariectomized / estradiol-primed Long-Evans rats. The numbers in
parentheses indicate the group sizes for each of the treatments. Numbers within the 100 mg/kg
columns at the right indicate the percent change from the 0 mg/kg group. *p<0.05.
Also, as predicted from our understanding of the continued exposure to progesterone and
corticosterone, four daily doses of atrazine resulted in a significant decrease in LH
Page 23 of 184
secretion. Again, this decrease was associated with an atrazine-induced increase in both
progesterone and corticosterone. The details of this study are provided Goldman et al.,
2011 (Appendix A).
In summary, these data demonstrate two important factors pertaining to the temporal
response to atrazine exposure. First, a single high dose of atrazine (100 mg/kg bw) affects
the HPG axis by increasing the amount of LH secreted during the afternoon LH surge. The
fact that this same single dose of atrazine increases adrenal hormone secretion (primarily
progesterone) would explain the enhancement of the surge. Second, multiple (4 daily)
doses of atrazine resulted in an attenuation of the LH surge. Again, as atrazine was found
to induce an immediate and significant increase in progesterone and corticosterone
secretion following each dose (Fraites et al., 2009; Goldman, 2011), it is important to
consider the implications of a brief LH surge attenuation.
2.3.3.2.3 LH Attenuation in Humans – Pharmaceutical Experience
Little is known about the direct effects of atrazine in humans and available epidemiology
studies are not sufficiently robust for making strong conclusions. However, one can infer
information about possible windows of susceptibility from what is known about human
physiology and from the pharmaceutical literature. In considering the relevance and
temporal concordance of a toxic effect in a test species to human health, a variety of
factors need to be considered. Among them is the potential window of susceptibility to the
toxic effect. For atrazine, the experimental data available indicate that, in rats, a 4-day
exposure is sufficient to attenuate the LH surge – the most sensitive endpoint in the
atrazine risk assessment. Since the estrous cycle in rats is 4 days long, this window of
susceptibility is not unexpected. However, in humans, the menstrual cycle lasts – on
average – 28 days. Thus, the question arises as to whether a brief exposure (e.g., a few
days) in humans could lead to an attenuation of the LH surge.
Evidence of chemically-induced decreases in GnRH or LH secretion is sparse in humans
and non-human primates relative to rodents. The available evidence in humans comes
primarily from the pharmaceutical arena. Nal-Glu, Cetrorelix®, and Ganirelix are three
GnRH antagonists used to block the LH surge and ovulation in women prior to in vitro
fertilization (IVF) procedures. In a series of experiments, regularly ovulating women
received two 5 mg injections of Nal-Glu on days 8 and 11 of the follicular phase of the
natural cycle (Frydman et al. 1992). This treatment resulted in a block of the spontaneous
LH surge. This work was further corroborated by Olivennes et al. who demonstrated that a
single 3 mg administration of the GnRH antagonist Cetrorelix® on day 8 of the follicular
phase was sufficient to block the LH surge. Ganirelix exposure during the late follicular
phase of the menstrual cycle has also been demonstrated to inhibit the LH surge and
ovulation by competing with the endogenous GnRH for receptor binding (Fauser et al.,
2002). One must consider these studies with caution with respect to how they relate to
potential effects of atrazine since the potency and pharmacokinetics of these
Page 24 of 184
pharmaceuticals relative to atrazine is unknown. However, these studies do help
qualitatively inform a potential window of vulnerability to chemicals disrupting the HPG axis
in women. Specifically, all of these pharmaceutical agents are administered during the late
follicular phase of the menstrual cycle (days 8-12 of the follicular phase) 2. Thus, one can
infer that the follicular phase (lasting ≈12 days) and possibly the late follicular phase (days
8-12 of the follicular phase) of the menstrual cycle may be a possible window of
susceptibility in humans.
Given the differences between the rat and human ovarian cycles, as well as the differences
in potency and likely MOA of pharmaceutical agents, these data are only considered in the
context of helping to identify if there is a time period during the menstrual cycle that is
particularly vulnerable to chemicals interfering with the LH surge. It is not intended to draw
any correlations between the mode of action, dose levels, or frequency of dosing needed to
elicit the LH suppression after exposure to a pharmaceutical agent or atrazine. Although
theoretically possible, a 4 day exposure to atrazine leading to LH surge attenuation in
humans and an adverse health outcome is unlikely, given the available dose response,
pharmacokinetics, and exposure data. As discussed in Section 4 of this paper, the area
under the plasma concentration-time curve (AUC) for atrazine and its chlorinated
metabolites is the critical dose metric. Based on the available drinking water monitoring
data, it is highly unlikely that the plasma AUC associated with the LH surge attenuation
would be achieved in such a short period of time (i.e., a high concentration of atrazine
and/or its metabolites would have to be present in the drinking water). Although a 4 day
atrazine exposure resulting in LH suppression is a conservative assumption, the Agency is
proposing to bound the critical window of exposure from 4 to 28 days as potentially relevant
to potential human health outcomes. As the September SAP noted in their report, “this
may be about as precise an estimate as can be obtained when starting with the
experimental animal data and the exposure requirements for LH surge suppression as
opposed to using outcomes that are more unequivocally adverse.”
2.3.3.3 Prepubertal Individuals: Atrazine-induced delays in sexual
maturation
In addition to the disruption in ovarian cyclicity, atrazine exposure has also been implicated
in delays in sexual maturation in both males and females following both perinatal and
peripubertal exposure. Pubertal development is directly related to the progressive
increases in the neurosecretory activity of GnRH neurons. In female rats, sheep, monkeys,
and humans (Grumbach, 2002), detailed analyses of peripubertal LH secretory patterns
have been conducted to provide surrogate measures of GnRH release throughout pubertal
maturation. These studies have revealed that the initial stages of pubertal maturation are
mediated by an acceleration of GnRH pulse generator activity (GnRH pulse frequency), an
increase in the amplitude of GnRH pulses, or both of these alterations in GnRH
2
In humans, the follicular phase lasts approximately 12 days, assuming a 28-day menstrual cycle
Page 25 of 184
neurosecretion. Terasawa and colleagues have directly measured GnRH release patterns
in female rhesus macaques and determined that all features of GnRH pulsatility (pulse
amplitude, pulse frequency, and mean GnRH release) are significantly increased
throughout pubertal maturation in this species. The work of Sisk et al. (2001) in the rat are
consistent with the hypothesis that maturation of the female rodent’s reproductive axis is
dependent upon a pubertal increase in GnRH pulse generator activity and a progressive
increase in the ability of the hypothalamus to generate surge-like releases of GnRH. The
cellular and molecular mechanisms mediating the pubertal acceleration of GnRH pulsatility
and the stimulation of putative GnRH mini-surges remain to be resolved.
2.3.3.3.1 Delays in Female Sexual Maturation
A number of studies have been conducted to evaluate the impact of atrazine and/or its
metabolites on pubertal development and estrous cyclicity in female rats (Laws et al., 2000,
2003; Ashby et al., 2002; Davis et al., 2011; Rayner et al., 2004). Collectively, these
studies have shown that atrazine delays the onset of puberty, as measured by a delay in
the age of vaginal opening (VO) and first estrus (Safranski et al., 1993). These findings are
consistent with the postulated neuroendocrine MOA for atrazine. Female sexual
maturation is the culmination of a complex cascade of cellular events at the hypothalamuspituitary-gonadal (HPG) levels that ultimately lead to the attainment of reproductive
capacity. Activation of the HPG axis, resulting in the pulsatile secretion of GnRH that
triggers a precisely regulated hormonal cascade of gonadotropins (LH and FSH) and
ovarian steroids, is critical to puberty onset. Disruption of GnRH and LH release can lead
to delays in pubertal development.
Peripubertal exposure to atrazine and/or DACT for 19-23 days delays pubertal
development in female rats at doses ≥ 34 mg/kg/day (Laws et al., 2000, Ashby et al. 2002,
Laws et al. 2003). In some instances animals failed to attain VO by the time of their
scheduled necropsy on PND 41. Interestingly, these delays in VO were not accompanied
by lower body weights. Thus, the delays do not appear to be related to generalized growth
delays. Gestational exposure to high doses of atrazine (100 mg/kg/day) during late
gestation (GD 14-21) have been shown to delay sexual maturation of female offspring,
however, exposures to lower doses (≤ 20 mg/kg/day) does not affect the age of pubertal
onset. A recent study by Davis et al. (2011) evaluated the effects of prenatal exposure to
atrazine on pubertal and postnatal reproductive indices in the female (Sprague Dawley) rat.
Exposures from GD 14-21 at doses ranging from 1-20 mg/kg/day did not elicit a delay in
VO or the timing of the first estrus. However, at 100 mg/kg/day atrazine exposure led to a
significant decrease in pup weight (seen at birth but resolved by PND21) and most
importantly a delay in VO. These results are consistent with the observations by Rayner
and coworkers (2004) that atrazine exposure via the milk of dams exposed to 100
mg/kg/day during GD15-19 led to a delay in VO without affecting estrous cyclicity once
sexual maturation was reached.
Page 26 of 184
While delays in female puberty onset – as determined by the time of VO – occur at doses ≈
20 times higher than the doses resulting in disruption of the LH surge, it is important to note
that the duration of exposure sufficient to cause delays in VO ranges between 5 (prenatal
exposure) and 23 days (peripubertal exposure). Thus, using the point of departure for the
LH surge attenuation as the basis for the risk assessment is protective of this effect. These
data also suggest that the critical duration of exposure that could lead to this effect –
although at high doses – can be as brief as 5 days.
2.3.3.3.2 Delays in Male Sexual Maturation
Over the last decade a number of studies demonstrated that atrazine delays male puberty
in different strains of rats following both peripubertal and perinatal exposure (Stoker et al.,
2000; Friedmann, 2002 ;Trentacoste et al., 2001; Rayner et al., 2006 and Rosenberg et
al., 2008; Pogrimic et al., 2009). These studies support the hypothesis that impaired
reproductive development is the result of an apparent delay in the maturation of the GnRH
pulse generating mechanism and lower LH concentrations leading to insufficient stimulation
of the gonads during the period that puberty would normally occur. The low testosterone
concentrations result in delayed maturation of the androgen dependent sex accessory
tissues.
A reduction in testosterone levels following atrazine exposure has been reported in a
number of studies in mammals, as well as other species revealing a consistency in the
effects of atrazine on androgens. It is well known that the development of the size of the
penis and cornification of the epithelium of the prepuce and preputial separation in
immature rats are regulated by androgens (Marshall, 1966). A decrease in testosterone
secretion during the juvenile period can delay preputial separation (Lyons et al., 1942) and
reduce the size of the androgen-dependent tissues, such as the ventral prostate and
seminal vesicles. Normally, testosterone levels rise gradually from PND 20-40, and
abruptly double by PND 50 (Matsumoto et al., 1986; Monosson et al., 1999).
In the male rat, atrazine exposure resulted in delays in the onset of puberty, as determined
by assessment of preputial separation (PPS) . Stoker et al. (2001) found that peri-pubertal
exposure to atrazine delayed puberty for 1.5 to 2.5 days, and decreased the size of seminal
vesicles and prostate. At PND 45, intratesticular testosterone was also decreased. In the
studies examining the Sprague Dawley (SD) rat (Trentacoste et al., 2001), decreases in
ventral prostate and seminal vesicle weights were observed along with 3-4 day delays in
puberty onset. In addition, Friedmann (2002) found a decrease in intratesticular and serum
testosterone following exposure to the peripubertal male SD rat. Thus, all three studies
found decreases in either serum or intratesticular testosterone and Trentacoste et al. also
found a decrease in LH. It should be noted that all these effects were observed at doses
that were at least one order of magnitude higher than the NOAEL for LH surge attenuation
currently used for risk assessment purposes.
Page 27 of 184
Ventral prostate, seminal vesicles, lateral prostates and epididymal weights are significantly
reduced at doses just above the doses which delay preputial separation (Stoker et al.,
2001). These effects provide further evidence of a decrease in androgen stimulation.
Decreases in androgen hormone concentrations also correspond with the effects observed
on pubertal endpoints. For example, Stoker et al., 2001 demonstrated that intra-testicular
testosterone was significantly decreased following atrazine exposure peripubertally. This
has corresponded to the work of others showing that serum testosterone is decreased in
SD rats when dosed during a similar period of PND 22 to 47 (Trentacoste et al., 2001;
Friedmann, 2002).
The study by Stoker et al., 2002 also examined the effect of the atrazine metabolites,
DACT, DIA and DEA on pubertal development in the rat. In this study, equimolar doses of
the metabolite were compared to the effect seen with atrazine in the earlier study (Stoker et
al., 2000). PPS was significantly delayed by DEA, DIA, and DACT. When the males were
sacrificed on PND 53 treatments with DEA, DIA or DACT at atrazine equivalent dose (AED)
≥50 mg/kg/day, caused a significant reduction in prostate weight. Seminal vesicle
epididymal weights and serum testosterone levels were also reduced This study
confirmed that the chlor-s-triazine metabolites of atrazine also result in similar effects on
pubertal development.
It should be mentioned that other factors, in addition to those changes within the HPG, can
affect the timing of puberty in the male rat including nutritional status (leptin/body weight)
and environmental (pineal gland/secretion of indole amines) influences (Ebling and Foster,
1989). 3 Nutritional status and body weight are known to have effects on puberty, which are
suggested to reflect the metabolic signals in the brain that serve as indices of the metabolic
state. These metabolic alterations associated with weight loss or decrease in growth rate
are inhibitory to the reproductive system and may be related to substances in the body that
can alter the release of GnRH such as insulin, amino acids necessary for precursors of
neurotransmitter synthesis and essential fatty acids (Ojeda and Urbanski, 1994).
The peripubertal exposure male study (Stoker et al., 2000) using Wistar rats showed
decreased body weight at the 200 mg/kg dose, but a pair-fed group was included to discern
the effects of changes in growth. There were changes in puberty at doses in which no
body weight decreases were observed. However, in a study with Sprague Dawley rats
Trentacoste et al., found that the food restriction group (receiving approximately 10% less
food which was equivalent to the 100 mg/kg atrazine treatment food intake) showed the
same effects on LH, testosterone and reproductive tract development. A recent paper
examined the effects of food restriction on the endpoints in the pubertal male exposure
3
The direction of change in daylength provides the seasonal time cue for the timing of puberty in many
mammalian species. The pattern of melatonin secretion from the pineal gland transduces the environmental
light/dark cycle into a signal influencing the neuroendocrine control of sexual maturation.
Page 28 of 184
period and found that reductions in body weight up to 10% did not influence pubertal onset
or the growth of androgen dependent tissues in the rat (Laws et al., 2007).
Perinatally a single 100 mg/kg atrazine dose to the dam resulted in an effect on pup growth
that may involve changes in milk quality, as the offspring showed a decreased weight of 7%
on PND 4 (Rayner et al., 2006). Work by Rosenberg and co-workers (2008) showed that
pre-natal (GD14-parturition) exposure to atrazine at doses ≥10 mg/kg/day led to a
significant delay in preputial separation (1-3 days) while anogenital distance on PND21
albeit at significantly higher doses. Measuring testosterone levels in both the serum and
intratesticular fluid of PND 60 rats revealed that testosterone levels were decreased at
doses ≥ 50 mg/kg/day. It should be noted that all of the effects on PPS and AGD occurred
in conjunction with nonsignifcant decreases in pup body weights (Rosenberg et al.,2008).
These alterations of early postnatal body weight could play a role in the delay in puberty in
these male offspring.
In summary, the MOA of altered pubertal progression in the rat and decreased androgen
production by the testes should be assumed to be relevant to humans, as GnRH and the
regulation of pituitary hormones stimulate the gonadal secretion of steroids and is critical
for the initiation of the set of events and time course of the HPG regulation of puberty in the
human. All the effects on sexual maturation and altered androgen status reported after ≈
30 days of exposure occur at dose levels ≥ 5X higher than those leading to the LH surge
disruption which serves as the basis of the Agency’s current risk assessment.
2.3.3.4 Atrazine-associated decrease in prolactin and prostatitis in
young rats
In rodents, non-bacterial prostate inflammation is typically noted in older males (e.g. greater
than one year of age) and can be induced with elevated prolactin concentrations
(hyperprolactinemia) (Tangbanluekal and Robinette. 1994). In 1999, Stoker et al. reported
an increase in prostatitis in the male offspring of mothers exposed orally to atrazine from
PND 1 to 4. This effect was not linked to an alteration to LH, but rather the atrazine related
suppression of suckling-induced prolactin release in the lactating dams. An increase in the
incidence of prostatitis was observed in the 120 day old male offspring of dams treated with
atrazine (≥ 12.5 mg/kg/day) from postnatal day 1-4. An increase in the incidence of
prostatitis was also reported by Rayner et al. (2007) dams were exposed to 100 mg/kg/day
atrazine during GD 15-19. The dose level eliciting the increase in the incidence in prostatitis
in the offspring is > 10-fold higher than the dose leading to the LH surge attenuation used
as the basis for the Agency’s risk assessment.
In order to understand the significance of this observation, it is necessary to understand the
development of the tuberoinfundibular dopaminergic (TIDA) neurons located within the
hypothalamus and their role in regulating prolactin secretion in the adult. Prolactin plays a
crucial role in the neonatal brain for normal TIDA neuron development. In the adult
Page 29 of 184
offspring, the impaired TIDA regulation is reflected by elevated prolactin levels
(hyperprolactinemia) (Shyr et al., 1986, Stoker et al., 1999; 2000). It is this elevated level
of circulating prolactin in the adult males that has been linked to an increased incidence of
prostatitis. Thus, an increased incidence of prostatitis in the offspring of dams exposed to
atrazine during the critical time for TIDA neurons activation (first postnatal week) may be
attributed to elevated blood prolactin concentrations due to impaired TIDA neuronal
maturation (Stoker et al., 1999). In summary, the data indicate that atrazine induces
prostatitis at doses ≥ 12.5 mg/kg/day and that – in rats – early postnatal exposure is a
critical window of susceptibility to this effect.
Stanko et al. (2010) evaluated reproductive development in male rats exposed prenatally to
either atrazine or an atrazine metabolite mixture (AMM). The authors reported an increase
incidence of prostatitis at doses ≥ 0.9 mg/kg AMM and at 100 mg/kg atrazine. This study is
the companion study to the Enoch et al. (2007) study. While Enoch et al. assessed the
female offspring of dams exposed during GD 15-19, Stanko and coworkers evaluated the
male offspring. Therefore, the limitations identified by the Agency – and confirmed by the
September 2010 SAP – in the Enoch et al. publication are also applicable to Stanko et al.
The Agency concluded that the shortcomings in the study design, data reporting and
analysis compromise the interpretation of the studies. A more detailed description of this
study is available in Appendix A of this document.
In rats, the dorsal and lateral prostates are the most homologous to the human prostate
(Price, 1963) and lateral prostate is most sensitive to prolactin, so the rat is a good model
to study the effects of disrupted prolactin on the developing prostate. It is unknown when
the TIDA neurons develop in the human fetus or whether this development is dependent on
the maternal prolactin concentrations (Ben-Jonathan et al., 2007 review). As such, it is
unclear what – if any – is the impact of atrazine exposure on developing prostatitis in
humans.
2.3.4 Mammary gland whole mounts
Rodent mammary glands are widely used as experimental models for the human breast,
and the pre- and postnatal developmental windows of the mammary glands lend several
advantages for studying disruptions in normal development (Fenton 2006). The effects of
prenatal atrazine exposure on mammary gland development in the rat have been
investigated by several groups with conflicting results. Enoch et al. (2007) reported that
Long Evans rats prenatally exposed to atrazine metabolite mixtures as low as 0.09 mg/kg/d
during late gestation (GD 15-19) exhibited a persistent disruption in mammary gland
development up until postnatal day (PND) 60. Similarly, Rayner et al. (2005) found that
regardless of the length of gestational exposure (the longest being GD 13-19), 100 mg/kg
atrazine stunted mammary gland growth up through PND 67. In contrast, Hovey et al.
(2011) observed no differences in mammary gland morphology in Long Evans rats exposed
to atrazine from GD 13-19. There were, however, differences in the methods of evaluation
Page 30 of 184
employed in these studies. Enoch et al. (2007) and Rayner et al. (2005) using a subjective
scoring method that relied on a holistic assessment of the mammary tissue, while Hovey et
al. (2011) utilized a quantitative approach. A brief review of this study is available in
Appendix A of this document.
In response to a recommendation during the September 2010 SAP meeting, the Agency
has conducted a set of experiments investigating the potential impact of in utero atrazine
exposure on mammary gland development in Sprague Dawley rats using both the
subjective scoring methodology described by Enoch et al. and a computer-based
quantitative methodology (morphometric analysis) (Davis et al. 2011). Mammary gland
development was assessed using whole mounts of the 4th abdominal mammary gland of
PND 45 old pups. PND 45 pups were selected based on consensus guidelines established
following a recent 2009 workshop
(http://www.silentspring.org/pdf/our_research/MammaryGlandWholeMountRoundRobinSu
mmary.pdf). No differences in the quantitative measures or in the subjective scores were
found indicating that gestational atrazine exposure had no demonstrable effect on normal
mammary gland development.
2.3.5 Summary
The neuroendocrine MOA of atrazine leads to a perturbation of the hormonal milieu in
laboratory animals. This perturbation – in turn – leads to a series of adverse outcomes at
different lifestages as observed in rats. Quantitatively, the most sensitive effect is the ≈
33% LH surge attenuation (BMDL = 2.56 mg/kg/day) observed after female rats of
reproductive age are exposed to atrazine for 4 days. The Agency is using the 33% LH
surge attenuation after a 4 day exposure as a precursor event to protect for other adverse
outcomes including estrous cyclicity disruption, and delays in sexual maturation occurring
at higher doses in laboratory animals.
In the case of atrazine, it has been noted that in addition to dose, duration of exposure is an
important parameter that must be considered in evaluating the relationship between dose
and attenuation of the LH surge. Currently available data indicate that a 4-day exposure is
sufficient to elicit a decrease of the LH surge in rats. Interestingly, this is the length of the
estrous cycle in rats and also the exposure duration needed for atrazine to reach pseudosteady state – a topic that will be discussed in greater detail in Section 4 of this draft issue
paper. Preliminary data, however, suggest that in rats even shorter atrazine exposures can
result in LH changes, albeit at high doses (100 mg/kg/day). Other effects of concern, such
as delays in puberty onset and decrease in suckling-induced prolactin release and
eventually prostatitis in young rats, identified in the animal toxicity database occur at higher
doses but have a different temporal profile compared to the LH surge attenuation. For
instance, prostatitis can be seen in rats exposed to 12.5 mg/kg/day of atrazine for 3 days
shortly after birth. Similarly, atrazine-induced delays in puberty onset have been reported
in both peripubertal male and female rats after exposures to atrazine (≥12.5 mg/kg/day) for
Page 31 of 184
approximately 20-30 days. Although drawing a direct temporal correlation between the
effects seen in the rat animal model and potential human health outcomes is not feasible at
this time, it is prudent to consider the possibility of a critical temporal window of < 90 days
(the current rolling average drinking water concentration). This may have an impact on the
frequency of drinking water monitoring that may be required. In Section 7 of this issue
paper the Agency will present a case study integrating the animal toxicity, pharmacokinetic
and water monitoring data to illustrate the relationships between critical windows of
exposure and the sampling frequency needed to assess whether atrazine levels exceed the
Agency’s level of concern for critical intervals.
Page 32 of 184
3. EPIDEMIOLOGIC EVIDENCE OF THE CARCINOGENIC POTENTIAL OF ATRAZINE
3.1
Introduction
3.1.1
Background
This section provides EPA’s review of recently published (September 2003 – May 2011)
epidemiologic evidence of the carcinogenic potential of the herbicide atrazine. This review
updates EPA’s analysis of this observational database presented in two previously
convened FIFRA science advisory panels (SAP): June 2000, and July 2003. Briefly for
reference, the June 2000 FIFRA SAP considered studies identified by EPA that evaluated
atrazine or triazine exposure in association with breast, ovarian, and prostate cancers as
well as non-Hodgkin lymphoma (NHL) and other lymphohematopoietic cancer sub-types.
The Final SAP report which provided recommendations to EPA concerning interpretation of
these data is presented in Appendix A. The SAP recommended EPA consider the evidence
in support of an association between atrazine and prostate and breast cancer to be
inconclusive. With regards to ovarian cancers, the SAP commented that while one study
was suggestive of an association, it required replication and improved study methods to
better inform causal inference. In addition, the Panel urged EPA to re-evaluate the studies
related to NHL, as the Panel believed there was some supportive evidence observed.
Considering the SAP recommendation, EPA concluded that while the data were not of
sufficient quality or quantity to conclude there was an association, EPA should continue to
monitor the literature, and re-convene future SAPs when additional epidemiologic data
were identified ((EPA), 2000).
In 2003, the EPA presented its analysis of several epidemiology studies which evaluated
the potential association between atrazine and prostate cancer. Mainly, this review
consisted of studies which indicated a possible increased risk of prostate cancer among an
occupational cohort of triazine manufacturing plant workers (i.e., the St. Gabriel
manufacturing plant cohort). The Agency also considered a preliminary nested case control
study from the Agricultural Health Study (AHS), and a correlation analysis of this
association in this evaluation. The Final SAP report which provided recommendations to
EPA concerning use of these data is presented in Appendix B. EPA concluded, in
consultation with the FIFRA SAP, that the increased incidence of prostate cancer in this
cohort was likely in part or in whole due to intensive prostate cancer screening program,
the prostate specific antigen (PSA) test was available on site to employees and many
employees participated in the program (EPA), 2003). Therefore, detection bias was a likely
explanation for the apparent risk concerns presented in the occupational studies.
Concerning the AHS study on prostate cancer, the Panel noted that although results of this
initial, hypothesis-generating analysis did not provide evidence of a positive association
between atrazine and prostate cancer, differences between agricultural and manufacturing
exposure profiles, as well other demographic differences between the two populations,
hinder a direct comparison of results. The Panel in its Final report stated the data were
Page 33 of 184
insufficient to support a conclusion regarding the carcinogenic potential of atrazine in the
human population. The Panel recommended EPA consider additional, follow-up studies
from the AHS as well as other peer-review sources, to clarify the nature of the association
with atrazine. As a result of this FIFRA SAP (2003) consultation, EPA concluded:
“The Agency does not find any results among the available studies that would lead us to
conclude that potential cancer risk is likely from exposure to atrazine. EPA plans to revisit
this conclusion upon receipt of new studies, especially those from [National Cancer
Institute] NCI’s Agricultural Health Study on atrazine and all cancers, prostate cancer, and
non-Hodgkin’s lymphoma, all of which are planned for completion in the next 1-2 years
[sic].” (Blondell & Dellarco, October, 28, 2003 2003 #1026)
EPA believes it is appropriate to present this analysis at this time as several new
evaluations on the topic have been published since 2003, including several evaluations by
researchers with the Agricultural Health Study (AHS).
3.1.2
Identification of Epidemiology Studies included in Current Review
This review of the epidemiologic literature of an association between atrazine exposure and
specific anatomical cancer sites includes studies previously reviewed by EPA and
presented to the FIFRA SAP panels (June 2000 and July 2003), as well as more recent
peer-reviewed publications, including a new evaluation by AHS researchers published in
May 2011. For the purpose of the current evaluation, emphasis is placed on epidemiologic
studies published since the time of the last EPA review, i.e., September 2003 to May 2011.
However, to provide a comprehensive evaluation, EPA presents epidemiology studies of
atrazine or triazine exposure in association with any cancer outcome published between
1985 and 2011 in this review. The current review is intended to update the Agency’s cancer
epidemiology evaluation to determine whether new information alters the Agency’s
previous conclusion regarding evidence of the carcinogenic potential of atrazine in the
human population.
EPA values and appreciates the counsel provided by previous FIFRA SAPs (February and
September 2010) regarding the draft “Framework” for incorporating epidemiology data into
the risk assessment process (U. EPA, 2010). Specifically, these previous Panels
encouraged EPA to employ a clear and consistent methodology for identifying studies
included in the review of epidemiologic literature, including a delineation of excluded
studies; consistent application of guidelines for adjudicating the results of individual
observational studies, e.g., consistent application of inclusion and exclusion criteria,
potential for bias in the studies; and, a simple and understandable method to synthesize
the evidence presented in the epidemiology database, as well as integrate with the
experimental animal database. While EPA is in the process of refining the proposed
Framework, EPA strives to adhere to the principles offered by previous Panels in this
respect. However, EPA also acknowledges that the current literature review reflects a
necessarily hybrid approach to performing a review of this literature, given the history of the
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previous atrazine cancer scientific evaluations, and the decision to a priori include all
previously reviewed materials as well as all AHS relevant atrazine cancer point estimates in
the current EPA review.
Upon EPA’s request, previous FIFRA SAP panels (February and September 2010)
provided several thoughtful suggestions to EPA regarding an epidemiological literature
review methodology. Specifically, the Panel noted the importance of a clear and
transparent literature search methodology, articulation of study inclusion and exclusion
criteria, and the use of a scoring system with which multiple raters may adjudicate the
quality of individual studies, among other suggestions. In the current review, inclusion
criteria were purposely broad and reflected only that atrazine or triazine is measured in
association with a cancer outcome. Upon title and abstract review, studies were excluded if
they did not measure an association between atrazine or triazine exposure and a health
outcome. Concerning the use of a scoring system, EPA considered this option but instead
decided a broader and open evaluation of the epidemiologic literature was merited at this
time while noting the strengths and limitations of each study. However, among the list of
studies remaining after title and abstract review, a group of scientists jointly adjudicated the
relevance of the study for inclusion, as well as “grouped” studies as generally “strongly” or
“weakly” providing evidence to inform the discussion. A full description of the literature
review methodology including a delineation of the search string utilized is presented in
Appendix C.
EPA considered several factors indicative of good quality epidemiologic analysis in
performing this review. Among these factors were the ability to accurately measure the
health effect (the outcome) as well as atrazine exposure; the identification and accurate
measurement of potential confounding factors; the sample size and statistical power to
measure the association; the appropriateness of the statistical analysis to the study design;
and, the interpretation of the overall study results. Key among these factors is the method
of exposure assessment. Specifically, whether authors were able to perform qualitative or
quantitative exposure assessment; the ability to measure atrazine exposure during critical
time window for the health effect; the range of the exposure captured in the study; and, the
relevance of the biomarker (if measured) to actual environmental exposures. In combining
evidence from individual studies and across disciplines, i.e., toxicology and epidemiology,
EPA considered factors such as presence of an exposure-response relation, temporality of
exposure-disease measurement, strength of individual risk point estimates, consistency of
findings across studies, knowledge of biologically plausible hypotheses to support the
observed findings, as well as professional judgment.
3.2
Epidemiology Literature of the Carcinogenic Potential of Atrazine
The present review includes research articles presenting evidence concerning the
carcinogenic potential of atrazine in the human population. However, the aspects of this
database which have already been part of an external review, i.e., FIFRA SAP or other
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external review processes, are not presented with the same level of detail in this chapter or
in the study reviews. The relevant FIFRA SAP final reports which provide a thorough review
of these studies as well as an overall synthesis of the epidemiologic evidence at prior
points in time are included as appendices to this chapter for reference (Appendices A and
B). Organized by anatomical cancer site, the chapter first briefly recollects studies
previously reviewed, followed by a more detailed summary of the recent epidemiologic
research. At the conclusion of each section, a synthesis of the epidemiologic data is
proffered. EPA’s review of individual investigations are included in Appendix D (studies
published prior to 2003), and Appendix E (studies published subsequent to 2003, including
several from the AHS). In addition, a summary table of study characteristics is presented in
Appendix F for the recently published epidemiology studies, the focus of the current EPA
review. Ultimately, a synthesis and integration of the cancer epidemiology studies with the
results of experimental toxicological investigations is presented in Section 3.3, using the
modified Bradford Hill criteria as an organizing tool, in addition to scientific judgment.
Importantly, EPA notes at this time that there was no evidence of tumors noted in any of
the experimental toxicology studies performed, for any of the cancer sites evaluated in the
epidemiology studies discussed below, with the exception of mammary gland tumors in rats
which were later determined to have development through a biological mechanism not
operational in humans (See full discussion in Section 3.3 below).
3.2.1
Overview of Epidemiologic Database
The atrazine cancer epidemiology database includes 40 studies reflecting cohort,
population-based case-control or nested case-control study designs. Among this number,
several ecologic studies were identified, but were not weighed heavily in the weight of
evidence evaluation, per the advice of previous Panels (U. EPA, 2010). Studies reflected
general population exposure, occupational manufacturing as well as agricultural exposure.
Various methods of atrazine and/or triazine exposure assessment were employed across
these studies. In general, the studies varied in terms of strengths and weaknesses.
The observational studies included in EPA’s current review of the atrazine cancer
epidemiology database mainly consider the herbicide’s potential carcinogenic effect upon
the reproductive and endocrine systems as well as the lymphohematopoietic systems.
Approximately one-third of the cancer studies published subsequent to 2003 relate to these
types of cancer endpoints. Investigations of atrazine exposure in association with
leukemias and lymphoma include Hodgkin’s disease, non-Hodgkin lymphoma (NHL),
multiple myeloma, leukemia, among other specific sub-types. Two new evaluations of
atrazine exposure in association with lymphomas and NHL specifically have been
published since 2003, however, a majority of studies published in this database published
prior to 2003, investigate atrazine’s link with leukemias and lymphomas. Other cancer sites
considered in the studies reflected in the current review include brain/glioma and colorectal
cancers, as well as two evaluations of atrazine exposure in association with pediatric
cancers including childhood leukemia. Given the importance of the AHS investigations to
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the question of atrazine carcinogenicity, as articulated by the FIFRA SAP (2003) (See
Appendix B), all other atrazine-cancer point estimates measured in either nested casecontrol or cohort studies are also included for reference. For this reason, a brief review of
AHS design and methods, and the strengths and limitations of this cohort study are
presented below. Results of specific AHS evaluations are incorporated within respective
cancer-specific discussion sections.
Following the discussion of the AHS, cancer-site specific studies are presented. Within
each sub-section studies published prior to 2003 are briefly recalled, followed by a more
expansive discussion of the recent epidemiologic data (2003-2011). EPA’s review of each
individual study is presented in Appendix D (pre-2003) and E (post-2003). A synthesis of
the observational studies within each cancer site-specific section as well as across all
cancer endpoints researched is offered at the conclusion of each section. EPA presents its
integrative discussion of both the toxicological and also the epidemiological evidence in
Section 3.3.
3.2.2
Agricultural Health Study (AHS) Atrazine Cancer Point Estimates
Since 2003 there have been six nested case-control analyses within the AHS which
evaluated use of a number of agricultural pesticides, including atrazine, in association with
specific anatomical cancer sites. These include investigations of prostate (Alavanja et al.,
2003), lung (Alavanja et al., 2004), colorectal (Lee et al., 2007), breast (Engel et al., 2005)
(among female spouses and female applicators in the AHS), pancreatic (Andreotti et al.,
2009), and melanoma (Dennis, Lynch, Sandler, & Alavanja, 2010). In addition, researchers
also published two cohort analyses specifically examining the association between atrazine
use and cancer outcomes; multiple cancer sites were evaluated in each study (Beane
Freeman et al., 2011; J. A. Rusiecki et al., 2004). This section provides general information
concerning the design and methods of the AHS. The results of these investigations are
considered within respective cancer-site discussion sections presented in this section.
3.2.2.1
Study Design and Methods
The Agricultural Health Study (AHS) is a large, prospective cohort study comprised of
private and commercial pesticide applicators in Iowa and North Carolina. The study
includes 57,310 licensed pesticide applicators, comprising 52,394 private applicators and
4,916 commercial applicators, as well as 32,346 spouses of pesticide applicators.
Researchers successfully recruited 82% of the target population into the AHS cohort which
represents a high recruitment rate for an epidemiologic study. While farmer and pesticide
applicator participants in the AHS are generally healthier than the overall U.S. population,
AHS participants have a higher rate of some cancers including prostate, lip, multiple
myeloma and ovarian cancers (Alavanja et al., 2005; Koutros et al., 2010). Lower rates of
smoking and alcohol use, and the physically demanding nature of the occupation are health
protective factors in the AHS population, and pesticides, viruses (animals), bacteria,
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sunlight, and other non-pesticide chemicals may be putative risk factors for carcinogenesis
among these farmers.
Pesticide applicators must obtain a license from the state to purchase and use restricteduse pesticides. Participants in the AHS were enrolled into the study at county pesticide
licensing facilities and were asked to complete a 21-page written enrollment questionnaire.
Enrollment occurred over a period of 4-years, December 13, 1993 to December 31, 1997
(“Phase I”). The Phase 1 “enrollment” questionnaire solicited information concerning types
of agricultural crops and livestock produced, types of pesticides used, pesticide application
methods, use of personal protective equipment, and, lifestyle factors such as smoking and
alcohol intake. A portion of the cohort also completed a “take-home” questionnaire (~40%)
in which additional lifestyle (e.g., diet) and chemical exposure information (i.e., an
additional 28 chemicals) was ascertained.
To determine case- and vital-status, cohort members were linked to state cancer registries
in Iowa and North Carolina and the state death registries and the National Death Index,
respectively. Linking was accomplished through use of social security number, first and
last name, date of birth, and gender. Study participants who were alive but no longer
residing in either study states were identified through the Federal Internal Revenue Service
address file, motor vehicle registration records, and the restricted-use pesticide license and
certification records of the departments of agriculture in Iowa and North Carolina. At this
time, less than 2% of the cohort has been “lost-to-follow-up,” i.e., can no longer be located
for purposes of the study. This is a very low percentage lost to follow-up, reducing the
chance of selection bias in this prospective cohort study.
To measure atrazine exposure, AHS researchers considered three metrics across the
studies including an atrazine cancer point estimate: (i) whether participants ever used the
insecticide; (ii) the cumulative exposure days (the total number of exposure days per year,
and total number of years in lifetime); and, (iii) an intensity weighted cumulative exposure
metric. Exposure information was collected via self-report questionnaire responses. The
“ever used” metric dichotomized atrazine exposure into ever/never categories where “ever
used” was considered as at least used one time in lifetime. The lifetime exposure- days
metric was calculated by taking the product of the total number of years atrazine was
applied and the number of days per year atrazine was applied. The intensity-weighted
lifetime exposure day metric includes: the frequency and duration of application (lifetime
exposure days), application methods, mixing and equipment repair status, and use of
personal protective equipment. These factors were weighted to reflect the intensity of the
exposure based on monitoring data available in the published literature. Methods of
pesticide exposure assessment used in the AHS have been reviewed extensively and
shown to have high reliability and validity (Blair et al., 2002; Hoppin, Yucel, Dosemeci, &
Sandler, 2002).
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There have been six nested case control studies published within the AHS since 2003:
prostate, breast, pancreatic, lung, colorectal, and cutaneous melanoma. In addition, there
have been two cohort analyses of the relation between atrazine and several different
cancer sites. The results of these specific evaluations are integrated within the cancerspecific discussions presented below.
3.2.2.2
AHS Strengths and Weaknesses
The AHS includes both strengths and weaknesses. The large sample size and sufficient
statistical power to investigate the potential association between atrazine use and several
cancer sites including prostate cancer is a strength of the study. In addition, given the
prospective study design the potential for recall bias (differential exposure measurement
error) is eliminated, as pesticide exposure information was collected prior to the
identification of disease. Exposure measurement error exists in this study (Blair et al.,
2011), however it is likely non-differential in nature and would attenuate, and not falsely
inflate, measured risk estimates. Selection bias is considered of low potential as very few
participants have been lost to follow-up in this cohort (i.e., few participants dropped out of
study or were unable to be followed through other means). In this cohort, cancer cases
were ascertained through linkage with state cancer registries, and include histopathological
confirmation as well as information as to stage and grade of cancer at diagnosis. High
quality outcome ascertainment reduces the potential for outcome measurement error. The
AHS collects a vast amount of information for use in statistical analysis including prostate
cancer risk factors such as age, race and family history of prostate cancer; cancer risk
factors such as smoking and alcohol use; and, use of other correlated pesticides. Lastly,
the AHS exposure measurement tools have been illustrated to be valid (Hoppin et al.,
2002) and reliable (Blair et al., 2002), and provide a quantitative measure of pesticide
exposure over the working lifetime.
In combination, these studies provide an important database of epidemiologic evidence
pertaining to atrazine carcinogenicity. However, it is important to note that most of these
studies are hypothesis-generating in nature and did not propose a specific biological
mechanism as to the carcinogenic mode of action of atrazine per se. The recent evaluation
(Beane Freeman et al., 2011) was able to test hypotheses related to prostate, breast,
ovarian and NHL as these sites have been previously suggested to be related to atrazine
use. These results are further discussed below.
3.2.3
Reproductive and Endocrine System Cancers
Because atrazine has a known neuroendocrine mode of action several researchers have
hypothesized a role for atrazine in the etiology of certain reproductive and endocrine
system cancers. Investigations of a link between the herbicide and prostate, breast,
ovarian, and thyroid cancer are available for consideration. Several studies have identified
that farmers have a greater risk of prostate cancer, although overall cancer rates are less
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than the rates in the general population (Alavanja et al., 2005; Koutros et al., 2010).
Similarly, there is some indication for an increased risk of ovarian cancer among women
who live near or work in agriculture (Alavanja et al., 2005). However, few analyses to date
have hypothesized a role for atrazine specifically, i.e., hypothesis-testing in nature, and
even fewer have investigated an association with female reproductive system cancers. In
many of these investigations, the number of female cancer cases exposed to atrazine is
small.
3.2.3.1
Prostate Cancer
In 2003, EPA reviewed several studies that investigated the association between atrazine
exposure and prostate cancer, and presented a review of these data to the FIFRA SAP
(EPA, 2003b) [See Appendix]. Briefly, an increased risk of prostate cancer was observed
among an occupational cohort of triazine manufacturing plant workers (P. A. MacLennan et
al., 2002; Sathiakumar & Delzell, 1997; N. Sathiakumar, E. Delzell, & P. Cole, 1996),
however further analyses by EPA and others, and supported by external peer review
including the FIFRA SAP, concluded that the availability of an on-site prostate cancer
screening program, i.e., the PSA test, likely explained some or all of the increased prostate
cancer incidence (Blondell & Dellarco, October, 28, 2003 2003 #1026; EPA, 2003b).
Authors hypothesized that the elevated cancer rates was due to detection bias, and that the
increased prevalence of PSA screening could explain some or all of the increase in
prostate cancer incidence observed in this occupational population. This is supported by
the observation that the increase in cancer risk was only observed among active workers
who had access to the screening, and a lack of exposure response gradient using a jobexposure matrix approach to measure exposure . As a result of several internal as well as
external peer review processes, reviewers determined that given the limitations of the study
design, small sample size and lack of refined exposure measurement, a possible role for
atrazine could not be ruled out, however it did not appear likely.
Subsequent nested case-control analyses of the triazine manufacturing plant workers using
refined exposure measurements did not alter the conclusion (P. A. Hessel et al., 2004).
However, the same limitations as noted above, primarily the small sample size and limited
exposure information in this occupational study precluded a determination that an
association between triazine exposure and prostate cancer was present or absent. Further,
the results of a nested case control study within the AHS population indicated little
evidence of an association with prostate cancer (OR 0.94, 95% CI 0.78, 1.14), and there
was no observation of an exposure-response trend (p trend>0.10) (Alavanja et al., 2003). In
another study, analyses using state cancer registry data (CA), and publicly available
pesticide use data indicate some evidence of a statistical correlation, however limitations of
the exposure analysis restricts use in causal inference (P. Mills, 1998; P. K. Mills & Yang,
2003). However, recently published AHS cohort analysis results addressed this question in
cohort analyses with sufficient statistical power to detect an association if one exists
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(Beane Freeman et al., 2011; J. A. Rusiecki et al., 2004). These data are presented below,
followed by a synthesis of the studies in this database.
In 2004, Rusiecki et al. published a cohort analysis among participants in the AHS of
atrazine exposure in association with many different cancer endpoints, including prostate
cancer. AHS study design and methods are noted in section 3.2.2.1. With reference to
prostate cancer, authors reported no evidence of a statistical association between atrazine
exposure and prostate cancer using either cumulative exposure or an intensity-weighted
cumulative exposure metric. Specifically, across quintiles of atrazine lifetime exposure
days, the following odds ratios were estimated in comparison to the lowest exposed group
(1-20 days): 21-56 days, OR 0.89 (95% CI, 0.66 to 1.21); 57-178.5 days, OR 0.75 (95% CI,
0.56 to 1.03); and greater than 178.5 days OR 0.88 (95% CI, 0.63 to 1.23) (J. A. Rusiecki
et al., 2004). The test for linear trend was not significant, p=0.26. Results were similar using
intensity-weighted lifetime exposure days. Authors state that in this study there was
sufficient statistical power to detect an association of 1.3 in the highest quartile, assuming a
linear trend across quintiles of atrazine exposure (1-β=0.89).
A new evaluation within the AHS cohort study with over twice as many prostate cancer
cases indicated similar results. Risk estimates in the upper quartiles of either lifetime
exposure days or intensity-weighted lifetime days as compared to the lowest quartile of
exposure were not significantly different from the null (Beane Freeman et al., 2011).
Compared to the lowest exposure group, odds ratio of prostate cancer in the upper
quartiles of atrazine exposure were 1.04, 1.06, 1.04, respectively. In addition, there was no
visual or statistical evidence of any exposure-response trend (p for linear trend 0.81). The
latter analysis reflected use of an updated exposure algorithm which reduced exposure
measurement error in comparison to the original algorithm used in the earlier analysis.
While reflecting two different study periods as well as two different case series (prostate
cancer cases updated through 2007), these two investigations within the AHS are similar in
design and methods. The initial investigation by Rusiecki et al. (2004) was hypothesis
generating in nature, however previous research, as noted above, provided support for a
possible association with prostate cancer. The follow-up investigation examined the same
question, within the same cohort, at a different point in time, and in many ways confirms the
original finding. Differences between the two studies include an increased number of
prostate cancer cases in the latter investigation, and use of the updated exposure algorithm
in the latter analysis. Both studies had sufficient statistical power to detect an association
with atrazine, if an association exists. Notably, the analysis by Rusiecki (2004) and BeaneFreeman (2011) both adjusted for use of other pesticides including several that are typically
highly correlated with atrazine use including alachlor and 2,4-D as well as other triazines
including cyanazine and metribuzin. Overall, the results of these two cohort analysis in
which atrazine use was compared between cases and non-cases of prostate cancer,
among other cancer sites evaluated, did not suggest an association is present among
pesticide applicators in the AHS, who demographically were primarily U.S. white men.
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Conclusions: Prostate Cancer - The results of several internal and external peer review
processes concluded that the association between triazine exposure among an
occupational cohort of manufacturing plant employees can likely be explained in part or in
whole by the availability of intensive PSA screening, i.e., evidence of detection bias (EPA,
2003b). Previously published analyses using California state cancer registry and pesticide
use data (P. Mills, 1998; P. K. Mills & Yang, 2003) suggest a possibility of a correlation
and/or an association with atrazine or triazine (simazine measure Mills et al. 2003),
however other limitations of these studies reduce their use in a weight of evidence analysis,
primarily the method of aggregate exposure assessment, and lack of an atrazine risk
estimate. The results of two cohort analyses within the AHS with sufficient statistical power
to detect an association if an association exists did not observe statistical evidence of a link
between atrazine use and prostate cancer. The magnitude and direction of the association
was not different from unity (i.e., a null association), and there was no evidence of an
exposure response trend using either lifetime exposure days or intensity-weighted lifetime
exposure days. The AHS study design assured the atrazine exposure preceded the
disease outcome. Sensitivity analyses performed using both the low-exposed and nonexposed groups as referent did not differ. Notably both analyses simultaneously controlled
for use of correlated pesticides in the statistical analyses, for which the analyses were
adequately powered to investigate, and no associations were observed.
As noted by previous SAPs (EPA, 2003b), the exposure profiles of the triazine
manufacturing plant workers and the AHS private (farmers) and commercial applicators
differ, and lack of association among farmers does not preclude the existence of a positive
association with triazine manufacturing plant workers. The higher magnitude of the
exposure, the different exposure profile, and the distinct population characteristics (age,
race, decade of exposure, other pesticide/chemical exposures) may indeed result in two
different measures of association. However, to date, the information is not sufficient to
support this supposition, and the AHS population is likely among the most highly exposed
occupational groups in the nation.
Although the primary risk factors for prostate cancer are age and family history, the specific
causes of prostate cancer remain unknown. In general, epidemiological evidence of a role
of atrazine in prostate cancer is minimal. In spite of the herbicide’s neuroendocrine MOA, a
clear role of atrazine in prostate carcinogenesis is not understood, and evidence from the
experimental toxicology literature would argue against a possible mode of action for
atrazine in prostate tumor formation (See section 3.3 below). At this time the lack of
evidence of a statistical association in the two cohort analyses available from the AHS
which reflects a prospective design assuring temporality of exposure-disease
measurement, consistency of results between manufacturing (P. Hessel et al., 2004; P. A.
MacLennan et al., 2002) and agricultural (Alavanja et al., 2003; Beane Freeman et al.,
2011; J. A. Rusiecki et al., 2004) populations, and lack of compelling biological hypothesis
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as to the mode of prostate carcinogenesis from the experimental studies, argue against a
link between atrazine and prostate cancer.
3.2.3.2
Breast Cancer
Several authors have evaluated the association between atrazine/triazine exposure and
breast cancer. The rationale for these investigations is the known neuroendocrine health
effects of atrazine, and earlier experimental studies which reported mammary gland tumors
in female rats and studies reporting an effect on mammary gland development. However,
more recent toxicological data suggest that the mode of action for atrazine’s effect on
mammary gland development in rats is not operational in humans (EPA, 2010; EPA, 2000)
(See Section 3.3). Nevertheless, there have been three ecologic studies evaluating this
potential association in the human population, two were published prior to 2003 and
previously reviewed by EPA (Bassil et al., 2007; Hopenhayn-Rich, Stump, & Browning,
2002; M. A. Kettles, S. R. Browning, T. S. Prince, & S. W. Horstman, 1997), and one was
more recent (Muir et al., 2004). The ecologic evaluations are collectively discussed below.
In addition, a population-based case control study in Wisconsin (J. A. McElroy et al., 2007)
using atrazine well water concentrations to measure exposure, and two recent evaluations
from the AHS (Beane Freeman et al., 2011; Engel et al., 2005) provide additional
information to examine this question.
There are three separate ecologic analyses of the association between
groundwater/drinking water exposure to atrazine and breast cancer. Two were previously
reviewed by EPA, and one is considered for the first time herein. Kettles et al. (1997)
conducted an ecologic study to examine the association between breast cancer incidence
and triazine herbicide exposure; in 2002 Hopenhayn-Rich updated the analysis. The
Kettles et al. (1997) study calculated county breast cancer incidence rates using Kentucky’s
statewide cancer registry for the years 1991-1994. Triazine herbicide exposure was
assessed using data on ground and drinking water contamination, corn acreage, and
pesticide usage by applicators. Researchers report a small, statistically significant
association between breast cancer and water contamination. However, the investigators
also indicate that the results were not consistent across study years. While the water
contaminant associations were not consistent between time periods, the investigators did
report that their composite exposure variable was associated with breast cancer during
both 1991-92 and 1993-94 (i.e., Medium and High Exposed County ORs = 1.09 (1.041.14), 1.07 (1.01-1.14) for 1991-92 and 1.14 (1.08-1.19), 1.20 (1.13-1.28) for 1993-94) (M.
Kettles, S. Browning, T. Prince, & S. Horstman, 1997). This study is discussed in the final
SAP report from the June 2000 meeting (Appendix A) and concluded these data were
insufficient to make a determination as to the relation between atrazine and breast cancer.
In 2002, Hopenhayn-Rich updated this analysis by refining exposure measurement and
including three additional years of cancer registry data, but utilized similar methods in other
ways. In this ecologic evaluation, authors reported that their study did not provide evidence
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that supports an association between atrazine exposure and county level incidence of
breast cancer(Hopenhayn-Rich et al., 2002).
Muir et al. (2004) performed an ecologic analysis of this association in two counties in
England, Lincolnshire and Leichestershire. Researchers included all cases of breast cancer
diagnosed among women, ages 45 years and greater in these two counties between the
years 1988 to 1991. Cancer cases were ascertained through regional cancer registry (Trent
Cancer Registry). Pesticide exposure was measured at the group level. A pesticide usage
survey for 1991 was used to determine the total kilogram of active ingredient applied per
kilometer by each ward within the two counties of England represented in the study.
Investigators included atrazine, aldicarb, lindane and cyanazine in the study as these were
identified as potentially estrogenic substances by the study authors and hypothesized to
play an etiologic role in breast cancer development. Authors reported no significant
correlations (“clusters”) of breast cancer by geographic area. Furthermore, authors
observed no association between average pesticide usages by ward and breast cancer
incidence rate ratio (IRR) in urban or rural areas of Lincolnshire, or urban areas of
Leichershire. Authors did note a statistically significant (p<0.05) positive change in slope of
breast cancer incidence rates and 1-unit change in kg of atrazine used per km2 (slope 0.17
(0.03, 0.31)) (Muir et al., 2004). The researchers noted the inherent limitations of the study
design, but suggest the results of this work supports excluding the possibility of a strong,
positive association between atrazine usage and breast cancer among women diagnosed
later in life (otherwise these studies would have detected greater associations).
Overall, these three ecologic studies provide limited, inconsistent evidence as to whether
an association exists between atrazine exposure and breast cancer. The slight suggestion
of an association in Kettles (1997) was not observed by Hopenhayn-Rich (2002) using
similar methods and similar study population. The study by Muir et al. (2004) does not
present internally consistent results, positive slope in only one of two study areas, and the
weak magnitude and statistical significance of the slope (coefficient) does not strongly
argue in favor of an association. Given the nature of the study design, these data offer
weak, inconsistent evidence of a possible link between atrazine and breast cancer.
Mills et al. (2006) performed a hypothesis-testing, cohort study using ecologic exposure
assessment methods to evaluate the association between atrazine exposure and breast
cancer among Latinas diagnosed with breast cancer. Invasive breast cancer cases were
identified from the California state cancer registry (CCR) between the years 1988 to 1999,
and compared with county level pesticide exposure, and grouped demographic data.
Pesticide use information from the years 1970 to 1988 was obtained from the California
state pesticide use registry (PUR). Authors were interested in six pesticides, including
atrazine and simazine. Authors calculated standardized incidence ratios (SIR) for 19881993 and then for 1994-1999 to reflect secular changes in breast cancer trends (screening,
diagnostic tools, knowledge in population), and to examine latency between exposure and
diagnosis. Adjusting for age and other co-variables, authors reported no association
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between atrazine exposure and breast cancer comparing the highest and lowest quartiles
of exposure (1988-1993: OR 0.86 (95% CI 0.70, 1.05); 1994-1999 OR 0.87 (95% CI 0.73,
1.04)
Mills et al. (2006) performed a large, cohort study of atrazine exposure and breast cancer in
a highly exposed group. Outcome classification was good, based upon high quality cancer
registry, and using state pesticide use records is an innovative method to assess exposure.
By design, authors assured pesticide exposure preceded disease diagnosis. Established
breast cancer risk factors were indeed significantly associated with breast cancer in this
study, which is reassuring of the study methods. The major limitation of this study is the
ecologic nature of the exposure assessment. Authors assumed PUR reports for the county
and group level confounder information were a good approximation of pesticide exposure
of women diagnosed with breast cancer and co-variable information, which likely resulted in
exposure misclassification. Because of the lack of individual level exposure and
confounder information, an association cannot be ruled out based upon this study.
However, in general, this study lacks evidence of an association.
McElroy et al. (2007) performed a population-based case-control study of atrazine
exposure and breast cancer among 22,495 women residing in the state of Wisconsin.
However, given the timing of exposure and disease information collection, the study was
essentially cross-sectional in nature. Cases included women who were diagnosed with
breast cancer during the years 1988 through 2001, and reported to the state tumor registry.
Controls were randomly selected from state DMV (20-65 years) and Health Care Finance
Administration (65-79 years) records to roughly approximate the age distribution of cases
(10-year age categories), and for whom no previous breast cancer diagnosis was made.
Investigators were interested in atrazine exposure from untreated well water used for
drinking water. Therefore, authors restricted the study population to those who resided in a
rural area for whom well-water would likely supply local drinking water defined as a region
with no public water system. Upon applications of the eligibility and restriction criteria, 3,275
case and 3,669 controls were selected into this study. The concentration of atrazine and
atrazine metabolites in well water which provided the sole residential drinking water source
for participants in this study was measured in a state survey of pesticide contamination
during three survey years: 1994, 1996, and 2001. To estimate atrazine exposure between
sampling years of 1994, 1996, and 2001 to the entire study period of 1988-2001, authors
utilized a “national neighborhood interpolation” method using ArcGIS. Authors considered
atrazine well water concentration within the entire state and within the highest agricultural
area of the state separately, the south central WI region. Overall, authors reported little
evidence of an association between atrazine concentration in well water and breast cancer.
Comparing those in the moderately high category to the lowest exposure category, minimal
increased odds of disease were reported (OR 1.10, 95% CI 0.90, 1.40) (J. McElroy et al.,
2007). Among participants in the highest atrazine exposure area, odds of breast cancer
was non-statistically significantly elevated to 1.3 (95% CI 0.30, 5.0), however precision was
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low and the number of cases in the upper category was small (n<5) (J. McElroy et al.,
2007).
This study presents limited evidence of a role for atrazine drinking water exposure in
relation to breast cancer. These study methods are an improvement over previously
published population-based studies of atrazine and breast cancer; however there are likely
significant information biases (resulting in exposure misclassification), and heterogeneity in
outcome classification, in addition to low sample size in the upper-exposure categories that
could explain the essentially null findings. There are several strengths to this method of
exposure assessment including the fact that this is an improvement over previous ecologic
studies, the interpolation across study years is supported by the fact that atrazine is
relatively persistent in water, and the restriction to rural residents likely isolates the most
highly exposed to atrazine in drinking water. Using these exposure methods, however,
there are numerous possibilities for exposure measurement error in this study, several of
which are acknowledged by the authors. Concerning residential history, the exposure
measure in this study for a woman diagnosed at 60 years likely represents her adult life
exposure, however a woman diagnosed at 25 years, the exposure may represents her
early life exposure experience. Additionally, the outcome measurement may be
heterogeneous: breast cancers diagnosed early in life (3rd decade) as opposed to later in
life (7th decade of life) most likely reflect different etiologies, and known differences
between estrogen receptor positive tumors in relation to possible influence by an endocrine
disrupting pesticide were not evaluated. The effect of both exposure and outcome
measurement error may have attenuated these results. However, as the authors state,
there is no strong indication of an association, if there is a positive association in nature, it
is likely modest. These data provide little evidence of a strong association between breast
cancer and atrazine exposure, but this study cannot exclude the possibility of a poorly
measured moderate association.
Within the AHS cohort, two studies evaluated an association between atrazine use and
breast cancer: a nested case-control analyses among female spouses of pesticide
applicators (Engel et al. 2005), and a cohort analysis including female pesticide applicators
(Beane-Freeman et al. 2011). AHS study design and methods are discussed above in
section 3.2.2.1. Engel et al. (2005) compared ever-use of several pesticides, including
atrazine, and breast cancer rates among female spouses of enrolled pesticide applicators.
Authors were able to assess both direct, ever-use of pesticide in lifetime, and indirect
pesticide exposure, reported use by the pesticide applicator husband. However, atrazinespecific exposure information was only available for the ever-use metric, i.e., detailed
exposure information including duration and frequency of use was not available. The
female spouses who responded to the supplemental questionnaire also provided
information concerning other demographic data, reproductive health and known breast
cancer risk factors. As noted above, cancer outcome information was ascertained through
linkage with state cancer registries and is considered to be more than 95% complete
(Alavanja et al., 1996). Adjusting for age, race and state, authors did not observe evidence
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of an association between ever use of atrazine and incident breast cancer among female
spouses of enrolled applicators when measuring direct exposure (OR ever use 0.7, 95% CI
0.40, 1.2), or indirect exposure (husband’s ever use OR 1.1, 95% CI 0.7, 1.6) (Engel et al.,
2005). Slight differences in estimated risk likely due to increased stability of indirect
measure (greater number indirectly exposed women). Considering use of any pesticides,
authors observed no trend by duration of use (direct report of number years, days per
year), or proximity to pesticide treated area. A slight trend by number of times per year wife
washes clothes worn by husband when applying pesticide was observed, but confidence
bounds overlapped across indirect exposure category. While exposure measurement is
limited in this study, ever-use directly reported by only 11 atrazine exposed cases, the
study lacks evidence of an association between atrazine use and breast cancer.
In the recent cohort analysis of atrazine use, authors estimated that association with breast
cancer among female applicators enrolled in the AHS (Beane Freeman et al., 2011). In this
study, authors used the ever-use metric only; there were too few breast cancer cases
among female pesticide applicators to evaluate an exposure-response trend using quartiles
of atrazine exposure. Authors elected to evaluate this cancer site due to a priori interest
based upon previous epidemiologic research, and atrazine’s neuroendocrine health effects.
Authors reported no significant association among nine atrazine exposed cases and 27
controls (OR 1.14, 95% CI 0.47, 2.50) (Beane Freeman et al., 2011). Similar results were
observed, according to these authors, when comparing breast cancer rates between
women greater than and less than the median number of lifetime exposure days. Authors
adjusted for age only in this study, as other breast cancer risk factors were not confounders
in this sample and the use of fewer co-variables retains statistical power in a small sample.
The strengths of the AHS in terms of design, exposure and outcome measurement, and
confounder information have been noted above. In this analysis, the small number of
female breast cancer cases and resultant inability to explore an exposure-response relation
is a limitation. However, strengths of the study include temporal association, exposure
precedes disease.
There have been seven evaluations of the association between atrazine or triazine
exposure and breast cancer since 1997 identified in this review. Three of these studies
utilized an ecologic study design, and offered limited, inconsistent evidence of a link in the
human population, and are more supportive of the null hypothesis, no true association.
Mills et al. (2006) evaluated the question among Latinas in the state of California using
state cancer registry data, and aggregate level exposure analysis. These authors reported
no evidence of an association in two time periods evaluated, however the measurement
error in the method of exposure assessment, and uncertainties presented may have
attenuated these results in this highly exposed population of Latina agricultural workers.
AHS investigators evaluated the breast cancer-atrazine link among female spouses of
licensed pesticide applicators (direct and indirect exposure) as well as female applicators
themselves. Neither investigation observed evidence of an association. Although the AHS
is generally a very strong study, estimating risks among women exposed to pesticides is
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more limited than among men. Neither Engel et al. (2005) nor Beane-Freeman et al. (2011)
observed strong statistical associations, nor were they able to explore exposure response
relation due to the small number of atrazine exposed females, measuring only ever-use of
atrazine during the lifetime. Both studies had wide non-statistically significant confidence
intervals indicating lack of power to detect a difference if one truly was present. Research
indicates chemical exposure during different points in the life cycle may critically impact
cancer risks. None of these investigations were able to explore this aspect of the question.
Considering this database in total, studies available reflect different designs; varied target
populations including general population in high-use areas, agricultural workers, and
occupational groups; evaluated both direct and indirect pesticide exposure; explored
exposure through drinking water, or occupational (dermal/inhalation) pathways; ever-use of
the herbicide as well as exposure-response, among other sources of variation. Overall,
these studies lack strong evidence of an association between breast cancer and atrazine
exposure among low general population levels, or presumably higher occupational
exposure levels. Limitations across these studies include aggregate exposure assessment,
exposure measurement issues in those studies that attempt to estimate exposure at the
individual-level, and small number of atrazine exposed females in occupational settings
(AHS). These studies are mainly initial, exploratory analyses, with the exception of BeaneFreeman et al. (2011) which estimated the association based upon a priori hypothesis.
However, none of the investigations were able to explore critical windows of exposure, or
exposure-response associations with sufficient number of exposed females in the higher
exposure categories. At this time, the database lacks evidence of a relation between
atrazine exposure and breast cancer.
3.2.3.3
Ovarian Cancer
Because of the neuroendocrine effects of atrazine exposure, some epidemiology studies
have been performed evaluating the link between atrazine and ovarian cancer, however
there are only a few. An elevated risk of ovarian cancer was reported comparing the AHS
population to the general population of the respective study states (IA/NC) (Alavanja et al.,
2005; Waggoner et al., 2011).
Within the atrazine cancer epidemiology database, two evaluations included a point
estimate for atrazine exposure in relation to ovarian cancer risk; one was previously
reviewed by EPA (EPA, 2000) (See Appendix B-1). Briefly, for reference, Donna et al.
(1989) performed a population-based case control study in Italy in the mid-1980s. Cases
were ascertained through hospital registries, assumed to be complete among all cases in
the region, and controls were selected from electoral roles of the same region. Among the
65 cases and 126 controls selected, pesticide exposure was assessed through
questionnaire and classified as ‘definite,’ ‘probable,’ or ‘non-exposed’ based upon
questionnaire responses which were independently rated by two industrial hygienists.
Relevant confounding variables were also collected and included in the analysis, e.g.,
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ovarian cancer risk factors. Authors observed two-fold elevated odds of ovarian cancer
among those who were classified as definitely exposed to triazines (OR 2.7, 95% CI 1.0,
6.9) (Donna et al., 1984; Donna et al., 1989). In addition, authors observed evidence of
exposure-response by duration of exposure and by probability of exposure (i.e., none,
probable, definite). This study supported earlier findings from the same research group,
however the earlier publication measured herbicide use only (Donna et al., 1984).
A strength of the study was the inclusion of histopathologically confirmed cases which
reduces outcome measurement error. In addition, authors were able to obtain relatively
detailed exposure measures through interviewer administered questionnaire, however,
exposure measurement error remains an uncertainty in this study. Case ascertainment was
estimated to be 96%, and controls were similar to the source population indicating selection
bias was unlikely. The relatively small sample size and the probabilistic assignment of
exposure category are weaknesses. As noted in previous EPA reviews of this study, the
inability to adjust for other pesticide exposure is an uncertainty. Factors in favor of a true
association with ovarian cancer include the magnitude and statistical significance of the
reported association in more than one study, evidence of an exposure-response trend,
temporality of exposure-disease relation through study design decisions, and general
plausibility of a role for triazine in endocrine cancers. Synthesis of these data with
experimental studies is discussed in Section 3.3.
In addition to this evaluations, Hopenhayn-Rich (2002) performed an ecologic analysis of
triazine exposure, measured in state drinking water monitoring data, and ovarian and
breast cancer. Regarding ovarian cancer and triazine/atrazine use authors observed no
evidence of an association, and authors in fact reported an inverse association between the
mid- and high-triazine exposure groups (ecologic assessment) (Hopenhayn-Rich et al.,
2002). A separate ecologic analysis did not observe a correlation between atrazine
exposure and ovarian cancer in women in Ontario (J. A. Van Leeuwen, D. Waltner-Toews,
T. Abernathy, B. Smit, & M. Shoukri, 1999). Under the conditions of these studies, the
results do not lend evidence in support of an association with atrazine exposure.
In a hypothesis-testing investigation, Young et al. (2005) performed a population-based
case control study of ovarian cancer in association with triazine exposure in women who
resided in the Central Valley, CA, an area of high agricultural land use and pesticide
exposure. Histopathologically confirmed epithelial ovarian cancer cases were ascertained
through two regional registries, and controls were selected through random digit dialing
(RDD) of female residents in the study area who had a land-line telephone. Ovarian cancer
cases diagnosed in 2000-01 were eligible; pesticide exposure information was obtained
through a variety of sources and reflected the time period 1974-1999. Response rates were
modest in this study: 40% for cases and 57% for controls. Researchers assessed
occupational and residential history as well as other ovarian cancer risk factors through a
self-administered questionnaire distributed to study participants. Occupational history
included information such as location of agricultural job site, crops worked, job titles, years
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of employment among other characteristics. However, participants were not directly queried
as to specific pesticides used in the workplace. Authors estimated triazine exposure
indirectly by linking California state pesticide use reports for the study period to participants’
reported job location, years of employment, and crops worked.. In addition, weighting
factors were used to estimate intensity of exposure by job title, e.g., mixing and/loading
pesticides versus harvesting.
Only 8.8% of participants reported using triazines, and participants used simazine and
cyanzine more than atrazine. Adjusting for several endocrine cancer risk factors, authors
observed a non-statistically significantly elevated risk of ovarian cancer in relation to total
triazines (OR 1.34 (95% confidence interval (95% CI) 0.77,2.33), but not in association with
atrazine OR 0.76 (95% CI 0.16, 3.55), estimate based upon 2 and 12 exposed cases and
controls (H. Young, P. Mills, D. Riordan, & R. Cress, 2005). Examining quintiles of total
triazine exposure, a suggestion of an exposure-response trend upon visual inspection,
however the test for linear trend was not significant at the p<0.10 significance level. Using
an alternative statistical modeling approach, authors modeled triazine exposure as a
continuous variable; no evidence of a significant slope was observed. Sensitivity analyses
regarding weights used in the job exposure matrix, and the investigation of effect
modification by reproductive cancer risk factors were not additionally informative.
Authors observed some evidence of a positive association between occupational and
residential triazine exposure and ovarian cancer, however there was no statistical evidence
observed for an association with ever-use of atrazine, and no statistical evidence of an
exposure response trend for duration of exposure to triazines. The low prevalence of
atrazine use among women in this cohort (triazine prevalence 8.8%, atrainze prevalence
likely <5%), and the number of exposed cases and controls was small (2 and 12,
respectively), likely explains the imprecise risk estimate for atrazine exposure. A study with
a larger sample size would enhance the precision of the risk estimate, and clarify the true
direction of the association, if any. In addition, the exposure assessment method likely led
to non-differential exposure misclassification, attenuating the risk estimates. The low to
moderate participation rates may indicate the possibility of non-response bias, i.e., those
who refused to participate may have a different exposure profile than those who agreed.
The reported comparability between included and excluded cases with respect to age and
stage and grade of tumor at diagnosis somewhat reduces the potential for this type of
systematic bias, however authors acknowledged this uncertainty in the study. Exposure
information was collected to reflect the time period prior to ovarian cancer diagnosis,
reducing the chance of temporal bias, and exposure was assessed similarly between cases
and controls. Statistical analyses were clear and appropriate, including the selection of
confounding variables in the analysis. However, triazine-specific models may have been
too small to support adjustment for several of the ovarian cancer risk factors included in
models, further reducing statistical power. Other specific strengths and weaknesses of this
study are discussed in Appendix B-5. Overall, the authors conclude that this study cannot
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support or refute the existence of a true association between atrazine/triazine exposure and
ovarian cancer.
A recent AHS investigation provided an estimate of the risk of ovarian cancer among
female pesticide applicators in relation to ever use of atrazine during the working lifetime.
There were too few atrazine exposed female applicators to explore an exposure-response
relationship. Controlling for age only, as other ovarian cancer risk factors were not
confounders in the analysis, authors observed a non-statistically significant elevated odds
of ovarian cancer, OR 2.91 (95% CI 0.56, 13.60) in association with ever-use of atrazine
(Beane Freeman et al., 2011). It should be noted that this analysis reflected only nine
ovarian cancer cases, 4 of whom reported ever using atrazine. All of these cases reported
lifetime number of days used below the median for all atrazine exposed cancer cases, i.e.,
the ovarian cancer cases were among the lower end of the exposure distribution. Authors
state that the role of chance, or possibly another unknown source of unmeasured
confounding may explain these results; however, there is some plausibility of the result
given the neuroendocrine effects of atrazine. Strengths and weaknesses of the AHS are
noted in section 3.2.2.1 above, however with reference to this evaluation, strengths of the
study include hypothesis-testing nature of the estimation, the ability to investigate this
question among a group of occupationally exposed women, and lack of confounding bias in
the analysis. However, the lack of ability to explore an exposure-response association, and
the imprecise nature of the risk estimate due to a small number of atrazine exposed cancer
cases are limitations which must be addressed in further studies.
There are four epidemiologic investigations which consider the relation between atrazine or
triazine use and ovarian cancer: (1) an ecologic analysis which reported an inverse
association; (2) a population-based case-control study in Italy which reported elevated odds
of ovarian cancer for the most highly exposed group, but for which the study was limited in
its ability to adjust for other pesticide and confounder exposures; (3) a population-based
case-control study in California using publicly available data on outcome and exposure to
estimate risks and, therefore, is reflective of a large sample size, but only a small number of
atrazine exposed cases and controls (2 and 14 respectively); and, (4) the hypothesistesting risk estimate presented in a recent AHS cohort analysis that reported elevated odds
of the cancer, but which only utilized an ever-use exposure metric. These data are
supplemented by general information that the incidence and mortality of ovarian cancer are
elevated among the AHS occupational cohort in comparison to the general population, but
this information is clearly not specific to atrazine. Each study through design or methods
reflects an appropriate temporal association (exposure precedes disease). However, given
the typically late stage at ovarian cancer diagnosis, and long period of latency (Adami,
2002), some uncertainty regarding the temporal association exists. Although elevated
associations were observed in both Donna et al. (1989) and Beane-Freeman et al. (2011),
estimates were somewhat imprecise most likely due to the small number of exposed and
highly exposed ovarian cancer cases, and exposure-response relationships were not fully
assessed in these studies. Overall, this database presents some indication of a possible
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association between atrazine and/or triazine use and ovarian cancer; however, the small
sample sizes, lack of ability to control for other pesticide use and possibility of unmeasured
confounders in the association limit the ability to infer a causal association at this time.
The key events in the neuroendocrine mode of action for atrazine in relation to ovarian
cancer is discussed in Section 3.3 below.
3.2.3.4
Thyroid Cancer
EPA is aware of only one study of the possible association between atrazine use and
thyroid cancer; a recently published atrazine cohort analysis through the AHS explored this
association. However, EPA notes that the relation was only assessed in male pesticide
applicators; there was only one atrazine exposed female applicator in the cohort. The
etiology of thyroid cancer is not well understood, although family history of thyroid cancer
and radiation exposure are known risk factors. Also, the etiology and epidemiology of
thyroid cancer differs between men and women and differs among the types of thyroid
tumors.
In the AHS, Beane-Freeman et al. (2011) compared lifetime atrazine exposure days among
twenty-nine exposed thyroid cancer cases in comparison with non-cases of thyroid cancer.
Author’s ascertained thyroid cancers among the cohort through linkage with state cancer
registries, and self-administered questionnaires provided information on use of other
pesticides, demographic characteristics and other cancer risk factors. Adjusting for several
potential confounding factors, researchers observed a statistically significant, four-fold
increased odds of thyroid cancer comparing the most highly exposed group to the referent
(the lowest exposed) (OR intensity-weighted upper quartile 4.84 (95% CI 1.31, 17.93))
(Beane Freeman et al., 2011). The association remained when researchers controlled for
body mass index, a known risk factor for thyroid cancer (OR 4.30 (95% CI 1.21, 15.28)).
Non-significant, but positive 2- to 4-fold associations were consistently observed across
quartiles of exposure using both the cumulative exposure metric and the intensity-weighted
exposure metric. The test for linear trend was significant at p<0.10, but not at p<0.05, for
intensity-weighted lifetime days. However, the lack of a clear exposure-response trend, and
the small number of atrazine exposed cases in some categories, e.g., n>5, were limitations
to this study and present some uncertainties in the interpretation of these results. The
authors conclude that this investigation, given the strengths of the AHS in design and
methods (mentioned above), provide some evidence of a positive link between atrazine
and thyroid cancer in male pesticide applicators who reported using atrazine during their
lifetime.
EPA identified no other evaluations of this relation in the literature. There is no known
biological mechanism postulated for atrazine at this time (See Section 3.3). Authors noted
that another AHS investigation examined pesticide use in association with hypothyroidism,
a suspected risk factor for thyroid cancer, and observed no association with atrazine
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(Goldner et al., 2010). Strengths of this one investigation of the topic include the design and
method of the AHS, i.e., prospective, extensive pesticide exposure information that has
been validated and reliable, appropriate temporal association to estimate risk, ability to
examine an exposure-response relation, and general plausibility of result. However, the
lack of an observed trend, paucity of other epidemiologic or experimental data providing
any comparative information, low statistical power and ability to measure risk with less
uncertainty due to small sample size, and lack of mechanistic information.
3.2.4
Lymphohematopoietic Cancers
In this section, cancer epidemiology studies of the association between atrazine and/or
triazine use and lymphohematopoietic cancers are reviewed. Over the last twenty years,
several studies have hypothesized a role for farming exposures and pesticides specifically
in relation to leukemia and lymphomas. Specifically, a series of population-based case
control studies located in the mid-western U.S. performed by the National Cancer Institute
(NCI) estimated the risk between pesticide use including atrazine and several
lymphohematopoietic outcomes (Brown et al., 1990; Brown, Burmeister, Everett, & Blair,
1993; Burmeister, 1990; Cantor et al., 1992; Hoar et al., 1986; Hoar Zahm, Weisenburger,
Cantor, Holmes, & Blair, 1993; Weisenburger, 1990). Among these studies, however, only
two specifically tested the hypothesis of atrazine exposure in association with one of these
cancer subtypes, non-Hodgkin lymphoma (NHL) (Hoar Zahm et al., 1993; Zahm et al.,
1993). In addition, cohort evaluations comparing cancer incidence (standardized incidence
rates, SIR) and cancer mortality (standardized mortality rates, SMR) rates between an
occupationally group exposed to triazines and the general population also estimated risk of
NHL and other sub-types (P. MacLennan et al., 2002; P. A. MacLennan, E. Delzell, N.
Sathiakumar, & S. L. Myers, 2003; Sathiakumar & Delzell, 1997; N Sathiakumar, E Delzell,
& P Cole, 1996). Each of these evaluations has been previously reviewed by EPA (Blondell
& Dellcarco, October, 28, 2003 2003 #1026; EPA, 2000, 2003a) [See Appendix B.3].
Since the time of the last EPA review, DeRoos et al. (2003) published a pooled analysis of
the association between pesticide use and NHL pooling data from three population-based
case control studies (IA/MN, KA, and NE) performed by the NCI in the 1980’s. These
authors employed a hierarchical regression technique which allowed for co-adjustment for
exposure to 47 different pesticides when measuring the specific putative pesticide-NHL
association including atrazine (De Roos et al., 2003). EPA also identified two hospitalbased case-control studies performed in France investigating the possible link between
atrazine and other pesticides with leukemias and lymphomas during two separate study
periods (Clavel et al., 1996; Orsi et al., 2009). In addition, two cohort analyses in the AHS
investigating the use of atrazine in relation to all cancer outcomes including leukemias and
lymphomas have been published since EPA last reviewed this database (Beane Freeman
et al., 2011; J. A. Rusiecki et al., 2004). Each of these studies is briefly discussed below.
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3.2.4.1
Population-Based Case-Control Studies: Midwestern U.S.
In the early 1990’s, the NCI established three population-based case control study
populations in the Midwestern U.S. to examine the association between several different
lymphohematopoietic cancers and farm-related exposures including pesticides. Across
these studies, cases of lymphohematopoietic (leukemias and lymphomas) were identified
through state-based cancer registries (IA, KA), or state-wide cancer surveillance, and
controls were identified from the general population. Demographic and pesticide exposure
was obtained through in-person (IA, MN) or telephone-based (KS, NE), structured interview
questionnaires administered by trained study staff members. Duration and intensity of
pesticide exposure was available for a subset of participants, and a small pesticide
validation study using sales records was performed (Hoar et al., 1986). Authors solicited
information about ever use of specific pesticides, farm activities, residential history, nonfarm occupations, and first and last year of use of specific pesticides. Pesticide exposure
information was obtained for 38 herbicides, 24 insecticides, 34 crop insecticides, and 16
fungicides. Atrazine exposure information was collected in most of these studies; Hoar et
al. (1986) however only measured triazine exposure. Across these evaluations,
researchers were able to control for the influence of other pesticide exposure as well as
cancer risk factors in the statistical analysis.
Brown et al. (1990) evaluated odds of leukemia in association with pesticide use, and
reported no evidence of a statistical association: ever use of either triazine (OR 1.1 (95% CI
0.8, 1.5), and atrazine use among 38 and 108 exposed cases and controls, respectively
(OR 1.0 (95% CI 0.6, 1.5). In a separate evaluation, no evidence of an association was
observed with multiple myeloma, although the number of atrazine exposure cases and
controls were small: 12 and 74, respectively (Brown et al., 1993).
Regarding an association with non-Hodgkin lymphoma (NHL), Hoar et al. (1986) reported
that NHL risk, but not soft tissue sarcoma or Hodgkin’s disease, increases with duration
(number of years) and frequency (days/year) of herbicide use (p for trend<0.05), and the
association between triazine use (ever/never) and NHL are elevated 2-fold (OR 2.5 (95%
CI 1.2, 5.4) (Hoar et al., 1986). When users of phenoxy or uracil herbicides, other highly
prevalent herbicides, are excluded from the ever-triazine exposure group the association
remained positively elevated, but uncertainty increased, (OR 2.2 (95% CI 0.4, 9.1)). The
latter analysis is based on only 3 and 11 triazine exposed cases and controls. Cantor et al.
(1992) reported some evidence of a link with farming exposures and NHL: corn growers
(OR 1.4 (95% CI 0.9, 2.4), herbicide users (OR 1.3 (95% CI 1.0, 1.6), and triazine users
(OR 1.6 (95% CI 1.0, 2.5) (Cantor et al., 1992). However, the risk of NHL in association
with ever-use of atrazine is only weakly suggested 20% increased odds of NHL (95% CI
0.9, 1.8), adjustment for use of other pesticides did not alter estimates.
Combining datasets from all three of the NCI sponsored, Hoar-Zahm 1993 reported a
significant association between atrazine use and NHL (OR 1.4 (95% CI 1.1, 1.8); however,
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in contrast to Cantor et al. (1992), adjustment for the use of other pesticides,
organophosphates and 2,4-dichlorophenoxyacetic acid (2,4-D) in particular, diminished this
estimate considerably (Hoar Zahm et al., 1993).Notably, there was no appreciable
difference in the risk of NHL among ever-users of atrazine and those who reported
personally handling atrazine in this analysis. When evaluation the same association among
women only, authors observed little evidence of an association, although the number of
atrazine exposed female NHL cases was limited (Zahm et al., 1993). Overall, these authors
suggest their evaluation presented little evidence of an association between atrazine use
and NHL.
Strengths of the studies include use of low likelihood of selection bias, high quality outcome
ascertainment including histopathological confirmation, performance of exposure validation
study (using pesticide sales records), and ability to adjust for other pesticides in the
atrazine-NHL association. Limitations include small numbers of exposed cases in some
instances, the hypothesis-generating nature of these investigations, and probable
exposure-measurement error each of which may lead to uncertainties in the risk estimates
reported. EPA notes other details are noted in Appendix D.
The question as to the role of co-exposure to other pesticides when measuring the
association between atrazine, or any other pesticide, and NHL was evaluated in a pooled
analysis using these three population-based case control studies (IA/MN, NE, KS). DeRoos
et al. (2003) estimated the association between atrazine exposure and NHL, as well as
several other pesticides, in an analysis which concurrently controlled for the influence of all
(n=47) other pesticide exposure using sophisticated analytic techniques. Hierarchical
regression techniques use available information (pesticide class, functional group,
carcinogenic classification) to estimate a “prior” distribution of the pesticide-NHL risk
relations, and adjust observed associations (results of pooled analysis) toward the “prior”
distribution (De Roos et al., 2003). In this way, authors used information external to the
studies themselves to inform the shape of the exposure-response curve. Hierarchical
regression can add precision and accuracy to risk estimates, however precision and
accuracy are only enhanced relative to the quality of the “prior” distribution.
Researchers reported statistically-significant 50% increased odds of NHL in association
with exposure to atrazine (hierarchical regression adjusted odds ratio (OR) 1.5 (95%
confidence interval (95% CI), 1.0 to 2.2)), adjusting for exposure to all (n=47) other
pesticides (De Roos et al., 2003). It is notable that this result, which is essentially a
weighted average of the three NCI case control studies discussed above (Hoar et al. 1986;
Cantor et al. 1992; Zahm et al. 1993), is closer to 1.0 than the adjustments performed in the
logistic model presented in the pooled analysis of the same question performed by Zahm et
al. (1993). The analytic methods in DeRoos et al. (2003) including the ability to mutually
adjust for many other pesticides may explain the different risk estimate. Overall, DeRoos et
al. (2003) cautioned that these data only provide limited evidence of any specific pesticidePage 55 of 184
NHL associations as results of many different statistical tests, the role of chance may
explain these (positive) findings.
EPA also identified epidemiology studies external to the NCI series of case-control studies,
and reported its interpretation of these data in the earlier evaluation of this database.
Burmeister et al. (1990) studied the relation between triazine exposure and multiple
myeloma and reported a non-significant 30% increased odds of exposure among those
with MM (Burmeister, 1990). In addition, in a correlation analysis using county level
pesticide use data (CA DPR), i.e., ecologic exposure assessment, and state cancer registry
statistics, Mills et al. (1998) reported a moderate correlation between leukemia and atrazine
use (P. Mills, 1998) (See Appendix D). In addition, an ecologic study comparing all cancers
reported to a regional registry and triazine usage, authors reported no evidence of a
significant correlation with NHL (J. Van Leeuwen, D. Waltner-Toews, T. Abernathy, B. Smit,
& M. Shoukri, 1999). Mills et al. (1998) in the same correlation analysis as noted above,
reported no evidence of a correlation between pesticide use and NHL among most subpopulations examined, with the exception of Hispanic females. These authors reported a
non-significant positive correlation with NHL among this group. It was also noted in
previous EPA reviews that an occupational cohort study reported elevated standardized
mortality rates (SMRs) for NHL (n=2 cases) among triazine manufacturing plant workers in
comparison to the general population of the area (P. A. MacLennan et al., 2003; P. A.
MacLennan et al., 2002). These analyses updated comparisons made between
manufacturing workers occupationally exposed to triazines and the general public
published in the mid-1990’s and also reported elevated rates of NHL among workers
(Sathiakumar & Delzell, 1997; N. Sathiakumar et al., 1996). Overall, these studies offer
weak evidence to our consideration of this question, and have been superseded by more
recent investigations.
3.2.4.2
Hospital-Based Case-Control Studies (France)
Clavel et al. (1996) performed a hospital-based case control study of hairy-cell leukemia
(HCL) and pesticide use, including triazines. Authors identified cases through oncology
departments of 18 regional hospitals, and control patients through other hospital
departments. Participation rates were modest, however participants were blinded as to the
study hypothesis, therefore it is unlikely participants declined to participate due to pesticide
usage. Pesticide exposure was assessed via self-administered questionnaire distributed via
mail, verified by an industrial hygiene expert in pesticide use who also classified exposure
intensity based upon information provided in the questionnaire. Authors reported elevated
odds of hairy cell leukemia for all farmers (OR 1.5, lower limit 1.0), corn growers (OR 3.2,
95% CI 1.6, 6.2), ever use of herbicides (OR 1.8, 95% CI 1.0, 3.1), and ever use of
triazines (OR 2.4, 95% CI 1.2, 3.5) (Clavel et al., 1996). However, when authors examined
the association among never users of organophosphates only, the association with
triazines was attenuated and becomes statistically non-significant OR 2.0 (95% CI 0.7, 5.6).
The correlation between organohphosphate and triazines is high in this study population.
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In a study with similar design and methods and study population, performed between 200004, Orsi et al. (2009) evaluated the association between farming including specific
pesticides, and lymphoid neoplasms. Outcomes examined included non-Hodgkin’s
lymphoma (NHL), multiple myeloma (MM), Hodgkin’s lymphoma (HL), and lymphoid
proliferative syndrome (LPS). Cancer cases were identified through medical record
evaluation in oncology centers at six hospitals in France; and, controls were identified from
the patients who attended different departments of the same hospitals (e.g., rheumatology,
orthopedics) unrelated to cancer treatment. Pesticide exposure information was collected
first by self-administered questionnaire, followed by a structured in-person questionnaire
administered by trained staff. Industrial hygienists with expertise in pesticide use evaluated
participant responses. Authors modeled both ever-use of pesticides, as well as total
number of years employed in agriculture as a measure of duration of exposure.
Authors reported associations between employment in farming, and corn farming
specifically in addition to ever use of specific pesticides in association with NHL, HL, and
MM. For reported triazine use specifically, associations were observed with HCL (OR 5.1,
95% CI 1.4-19.3) (Orsi et al., 2009). In addition, triazine use was associated with NHL
(diffuse large cell lymphoma 8/20 OR 2.1 [95% CI 0.8–5.0]) and follicular lymphoma (FL)
4/20, OR 2.3 [95% CI, 0.7–7.7]), but not with chronic lymphocytic leukemia 4/17 OR 0.9
[95% CI 0.3–3.0] (Orsi et al., 2009). Small sample size, particularly low numbers of triazine
exposed cases of lymphoma sub-types resulted in imprecise measures of the association.
The strong magnitude of the association suggests additional studies employing a larger
sample may result in more stable, positive associations; however, the width of the
confidence limits which include 1.0 in each instances suggests results could be due to
chance. The statistically significant association with hairy-cell leukemia is noted; however,
this confidence interval is also quite wide.
Authors suggest an association between triazine use and lymphohematopoietic cancers
may be present, specifically with HCL, NHL and FL. Important to this effort, EPA notes
triazines and not atrazine per se was measured. Strengths of these studies include the
performance of several sensitivity analyses to reduce some sources of uncertainty, and use
of two levels of pesticide exposure data collection by trained, blinded interviewers.
However, the relatively low statistical power and relatively wide confidence bounds suggest
lack of precision in the estimates and an inability to exclude chance as an explanation for
these findings. In addition, as acknowledged by the authors, systematic bias could not be
entirely ruled out with respect to representativeness of the control series of the population
from which cases arose. Exposure assessment reflected ever-use over a lifetime and
disallowed more refined analysis for risk assessment. Overall, the potential for bias in
terms of selection and exposure measurement error, the highly correlated nature of multiple
pesticide use and the inability to control for this in the Orsi et al. (2009) study, among other
critiques noted in the appendices weaken these results.
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3.2.4.3
AHS Estimates of Atrazine and Lymphohematopoietic Cancers
Within the AHS, Rusiecki et al. (2004) performed a cohort analysis of cancer (all) in
association with lifetime use of atrazine. While authors examined the link between atrazine
use and several cancer outcomes, the results for NHL and MM are discussed here only. A
brief discussion of AHS methods and design as well as strengths and weaknesses is
presented above in Section 3.2.3.1.
Using a measure of both cumulative exposure (total number of days per working lifetime,
lifetime exposure days (LTED)) and also intensity-weighted cumulative exposure (intensityweighted LTED, IWED), exposure modified by use of personal protective equipment (PPE)
and other use practices, researchers reported evidence of a positive, non-statistically
significant associations with NHL (OR(IWED) 1.75 (95% CI 0.73 to 4.20, n=20), as well as
with multiple myeloma (OR 2.17 (95% CI 0.45 to 10.32) (same comparison) comparing the
upper-quartile of atrazine exposure (175 exposure days) with the lowest exposed group (120 exposure days) (J. A. Rusiecki et al., 2004). A suggestion of a positive trend across
quartiles of atrazine exposure was noted, albeit the test for trend was not significant
(p>0.10). No evidence of a statistical association between atrazine exposure and leukemia
was observed. In this analysis, authors controlled for age, sex, alcohol consumption,
residence on a farm, smoking status, educational level, family history of cancer, state of
residence, and use of 10 most highly correlated pesticides with atrazine (J. A. Rusiecki et
al., 2004). Recently, Beane-Freeman et al. (2011) updated this analysis within the AHS
with approximately twice as many cancer cases. Adjusting for standard confounding
variables as well as other pesticide use authors observed no evidence of an association
with any lymphohematopoietic cancer (Beane Freeman et al., 2011). Specifically, authors
reported no association with NHL or any NHL sub-type, or with leukemia or multiple
myeloma. Regarding NHL specifically, the updated analysis included 152 cases while the
initial evaluation included 68 cases. The increased statistical stability produced by the
larger sample size appears to have clarified the nature of the association between atrazine
and NHL in the AHS cohort of private and commercial pesticide applicators.
Therefore, while the result of an initial, hypothesis-generating study in the AHS reported
suggestive, positive associations with NHL and MM, due to elevated risks in the upperquartile, and the suggestion, albeit not statistically significant, of an exposure-response
trend, recent re-evaluation of the data did not replicate this initial finding. The recent
evaluation with twice as many cases of non-solid tumors report no evidence of an
association with atrazine use within the AHS cohort. The increased sample size may have
reduced the role of random variability in the estimates (i.e., wide confidence intervals), and
the use of an updated AHS exposure algorithm may have reduced exposure measurement
error in such a way as to move the estimate toward the null values. Positive associations
initially observed may also be the result of multiple statistical tests; however, earlier studies
(discussed above) suggest a possible link with NHL.
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3.2.4.4
Conclusions: Lymphohematopoietic Cancers
Several investigators have evaluated the association between pesticide exposure including
atrazine/triazine exposure specifically and lymphohematopoietic cancers since the early
1990’s. Studies published prior to the 2003 EPA review provided little evidence of a link
between leukemia or multiple myeloma and atrazine use (Brown et al., 1990; Brown et al.,
1993; Burmeister, 1990), however the small sample size and role of random variability in
addition to non-differential exposure measurement error may have influenced these results.
The recent AHS study with enhanced design, lifetime exposure measurement, outcome
ascertainment, and increased sample size, reported risk estimates for leukemia and
multiple myeloma that did not differ from the null (Beane Freeman et al., 2011). The latter
study reduces uncertainty around this association, and is supportive of the conclusion of no
association among occupationally exposed applicators.
The results of both individual case-control studies and pooled analyses based upon these
studies provided some evidence of an association between atrazine and NHL (Cantor et
al., 1992; De Roos et al., 2003; Hoar Zahm et al., 1993; Zahm et al., 1993), however the
authors themselves cautioned against considering specific pesticide-NHL associations
reported as evidence of a causal link. For example, NHL risk was estimated in association
with numerous pesticides, including 33 herbicides, in these studies; no adjustment for
multiple statistical tests was performed. However, these studies provided adequate control
for systematic biases such as selection bias (comparability of controls to population from
which cases arose), recall bias (participants blinded to study hypothesis), and some control
for confounding due to use of other pesticides. The hospital-based case control studies
performed in France during two separate study periods present some evidence of a
positive link, however the potential for systematic error in the conduct of these studies
including selection bias due to method of control identification, weakens the strength of this
evidence in the overall assessment of the epidemiologic database (Clavel et al., 1996; Orsi
et al., 2009).
In 2004, the initial AHS evaluation (Rusiecki et al. 2004) suggested a positive, elevated risk
of multiple myeloma, but neither the risk estimates nor the test for trend was statistically
significant although a positive exposure response trend was suggested in the data (J. A.
Rusiecki et al., 2004). However, this finding was not replicated in subsequent AHS cohort
analysis (Beane Freeman et al., 2011). These authors observed no evidence of a statistical
association between atrazine use and leukemia, multiple myeloma, NHL and NHL subtypes, although there were fewer than 5 exposed cases per exposure category in some of
the analyses. Differences in study design, i.e., population-based case-control and cohort,
and exposure assessment methods between the earlier studies and the recent AHS study,
as well as difference in sample size and the number of exposed cases between the earlier
and later AHS cohort analysis, may explain these findings. Overall, the database lacks
evidence of an association between atrazine and triazine exposure and these lymphoma
and leukemia sub-types, including NHL.
Page 59 of 184
3.2.5
Other Cancer Sites
In addition to studies of atrazine’s potential influence on lymphohematopoietic cancers or
reproductive and endocrine system tumors noted above, EPA has identified additional
investigations of a potential link between atrazine with other cancer sites in both adults and
children. These studies reflect atrazine’s potential influence upon tumors in the brain
(glioma) and digestive tract including colon. Other cancer endpoints evaluated within AHS
studies, but not yet discussed in the chapter, are also summarized herein to aid
completeness of the data presentation. These studies are briefly mentioned herein.
3.2.5.1
Brain/Glioma
EPA identified a hypothesis-generating evaluation of the relation between farm exposures,
including atrazine, and brain cancer. Results were reported separately for men (Ruder et
al., 2004) and women (Carreon et al., 2005). The study was performed in the upper
Midwest U.S. by a National Institute of Occupational Safety and Health (NIOSH) research
team. Authors performed a population-based case control investigation of the association
between pesticide exposure and gliomas, a type of brain cancers, among persons living in
the upper mid-western states of Iowa, Michigan, Minnesota, and Wisconsin. In the study
among women, cases were ascertained (1995-97) through interactions with medical offices
or neurosurgeons, and were histopathologically confirmed. Proxy respondents were used in
instances in which the case was deceased. Controls were non-cases of glioma. Controls
may have had other cancers, and were identified through either state DMV (ages 18-65) or
state health care files (ages 65-80). Pesticide exposure as well as other demographic
information was obtained through self-reported questionnaire; trained industrial hygienists
reviewed written responses and compared reported pesticide trade names with database to
determine active ingredients used. Data collection included comparability of methods
between cases and controls including a standardized interview, intermittent re-training of
interviewers within the study period, and blinding of interviewers to the study hypothesis.
Among 341 female cases and 527 controls, authors reported little evidence of an
association between triazine or atrazine use and gliomas. The results include: ever-use of
an herbicide, OR 1.0 (95% CI 0.6, 1.5); triazine OR 1.0 (95% CI 0.6, 1.7); and atrazine OR
1.1 (95% CI 0.7, 1.8) (Carreon et al., 2005). Results for men were similarly unremarkable.
Authors observed no association between glioma and triazine use (OR 0.9, 95% CI 0.6,
1.2), and they did not separately estimate glioma odds in relation to atrazine use (Ruder et
al., 2004). Although both evaluations were derived from the same study, Carreon et al.
(2005) provided more extensive detail concerning study design and methods. For example,
details concerning case and control participation rates were not provided in the study by
Ruder et al. (2004). However, in both evaluations the risk estimate for atrazine excluding
proxy respondents was similar.
Page 60 of 184
Authors performed a hypothesis-generating evaluation of pesticide exposure in relation to
glioma diagnosis. There are many strengths to this study including obtaining information
from those who refused to participate in the study to compare characteristics and assess
potential systematic bias; use of a 2-year lag in exposure through design to assure
exposure among cases is not over-estimated; moderately high recruitment rate; and, use of
several quality assurance and control methods including standardized interview techniques,
as well as interviewer training and blinding to study hypothesis to reduce observer bias.
However, there are some limitations as well including the method of selection of cases and
controls and the possibly under-ascertainment of cases, possible heterogeneity in outcome
classification, and quality of exposure measurement. Overall, the uncertainties presented in
this study make it difficult to discern whether the lack of an observed association reflects
the true population risk of glioma in relation to pesticide exposure, or reflects errors in study
design, i.e., selection bias or exposure measurement error as noted by the study authors.
Other than results from the NIOSH studies, EPA identified two ecologic evaluations of the
possible link between atrazine exposure and brain cancer. Mills et al. (1998) suggested a
moderate correlation between this exposure and disease, while Van Leeuwen et al. (1999)
reported no significant association (P. Mills, 1998; J. A. Van Leeuwen et al., 1999). Overall,
the NIOSH studies provide insufficient evidence to determine whether an association
between atrazine or pesticide exposure and glioma exists. EPA notes that the result
evaluation within the AHS does not report a significant, positive association with any brain
cancer (Beane Freeman et al., 2011).
3.2.5.2
Pediatric Cancers
In an ecologic analysis, Thorpe et al. (2005) compared concentration of atrazine and other
environmental contaminants in residential drinking water, in Maryland with state cancer
registry data on childhood cancers to estimate risk of childhood cancers in association with
contaminants. Several pediatric cancer sites were evaluated. Authors reported nonsignificantly elevated odds of bone cancer and leukemia among 689 pediatric cancers
reported during the study period. There are few studies of pediatric cancers in association
with pesticide use, and fewer in relation to atrazine use (Thorpe & Shirmohammadi, 2005).
Therefore this study contributes to our understanding of the potential relation. Moderately
elevated risk estimates were observed in this ecologic analysis, albeit non-statistically
significant. However, in addition to the standard limitations of ecologic studies for use in
causal inference, this study assumed all participants used well water for drinking water, did
not address the uncertainty presented by residential mobility during pregnancy which may
have also contributed to childhood cancers, and could not provide much information as to
whether prenatal or postnatal exposures may be more strongly associated with pediatric
cancers. Significant uncertainties are present in this study, which limits its use in
determining whether a true link with atrazine exists.
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Rull et al. (2009) performed a population-based case control study of pediatric acute
lymphocytic leukemia (ALL) within the Northern California Childhood Leukemia Study
(1995-2002). Maternal residence at birth was compared with state pesticide use records
(PUR) to estimate pesticide exposure. Authors considered exposure to 118 active
ingredients for which there was some evidence of cancer, developmental or reproductive
health effects, or evidence of endocrine disruption (adjudicated by authors); or, frequency
of use was high. Using proximity of maternal home as a measure of exposure, authors
evaluated pesticide exposure over the child’s lifetime as well as during the first year of life
only.
Authors reported borderline significant associations between those in the moderately
exposed category of triazines (OR 1.9 (95% CI 1.0, 3.7), but not among those in the most
highly exposed category (R. P. Rull et al., 2009). Adjusting for exposure to other classes of
pesticides, the magnitude of the association in the moderately exposed category increased,
but the precision decreased (OR 4.1, (95% CI 1.5, 11.1)) (R. Rull et al., 2009). Associations
were also observed for herbicide categories, but results were not statistically significant
(moderately exposed: OR 1.2 (95% CI 0.8, 1.9), and highly exposed: OR 0.9 (95% CI 0.5,
1.5)) for lifetime exposure to herbicides. There was no association between pediatric
triazine exposure during the first year of life and ALL risk in this study. Triazines to which
this study population was exposed included atrazine, cyanazine, prometryn, pymetrozine,
and simazine.
Authors performed a hypothesis-generating, population based case-control study of
pediatric ALL in relation to pesticide exposure. Strengths of the study include method of
case ascertainment, within 72 hours of diagnosis; the recruitment rate was high (84%); and
the cases and controls were similar on several (matching) factors, reducing the potential for
selection bias and residual confounding. The major limitation of this study is the method of
exposure assessment, i.e., using maternal proximity to farm-site as a proxy for exposure,
and the lack of information on critical windows of exposure for development of a pediatric
cancer, and lack of precision of estimation of triazine with acute lymphocytic leukemia.
Overall, given both the noted limitations of this study as well as the lack of statistical
evidence of an association, the study is not strongly supportive of a link with atrazine.
Although not triazine or atrazine specific, within the AHS, authors evaluated childhood risk
of cancer among children of parents enrolled in the AHS and reported an overall slight
excess risk (OR 1.36, 95% CI 1.03, 1.79) (Flower et al., 2004). When examining specific
cancers, risk of lymphoma was overall elevated among children of AHS families. Authors
observed few pesticide-specific associations; but statistical power may have limited the
ability to detect associations. Authors suggested non-pesticide farm exposures may explain
these overall findings, such as Epstein-Barr virus.
At this time the database is insufficient to determine whether an association exists between
atrazine or triazine exposure and pediatric cancers. Importantly for pediatric cancers,
Page 62 of 184
neither of these initial evaluations measured pesticide exposure during critical windows of
development, including maternal exposures. The ecologic study is not of sufficient quality to
support the hypothesis of a relation between atrazine and pediatric cancers. The lack of an
exposure-response trend by proximity to field, and imprecision of risk estimate due to small
number of cases in upper exposure category as well as some degree of non-differential
exposure misclassification, among other factors, weakens the ability to draw a conclusion
based upon these data.
3.2.5.3
Colon Cancer
Hoar et al. (1985) published a brief report of a case control analysis of pesticide use and
colon cancer. Authors selected cases diagnosed with colon cancer in Kansas during 197682, and population-based controls. As a brief report, limited detail concerning data
collection procedures or statistical methods used to evaluate the association between colon
cancer and exposure to herbicides was provided. Hoar et al. (1985) reported that
employment on a farm was associated with a non-significant increase in colon cancer risk
(OR = 1.6, 95% CI: 0.8 – 3.5), which was similar to the risk in farmers who did not report
using herbicides, and reported use of triazines was not associated with colon cancer (OR
1.4, 95% CI 0.2, 7.9) (Hoar, Blair, Holmes, Boysen, & Robel, 1985). With regard to selfreported use of herbicides, the investigators reported that the risk of colon cancer among
farmers who reported mixing herbicides was similar to the risk in farmers who never
reported using herbicides and there was a lack of dose-response relationship between
years of herbicide use and colon cancer risk. Based on these reported findings, the
investigators suggest that the study results do not support an association between colon
cancer and herbicide exposure including triazines. In addition to this estimate, previously
mentioned analyses in which cancer rates were compared between a cohort of triazine
manufacturing plant workers and the general population did not report an elevated rate of
either colon or rectal cancer incidence or mortality (P. MacLennan, E. Delzell, N.
Sathiakumar, & S. Myers, 2003; P. MacLennan et al., 2002), and an ecologic analysis did
not observe a link between atrazine and colon cancer (J. A. Van Leeuwen et al., 1999).
Lee et al. (2007) reported colorectal cancer risk estimates for several pesticides including
atrazine among 305 incident cases of colorectal cancer and 56, 813 non-colorectal cancer
cases. Overall, authors observed no evidence of an association between atrazine use and
colorectal cancer (OR 0.9 (95% CI 0.7, 1.2)), or colon cancer (OR 0.8 ( 95% CI 0.6, 1.1)),
or cancer of the rectum (OR 1.2 (95% CI 0.7, 2.0)) (Lee et al., 2007). In this investigation,
authors only reported the atrazine ever-use exposure metric, as no evidence of an atrazine
exposure-response trend was observed. Recent cohort analyses within the AHS did not
observe evidence of an association between lifetime atrazine use and either colon or rectal
cancer (Beane Freeman et al., 2011; J. A. Rusiecki et al., 2004).
The magnitude and direction of the risk estimates were suggestive of no association, and
among the studies with sufficient sample size to evaluate an exposure-response trend,
Page 63 of 184
none was observed. While the database is a small one, the more recent evaluations within
the AHS have several strengths over previously published studies include the prospective
design, larger sample size, and measurement of other potentially confounding variables
including use of other pesticides. Among a nested case-control study and two cohort
analyses which estimated colorectal cancer risk in association with atrazine, no evidence of
an association was observed. Authors did not propose a mechanism through which
atrazine could influence colorectal cancer risk; animal studies did not observed tumors in
these sites (See Section 3.3). Overall, this database lacks evidence of an association.
3.2.5.4
Other AHS
In the interest of providing a complete delineation of the atrazine or triazine and cancer
point estimates reported in the AHS, EPA notes the following additional results which have
not yet been mentioned in cancer specific sections above. Neither the earlier nor the
updated cohort analysis noted an association with all cancers combined, nor with several
additionally evaluated cancer sites including: oral, esophagus, pancreas, melanoma,
kidney, larnynx, brain or liver. Suggestive findings regarding other anatomical cancer sites
noted by Rusiecki et al. (2004) in the earlier cohort analysis were not replicated in the
updated study (Beane Freeman et al., 2011). Specifically, the odds of either lung or bladder
cancer were not significantly different by atrazine exposure category in the more recent
AHS study (Beane Freeman et al., 2011). Other anatomical cancer sites for which some
statistical evidence of an association was noted, but for which no exposure-response trend
was observed, or lacked an a priori hypotheses, were not considered to provide strong
biological evidence of an association (esophageal and oral cancers, and inverse
associations with pancreatic and laryngeal cancers).
As noted above, AHS researchers also performed several nested case-control analyses
since the last EPA evaluation. Among these, neither the study of lung cancer nor
cutaneous melanoma included specific risk estimates for atrazine use, due to lack of an
observed exposure-response association, however EPA notes that lack of an observed
association is important evidence to consider in the totality of the atrazine cancer
epidemiology database (Alavanja et al., 2004; Dennis et al., 2010). Andreotti et al. (2009),
compared pesticide use, including atrazine, among 93 pancreatic cancer cases and 82,503
cancer-free applicator and applicator spouse controls, and reported no evidence of an
association with atrazine use (OR 0.70 (95% CI 0.4-1.2)) (Andreotti et al., 2009). Due to the
low numbers of atrazine-exposure pancreatic cancer cases, no exposure-response
analysis was attempted.
Across these AHS studies, evidence suggesting a statistical association is lacking, results
did not significantly differ from unity.
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3.3
Summary of Evidence Concerning the Carcinogenic Potential of Atrazine
3.3.1
Synthesis and Integration of Toxicology and Epidemiology Literature
Over the past several decades, there have been numerous experimental toxicological as
well as epidemiologic evaluations of the carcinogenic potential of atrazine. EPA has
presented its evaluation of earlier iterations of this combined database in both 2000 and
2003 as noted in section 3.1 (Blondell & Dellarco, October, 28, 2003 2003 #1026; EPA,
2000, 2003a). In April 2010, EPA presented the most current assessment of the atrazine
cancer toxicological database (EPA, 2010). In this section, EPA will consider the most
current assessment of the atrazine cancer epidemiology database in the context of the
available experimental data, combining the evidence from these two streams of
information.
EPA has extensively considered the experimental studies of the carcinogenic potential of
atrazine. Briefly, in the 1986 review, EPA considered atrazine a “Group C” carcinogen, i.e.,
a possibly human carcinogen, based upon increased incidence of mammary gland tumors
in Sprage-Dawley rats, and lack of mechanistic data to evaluate the mode of action and the
relevance in the human population. Subsequently, mechanistic data were produced
concerning the mode of action of atrazine which indicated that the mode of mammary
tumor development in the rat was not operational in the human (EPA, 2000; SAP 2000).
There were no other tumor sites noted in the animal bioassays (mice and rats) other than
the mammary tumors in female rats. Additional mechanistic experimental data included
laboratory-based genotoxicity and mutagenicity studies, as well as studies of atrazine’s
tumor promoting potential. Study results indicated that atrazine was neither mutagenic or
genotoxic, nor was there evidence of tumor promotion in the thyroid, pituitary, ovarian or
breast cancer experimental systems evaluated (EPA, 2010; EPA, 2000). Studies of the
tumor promotion potential of atrazine included both whole body and human cancer cell line
systems. Furthermore, experimental studies published or submitted to EPA since the
previous EPA review (i.e., the 2003 IRED) did not reveal additional evidence of tumor
formation or promotion in animals (EPA, 2010). Based on the weight of evidence (including
both empirical and mechanistic studies), atrazine exacerbates a condition to which the SD
rat is normally predisposed due to an endocrine environment that is the normal cause of
reproductive senescence in this strain for which there is no human counterpart. Thus, the
experimental evidence does not indicate a role for atrazine in the carcinogenic process in
humans.
In its review of earlier iterations of the atrazine cancer epidemiology database, studies
published prior to the 2003 IRED, EPA stated that the data were insufficient to conclude
whether an association between atrazine and cancer was present, and that the database
lacked adequate information upon which EPA could perform a cancer risk assessment
(EPA, 2000, 2003a, 2003b). In this chapter, EPA has considered the results of atrazine
Page 65 of 184
cancer epidemiologic evidence published between the mid-1980’s to the present, inclusive
of epidemiologic data previously reviewed as well as newly published studies.
Epidemiologic studies have mainly focused upon cancers of the lymphohematopoietic
system (leukemia and lymphoma including NHL), the reproductive and endocrine systems
(prostate, breast, ovarian and thyroid cancer), and a small number of studies considering
other cancer endpoints.
While there were earlier suggestions of a possible link between atrazine and
lymphohematopoietic cancers in a few studies published in the early 1990’s, overall, the
evidence was weak. Even among the studies which reported a suggestive, positive relation
between atrazine and NHL, authors cautioned against the ability to link any one pesticide
with NHL in these studies due to the number of statistical comparisons made, the modest
magnitude and marginal statistical significance of the associations estimated, the
attenuation of the association upon mutual adjustment for other pesticide exposure, and
lack of an exposure-response trends, among other factors (De Roos et al., 2003; Hoar
Zahm et al., 1993; Zahm et al., 1993). Two hospital-based case-control studies in France
also offered some additional information to consider the question; however concern
regarding possible systematic error in the selection of the control series, role of
confounding bias, lack of an exposure-response trend and often small sample size in the
evaluation of specific leukemia and lymphoma sub-types, limits use of these data (Clavel et
al., 1996; Orsi et al., 2009). More recently, within the AHS, although initial cohort analysis
results reported some evidence of a non-statistically significant, positive relation between
lifetime atrazine use and NHL and multiple myeloma based upon suggestion of an
exposure-response trend (J. A. Rusiecki et al., 2004), a follow-up study with twice the
number of cancer cases did not observe an association between atrazine and any leukemia
or lymphoma sub-type (Beane Freeman et al., 2011). In addition, within the experimental
database, there is no evidence of an increase in lymphoma non-solid tumor formation,
including NHL, when test animals are dosed with atrazine, simazine or propazine. In
addition, there is no evidence in the cancer epidemiology literature that hormones or other
reproductive factors are related to the development of leukemias or lymphomas (Adami,
2002). Therefore, at this time, there is no experimental evidence to suggest a role for the
herbicide in the etiology of these tumors, nor do the epidemiology studies performed within
agricultural, occupational populations, and demographically consisted primarily of white,
males, suggest evidence of an association.
Given the known neuroendocrine effects of atrazine, several investigators evaluated a
potential link between atrazine exposure and cancers of the reproductive and endocrine
systems including prostate, breast, ovarian as well as thyroid cancer. In 2003, EPA
concluded, in consultation with the FIFRA SAP, that results of studies of triazine
manufacturing plant workers which suggested an association with prostate cancer were
likely due to the presence of an on-site prostate cancer screening program, the PSA test, in
which most of the male employees participated during the time period of this study (EPA,
2003a, 2003b). Follow-up studies within this occupational cohort reflecting more refined
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triazine exposure information did not alter this conclusion (P. A. Hessel et al., 2004). Two
studies within the California cancer registry suggested a possible correlation and/or
association with atrazine (P. Mills, 1998) or simazine (P. K. Mills & Yang, 2003), however
the aggregate level exposure measurement and lack of risk estimate for atrazine per se in
the latter study, presents uncertainty in the use of these data.
An initial nested case-control studies within the AHS (Alavanja et al., 2003) observed no
evidence of a relation between ever-use of atrazine and prostate cancer, and, more
recently, two cohort evaluations with sufficient statistical power to detect an association
between atrazine use and prostate cancer, if an association exists, did not report evidence
of a link with prostate cancer, i.e., risk estimates did not differ from the null (Beane
Freeman et al., 2011; J. A. Rusiecki et al., 2004). During the 2003 FIFRA SAP evaluation,
the Panel suggested that differences in exposure profile, including the periodicity and
intensity of exposure, between triazine manufacturing plant workers and corn and soybean
pesticide applicators, precluded consideration of these data as part of a whole database
(EPA, 2003b). However, EPA believes that the AHS cohort represents one of the most
highly exposed occupational cohorts available for study, and lack of evidence of an
association in this cohort which has adequate statistical power to detect an association, is
strongly suggestive of no evidence of an association among occupationally exposed
primarily white, males in the U.S. population. Furthermore, the results of chronic toxicology
studies in two species (rat and dog) indicated no evidence of increased incidence of
prostate tumors. As noted briefly above, there is no evidence of a role for atrazine in
prostate tumor formation in either the animal bioassays, or within in vitro studies of tumor
promotion, genotoxicity or mutagenicity with atrazine. There is no known mechanism for
atrazine’s possible role in prostate tumor formation in humans. Considering both the
epidemiologic and toxicologic studies, the current database lacks evidence of an
association between atrazine and prostate cancer.
The results of three ecologic epidemiology studies provided inconsistent and generally
weak evidence concerning a possible association between atrazine and breast cancer
(Hopenhayn-Rich et al., 2002; M. A. Kettles et al., 1997; Muir et al., 2004). One ecologic
study indicated a small, but significant 20% increased odds of the disease among the
exposed (Kettles et al. 1997), however a follow-up analysis using the same methods in the
same study population failed to replicate the finding (Hopenhayn-Rich et al. 2002). Muir et
al. (2004) found evidence of a positive association with breast cancer, but in only one of the
two study sites, thereby inconsistent evidence. A study among Latinas who reported
employment in agricultural work residing in California, using state cancer registry data and
reported pesticide use and other self-reported data, lacked evidence in support of an
association with breast cancer, however, the ecologic nature of the exposure assessment
again precludes making any strong conclusions from this study (P. Mills & Yang, 2006). In
a population-based case control study in a high-use area of the upper-midwest (WI),
authors reported no evidence of an association with breast cancer, however the upperexposure quartiles included fewer than 5 exposed cases (J. A. McElroy et al., 2007). Within
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the AHS, considering either female spouses of pesticide applicators (Engel et al., 2005) or
female applicators themselves (Beane Freeman et al., 2011), no association between everuse of atrazine and breast cancer was observed. However, these authors were not able to
address exposure-response trends given the relatively small number of cases included in
the analysis. Strengths of this database include the reflection of several different target
populations across the studies including women in the general population, although
residing in high-use areas, female agricultural workers, female spouses residing on a farm
or residing with a commercial pesticide applicator, and female professional applicators, as
well as study designs. In addition, investigations measured both indirect, i.e., exposure
through laundering atrazine contaminated clothing, as well as direct use of the herbicide.
Limitations include use of aggregate exposure measurement techniques (ecologic) as well
as lack of ability to investigate exposure response association, and relatively small sample
of atrazine exposed females in many of the studies. The observational data are insufficient
to determine whether or not a link between atrazine and breast cancer may exist, and at
this time, are suggestive of no true association. Further, the initial observation of mammary
tumors in early experimental animal studies (female Sprague-Dawley rats), were
determined through follow-up mechanistic studies to be due to a mode of action that is not
operative in humans, i.e., reproductive senescence due to high estrogen production
resulting in constant estrogen stimulation of the mammary tissue. Therefore, at this time,
considering the current toxicological and experimental evidence, the database lacks
evidence of an association between atrazine and breast cancer.
The epidemiologic database for the relation between atrazine and ovarian cancer is small,
and weakly suggestive of a possible association across three studies, however the possible
role of random variability, bias or confounding in the risk estimates observed cannot be
excluded. A recent ecologic analysis suggested an inverse (protective) association
between atrazine exposure and ovarian cancer in an area of agricultural use in the U.S.
(Hopenhayn-Rich et al., 2002), and an earlier ecologic study reported no evidence of an
association (J. A. Van Leeuwen et al., 1999). However, an earlier case-control study in Italy
suggested a potential 1.5- to 2-fold increased odds of ovarian cancer among those
“possibly” and “definitely” exposed to triazines (Donna et al., 1989). Earlier evaluation of
this study by both EPA and the FIFRA SAP suggested uncontrolled confounding by other
pesticide exposure may explain results.
Using state cancer registry data and pesticide reporting survey information, Young et al.
(2005) reported slight increased risk for total triazine users, but not for atrazine exposed
women (H. Young et al., 2005). However, the prevalence of atrazine exposure was less
than eight percent in this study, limiting the ability to detect an association. Recently, AHS
researchers reported an elevated odds of ovarian cancer among ever-users of atrazine in a
population of female pesticide applicators. Investigators were not able to evaluate an
exposure-response relation, and only reflected nine ovarian cancer cases, four of whom
reported atrazine use. Overall, there are weak, but somewhat suggestive observations of
an association between ovarian cancer and atrazine use, however significant uncertainties
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in the observational database exist. At this time, data are insufficient to inform whether an
association may exist.
The experimental animal studies do not strongly suggest a role for atrazine in ovarian
cancer etiology, however uncertainties in both databases must be considered. Animal
bioassays, as noted above, do not show increased incidence of ovarian tumor formation
upon administration of atrazine, simazine or propazine (common mechanism group). The
mode of action through which atrazine influences neuro-endocrine effects, i.e., suppression
of the lutenizing hormone (LH) surge and reduced number of ovulatory cycles, the
“incessant ovulation” hypothesis, would likely lead to a decreased, and not an increased
risk of this cancer in women. Suppression of the LH surge would result in altered pituitary
and steroid hormone concentration in the ovary, and reduce the mitogenic potential of
estrogens in the target tissue as well as the trauma of ovulatory cycles suspected to be
linked to ovarian cancer etiology. However, epidemiologic evidence against the “incessant
ovulation” hypothesis exists, including the observation that both pregnancy and lactation
result in increased, and not decreased, levels of estradiol and follicle-stimulating hormone
(FSH), respectively, and also appear to be protective against ovarian cancer (Henderson,
Ponder, & Ross, 2003). Furthermore, recent in vitro studies performed by EPA indicate that
while it does not appear that atrazine is an aromatase inhibitor, atrazine does appear to
increase CYP19 (the aromatase gene) messenger RNA (mRNA) synthesis, and may
increase concentration of aromatase. The aromatase enzyme converts testosterone to
estradiol; increasing aromatase could lead to increased concentrations of estradiol.
However, an increase in aromatase has not been detected in vivo. Although there are
some indications of an association with ovarian cancer and a biologically plausible role for
the herbicide, although no known alternative mode of action at this time, overall considering
the strengths, weaknesses and uncertainties of the limited number of studies available, the
current database lacks strong evidence of a true association.
Among all the epidemiology studies included in this evaluation, only one study offers a risk
estimate for the association between thyroid cancer and atrazine exposure: the recent AHS
cohort analysis (Beane Freeman et al., 2011). In this evaluation, authors report evidence of
a statistically significant four-fold increased odds of thyroid cancer among male pesticide
applicators; the estimate remains elevated and significant when adjusting for body mass
index, a known risk factor for thyroid cancer. However, authors note the lack of a clear
exposure-response trend, and the small number of atrazine exposed cases in some
categories, e.g., n<5. These and other limitations to this study present some uncertainties
in the interpretation of these results. There is no evidence of an increased incidence of
thyroid tumors in experimental animal bioassays, nor does atrazine act as a tumor
promoter in experimental systems, or illustrate the ability to alter thyroid hormone levels or
thyroid-stimulating hormone (EPA, 2010). In the AHS, atrazine was not associated with
thyroid disease (Goldner et al., 2010). Epidemiologic evidence indicates a likely link
between reproductive factors including steroid and peptide hormones in the etiology of this
cancer; the striking difference in the trends and pattern of thyroid cancer between men and
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women support this hypothesis (Adami, 2002). However, a clear mode of action is lacking,
and no toxicological evidence exists as to a mode of action for atrazine in thyroid tumor
development. Thus, at this time, evidence is insufficient to determine the nature of the
association between thyroid cancer and atrazine.
In addition to these more commonly assessed atrazine-cancer associations, EPA identified
a few other studies. Considering pediatric cancers, the evidence is currently insufficient to
make a determination as to whether a relation exists. One ecologic study of the association
between atrazine concentration in drinking water and all pediatric cancers reported to the
Maryland state cancer registry, only provided unadjusted risk estimates as the result of
simple contingency table analysis. Other methodological limitations noted by EPA in
Appendix E render this study of low quality. In the AHS, parental atrazine use is weakly,
non-significantly associated with childhood cancers (combined) (OR 1.27 95% CI 0.70,
2.30), but multiple statistical tests were performed (Flower et al., 2004). An evaluation of
acute lymphocytic leukemia (ALL) among children in the Northern California childhood
cancer cohort (R. P. Rull et al., 2009) suggested a non-significantly elevated risk of ALL
with triazine exposure, however lack of exposure refinement including information on
critical windows of effect in development result in uncertainties that preclude the Agency
from reaching any definitive conclusions based upon this study. Maternal hormone levels
have been associated with childhood leukemias in some studies (Adami, 2002); however, a
potential role for atrazine in the alteration of maternal hormone and subsequent childhood
leukemia risk is not available. Overall, this database is insufficient to inform whether a
causal association may exist.
Studies of a link between atrazine or triazine with colorectal cancer do not present
compelling evidence of an association at this time, overall this database is weak. The
magnitude and direction of the measured associations were not strong, exposure-response
trends were lacking, and the number of cases was often small (Beane Freeman et al.,
2011; Hoar et al., 1985; Lee et al., 2007; P. K. Mills & Yang, 2007; J. A. Van Leeuwen et
al., 1999). Concerning glioma (brain cancer), a population-based case-control study in the
upper Midwest of the U.S., suggest no association between atrazine exposure and glioma
in men (Ruder et al., 2004) or women (Carreon et al., 2005), however under-ascertainment
among the elderly given difficulty in diagnosing this cancer in the elderly population, and
other sources of information bias may have influenced this result. EPA notes the recent
AHS cohort study observed no link between atrazine use and brain cancer among
occupationally exposed men and women (Beane Freeman et al., 2011). Overall, the
studies of an association between either digestive system tumors, or brain gliomas are
limited in number, and reflect uncertainties that hinder use in making causal inference. At
this time, the database lacks evidence of a link between atrazine and these types of
cancers.
To be exhaustive in the consideration of all the available evidence, EPA considered all
available evidence presented in other AHS studies which have estimated the association
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between atrazine exposure and several other anatomical cancer sites not yet discussed.
Overall, no additional evidence of an association was identified across these atrazinecancer risk estimates. Specifically, these include: lung, pancreatic, cutaneous melanoma,
oral, esophageal, kidney, larynx, and liver cancer (Beane Freeman et al., 2011; J. Rusiecki
et al., 2004). As already stated, results of animal bioassays did not indicate increased
incidence of any tumors, other than mammary which is discussed above.
3.3.2 EPA Conclusion
As a result of extensive investigations within both experimental and observational studies,
the cancer database does not strongly suggest a link between atrazine exposure and
increased cancer incidences in the human population. Recent cohort analyses among an
occupationally exposed population of pesticide applicators (AHS) indicated no evidence of
an association with leukemias, or lymphomas including several sub-types of lymphoma.
Researchers observed no increased incidence in these tumors in experimental studies. The
AHS reports were also strongly supportive of the conclusion of no association between
atrazine and prostate cancer, as a result of studies with sufficient statistical power (1β>0.85) to detect an association, no other peer-reviewed papers were identified since the
last EPA evaluation. The database for breast cancer is small, and, at this time, is not
strongly supportive of a link with atrazine given limitations of study design, sample size, and
lack of exposure-response information. EPA determined that the mode of action through
with atrazine influences an increased incidence of mammary gland tumors in animals is not
operative in humans. Indications of a role of atrazine in ovarian cancer in epidemiology
evaluations is based on a small number of studies, some of which reported positive
association (Beane Freeman et al., 2011; Donna et al., 1984; Donna et al., 1989; H. A.
Young, P. K. Mills, D. G. Riordan, & R. D. Cress, 2005). However, available information
concerning atrazine’s known mode of action, decrease LH surge and exposure to
estrogen/ovulation, suggest a relation is not likely. There is only one study available which
estimated an association between atrazine and thyroid cancer. While statistical results are
somewhat suggestive of an association, the exposure-response trend was not monotonic,
based on a small number of exposed cases, and not yet replicated in other populations.
Other atrazine cancer epidemiology studies were not suggestive of an association including
cancers of the digestive tract (colorectal, pancreas), brain, and lung, pancreatic, cutaneous
melanoma, oral, esophageal, kidney, larynx, and liver cancer. The data regarding pediatric
cancers are insufficient to draw a conclusion. As noted several time above, there were no
other animal tumors identified in the atrazine bioassays submitted to EPA.
While some epidemiology studies are weakly suggestive of an association between
atrazine exposure and cancer incidence in the human population, the preponderance of the
different lines of evidence – including both experimental and epidemiology studies – does
not substantiate a role of atrazine on human carcinogenesis. Thus, the weight of the
evidence supports that atrazine is not likely to be carcinogenic in the human population.
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4. PROPOSED UPDATES TO THE DOSE-RESPONSE ASSESSMENT
4.1
Benchmark Dose Analysis
Detailed information about the BMD analysis summarized below can be found in Appendix
C.
Numerous scientific peer review panels over the last decade have supported the Agency’s
application of the BMD approach as a scientifically supportable method for deriving points
of departures (PoDs) in human health risk assessment, and as an improvement over the
historically applied approach of using no-observed-adverse-effect levels (NOAELs) or
lowest-observed-adverse-effect-levels (LOAELs). The NOAEL/LOAEL approach does not
account for the variability and uncertainty in the experimental results, which are due to
characteristics of the study design, such as dose selection, dose spacing, and sample size.
With the BMD approach, all the dose response data are used to derive a PoD. Moreover,
the response level used for setting regulatory limits can vary based on the chemical and/or
type of toxic effect and for a given chemical, all available studies can be evaluated using a
common response level which aids interpretation (USEPA, 2000).
In recent months, the Agency pursued performing BMD analysis on a variety of endpoints
(attenuation of LH, prostatitis, delayed PPS, delayed vaginal opening). However, after an
initial scoping exercise, it became clear that the LH data were the only datasets with
sufficient dose-response data to support empirical modeling. For the other endpoints,
toxicological effects have only been observed at 1) only at very high doses; 2) only at single
dose levels; or 3) exhibit flat dose-response curves. As such, the only BMD analyses
provided here and in Appendix C relate to LH attenuation. Although, it would be preferred
to model additional endpoints the other outcomes reported following atrazine exposure are
less sensitive than the LH data. As such, the Agency is focusing on the data which are
most sensitive and thus most protective of human health.
Generally, EPA calculates both the BMD (central estimate) and the BMDL (the BMDL
corresponds to the 95% lower bound on dose). As a matter of science policy, EPA uses
the BMDL as the point of departure. A key consideration when performing BMD modeling
is determining the response level—known as the benchmark response (BMR). In the case
of continuous endpoints, like LH attenuation, the BMR most often represents an X%
change from background levels (or untreated controls). Typically, the BMR is selected on
the basis of a combination of biological (mode of action, quantitative link between key
events, historical/concurrent controls) and statistical considerations (sample size,
variability, etc). However, in the absence of information concerning the level of response
(or % change) associated with an adverse effect, the Agency’s draft BMD guidance
suggests that the BMD and BMDL corresponding to a change in the mean response equal
to one standard deviation from the control mean be used as the BMR. In the case of
atrazine, the level of attenuation of the LH surge considered to be adverse is a function of
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several factors including but not limited to the life-stage and functional outcomes under
consideration (e.g., estrous cyclicity disruptions in rats). Moreover, the differences in
reproductive cycles/aging between rodents and humans add an additional level of
complexity to establishing a specific BMR value. These differences notwithstanding, in
general, the reproductive physiology among mammals is remarkably similar making the use
of the LH surge as a critical effect of concern appropriate. This perturbation of the LH
surge is the cornerstone of the cascade of events leading to the adverse reproductive
outcomes attributed to atrazine exposure. Activation of the HPG axis resulting in the
pulsatile secretion of GnRH and LH is critical to puberty onset. For instance, decreased LH
during puberty would lead to insufficient stimulation of the gonads to reduce the circulating
hormone levels needed for development of sex accessory tissues in males and females.
Moreover, researchers have found that disruption of GnRH release and the ensuing
dampening of the LH surge can lead to delays in vaginal opening and preputial separation.
Based on the available data, attenuation of the LH surge continues to be the most sensitive
endpoint in the developmental/reproductive toxicity profile for atrazine. Attenuation of the
LH surge occurs at doses that are either comparable to or ≈ 4- to 30-fold lower than those
eliciting other developmental/reproductive effects (e.g., delayed ossification, estrous
cyclicity disruptions, delays in sexual maturation, and prostatitis). Taking into account these
challenges in establishing a response level, a BMR of one standard deviation from the
control mean was chosen as the suggested choice by the EPA BMDS 2000 guidance
whenever the biologically significant degree of change in a response is not clearly defined.
There are various options for selection of the appropriate dose metric for atrazine including
administered dose and average daily steady state AUC for plasma triazines (i.e., atrazine
and its metabolites). The average daily steady state AUC dose metric may be an
appropriate internal dose metric to better characterize the link between atrazine exposure
and attenuation of the pre-ovulatory LH surge since it considers the contributions of all the
relevant triazine species. BMD modeling was conducted using the administered atrazine
dose for each of the studies identified below and using the corresponding AUC metric for
the study selected as the most robust for deriving a point of departure (4-day exposure data
from Cooper et al., 2010). AUC estimates were derived as indicated below using the
corresponding linear expression that relates this dose metric to administered doses of
atrazine.
Y = 13.0x -8.57
Where Y is the daily steady state estimate and x is the administered dose of atrazine
4.1.1 Identification of Critical Studies
Several oral toxicity studies in the rat provided LH data that were considered for doseresponse modeling. These include Morseth et. al. (1996a & b), Minnema (2001; 2002) and
Cooper et al. (2010). Recent data from the NHEERL provided the most robust LH data in
terms of dose selection (number of dose levels - particularly low dose range - and the
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spacing between dose levels) and variability of the data. The study design addressed the
low-dose region of the dose-response curve and exhibited less data variability (i.e., smaller
standard deviations).
A one month oral study by Morseth et al . (1996a) established a LOAEL of 40 mg/kg/day
and a NOAEL of 5 mg/kg/day for an effect on LH surge. A limitation of the study is the large
variability of the data (as reflected by standard deviations). A subsequent 6 month study
(Morseth et al . 1996b) established a LOAEL of 3.65 mg/kg/day for attenuation of LH surge;
the NOAEL was 1.8 mg/kg/day. The data from this study also indicated large variability.
The NOAEL from this study was the basis for the point of departure used to establish the
chronic reference dose (cRfD) in the EPA’s 2003 human health risk assessment for
atrazine.
Minnema (2001) established a LOAEL of 40 mg/kg/day for attenuation of LH surge in rats
treated with atrazine for 1 month. The NOAEL was 5 mg/kg/day. These data also
displayed variability. In a follow-up 6 month dietary study (Minnema 2002), atrazine (1.8,
3.4, 4.9, and 29.1 mg/kg/day) was without effect on LH surge in ovariectomized female SD
rats. Confidence in this study is low; BMD/BMDL values were not calculated for Minnena
(2002). Based on historical control data, the induction of LH surge in control animals
seems to be suboptimal. Rats exhibited an infection that may have impacted LH surge
response. In addition, estradiol data were not submitted to evaluate LH induction
response.
In a recent study by Coder (2011), a decreased estradiol induced LH surge was observed
in female SD rats following oral gavage administration of atrazine (0. 6.5, 50, or 100
mg/kgday) for 4 days. However, no statistically acceptable BMD/BMDL values were
obtained with the LH surge data from this study. The Coder (2011) study is limited by the
poor selection of doses in the low dose region of the response curve.
4.1.2 Methods & Results
EPA’s Benchmark Dose Software (BMDS) version 2.1.2 was utilized to estimate BMD
estimates from the available LH studies. All models pertinent for continuous data were
evaluated including exponential, Hill, power, polynomial and linear. Criteria used to assess
the best fit included statistical (goodness-of-fit) values, model criteria (Akaike Information
Criteria; AIC), BMD/BMDL ratios, and visual inspection.
The most reliable model fits and BMD and BMDL estimates were obtained from the 4-day
data from Cooper et al. (2010). In contrast to the NHEERL dataset, less reliable model fits
and failure of some models to compute BMD/BMDL values were encountered with the
other available studies that had more data variability and less well-defined dose selection.
There is uncertainty in the BMD/BMDL estimates from these other studies. The table below
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summarizes the results of BMD analyses of the available LH studies and the level of
confidence in the estimates. Detailed results are provided in Appendix C.
Table 1: Summary of Administered Dose BMD analyses
LH Studies
BMD
BMDL
NHEERL 4 day
(oral gavage)
4.92
2.42
Minnema 1 Month
(oral gavage)
653.3
245.1
Morseth 1 Month
(oral gavage)
523.7
174.7
Morseth 6 Month
(dietary)
36.2
17.1
4.30
3.40
Coder 4 day
(oral gavage)
Confidence
High – robust dataset (less
data variability; well-defined
dose selection); describes LH
response very well.
Low – data variability; both
BMD and BMDL estimates
outside experimental range;
large BMD/BMDL ratio; failure
of some exponential models to
compute estimates.
Low – data variability; BMD
estimate outside experimental
range; large BMD/BMDL ratio;
failure of some exponential
models to compute estimates.
Low – data variability;BMD
estimate outside experimental
range; failure of some
exponential models to
compute estimates.
Low – No statistically
acceptable BMD/BMDL
values
For the Cooper et al. (2010) 4-day data, EPA calculated the BMD/BMDL for the
administered dose and internal dose metrics. When administered dose was used as the
dose metric, the exponential model (subset model 4) provided the best fit to the data, and
based on statistical (goodness-of-fit values), modeling criteria (Akaike Information Criteria;
AIC values), and visual inspection, BMD and BMDL estimates were 4.92 and 2.42
mg/kg/day, respectively (see Cooper et al. 2010) BMD output and summaries provided in
Appendix C). The BMD and BMDL estimates described the LH surge data very well.
BMD analyses were also carried out with the corresponding average steady state plasma
levels and average daily steady state AUC. The Hill model provided the best fits for both
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dose metrics. The following table summarizes BMD and BMDL estimates corresponding to
administered dose and AUC as the dose metrics.
Table 2: BMD Modeling Results with average steady state plasma triazines and AUC estimates
BMD and BMDL
estimates (BMR =
1SD from control
mean)
BMD
BMDL
SS levels (mg/L) of
Total Triazine
Equivalents
Daily AUCss
(mg/L-hr)
2.41
1.03
57.9
24.7
BMDL values of 1.03 mg/L and 24.7 (mg/L-hr) were estimated for steady state levels of
plasma triazines and daily AUCss, respectively. These BMDL estimates would be
particularly useful for comparison to drinking water monitoring estimates.
Exponential Model 4 with 0.95 Confidence Level
Exponential
25
Mean Response
20
15
10
5
BMDL
0
BMD
10
20
30
40
dose
50
60
70
12:24 05/04 2011
Figure 4: EPA BMDS Exponential Model (Constant Variance; BMR = 1 SD from control mean)
Results for Attenuation of LH Surge in Female Rats Administered Atrazine by Gavage - NHEERL
Data.
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Exponential Model 5 with 0.95 Confidence Level
Exponential
25
Mean Response
20
15
10
5
BMDL
0
BMD
2
4
6
8
10
12
dose
08:46 05/09 2011
Figure 5: EPA BMDS Hill Model (Constant Variance; BMR = 1 SD from control mean) Results for
“Area Under the Triazine Equivalent Plasma-Concentration Time Curve” as the Dose Metric NHEERL Data (Attenuation of LH Surge in Female Rats Administered Atrazine by Gavage).
Page 77 of 184
Exponential Model 5 with 0.95 Confidence Level
Exponential
25
Mean Response
20
15
10
5
BMDL
0
BMD
50
100
150
dose
200
250
300
09:12 05/09 2011
Figure 6: EPA BMDS Hill Model (Constant Variance; BMR = 1 SD from control mean) Results for
“Daily Steady State AUC for Triazine Equivalents” as the Dose Metric - NHEERL Data (Attenuation
of LH Surge in Female Rats Administered Atrazine by Gavage).
4.1.3
Proposed Point of Departure
Plasma levels of atrazine and its metabolites appear to reach pseudo steady state at or
near four days of exposure in the adult rat. Specifically, after four days of exposure, the
plasma levels of total triazine equivalents plateau during periods of repeated dosing.
Similarly, data from multiple laboratories ranging in duration from four days up to six
months of exposure show that attenuation of LH is fairly constant at a given dose and
suggest PD steady state. As such, given both PK and PD steady state, it is prudent to
select the most robust study from those studies where steady state has been reached (i.e.,
any study four days or longer). Data from Cooper et al. (2010) contain multiple doses
across a broad range and provide comparatively low variability. As such, the data from
Cooper et al. (2010) are proposed as the most appropriate for deriving a PoD.
Specifically, the BMDL of 2.56 mg/kg/day (based on AUC dose metrics) is proposed as a
point of departure (PoD) for atrazine risk assessment for repeated exposure scenarios.
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Possible approaches for extrapolating this animal PoD to humans is discussed in Section 8
below.
4.2 Pharmacokinetic Analysis
4.2.1 Background
Characterization of the pharmacokinetics and internal dosimetry of atrazine and its
metabolites represents a critical step for elucidating the link between exposure and
attenuation of the pre-ovulatory LH-surge for the application of a mode of action approach
to risk assessment. LH attenuation resulting from atrazine exposure has been identified as
an early key event in the progression towards apical toxicities (for example, disruption of
ovarian function including estrous cyclicity and delays in puberty onset), However, the
temporal relationship between atrazine exposure and LH attenuation has not been fully
characterized.. One of the information gaps noted at the September 2010 SAP meeting
was the lack of detailed pharmacokinetic information for atrazine and its individual
metabolites (FIFRA 2010c). Pharmacokinetic information is particularly important since
atrazine as a parent chemical is short-lived and its chloro-s-triazine metabolites are
presumed to be responsible for most, if not all, of the neuroendocrine mode of action
effects including LH attenuation.
The current understanding of atrazine metabolism is that upon entering the body via the
oral route of administration, it is quickly and extensively metabolized to the monodealkylated chloro-s-triazine metabolites deethylatrazine (DEA) and deisopropylatrazine
(DIA). A second round of CYP-450 mediated dealkylation results in diaminochlorotriazine
(DACT), the most abundant metabolite of atrazine frequently observed in animal toxicity
studies and human biomonitoring (depicted in Figure 7). Atrazine can also undergo
dechlorination to form glutathione conjugates. This metabolic pathway, however, is
assumed to be more of a detoxification pathway since the chlorinated triazine ring is
considered to be the toxicologically important moiety. It has been postulated, based on
urine radioactivity percentages, that glutathione conjugation may represent as much as
30% of atrazine metabolism (Timchalk et al. 1990).
Page 79 of 184
Glutathione conjugation
Atrazine
Dealkylation
Dealkylation
CYP450
Deethylatrazine (DEA)
Deisopropylatrazine (DIA)
CYP450
Dealkylation
Dealkylation
Diaminochlorotriazine (DACT)
Figure 7: Metabolic Scheme for Atrazine
The following section is an update to the pharmacokinetic information evaluated by the
Agency for atrazine and its metabolites that now includes different species including rats,
monkeys, and humans. This information is being used to propose an interim
pharmacokinetic modeling approach that relates orally administered doses of atrazine to
plasma triazine levels given that a thorough review and evaluation of a recently submitted
PBPK model by Syngenta has not been completed.
4.2.2 Updates on the Pharmacokinetics of Atrazine and Its Metabolites in the
Rat
Additional details of the pharmacokinetics of atrazine and its metabolites are emerging
based on the need to characterize the temporal relationship between atrazine exposure
and attenuation of the LH surge. In a recent effort aimed at characterizing the
pharmacokinetic behavior of atrazine and its metabolites, Syngenta in collaboration with
Page 80 of 184
WIL Research Laboratories, LLC, embarked on a time-intensive in vivo pharmacokinetic
study with female Sprague Dawley rats following oral administration of atrazine (Coder
2011). The study consisted of two modes of oral administration: once daily bolus dosing via
oral gavage for 4 days or distributed dosing for 4 days through the diet. Particular attention
was given to capturing the elusive short in vivo plasma half-life of atrazine following oral
administration since previous studies have not sampled plasma early enough post-dosing
to properly characterize it. In the new study, plasma was sampled and analyzed for
atrazine, DEA, DIA, and DACT as early as 5 minutes following oral gavage dosing. The
treatment duration of 4 days was selected based on the critical study that established the
LOAEL for LH attenuation at 3.12 mg/kg bw/day over a 4 day period (Cooper et al. 2010).
Also included in the pharmacokinetic study was a 4-day washout period during which
dosing with atrazine did not occur in order to characterize the clearance of all 4 plasma
chloro-s-triazines (i.e., atrazine, DEA, DIA, and DACT) resulting from once daily dosing for
4 days. It should be noted that a mass balance analysis of the administered atrazine dose
was not performed in the study at any of the levels tested.
4.2.3 Oral Gavage Dosing of Sprague Dawley Rats with Atrazine
The results from oral gavage dosing indicate that atrazine is rapidly absorbed and cleared
from rat plasma at all doses tested. Figure 8 shows the 24-h plasma profile of atrazine
following oral gavage dosing at 3, 10, and 50 mg/kg bw atrazine. Maximum plasma levels
(Cmax) were reached as early as 20 minutes following dosing at 3 and 10 mg/kg bw
atrazine and much earlier at 10 minutes following dosing with the higher dose of 50 mg/kg
bw atrazine. The increase in Cmax was less than proportional with respect to dose,
increasing by only 2.8- and 1.7-fold as compared to 3.3-(3 to 10 mg/kg bw) and 5-fold (10
to 50 mg/kg bw) increases in dose levels, respectively. In contrast, the area under the
plasma concentration-time curve (AUC) estimated from zero to infinity increased more
directly in proportion with dose (Table 3). When plasma levels during the elimination phase
were plotted on a semi-logarithmic scale (i.e., lnCp) versus time, a significant linear
correlation was observed for all doses of atrazine tested. A plasma elimination half-life as
short as 44 minutes was estimated for the low dose of 3 mg/kg bw but was noted to
increase with dose to 1.2 and 2.9 hours at 10 and 50 mg/kg bw atrazine, respectively
(Table 3).
Page 81 of 184
40
3 mg/kg bw/day ATRA
Plasma levels(µg/L)
35
10 mg/kg bw/day ATRA
30
50 mg/kg bw/day ATRA
25
20
15
10
5
0
0
1
2
3
4
5
6
7
8
10 12 14 16 18 20 22 24
time (hr)
Figure 8: Plasma profile of atrazine for 24 hours following oral gavage dosing of rats with 3, 10, or 50
mg/kg bw atrazine. Each time point represents average of six measurements ± SD. Data is from
Coder 2011.
The plasma profiles of the mono-dealkylated metabolites DEA and DIA were found to
parallel that of atrazine. Figure 9 shows the individual plasma profile of DEA or DIA in
comparison to that of atrazine at the low dose of 3 mg/kg bw atrazine. Several features can
be noted from Figure 9. First, the same time to maximum plasma levels (i.e., tmax) of 20
minutes as atrazine is observed for DEA and DIA. Such parallel absorption profiles into
plasma for these two metabolites is indicative of a first pass effect, that is, a fraction of
atrazine is metabolized to DEA and DIA in either the GI tract or liver prior to reaching the
systemic circulation. Second, although plasma levels differ in magnitude, the same generic
elimination profile as atrazine was observed for DEA, and DIA (see elimination decay after
tmax in Figure 9).Therefore, the plasma clearance of atrazine does not appear to be due to
metabolism to either DEA or DIA since its plasma profile does not decrease in
correspondence to the appearance of either of these two species. Since atrazine is known
to be short-lived as a parent chemical and barely detected in urine, if at all, (Ross et al.
2009; Catenacci et al. 1990; Ikonen et al., 1988), its fast disappearance from plasma may
be due to metabolism to species other than DEA and DIA.
Page 82 of 184
Table 3: Pharmacokinetic parameters estimated for atrazine and its chlorinated metabolites DEA,
DIA, and DACT following oral gavage dosing of rats with 3, 10, and 50 mg/kg bw atrazine. Data
analyzed is from Coder 2011.
ORAL GAVAGE DOSING
ATRAZINE
AUC
Cmax
(µg*hr/L)
(µg/L)
0→∞
6.43 ± 1.42
6.41
18.04 ± 6.38
24.87
31.07 ± 10.31
109.6
DEA
58.17 ± 7.40
153.5
141.00 ± 22.61
465.3
219.67 ± 71.50
2277
DIA
220.67 ± 43.03
370.3
614.17 ± 93.20
1336.0
1066.0 ± 419.0
7255.0
DACT
Atrazine dose
(mg/kg bw/day)
tmax
3.0
10.0
50.0
20 min
20 min
10 min
3.0
10.0
50.0
20 min
1 hr
45 min
3.0
10.0
50.0
20 min
1 hr
45 min
3.0
2 hr
682.00 ± 95.01
10.0
6 hr
50.0
8 hr
kel
(/hr)
Elimination
Half-life
0.941 ± 0.069
0.569 ± 0.047
0.238 ± 0.013
44.2 min
1.22 hr
2.91 hr
0.240 ± 0.009
0.0928 ± 0.0034
0.0678 ± 0.0035
3.40 hr
7.50 hr
10.22 hr
0.564 ± 0.030
0.2425 ± 0.0146
0.176 ± 0.0065
1.20 hr
2.86 hr
3.95 hr
8909.2
0.0873 ± 0.006
7.90 hr
2125.00 ± 398.00
29492.0
0.0845 ± 0.0070
8.20 hr
8321.67 ± 1630.57
149124.9
0.0752 ± 0.0102
9.22 hr
Page 83 of 184
Plasma Levels ( µg/L)
Atrazine
DEA
60
40
20
10.0
7.5
5.0
2.5
0.0
0
1
2
3
4
5
6
7
8
7
8
time (hr)
Plasma Levels ( µg/L)
300
Atrazine
DIA
200
100
10
8
6
4
2
0
0
1
2
3
4
5
6
time (hr)
Figure 9: The parallel plasma profiles of atrazine versus DEA or DIA following oral gavage dosing of
rats with 3 mg/kg bw atrazine. Data is from Coder 2011.
Oral gavage dosing with the higher doses of 10 and 50 mg/kg bw atrazine resulted in much
longer tmax values of 60 and 45 minutes, respectively, for both DEA and DIA while that of
atrazine remained at 20 minutes for 10 mg/kg bw and even decreased to 10 minutes at 50
mg/kg bw (Table 3). This observation suggests that the increases in tmax for DEA and DIA
are likely due to saturation of first pass metabolism resulting in prolonged absorption of
these two species into plasma. Limited solubility of DEA and DIA produced in the GI tract at
the dose levels tested is a less likely possibility since a biphasic absorption phase in the
plasma profile would be observed in such case as previously reported by McMullin and coworkers (McMullin et al. 2007).
Although comparable tmax values were exhibited by DEA and DIA, plasma levels (Cmax
and AUC) were several folds higher for DIA (Table 3). As with atrazine, both DEA and DIA
Page 84 of 184
Plasma Levels (µg/L)
exhibited fast linear elimination kinetics. At the low dose of 3 mg/kg bw atrazine, plasma
elimination half-life estimates were 3.4 and 1.2 hours for DEA and DIA, respectively. Dose
dependent decreases in plasma elimination were also noted for DEA and DIA. Plasma halflife estimates for DEA increased to 7.5 hr (~2.2 fold decrease) and 10.2 hr (~ 3-fold
decrease) at 10 and 50 mg/kg bw atrazine, respectively. Comparable decreases in plasma
clearance were seen for DIA with plasma half-life increasing to 2.9 (~2.4-fold clearance
decrease) and 3.9 hrs (~3.2-fold clearance decrease) at 10 mg/kg and 50 mg/kg bw
atrazine, respectively. The only metabolite that did not exhibit dose-dependent decreases
in plasma clearance was DACT for which a plasma elimination half-life was consistently
estimated at 8-9 hours (Table 3). DACT was also the predominant species detected in rat
plasma with estimated AUC values 20-24 fold higher than those estimated for DIA and
even higher for DEA. A much longer tmax of 2 hr was seen for DACT at 3 mg/kg bw
atrazine while plasma levels of DIA and DEA (primarily DIA) were seen decreasing (Figure
10). Peak plasma levels of DACT did not decrease much until about 14 hours post-dosing
when plasma levels of DEA and DIA had been significantly depleted. Such observation is
consistent with DACT being formed from the precursor metabolites DEA and DIA after
these species have entered the systemic circulation. The increases in plasma half-life at 10
and 50 mg/kg bw atrazine observed with DEA and DIA (Table 3) are likely due to saturation
of the metabolic systems responsible for their metabolism to DACT. The lack of dosedependent changes in the clearance of DACT is consistent with this species being a
terminal metabolite and/or the lack of saturation for further metabolism at the dose levels
tested.
Atrazine
DEA
DIA
DACT
800
600
400
250
200
150
100
50
0
0.00
0.25
0.50
0.75
1.00 2
4
6
8
10 12 14 16 18 20 22 24
time (hr)
Figure 10: 24-h plasma profile of atrazine and its chlorinated metabolites DEA, DIA, and DACT
following oral gavage dosing of rats with 3 mg/kg bw atrazine. Each time point represents average of
six measurements ± SD. Data is from Coder 2011.
Page 85 of 184
Repeated daily dosing with atrazine did not change the pharmacokinetic behavior of any of
the species analyzed (i.e., atrazine, DEA, DIA, or DACT) as compared to single dose
behavior. The entire 4-day treatment/4-day washout plasma profile at 3 mg/kg bw atrazine
is shown in Figure 11. The most abundant species was DACT followed by DIA, DEA, and
atrazine as the least abundant. DACT was also the species with the slowest clearance,
followed by DEA, DIA, and atrazine. With the exception of DACT for which a bit of
accumulation is observed due to sustained peak plasma levels, all 4 species analyzed can
be expected to be cleared from plasma by 24 hours, constituting a repeated episodic
pharmacokinetic profile upon repeated (once per day) daily dosing.
In summary, atrazine as a parent chemical exhibited very fast pharmacokinetics with a
tmax as short as 20 minutes and a plasma half-life of 44 minutes at the low dose of 3
mg/kg bw. The tmax information for atrazine in comparison to those for DEA and DIA is
consistent with the formation of these two metabolites being due to first pass metabolism.
The parallel plasma elimination profiles of DEA and DIA relative to that of atrazine do not
support the fast plasma clearance of atrazine being due to metabolism to DEA and DIA.
Without mass balance in a pharmacokinetic study, it is difficult to disregard other
metabolites as a possibility to explain the fast plasma clearance of atrazine since urinary
excretion of intact atrazine is likely to be minimal.
3 mg/kg bw/day atrazine: oral gavage dosing
Plasma Levels (µg/L)
800
Atrazine
DEA
DIA
DACT
600
400
200
0
0
20
40
60
80
100
120
140 160 180
time (hr)
Figure 11: Plasma profile of atrazine, DEA, DIA, and DACT from repeated once a day oral gavage
dosing of rats with 3 mg/kg bw atrazine for 4 days. Values represent the mean of six replicates ± SD.
Data is from Coder 2011.
Page 86 of 184
4.2.4 Dietary Exposures of Sprague Dawley Rats to Atrazine
Dosing with atrazine through the diet resulted in plasma levels for all 4 monitored
chlorotriazines (i.e., atrazine, DEA, DIA, and DACT) being more variable and overall much
lower as compared to oral gavage dosing at comparable dose levels. Figure 12 shows the
plasma profile of all 4 species at the equivalent dietary dose level of 3 mg/kg/day.
Maximum plasma levels of all species analyzed were reached much later at between 12.5
and 20 hours, reflecting the consumption of diet by rodents during the night. Only the
plasma profile of DACT can be deciphered with the high level of variation observed.
Plasma levels of DACT seem to reach a more defined pseudo steady state level by the
fourth day of daily dosing, probably reflecting the more frequent dosing with atrazine
through the diet and the longer half-life of this compound as compared to the other
metabolites. A similar behavior was observed at the higher dose levels of 10 and 50 mg/kg
bw atrazine (data not shown).
3 mg/kg bw/day atrazine: dietary dosing
Plasma Levels (µg/L)
500
ATRA
DEA
DIA
DACT
400
300
200
100
0
0
24
48
72
96
time (hr)
120
144
168
192
Figure 12: Plasma profile of atrazine, DEA, DIA, and DACT from dietary dosing of rats with atrazine at
a nominal dose of 3 mg/kg bw/day atrazine for four days. Data at each time point represents
measurements in 6 animals. Data points are not connected to help elucidate pattern. Data is from
Coder 2011.
The fluctuating behavior of plasma levels resulting from dietary dosing made analysis
difficult and thus only the elimination phase of each triazine species was analyzed for
comparison to oral gavage dosing. In the case of atrazine, no analysis was performed due
to plasma levels being too low and highly variable for any informative analysis. The results
for DEA, DIA, and DACT indicate, as with oral gavage dosing, that these species exhibit
Page 87 of 184
linear elimination kinetics (assessed by plotting lnCp vs. time). As listed in Table 4, the
estimated first order elimination rate constants are very comparable to those estimated via
oral gavage dosing. The estimated plasma half-life of DEA of 3.7 hr is very comparable to
that of 3.4 hr estimated via oral gavage. Greater differences were observed at 10 and 50
mg/kg bw/day, but with no clear pattern since a 1.2-fold slower clearance was observed at
10 mg/kg bw but an increase of about the same magnitude was observed at 50 mg/kg bw
(Table 4). In the case of DIA, with the exception of 3 mg/kg bw atrazine for which an
approximately 2-fold decrease in clearance was observed (plasma half-life of 3 hr versus
1.2 hr via oral gavage dosing), the results with 10 and 50 mg/kg bw atrazine were
comparable to those estimated via oral gavage dosing. Plasma clearance estimates were
particularly consistent for DACT as compared to oral gavage dosing.
Table 4: Pharmacokinetic Elimination Parameters estimated for atrazine and its chlorinated
metabolites DEA, DIA, and DACT following dosing of rats with atrazine through the diet at nominal
doses of 3, 10, and 50 mg/kg bw. Data is from Coder 2011.
Dose
(mg/kg bw/day)
3.0
10.0
50.0
3.0
10.0
50.0
3.0
10.0
50.0
Dietary Dosing with Atrazine
Elimination Rate
Elimination Plasma
-1
Constant (hr )
Half-life (hr)
DEA
0.187 ± 0.012
3.70
0.0761 ± 0.004
9.10
0.104 ± 0.005
6.65
DIA
0.231 ± 0.016
0.248 ± 0.015
0.243 ± 0.013
DACT
0.0897 ± 0.0019
0.0729 ± 0.0021
0.0734 ± 0.0018
3.00
2.80
2.85
7.73
9.51
9.44
4.2.5 Plasma Clearance Discrepancy Based on Studies with Radiolabeled
Atrazine
Analysis of the new pharmacokinetic dataset submitted to the Agency in which atrazine,
DEA, DIA, and DACT are monitored in rat plasma following once daily oral dosing with
atrazine indicates a plasma clearance discrepancy as compared to studies performed with
radiolabeled atrazine. None of the 4 species monitored in the new study exhibits a plasma
clearance that is comparable to that estimated based on radiolabeled atrazine. All four
species (i.e., atrazine, DEA, DIA, and DACT) can be expected based on their estimated
plasma half-lives to be cleared from plasma by 24 hours, constituting an episodic
pharmacokinetic profile that contrasts the bioaccumulative profile observed based on
Page 88 of 184
radiolabeled atrazine (Thede 1987). Pharmacokinetic studies with radiolabeled atrazine
have been carried out with a metabolically stable [14C]-radiolabeled triazine ring that allows
for the study of the overall disposition of all species associated with the radiolabel (Thede
1987; Paul et al 1993). In general, radiolabel disposition studies achieve a high degree of
mass balance (some very close to 100%) due to minimal sample preparation being
required for quantification of radioactivity.
At the September 2010 SAP meeting, the Thede 1987 study was presented as a key
pharmacokinetic study for estimating plasma AUC based on radiolabeled triazine
equivalents. The study consisted of repeated once daily dosing of young female Sprague
Dawley rats with a wide range of radiolabeled atrazine doses (1-100 mg/kg bw/day). The
resulting plasma profile (shown in Figure 13) clearly shows accumulation of the radiolabel
continuing until the fourth day of daily dosing when pseudo steady state plasma levels
seem to be reached (Thede 1987). Given that the LOAEL for LH attenuation was also
observed in rats after 4 days of once daily dosing with atrazine (Cooper et al. 2010), the
bioaccumulative pharmacokinetic profile resulting from daily dosing with radiolabeled
atrazine, not the episodic profile observed with atrazine, DEA, DIA, or DACT, is consistent
with the temporality of the LH attenuation endpoint.
The plasma elimination from the Thede 1987 study was noted to be linear with a consistent
elimination rate constant of 0.010-0.015 hr-1 (plasma half-life of 53.3 - 69.3 hrs) for the 1, 3,
7, and 10 mg/kg bw/day dose groups (Table 5) that indicates accumulation upon repeated
once daily dosing with radiolabeled atrazine. At the higher doses of 50 and 100 mg/kg
bw/day atrazine, the elimination rate constant was substantially higher (>20-fold) compared
to the other dose groups at about 0.3 hr-1. The nature for the difference is not known, but it
may be reflective of the limited time points examined, particularly at these high dose levels.
Page 89 of 184
Figure 13: Plasma profiles of radiolabeled triazine equivalents resulting from repeated once daily
dosing of rats with radiolabeled atrazine at 1, 3, 7, 10, 50 and 100 mg/kg bw atrazine. Data is from
Thede 1987.
Table 5: First order elimination rate constants estimated from plasma data in the Thede 1987 study
involving repeated once daily dosing of rats with radiolabeled atrazine (Thede 1987).
-1
ATRA dose (mg/kg/day) Elimination rate constant (hr )
1
0.010
3
0.015
7
0.013
10
0.014
50
0.376
100
0.308
4.2.6 Additional Rat Radiolabeled Atrazine Studies Evaluated for Plasma
Clearance
One of the limitations of the Thede 1987 study noted at the September 2010 SAP meeting
was the limited number of plasma data points for characterizing the elimination phase of
the radiolabel (FIFRA SAP 2010b). The elimination phase consisted of plasma data at only
3 time points corresponding to one animal of each dose group (i.e., 1, 3 ,7 , and 10 mg/kg
bw). In essence, the elimination rate constant of 0.010-0.015 hr-1 was estimated with 12
Page 90 of 184
data points from 4 individual animals. Recognizing that radiolabeled atrazine studies offer
at this time the best information to inform internal dosimetry and risk assessment, the
Agency embarked on an effort to gather more evidence for the plasma clearance obtained
from analyzing the plasma data from the Thede 1987 study. Thus, other rat radiolabeled
atrazine pharmacokinetic studies regardless of dose, sex, or dosing regimen (single or
repeated) were gathered for evaluation.
Paul et al (1993)
In the first study by Paul et al. 1993, single oral gavage doses of 1 or 100 mg/kg bw
radiolabeled atrazine were administered to groups of three male and female SD rats and
the resulting levels of radioactivity in plasma and other tissues were measured at different
times post-dosing (Paul et al. 1993). At both administered dose levels, plasma
measurements were limited to male rats and at the following time points: 2, 48, 168, and
336 hrs for 1 mg/kg bw and 24, 72, 168, and 336 hrs for 100 mg/kg bw. As seen in Figure
14, the plasma elimination phase at both dose levels exhibited linear behavior when plotted
on a semi-logarithmic plot versus time. The resultant elimination rate constants were 0.013
and 0.014 hr-1 which are consistent with those estimated for the 1-10 mg/kg bw/day dose
groups from Thede 1987 study (Thede 1987). It should be noted that the plasma analysis
of the 100 mg/kg bw/day dose group in the Thede study resulted in a much higher
clearance rate as compared to those obtained from Paul et al. 1993. The nature for
discrepancy is unknown.
Other tissues were also examined for radioactivity clearance in the Paul et al. 1993 study
including the brain, lungs, liver, kidneys, and others. Reported clearance values for all
tissues examined were much greater than 24 hours, estimated at as a range of 59-300
hours (Paul et al. 1993) (data not shown).
Page 91 of 184
C- atrazine
0.6
0.4
Paul et al. 1993
0.2
0.0
0
48
96
144
192
240
288
336
Single dose of 100 mg/kg bw
Plasma radiolabeled equivalents (ppm)
14
14
C-atrazine
25
20
15
Paul et al. 1993
10
5
0
0
24
48
72
96
120 144 168 192 216 240 264 288 312 336
time (hr)
time (hr)
ln (Plasma radiolabeled equivalents) (ppm)
Single dose of 1 mg/kg bw
14
Single dose of 100 mg/kg bw
C-atrazine
0
Slope = - 0.013 ± 0.002
R2 = 0.74
-2
-4
-6
Paul et al. 1993
-8
0
48
96
144
192
time (hr)
240
288
336
ln (Plasma radiolabeled equivalents) (ppm)
Plasma radiolabeled Equivalents (ppm)
Single dose of 1 mg/kg bw
4
14
C-atrazine
Slope = - 0.014 ± 0.001
R2 = 0.94
2
Paul et al. 1993
0
48
-2
96
144 192 240 288 336 384
time (hr)
-4
Figure 14: Plasma profile and analysis of elimination of a single oral gavage dose of 1 and 100 mg/kg
bw radiolabeled atrazine in rats. Data is from Paul et al. 1993.
Simoneaux 1985
In another study with radiolabeled atrazine by Simoneaux 1985, thirty six male rats (3 per
time point) were given oral doses of either 0.4 or 4.0 mg/kg bw 14C-atrazine for seven days
and sacrificed at 5, 7, 9, 10, 14, and 18 days post-dosing at which point plasma was
sampled for radioactivity (Simoneaux 1985). The plasma profiles are given in Figure 15.
Similar to the other two studies already discussed, analysis of the elimination phase at both
dose levels revealed linear behavior when plotted on a semi-logarithmic scale versus time.
When the resultant rate constants (estimated as per day from the reported plasma data)
are converted to per hr, the resultant values of 0.013 and 0.014 hr-1 are remarkably
consistent with those estimated from the Thede 1987 and Paul et al. 1993 studies.
In summary, one of the caveats of the plasma clearance analysis from the Thede 1987
study has been addressed by the evaluation of two additional rat pharmacokinetic studies
with radiolabeled atrazine. The two additional studies were carried out in different
laboratories with different sexes and dosing regimens (single and repeated dosing) and
along with the Thede 1987 study support an elimination half-life in the order of 53.3 - 69.3
Page 92 of 184
hrs (kel of 0.013-0.015 hr-1), indicative of bioaccumulation upon repeated daily dosing with
atrazine.
C-Atrazine
Repeated dosing with 4.0 mg/kg bw
0.10
0.08
0.06
0.04
Simoneaux 1985
0.02
0.00
0
2
4
6
8
10
12
14
18
16
time (days)
Ln (Plasma radiolabeled equivalents) (ppm)
Elimination Phase Analysis: 0.4 mg/kg bw/day
14
0
10
12
14
16
Simoneaux 1985
-3
-4
-5
-6
18
time (days)
-2
slope = -0.236 ± 0.031 day
= -0.010 ± 0.001 hr -1
-1
14
C-Atrazine
1.5
1.0
0.5
Simoneaux 1985
0.0
0
2
4
6
8
10
12
14
16
18
time (days)
Elimination Phase Analysis: 4.0 mg/kg bw/day
C-atrazine
1
-1
Plasma radiolabeled equivalents (ppm)
14
Ln (Plasma radiolabeled equivalents) (ppm)
Plasma radiolabeled equivalents (ppm)
Repeated dosing with 0.4 mg/kg bw
14
C-atrazine
1
0
10
-1
12
14
16
18
time (days)
Simoneaux 1985
-2
-3
-4
slope = -0.258 ± 0.019 day-1
= -0.011 ± 0.008 hr -1
Figure 15: Plasma profile and analysis of elimination of repeated oral gavage dosing with either 0.4 or
4 mg/kg bw 14C- atrazine in rats for seven days. Data is from Simoneaux 1985.
4.2.7 Pharmacokinetic Studies with Radiolabeled Atrazine in Monkeys
All of the pharmacokinetic studies discussed thus far were perfomed in rats. In an effort to
investigate the pharmacokinetics of radiolabeled atrazine in non-human primates, Hui et
al., 2011 dosed adult rhesus monkeys with either 1, 10, and 100 mg of [14C]- radiolabeled
atrazine via oral gavage and sampled plasma for radioactivity at various time points postdosing (Hui et al. 2011). An intraveneously administered dose of 0.25 mg of [14C]-atrazine
was also included in the study to estimate the oral bioavailability of atrazine. As with other
radiolabel studies already discussed, a high degree of mass balance of over 95% of the
administered dose was achieved. As with other species, urinary excretion was the primary
route of body excretion followed by fecal excretion. An oral bioavailability greater than 60%
was estimated for atrazine. Analysis of plasma radioactivity levels indicates that there were
dose-dependent changes in absorption across the different doses of atrazine tested.
Page 93 of 184
However, as was the case in rats, plasma clearance was linear. In fact, the plasma profiles
for the three different doses could be reproduced with a one comparment linear model with
optimized parameters (i.e., volume of distribution and elimination rate constant). Estimates
of the steady state volume of distribution via non-compartmental analysis were about the
same for the three dose groups given the variability reported around the mean values. An
average value of about 3.4 L/kg bw was estimated for the three doses tested. Moreover, as
similarly discussed for rat studies, both plasma Cmax and AUC increased with dose, but
AUC notably scaled more directly with dose (Hui et al. 2011). The estimated plasma
elimination rate constants along with other pharmacokinetic parameters are listed in Table
6 below. It should be noted that the estimated monkey plasma elimination rate constant of
0.03-0.06 hr-1 is only 2-4 fold greater than the estimated rat value of about 0.013 hr-1 with
considerable variability of as much as 50% (Table 6). The corresponding plasma half-life in
monkeys is estimated to be on average about 20 hours. Thus, bioaccumulation can also be
expected to occur in monkeys upon repeated daily dosing with atrazine.Thus, there is a
remarkable consistency in the clearance across different doses and species (rats and
monkeys) resulting from the linear pharmacokinetic behavior exhibited by radiolabeled
atrazine.
Table 6: Estimated plasma pharmacokinetic parameters in monkeys following oral gavage dosing
14
with 1, 10, and 100 mg [ C]-atrazine. Data is from Hui et al. 2011.
14
C-Atrazine Dose
(mg)
1
10
100
Cmax
(mg/L)
0.05
± 0.02
0.32
± 0.02
3.04
± 0.71
AUC
0→∞
((mg/L)*hr)
1.61
± 0.35
13.42
± 3.58
151
± 24
Vdss
(L/kg bw)
kel
-1
(hr )
2.427
± 0.397
4.413
± 0.781
3.255
± 0.597
0.04
± 0.03
0.06
± 0.03
0.03
± 0.00
t1/2
(hr)
20 ± 8
16 ± 9
24 ± 1
4.2.8 Pharmacokinetic Studies with Atrazine in Humans
As with the majority of environmental chemicals, human information on the
pharmacokinetics of atrazine is very limited. In one of the few controlled human studies
available, atrazine was administered orally to six volunteers (bw range of 74 to 117 kg) via
a single oral dose of 0.1 mg/kg bw (Davidson 1985). Urine was collected for each subject
for up to 168 hours post-dosing while blood samples were obtained from a single subject at
various time points starting at 2 hrs post-dosing. Atrazine, DEA, DIA and DACT were
measured in blood and urine samples using analytical methods with 0.005 ppm minimum
detection levels.
Page 94 of 184
4.2.8.1 Human Blood Pharmacokinetic Information on Atrazine and its
Chloro-s-triazine Metabolites
It should be noted that the information reported for blood samples refers to whole blood
rather than plasma. This is particularly important in comparing results to rat findings since
accumulation of up to 1.5% of administered radiolabel has been reported in rat red blood
cells (Thede 1987; McMullin 2003). Although the human blood information reported may be
limited (from a single subject), at minimum, pharmacokinetic behavior can still be compared
for consistency across different species. From the blood information reported, the most
important finding from a species extrapolation perspective is the linear first order behavior
exhibited by DIA and DACT which has been consistently observed in all animal species
discussed thus far. Peak blood levels of DIA were reported to be reached within 2 hours
and decreased rapidly thereafter with an apparent linear elimination rate constant of 0.25
hr-1 (half-life of 2.8 hr) (Table 7) which is only about 2.2-fold lower than that estimated for
rats at 3.0 mg/kg bw atrazine (Table 3). The rate of appearance of DACT in human blood
was noted to be slower than DIA with a tmax of about 5 hr, consistent with oxidative
metabolism from the precursor metabolites DEA and DIA. A first order elimination rate
constant of 0.039 hr-1 (half-life of 17.5 hr) was estimated for DACT which is about 2-fold
lower than the rat value of about 0.08 hr-1 (half-life of 8-9 hours) estimated for rats (Table
3). Atrazine and DEA were detected in human blood at levels below quantitation at the
administered dose level of 0.1 mg/kg bw atrazine. Such finding is consistent with rat
findings in that these two plasma chlorotriazines represent the least abundant out of the 4
chlorotriazines monitored in plasma. Moreover, atrazine would be barely detected at 2 hr
(the first time point analyzed) if the rat plasma half-life of about 44 minutes at 3 mg/kg bw
happens to be similar or even shorter in humans. Thus, the human blood information
provided in the Davidson 1987 report is at minimum qualitatively similar to the
pharmacokinetics of atrazine and DEA observed in rats and other species. From a species
extrapolation perspective, it should be underscored that DIA and DACT exhibited linear
elimination kinetics that is about 2-3 fold lower than that observed in rats (Table 3). When
the elimination rate constant of about 0.013 hr-1 estimated from the Thede 1987 study and
supported by other rat radiolabeled atrazine studies is allometrically scaled for a human
female with a bw of 60 kg (for example), the resulting kel of 0.0033 hr-1 is less than 4-fold
lower. Thus, allometric scaling of adult rat plasma triazine linear elimination kinetics
represents a reasonable approach for estimating the elimination kinetics in adult humans.
4.2.8.2
Human Urine Pharmacokinetic Information on Atrazine and its
Chloro-s-triazine Metabolites
Most of the information reported in the Davidson 1985 report is based on urinary levels of
DEA, DIA, and DACT; atrazine was not detected in urine (Davidson 1987). The urinary
excretion information for these three chloro-s-triazine metabolites were pooled and
analyzed as atrazine equivalents for pharmacokinetic behavior. The kinetics of each
Page 95 of 184
subject was found to be adequately described by a one-compartment linear model of
urinary excretion. An estimated first order elimination rate constant of 0.065 hr-1 was
estimated based on renal excretion as compared to 0.039 hr-1 estimated for DACT from
blood information from a single subject (Table 7). Given the limited information and
experimental variability, the blood and urinary clearance results were deemed by the study
author to be comparable (Davidson 1987). By far, however, the most important result
reported from the urinary excretion information was a very significant mass balance
discrepancy when only atrazine, DEA, DIA, and DACT were monitored. Only a total of
14.5% of the atrazine dose administered was accounted for when only these four
metabolites were monitored. It should be noted that urinary excretion is well-established as
the primary route of excretion of atrazine (i.e., if present at all) and its metabolites (DEA,
DIA, and DACT) and as also supported by the rapid absorption from the GI tract and peak
plasma levels being reached as early as 44 minutes post-dose. Thus, biliary/fecal excretion
is unlikely to explain the magnitude of the mass balance discrepancy reported in the study.
The most likely possibility as reported by the study author was that a large portion of
atrazine metabolites have not been identified. Several metabolites including conjugates of
glutathione, glucoronide and sulfate were reported as possibilities (Davidson 1987). Such
conclusion is consistent with the notion that the metabolism of atrazine has not been
completely characterized.
Table 7: Estimated Pharmacokinetic Parameters from Human blood and urine
a
Chlorotriazine
Atrazine
DEA
DIA
DACT
Whole Blood
Elimination rate constant
-1
(hr )
0.25
0.039
Urine
tmax (hr)
b
b
≤2
~5
Atrazine
DEA
DIA
DACT
-
Elimination Half-life
(hr)
2.8
17.8
b
-
0.29
0.30
0.060
a
information was from only one human subject
b
Below quantitation levels
c
Only the α phase is reported since the β phase was considered minor.
Page 96 of 184
c
2.4 ± 1.0
c
2.3 ± 0.5
11.5 ± 0.4
4.2.9 Updates on the Metabolism of Atrazine
Focusing on DEA, DIA, and DACT as the major toxicologically active metabolites of
atrazine’s neuroendocrine mode of action is a reasonable approach given the evidence to
date, assuming that the remaining metabolites are toxicologically inactive dechlorinated
glutathione conjugates. Recent reports have identified novel reactive metabolites of
atrazine from in vitro human liver microsomal preparations. In one of the most recent
studies by Leblanc et al. 2011, a new untargeted sensitive analytical chemistry method was
used to identify novel metabolites of atrazine (Leblanc et al. 2011). A new phase I
metabolic scheme for atrazine was proposed based on the formation of a newly identified
N-oxide metabolite which undergoes hydrolysis to form a reactive electrophilic imine
species which can conjugate with cellular nucleophiles like glutathione (GSH). As indicated
in the updated metabolic scheme in Figure 16, nitrogen hydroxylation and subsequent
imine formation takes place at the ethyl side chain on the triazine ring. In another recent
study by Joo et al. 2011, 1-hydroxyisopropyl atrazine and 2-hydroxyethylatrazine were
identified along with DEA and DIA as the major metabolites from human liver microsomal
incubations (Joo et al. 2011). These metabolites are also included in the updated metabolic
scheme in Figure 16.
Although the N-oxide, imine, and chlorinated glutathione conjugate metabolites as well as
1-hydroxyisopropylatrazine and 2-hydroxyethylatrazine depicted in Figure 16 were
identified using in vitro human liver microsomal preparations, at minimum, these in vitro
studies raise questions about the potential formation for these additional metabolites in vivo
which may explain the plasma clearance of atrazine which according to the plasma profiles
in Figure 9 does not seem to be directly due to either DEA or DIA. It should be noted that
the new metabolites in Figure 16 remain chlorinated and thus may be assumed to be
relevant in LH attenuation if the chlorinated triazine ring is indeed the important moiety for
such effect.
Page 97 of 184
GSH
2-hydroxyethylatrazine
1-hydroxyisopropylatrazine
Chlorinated conjugate
Imine
GSH
N-oxide
Non-chlorinated
Conjugate
Atrazine
Deisopropylatrazine (DIA)
Deethylatrazine (DEA)
Diaminochlorotriazine (DACT)
Figure 16: Updated metabolic profile for atrazine. Newly identified metabolites are above the dashed
line for comparison to Figure 7. New metabolites are from Leblanc et al. 2011 and Joo et al. 2011.
4.2.10 Summary of Pharmacokinetic Information
Atrazine and its metabolites exhibit linear kinetic behavior when monitored in plasma
individually (atrazine, DEA, DIA, and DACT) or as a whole through the use of radiolabeled
triazine ring across a wide range of orally administered doses and different species
including rats, monkeys, and humans. When only DEA, DIA, and DACT are individually
monitored in rats, the species with the slowest clearance is DACT with an estimated
plasma elimination half-life of 8-9 hr. With a plasma half-life of 8-9 hrs, a female rat being
dosed daily can be expected to clear most, if not all, of the chemical by 24 hours, resulting
in a repeated episodic plasma profile such as that described for DACT in Figure 11
following daily gavage dosing with atrazine. Thus, if only atrazine, DEA, and DIA, and
DACT are monitored, little or no plasma bioaccumulation can be expected to occur upon
repeated daily dosing. Moreover, if the pharmacokinetics of these 4 species are pooled and
described as molar chlorotriazine equivalents in a one-compartment linear model,
Page 98 of 184
clearance would be dominated by DACT as the most abundant and slowly cleared
metabolite. Indeed, McMullin et al. 2003 reported a one-compartment linear model based
on atrazine, DEA, DIA, and DACT as chlorotriazine equivalents with an estimated plasma
elimination rate constant of 0.08 hr-1 (t1/2 =8.8 hr) which is in excellent agreement with the
values estimated for DACT at the three atrazine dose levels of 3, 10, and 50 mg/kg bw in
Table 3 (McMullin 2003). In contrast, when the pharmacokinetics of atrazine are monitored
in rats via a radiolabeled triazine ring, a much longer plasma half-life in the range of 53-69
hours is observed across different studies and doses, suggestive of bioaccumulation upon
repeated daily dosing. Bioaccumulation of plasma radiolabeled triazine equivalents is clear
from available repeated daily dosing pharmacokinetic studies including Thede 1987 (Figure
13) and Simoneaux 1985 (Figure 15) . Thus, a plasma clearance discrepancy exists
between radiolabeled triazine studies and those that only monitor atrazine, DEA, DIA, and
DACT. The discrepancy may be due to unidentified/unmonitored metabolites of atrazine in
vivo such as those reported from recent in vitro metabolism studies (Figure 16) (Leblanc et
al. 2011 and Joo et al. 2011). It should be noted that glutathione conjugates formed via
substitution of the chlorine atom on the triazine ring are unlikely to explain the clearance
discrepancy since these are presumed to be cleared rapidly, a typical consequence of
phase II metabolism, and have been estimated to represent about 30% of the administered
atrazine dose (Timchalk et al., 1990 and McMullin et al. 2007). Because of this plasma
clearance discrepancy, the Agency is relying on studies based on radiolabeled atriazine on
the basis that such studies account for known and unknown metabolites of atrazine and
achieve a high degree of mass balance, although the toxicological relevance of these
metabolites related to atrazine’s neuroendocrine mode of action is unclear.
4.2.11 Progress on Physiologically Based Pharmacokinetic Modeling Efforts
In the case of chemicals like atrazine with a very short in vivo plasma half-life and
toxicologically active metabolites, pharmacokinetic modeling is necessary for the
application of a mode of action approach to risk assessment. Some of the current
pharmacokinetic modeling needs for atrazine include the estimation of relevant internal
measures of parent chemical and toxicologically active metabolites (related to the
endocrine mode of action), reliable means for extrapolating such internal measures across
different species and life-stages, routes, and conditions of exposure, and most importantly
for exposure assessment, means for reconstructing the human ingested dose through
drinking water. The preferred modeling approach for doing the aforementioned
extrapolations and dose reconstructions involves a properly calibrated and evaluated PBPK
model.
Previous efforts to develop a PBPK model for atrazine and its metabolites have been
hampered in part by sub-optimal pharmacokinetic data for model parameterization. The
integrated composite PBPK model for atrazine and its chloro-s-triazine metabolites
developed by McMullin and co-workers was parameterized with plasma data obtained from
oral gavage dosing of rats with atrazine, DEA, DIA, or DACT at molar equivalent levels of
Page 99 of 184
150 mg/kg bw atrazine (McMullin et al. 2007). At such high dose levels, limited solubility in
the gastrointestinal tract and continuing absorption of more slowly dissolving chemical
resulted in complex plasma profiles difficult to interpret for model parameterization,
particularly if oral uptake is not adequately described in the model. The final composite
model could not simulate the pharmacokinetics of atrazine or its metabolites, resulting in
overprediction of plasma levels of atrazine while underpredicting plasma levels of its
metabolites. In addition to inadequate data for model parameterization, the model lacked
postulated target tissues such as the brain and was limited to liver and rest of body
compartments (McMullin et al. 2007)
Some of the shortcomings of the McMullin 2007 PBPK model have been recently
addressed in a PBPK model recently submitted to the Agency by Syngenta in collaboration
with the Hamner Institutes (Campbell et al. 2011). Although the Agency has not performed
a thorough review and evaluation of the model, a preliminary review indicates that the
original McMullin 2007 model (McMullin et al. 2007) has been expanded to include
compartments of suspected target tissues including mammary gland, hypothalamus,
pituitary, testes/ovaries, and adrenals. The oral uptake of atrazine has also been refined by
removing a saturable uptake description and replacing it with separate compartments for
insoluble/bound and free fractions of atrazine as well as describing the potential slow
release of bound atrazine. New empirically determined tissue:blood partition coefficients
and hepatic in vitro metabolic rates were also incorporated. Most importantly, however, was
the model calibration performed with the new in vivo time-intensive rat pharmacokinetic
study described above where the chloro-s-triazine metabolites DEA, DIA, and DACT were
individually monitored in female SD rats following dosing with atrazine via the oral route of
administration (Coder 2011). The Agency is still evaluating the newly improved PBPK and
will consider using it after it has undergone a thorough review and evaluation process. In
the meantime, for purpose of this integrated toxicity-exposure assessment, an interim
pharmacokinetic modeling approach is presented below.
4.2.12 Proposed Interim Pharmacokinetic Modeling Approach
Non-compartmental pharmacokinetic analysis was introduced at the September 2010
meeting as a reliable way of estimating plasma AUC of radiolabeled triazine equivalents as
measure of internal exposure (FIFRA SAP 2010c). Daily pseudo steady state AUC
estimates were obtained from the Thede 1987 study for internal dose response analysis
associated with LH attenuation. Non-compartmental analysis works best when linear
elimination kinetics is observed in the plasma profile; that is, when elimination proceeds at
a rate that is proportional to the remaining plasma concentration. From a modeling
perspective, the most attractive feature of linear pharmacokinetics is its predictability based
on a constant plasma half-life. In the case of atrazine and its metabolites, linear elimination
kinetics is the most consistent feature observed when monitored individually (i.e., atrazine,
DEA, DIA, and DACT) or as a whole in radiolabel studies. Thus, the behavior of plasma
triazines may be described by a series of linear processes collapsed into a simplified
Page 100 of 184
deterministic one-compartment model as indicated in Figure 13. The oral uptake of atrazine
administered orally can be reasonably assumed to be fast and complete given its estimated
short tmax of 20 minutes or less (Table 3). The first order elimination rate constant (kel)
indicated in the model is representative of all clearance processes for plasma triazines that
consists primarily of urinary and biliary/fecal excretion. Metabolism is not reflected in kel
since the radiolabeled triazine ring is metabolically stable and thus, although metabolism is
important in the clearance of individual species including atrazine, DEA, DIA, and DACT, it
does not affect the clearance of the triazine ring.
Deterministic models such as the one compartment linear model proposed for plasma
triazines differ from statistical/probabilistic modeling approaches in that their parameters
are independently estimated rather than fitted to a particular dataset. In the case of kel in
Figure 17, an average value of 0.013hr-1 estimated for rats across different studies and
doses of atrazine is most appropriate for relating pharmacokinetic behavior to LH
attenuation in rats.
Oral dose of
Atrazine
(mg)
Fast/complete
absorption
Plasma Triazines
(Cp, mg/L)
Vdss (L)
kel (hr-1)
Triazines eliminated
Figure 17: Proposed Interim pharmacokinetic model for atrazine and its metabolites
Another feature of the behavior of plasma triazines as it relates to the toxicological endpoint
of concern (i.e., LH attenuation) is that pseudo steady state is reached (or nearly reached)
at the time when LH attenuation has also been observed. The condition of steady state
plasma levels for LH attenuation further supports the use of a one-compartment model
which assumes by definition that plasma and tissues are at least at equilibrium. Thus,
plasma triazines and the two parameters (Vd and kel) that govern their behavior in the one
compartment linear model are presumed to represent steady state values. In the case of
kel, it was already demonstrated from analyzing plasma data from the Paul et al. 1993
study that its values does not change in single dose versus repeated dosing studies.
There are several ways of estimating Vdss based on the available information. First, noncompartmental analysis offers a way of estimating Vdss through an area analysis as
depicted by equation 1. AUCM refers to the area under the first moment curve and is
calculated in a similar way as AUC except that it is based on the product of plasma
concentration and time (Benet 1979). Using the plasma data from Paul et al. 1993 study at
100 mg/kg bw atrazine (Paul et al. 1993), a Vdss estimate of 6.55 L/kg bw was obtained.
Page 101 of 184
Detailed calculations are given in Appendix A.9. The high dose of 100 mg/kg bw atrazine
was selected because plasma levels are well-defined at the later time points at this dose
level as compared to 1 mg/kg bw (Paul et al. 1993).
AUCM (mg*hr2/L)
0
(1) Vdss (L/kg bw) = Dose (mg/kg bw) *
∞
(AUC mg*hr/L)2
0
∞
In an attempt to evaluate the one-compartment linear model in Figure 17, the
corresponding equation that relates atrazine dose rate to steady state plasma triazine
levels (i.e., equation 2) was used to predict the average pseudo steady state plasma
radiolabeled equivalents reported in the Thede 1987 study. Using a Vdss of 6.55 L/kg bw
and kel of 0.013 hr-1, both of which were estimated independently from the single dose
plasma data from the Paul et al. 1993 study using non-compartmental pharmacokinetic
approaches, equation 2 yielded predictions of steady state plasma triazines that are very
consistent with those from the Thede 1987 study (Table 8).
Table 8: Evaluation of the one-compartment linear model for relating an atrazine dose to steady state
plasma triazines
animal 11
animal 21
2
Atrazine dose rate (mg/kg/day) ave. steady state plasma levels (mg/L) ave. steady state plasma levels (mg/L)2 Predicted value 3
1
0.56
0.62
0.49
3
1.88
2.16
1.47
7
3.84
3.45
3.43
10
5.11
4.83
4.89
50
22.80
25.61
24.47
100
53.10
56.84
48.93
1
Data is from Thede 1987
2
average steady state plasma levels were estimated by taking the mean value of plasma levels from day 4 through 8, i.e., when pseudo steady state was reached and maintained.
2
Using the one-compartment linear model expression that relates dose rate to steady state plasma levels (Equation 2)
Equation (2) can also be rearranged to estimate Vdss based on the plasma levels from the
Thede 1987 study (Table 5). An average Vdss value of 5.71 ± 0.65 L/kg bw for atrazine
doses of 1-10 mg/kg bw/day was obtained. Thus, this second approach gave a Vdss
estimate that is consistent with that obtained through non-compartmental analysis.
However, the Vdss estimate of 6.55 L/kg bw estimated from the Paul et al. 1993 study was
selected since it is based on an independent dataset that reasonably reproduces the
average pseudo steady plasma triazine levels (Css) at all doses tested in the Thede 1987
study.
Page 102 of 184
(2) Css (mg/L) =
Dose rate (mg/kg bw/day)
Vdss (L/kg bw) * kel (hr-1) * 24hr/day
It should be noted that the estimated value of 6.55 L/kg bw for Vdss is comparable to the
range estimated for the monkey of 2.427 - 4.413 L/kg bw (Table 6). Thus, there is
reasonable consistency across different rat studies (Paul et al. 1993 and Thede 1987) and
species (rats and monkeys) for which there is plasma triazine information from dosing orally
with radiolabeled atriazine.
The one compartment linear model depicted in Figure 17 represents a reasonable
approach at this time for informing the pharmacokinetic behavior and internal dosimetry of
atrazine and its metabolites given that the recent PBPK model submitted by Syngenta is
still being reviewed. However, the Agency is cognizant of the emerging science with the
ultimate goal of having a well-calibrated and evaluated PBPK model for atrazines and its
metabolites.
4.2.13 Implications for Water Monitoring Frequency
The ongoing re-evaluation of the health effects associated with atrazine includes the
drinking water monitoring frequency currently based on the 2003 risk assessment involving
90-day rolling averages of atrazine and its metabolites DEA, DIA, and DACT. Ideally the
frequency of drinking water monitoring should be reflective of the temporal pattern of the
toxicological endpoint of concern used in risk assessment, i.e., the duration of exposure
needed to elicit the toxicological endpoint. Given that the state of science has changed for
atrazine, the Agency is in position to re-evaluate current risk estimates based on the
current water monitoring frequency. The temporal pattern of LH attenuation is related to the
pharmacokinetic behavior and internal dosimetry of all relevant plasma triazines. There are
two features in the pharmacokinetic behavior of triazines that can be used to inform the
frequency for water monitoring frequency. First, the linear behavior that can be reasonably
described by the simplified one compartment linear model in Figure 17. The analytical
solution for a one-compartment linear model relates plasma levels of triazines to
administered doses of atrazine (Equation 3). The linear behavior of plasma triazines can be
reasonably expected to remain linear at lower doses of atrazines due to an even lower
probability of saturating ADME processes. Second, upon repeated dosing with atrazine,
plasma triazines reach (or nearly reach) pseudo steady state when LH attenuation is
observed. The steady state condition indicates that absorption and distribution processes
are offset by clearance processes and that while brain (a postulated site of action for LH
attenuation) and plasma triazine levels may be not equal, they will be at equilibrium,
making plasma triazine levels a reasonable surrogate for internal dose response analysis.
Page 103 of 184
4.2.13.1 Rat Forward Internal Dosimetry for LH Attenuation
The current understanding of the linkage between pharmacokinetics and LH attenuation
following atrazine exposure in rat studies is depicted in Figure 18. An oral gavage dose of
atrazine reasonably assumed to be completely absorbed from the GI tract (step 1) will be
distributed in a body volume of distribution to give plasma levels of triazines (step 2) which
can be cleared by the body with an overall first order rate constant (kel) that accounts for all
plasma clearance processes (primarily urinary and biliary/fecal excretion). Upon continued
exposure to atrazine (step 3), plasma levels of triazines will accumulate until steady state is
reached (or nearly reached) when optimal LH attenuation is observed. The proposed
critical duration is the time to reach steady state plasma triazines. It should be noted that
other metrics such as the time above a critical threshold of plasma levels have also been
considered. Based on the currently available information where 4 days is the shortest
exposure duration evaluated (Cooper et al. 2010) and the remarkably similar LOAELs
reported for studies of different durations (as short as 4 days and as long as 6 months), the
Agency is currently presenting steady state plasma levels in its derivation of an internal
dose metric associated with attenuation of the LH surge. As additional information becomes
available and as the Agency completes its review of the recently submitted Syngenta’s
PBPK model , the Agency will consider other internal dose metrics and adapt appropriately.
RAT: FORWARD DOSIMETRY FOR LH ATTENUATION
INPUT
Atrazine
Oral gavage dose (mg/day)
(1)
100% absorption
Absorbed Dose (mg/day)
(2)
Body volume of distribution (Vd, L)
Plasma triazines (Cp, mg/L)
kel (hr-1)
clearance
Continued exposure
(3)
Elimination
OUTPUT
bioaccumulation
Steady state
(Vdss, L)
Steady state plasma triazines (Cpss, mg/L)
(4)
Duration
Daily steady state AUC ((mg/L)*hr)
Optimal
LH attenuation
Figure 18: Proposed linkage between atrazine exposure and optimal LH attenuation in rat studies
Page 104 of 184
4.2.13.2 Human Reverse Dosimetry from Rat Internal Dosimetry for LH
Attenuation
The starting point for the human situation is the assumption of pharmacokinetic
equivalency. In other words, if the same plasma triazine AUC as the rat is ever reached in
humans, an equivalent LH attenuating effect can be reasonably expected (step 1 in Figure
19) (US EPA 2006). Notably, AUC is the product of levels times duration (i.e., “how much
and for how long”). Therefore, a given plasma AUC may be “matched” by high levels and
short duration or alternatively by moderate levels for a longer duration. The same one
compartment linear model expression but with allometrically scaled human parameters
(equation 4) can be used to match the rat AUC PoD associated with LH attenuation. These
types of forward and reverse calculations provide the basis for species extrapolation in risk
assessment particularly when a pharmacokinetic model is available. In the case of atrazine,
these calculations can be performed using simplified empirical calculations based on the
consistent linear pharmacokinetic behavior of triazines observed across different studies,
doses of atrazine, and species including humans.
(3)
(Dose rate) rat
Vdss rat * kel rat
PoD Daily Plasma AUC (Dose rate)
human
= Associated with =
Vdss human * kel human
LH attenuation
In contrast to animal toxicity studies where the frequency and level of dosing are controlled,
the dose rate in humans is determined primarily by the water consumption rate and the
chlorotriazine content of the water (step 3 in Figure 19), both of which are variable. In
particular, water levels of chlorotriazines can be highly variable (See hypothetical water
chemograph in Figure 20) due to being dependent on numerous factors including
application rate, proximity to growing field, and time of season. Nonetheless, an estimate of
the equivalent human time to reach steady state can be obtained via allometric scaling of
the rat elimination rate constant and assuming that it would take between 3-5 plasma halflives to reach steady state. A rat kel of 0.010 -0.013 hr-1 allometrically scaled for a human
body weight of 60 kg results in kel of approximately 0.00343 -0.00471 hr-1and a plasma
half-life estimate of about 6 - 8 days. Thus, an equivalent human time to reach steady state
plasma triazine levels based on a constant exposure level and frequency can be estimated
to be between 21-30 days.
The highly variable levels of atrazine and its metabolites in drinking water can be analyzed
through the use of an integrated average daily level of exposure given the predicted
duration of concern of 21-30 days. The observation that LH attenuation is driven by
sustained rather than acute exposures to atrazine further supports the use of an integrated
average daily dose metric. Rolling averages can be used to “carry over” exposures from
previous days and avoid gaps in the exposure estimates.
Page 105 of 184
HUMANS: PROPOSED REVERSE DOSIMETRY FOR LH ATTENUATION
Rat Daily Steady State AUC (mg/L*hr)
LH attenuation
≅
(1)
Human average daily AUC ((mg/L)* hr)
duration
Average daily plasma concentration (Cpave, mg/L))
Equivalent time to steady state
(Vdss, L)
(2)
Bioaccumulation
Continued exposure
Plasma triazine concentration (Cp, mg/L)
kel (hr-1) *
clearance
Elimination
Body volume of distribution (Vd, L) *
Absorbed dose (mg)
(3)
Water Consumption
(L/day)
100% absorption
Chlorotriazines in water
(µg/L)
Figure 19: Proposed linkages between atrazine exposure and LH attenuation in adult humans. The
asterisks indicate the parameters that were allometrically scaled from adult rats.
Hypothetical Water Chemograph
Triazine Levels
50
40
30
20
10
0
Critical
duration
Time
Figure 20: Hypothetical water chemograph showing the typical variation in chlorotriazine levels as a
function of time.
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4.2.14 Calculating Average Chlorotriazine Levels in Drinking Water for
Human Plasma Triazine AUC Estimates
At the April 2010 Science Advisory Panel (SAP) meeting, the panel recommended that the
Agency consider a drinking water monitoring approach based on the area under the
concentration-time curve (AUC) for atrazine and its chlorinated metabolites that can be
used in conjunction with an integrated internal dose response assessment (FIFRA SAPa).
Based on the currently available data, the Agency is presenting the use of pseudo steady
state plasma triazine levels to derive a plasma PoD AUC estimate associated with LH
attenuation. This value can be compared to a daily average AUC for drinking water
(AUCwater) for a given duration of concern on the basis that humans are unlikely to reach
steady state plasma levels of triazines as in rat studies, (Step 2 in Figure 19). AUCwater
can be estimated using the trapezoid rule whereby the area for a given exposure duration
would be divided into individual trapezoids as dictated by the water sampling frequency.
The sum of the individual trapezoids would be AUCwater. In the case the water sampling
frequency allows the estimation of a single trapezoid, AUCwater may be under- or overestimated depending on the shape of the individual trapezoid as depicted in Figure 21A. In
essence, the more frequent the water sampling, the better the estimation of AUCwater
(Figure 21B).
Time-weighted average levels of chlorotriazines in water can be estimated from AUCwater
according to equation 4. Rolling averages can be computed to “carry over” exposures from
previous days and avoid gaps in the estimates. In combination, equations 5 and 6 offers an
approach for estimating a human average daily AUC for plasma triazines that accounts for
an integrated dose rate for a duration of concern, body distribution and plasma clearance
information. The resulting human plasma AUC can be compared to the rat PoD AUC
estimate for a margin of exposure (MOE) calculation that is based on internal rather than
external measures of exposure (Equation 6).
B
Chlorotriazine Levels
Chlorotriazine Levels
A
time
time
Figure 21: The trapezoid rule for estimating the area under a water chemograph for a given duration
of exposure.
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(4) [atrazine]ave (mg/L) = AUCwater ((mg/L)*hr) for given duration
# days * 24 hrs
From water chemograph
(5)
Human average
daily plasma AUC
=
From human consumption information
[triazine water levels]ave (µg/L) * water consumption (L/day)
kel (hr-1) * Vdss (L)
Allometrically scaled from rat values
(6) MOE =
Rat PoD BMDL plasma AUC
Human plasma AU C
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5. SCIENTIFIC CONSIDERATIONS IN POTENTIAL SENSITIVITY OF INFANTS &
CHILDREN
5.1
Background
The FFDCA, as amended by the FQPA (1996), requires the Agency to give special
attention to the potential risk to infants and children. Specifically, FQPA instructs EPA, in
making its “reasonable certainty of no harm” finding, that in “the case of threshold effects,
an additional tenfold margin of safety for the pesticide chemical residue and other
sources of exposure shall be applied for infants and children to take into account potential
pre- and post-natal toxicity and completeness of data with respect to exposure and
toxicity to infants and children.” Section 408 (b)(2)(C) further states that “the
Administrator may use a different margin of safety for the pesticide chemical residue only if,
on the basis of reliable data, such margin will be safe for infants and children.” This
additional margin of safety is referred to as the “FQPA Safety Factor.”
In this section, the Agency describes the state of the science with respect to the potential
for pre- and post-natal toxicity and the completeness of data with respect to toxicity to
infants and children. This discussion focuses on experimental toxicology relevant for
considering pre- and post-natal toxicity. Although exposure considerations are a
component of the FQPA Safety Factor analysis, this section of the draft issue paper does
not include drinking water exposure. Drinking water exposure and statistical approaches
for assessing monitoring frequency are covered in Section 6.
Several key experimental toxicology studies important for the FQPA analysis have been
conducted since the September 2010 SAP. These include studies evaluating 1) the
potential hormonal changes and outcomes from gestational exposure to male and female
rats; 2) behavioral changes in male rats (e.g., rough and tumble play) following gestational
exposure (GD 14-21) and 3) other possible latent effects manifested in adults from
gestational and lactational exposure. Although additional experimental toxicology studies
are still on-going to better characterize the potential adverse health outcomes resulting
from atrazine exposure (including the duration of exposure that may lead to an adverse
health outcome), the available data do not indicate that pre- and/or post-natal exposure
leads to increased sensitivity in the young relative to the attenuation of the LH surge that
serves as the basis for the atrazine risk assessment. The Agency is evaluating potential
approaches for assessing drinking water monitoring strategies. The uncertainties and
confidence in the performance of water monitoring sampling strategies to potentially
capture atrazine levels exceeding the Agency’s level of concern are an important
consideration in the determination of the FQPA Safety Factor. Section 6 of this issue paper
will provide examples of how these uncertainties might be addressed by evaluating the
performance of different sampling strategies and some accompanying models for their
interpretation. As such, this draft issue paper does not propose a new FQPA Safety
Factor. Instead, the Agency will solicit comment from the SAP on 1) the current state of the
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science with respect to assessing potential sensitivity to the young and 2) the overall
approach for determining the FQPA Safety Factor.
5.2
Experimental Toxicology
5.2.1
Gestational Exposure
Recently, EPA’s ORD conducted a series of experiments to further evaluate the impact of
gestational exposure on reproductive development in both males and females. These
studies in conjunction with those evaluated by the Agency in preparation for the April and
September 2010 SAPs provide insight into the overall pattern of toxicity associated with
atrazine exposure during this lifestage not only in terms of the dose levels but also the
critical durations of exposure capable of eliciting an adverse effect.
With respect to toxicity outcomes following gestational exposure, Fraites et al. (2011) did
not observe effects on male reproductive development or the androgen-dependent
endpoints measured in the study after in utero exposure during gestation (GD 14-21)
including (i) testosterone production at birth and on PND 59, (ii) rough and tumble play
behavior, (iii) AGD and PPS, and (iv) androgen-dependent organ weights at doses as high
as 100 mg/kg/day. This is consistent with the findings reported by Rayner et al. (2007) who
observed no change in the timing of male puberty, but did report a higher incidence in
prostatitis at 100 mg/kg/day. In contrast, Rosenberg et al. (2008) reported delays in PPS at
50 mg/kg/day. Another high dose effect reported after gestational exposure to atrazine is a
delay in mammary gland development of female offspring (Rayner et al., 2005, 2007). This
effect, however, was not replicated by Davis et al. (2011) at doses as high as 100
mg/kg/day when evaluated either using a subjective scoring approach (as described by
Rayner and coworkers) or a morphometric analysis. Table 9 summarizes the toxicological
data available on atrazine after gestational exposure. Detailed reviews for all the studies in
the table are part of Appendix A.
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Table 9: Summary of available atrazine experimental toxicology studies from the gestational period
AUTHOR
(YR)
Rosenberg
(2008)
Rayner et al.
(2005)
Rayner et al.
(2005)
Rayner et al.
(2007)
EXPOSURE
NOAEL/LOAEL
mg/kg/day
Gestation
GD14parturition
10/50
GD 13-19
NA/100
GD 17-19
NA/100
GD 15-19
NA/100
Fraites et al.
(2011)
GD 14-21
20/100
Davis et al.
(2011)
GD 14-21
20/100
EFFECT
Delayed PPS
Delay in mammary gland
development
Delay in mammary gland
development
Prostatitis
Decreased pup weight
and pup viability (PND 14)†
Decreased pup weight
and pup viability (PND 04). Delay in VO
†
Decreased maternal body weight was also observed at the 100 mg/kg/day dose level
5.2.2 Multi-Lifestage Exposure
A multifaceted study evaluating the impact of atrazine exposure across several lifestages
has been submitted by the atrazine registrant – Syngenta. The purpose of this study was
to evaluate the effects of atrazine on sexual maturation, estrous cyclicity, and the LH surge
in Sprague-Dawley [Crl:CD(SD)] rats following at atrazine doses of 0, 6.5, 25 or 50
mg/kg/day administered via gavage. Animals were divided into two cohorts. Cohort I
animals were exposed from the time of conception through sexual maturation (VO) or PND
133. Half of the animals dosed until PND 133 were evaluated after a 20 days recovery
period (PND 134-154) while the remainder of the animals were evaluated on PND 133.
Cohort II animals were exposed only post-natally from PND 21 through sexual maturation
(VO + 5 days) or for 14 days as adults of reproductive age. Table 10 (excerpted from
MRID 48381001) describes the dosing regimen for these animals.
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Table 10: Dosing regimen Multi-Lifestage Study
Cohort I animals (all subsets) exposed to 50 mg/kg/day atrazine exhibited a 1.4-2.3 day
delay in VO (mean = 1.6 day delay). Unlike the findings reported by several investigators
(Foradori et al., 2009; Cooper et al., 2007; Morseth et al., 1996, Davis et al., 2011)), no LH
surge attenuation was observed at any dose level. Corticosterone and estrous cyclicity
also appeared to be unaffected by atrazine exposure. The study author, however,
acknowledged that the corticosterone measures were not obtained at the optimal time and
this may account for the discrepancy between the observations in this study and findings
previously reported by other researchers including Laws et al. (2009), Fraites et al. (2009),
Morseth et al. (1996), and Cooper et al.(1996).
In the case of Cohort II animals, no effect on VO was noted in animals whose exposure
began on PND21 (prior to the typical age of VO) at any dose level. LH surge and
corticosterone levels also appeared to be unaffected in this cohort (all subsets).
A unique feature of this study is that it is the only one available in the atrazine database
that evaluates the impact of atrazine exposure through multiple lifestages. The
preponderance of the data available to date supports the conclusion that LH surge
attenuation occurs at doses lower than those used in this study. The reason for the
inconsistency between this study and the body of peer reviewed literature evaluating the
impact of atrazine exposure across different life stages over the last 15 years is unclear. A
detailed review of this study is included in Appendix A of this document.
5.3. Summary
During the September 2010 SAP meeting, the Panel discussed several studies evaluating
the impact of atrazine exposure during development. At that time, the Agency concluded –
and the SAP concurred – that the available data did not identify a unique quantitative
susceptibility in the developing organism. None of the available studies with atrazine
evaluating the rats exposed during gestation, lactation or the peri-pubertal periods have
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shown effects at doses lower than those eliciting the LH surge attenuation in adult female
rats after 4 days of exposure. Three additional studies evaluating the effect of atrazine
exposure across lifestages have become available within the last few months. These
studies reinforce the conclusions reached during the September SAP meeting since all of
the effects observed in the young in these set of studies occurred at doses ≈ 25 times
higher than the dose EPA is proposing to use (BMDL of 2.56 mg/kg/day) as the PoD for
human health risk assessment (derived from LH data collected after four days of exposure
to adult females).
In making determinations regarding the FQPA Safety Factor, not only should potential lifestage sensitivity be considered but the need to ensure the exposure assessment will not
underestimate risks during development. During the September 2010 SAP meeting the
Agency proposed a water monitoring frequency between a few days and four weeks based
on durations of exposure that may result in adverse human health outcomes. In its
December 2010 report, the SAP commented that based on the assumptions and
extrapolations to the proposed critical window of exposure and the overall uncertainties
these assumptions introduce, “the imprecision in the Agency’s proposed sampling
frequency seems justified. This may be about as precise an estimate as can be obtained
when starting with the experimental animal data and the exposure requirements for LH
surge suppression…” Based on the SAP’s feedback, the Agency will continue to use
attenuation of the LH surge as the critical effect for the atrazine risk assessment and will
conduct an analysis of the water monitoring frequency ranging from a few days to 4 weeks.
Section 7 of this draft issue paper will present various approaches to assess the inherent
uncertainties associated with various drinking water monitoring strategies.
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6.EVALUATING ATRAZINE DRINKING WATER MONITORING DATA FOR USE IN
HUMAN HEALTH ASSESSMENTS
6.1 Introduction
The 2003 risk assessment for atrazine (USEPA, 2003) determined that LH surge
attenuation was the most sensitive toxicological endpoint for the human health risk
assessment associated with exposure to atrazine and other chloro-triazines. Based on the
best available information at the time, the USEPA identified a 90-day critical duration of
exposure, based on results showing LH surge attenuation following dietary dosing in a 6
month toxicity study and corresponding to the period within the year during which most
elevated concentrations of atrazine are likely to occur in drinking water. As a condition of
reregistration, the USEPA required the registrant, Syngenta, to monitor vulnerable
community water systems (CWS) to ensure that 90-day average concentrations did not
exceed the toxicological level of concern (37.5 µg/L total chloro-triazines) for that duration.
Syngenta proposed monitoring weekly during the use/ runoff season and bi-weekly during
the rest of the year to estimate total chloro-triazine (TCT) exposure over a 90-day time
frame for both source (raw) and treated (finished) waters at vulnerable CWS in the atrazine
use area. Any CWS that exceeded annual mean concentrations of 1.6 µg/L atrazine based
on quarterly Safe Drinking Water Act (SDWA) sampling was included in this more
intensively sampled Atrazine Monitoring Program (AMP). Since 2004, the AMP has
monitored TCTs in water at up to 166 CWS. No 90-day average concentration has
exceeded the toxicological level of concern in that time.
New data related to the hazard potential and dose-response for atrazine resulted in this reevaluation of human health effects for atrazine. Those studies suggest a shorter duration of
exposure may be appropriate for the toxicological endpoint of concern. With a shorter
duration of concern, the USEPA needs to consider how well the existing AMP monitoring
design characterizes shorter durations of exposure and whether the monitoring program
needs to include more frequent sampling or other means to augment existing data.
In the April and September 2010 FIFRA SAP consultations on atrazine, the USEPA
reviewed methods for designing monitoring studies to capture exposures of concern and
approaches for analyzing and interpreting existing monitoring data and characterizing the
uncertainties in those estimates for use in human health risk assessments (USEPA, 2010a
and 2010b). The SAP commented that the toxicological exposure timeframe of interest will
define the importance of peaks and determine the most useful approaches both for
designing a monitoring study and for evaluating the utility of existing monitoring studies
(FIFRA SAP, 2010a). However, the SAP noted that, in light of uncertainty over a critical
exposure period ranging from a few days to a few weeks, the best course of action may be
attempting “to capture the pattern of atrazine concentrations in the source water of each
CWS based on the characteristics of that particular water system, as opposed to a onesize-fits-all approach” (FIFRA SAP, 2010b).
Page 114 of 184
Identifying monitoring sites and determining the sampling frequency require careful
consideration to avoid underestimating true atrazine concentrations (FIFRA SAP, 2010b). A
predictive model can be used to target sites that merit the most detailed monitoring and
target the time periods when atrazine is most likely to be present (FIFRA SAP, 2010a,
2010b). The Panel recommended that the USEPA give thought to “using the simulation
models and CWS characterizations as part of the monitoring process. In particular, it is
feasible that models will eventually be accurate enough to provide predictions of atrazine
concentrations in source waters to a CWS for the coming crop season. Instead of requiring
a CWS to collect and analyze water samples in their output stream (drinking water) at some
predefined frequency (e.g., daily or weekly in the case of some sites), it should be possible
to use the models to facilitate targeting sampling to periods of time most likely to
experience an exceedance.” (FIFRA SAP, 2010b)
To estimate exposures from monitoring data sampled at intervals, particularly where peak
concentrations over shorter durations are important, the USEPA needs methods that can
predict values that may be greater than those sampled (FIFRA SAP, 2010a). The SAP
recommended combining regression-based models, such as the United States Geological
Survey’s (USGS) Watershed Regression on Pesticides (WARP), with either a deterministic
model or with a statistical approach using kriging or random-function models (FIFRA SAP,
2010b).
In preparation for the upcoming registration review for atrazine scheduled to begin in 2013,
the USEPA has focused on methods for characterizing the uncertainties resulting from the
existing AMP monitoring study design. Such methods include providing the uncertainty
bounds in estimated exposures based on how well the sampling design captures the
temporal variability of pesticide concentrations in water at locations where concentrations
are expected to be greatest. Additional uncertainties related to the number of years of
monitoring and site locations are also considered. While the discussion in this section
focuses on estimating atrazine concentrations, the human health assessment will be based
on TCTs. In most surface water sources, the elevated exposures will be dominated by
atrazine; in some instances, simazine or chloro-triazine degradates of atrazine may also
occur.
6.2 Key Questions to Address in Analyzing Monitoring Data
Pesticide occurrence in water is the result of a complex interaction of pesticide properties,
use patterns, application methods and timing, weather, watershed characteristics (e.g., soil,
slope, land cover type), and hydrology (Gilliom et al., 2006). While pesticide concentration
patterns resulting from this interaction are difficult to adequately characterize both
geographically and temporally with monitoring (ILSI, 1999; USEPA, 2000; Stone and
Gilliom, 2009), an understanding of the factors affecting pesticide distributions in water can
be used to predict pesticide occurrence in place and time and to characterize the degree of
uncertainty resulting from a monitoring study design for use in exposure estimates.
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For a pesticide with an established use pattern, such as atrazine, the variability of pesticide
occurrence in surface waters can be described by:
• The spatial pattern related to pesticide use, crop and management practices, soil
and hydrologic vulnerabilities, and rainfall distribution;
• Seasonal and daily temporal patterns at any given location related to intensity and
timing of pesticide applications and coincidence of rainfall events;
• A temporal correlation in which pesticide concentrations found on one day are
related to concentrations occurring on the previous and following days;
• Year-to-year temporal patterns at any given location reflecting changes in cropping
and pesticide use as well as variations in rainfall from year to year.
A well-designed surface water monitoring study takes into account both spatial and
temporal patterns of exposure, focusing sampling on when and where the pesticide is used.
Considerations for sampling frequency include not only the duration of toxicological
concern, but also the pesticide properties and use patterns, expected interactions of the
pesticide with the environment, and the nature of the water body and watershed. The
USEPA considers the following questions in determining the extent to which monitoring
data can be incorporated in exposure assessments:
•
Given monitoring of a specified sampling frequency, what can we say about the
actual structure (magnitude, frequency, duration, time series distribution) of the
concentrations at that site in a given year? This relates to the short-term temporal
variability and associated temporal correlations in pesticide occurrence in water.
•
What can we say about the nature of pesticide occurrence at the given site in other
years? How much variability in magnitude and duration of exposures can we expect
from year to year? This relates to longer-term temporal variability.
•
What can we say about pesticide exposures at other sites based on the results at
one or a few sites? This relates to the spatial variability of pesticide concentrations in
water.
•
Given the spatial variability in pesticide occurrence, how do we link monitoring data
with sound statistical designs? How do we deal with non-random monitoring
designs, such as the current atrazine CWS monitoring program?
•
How do we combine monitoring results across datasets, taking into account such
factors as watershed sizes, water body types, climate/weather differences, and
different study purposes and designs?
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•
How do we best analyze datasets with censored data (i.e., below the limit of
quantification)? This question spans a number of issues, including timing (were
samples taken when the pesticide was most likely to occur in water?), spatial
distribution (were samples taken from areas where the pesticide was used?), and
analytical methods (are the analytical methods sufficient to detect the pesticide at
concentrations of concern?). Determining the reason for non-detects can be useful
in interpreting the extent of risk both spatially and temporally and can aid in the
development of risk mitigation practices, if necessary.
•
How do we best integrate modeling and monitoring data to provide water exposure
estimates?
Each question addresses aspects of the uncertainties in monitoring analyses that need to
be considered in the context of the risk assessment. The recent series of SAP
consultations on human health and drinking water for atrazine have focused on addressing
the short-term temporal variability based on monitoring sampling frequency. Section 6.3
evaluates a cross-section of approaches for evaluating monitoring data and uncertainties,
ranging from conventional statistical analyses to mechanistic models and combinations in
between. However, in determining the uncertainty in monitoring data, the USEPA must also
consider year-to-year variability. How many years of monitoring are sufficient to
characterize the likely range in exposures resulting from year-to-year variability? Section
6.4 briefly explores ways of using monitoring and modeling together to estimate the
potential long-term range pesticide concentrations at a given location.
The USEPA typically addresses the spatial variability in pesticide concentrations in water
by focusing on vulnerable water bodies that are expected to have the greatest exposures.
For the atrazine monitoring program, CWS were considered vulnerable based on quarterly
compliance monitoring data. Section 6.5 will provide a brief discussion on spatial variability
and sound statistical designs, as well as potential applications of lessons learned regarding
watershed characteristics that contribute to higher atrazine concentrations from a separate
assessment of atrazine monitoring for ecological exposures (USEPA, 2007, 2009a, and
2009b).
6.3 Short-term Temporal Variability: Daily Variations within a Given Year
In a given year, pesticide concentrations in surface water bodies generally occur at low or
undetectable levels except during the active use period, where short-term seasonal pulses
of higher concentrations are related to the timing of pesticide use and rainfall patterns.
Such short-term pulses in pesticide concentrations range from less than a day to several
weeks, depending on the interaction of pesticide properties, use practices and patterns,
watershed characteristics, weather, and management practices. Atrazine, with relatively
widespread use and properties favorable to pesticide transport (mobility and persistence),
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may be detected year-round, with periods of high concentrations ranging from days to
weeks during the spring and summer. Consequently, monitoring data for atrazine provides
a good test for evaluating alternative methods for data analysis, as trends in time and
space can be observed above the level of quantification.
Within this seasonal pattern, pesticide concentrations in water show levels of temporal
autocorrelation (serial correlation) that are not well accounted for with traditional statistical
methods (Crawford, 2004). Pesticides such as atrazine show a serial correlation in which
the probability of detecting the pesticide in water on a particular day is greater when the
pesticide was detected on the previous day. Sampling designs and methods of interpreting
monitoring data need to account for the temporal co-dependence of pesticide
concentrations in water: the exposures measured on one day are related to the exposures
measured on preceding and subsequent days.
The extent to which monitoring studies capture this short-term variability depends on how
frequently and how targeted to the use season the sampling events occur. The AMP study
focused weekly samples during the anticipated high use/runoff period to estimate 90-day
average concentrations of concern. To estimate shorter duration exposures from
noncontinuously-sampled monitoring data, particularly where peak concentrations may
become more important, the USEPA needs methods that can predict values that may be
greater than those sampled (FIFRA SAP, 2010a).
The Agency considered several approaches for characterizing time series distributions and
uncertainties based on sampling design, beginning with statistical simulations and moving
on to geostatistical approaches, combinations of WARP with statistical models, and
deterministic models. While the emphasis is on analyzing the CWS monitoring data
collected for atrazine, such approaches could be applied to other monitoring datasets and
other pesticides.
The USEPA used a limited number of intensive monitoring datasets to provide a proof-ofconcept for the approaches presented below. A more complete analysis, involving the full
set of intensively sampled datasets, grouped by watershed area, water body type, and
geography, could be used for estimating uncertainty factors and time series for the weekly
CWS monitoring data collected for the AMP.
6.3.1 General Approach for Assessing Uncertainties in Monitoring Related
to Short-term Temporal Variability
To assess how well monitoring at a specified sampling frequency estimates the actual
magnitude, frequency, and duration of the concentrations at that site, we must consider
daily variations in pesticide concentrations and the temporal auto-correlation in those
concentrations. The USEPA is considering whether shorter durations of exposure (ranging
from 4 to 28 days) are more appropriate for assessing atrazine effects on humans. The
Page 118 of 184
question applied to atrazine is how well exposure estimates based on 7-day sampling
intervals during the high use season reflect actual exposures for 4-, 14-, or 28-day toxicity
durations of concern, as well as for the current 90-day duration. In this analysis, the USEPA
also evaluated uncertainty in estimates based on different sampling intervals (ranging from
4 to 28 days).
The SAP emphasized that evaluations of different exposure estimation methods need to be
benchmarked against intensive, preferably daily, monitoring data for an adequately
representative range of sites (FIFRA SAP, 2010a and 2010b). While the AMP covers the
geographic range of intensive atrazine use, the weekly sampling interval is not sufficient
because it provides a “biased representation” of actual concentration profiles (FIFRA SAP,
2010a). The USEPA proposed using the more intensively sampled monitoring data from
Heidelberg University’s National Center for Water Quality Research (NCWQR) and the
more frequently-sampled headwater streams from Syngenta’s Atrazine Ecological
Exposure Monitoring Program (AEEMP) to evaluate sampling frequency and exposure
estimation methods (USEPA, 2010a and 2010b).
The NCWQR (formerly known as the Heidelberg College Water Quality Laboratory) has
intensively sampled a number of pesticides, including atrazine, on selected Ohio rivers
since the 1980s (NCWQR, 2010). The Heidelberg dataset reflects frequent sampling over
many years in a geographically limited area. Syngenta’s AEEMP includes a subset of sites
with intensive atrazine measurements using an autosampler for a number of headwater
watersheds across the atrazine use area. While this dataset adds to the geographic
diversity in monitoring, it represents watersheds that are smaller than most watersheds that
supply CWS. Thus, generalizations from these datasets need to be done in the context of
both geographic distribution and representative watershed sizes. With this context in mind,
these two datasets do provide a basis for evaluating the ability of data analysis methods to
evaluate the uncertainty in monitoring datasets and their ability to predict the likelihood of
pesticide concentration excursions outside the range of the measured data in between
sampling events. In keeping with SAP recommendations, the USEPA will attempt to match
the intensively-sampled sites to CWS sites based on water body and watershed
characteristics to the extent possible (FIFRA SAP, 2010b).
Syngenta submitted a report summarizing a plan to monitor daily during the use season at
six CWS that are included in the AMP (Merritt, 2011). The six CWS – three in Kansas, two
in Indiana, and one in Ohio – draw water from single sources of flowing water. This
monitoring data will provide more intensive sampling of source and treated water samples
at CWS drawing from flowing waters that can be used to further evaluate the approaches
proposed in this section. Analogous daily monitoring data do not exist for reservoirs or
other static water bodies. A different temporal exposure profile would be expected from
reservoirs, which integrate pesticide loading from their watersheds and have slower flowthrough, resulting in a greater residence time and smaller temporal variations than flowing
water bodies (Blomquist et al., 2001). The SAP suggested collecting more intensivelyPage 119 of 184
sampled data for other representative water bodies, such as reservoirs (FIFRA SAP,
2010b). An effort to target CWS drawing from a single reservoir water source similar to that
described in Merritt (2011) could be completed in time for the 2013 registration review,
providing additional data to analyze uncertainties in sampling frequencies for static water
bodies.
Given the uncertainty in defining the duration of toxicological concern, the USEPA plans to
use a multi-step approach for analyzing the uncertainty in exposure estimates from
monitoring data. The first step is a statistical analysis of monitoring frequency by simulating
different sampling frequencies using the intensively sampled datasets described above.
This approach, described in Section 6.3.2, addresses the question:
Given a sampling frequency of X (samples per month or average interval between
samples) and an exposure duration of concern of Y (4 to 28) days, what is the relative
performance of each sampling strategy (X), based on measures representing the
differences of its estimates (Y) from the known true values? These differences may be
used to quantify the uncertainty from sampling frequency.
A bias or uncertainty factor generated from the analysis could be applied to bracket the
exposure estimates from the weekly CWS samples for comparison against the
concentration and duration of concern. If the bracketed exposure estimates exceed the
concentration of toxicological concern for the specific duration of concern, further analysis
using geostatistical methods, hybrid geostatistical-WARP, or deterministic modeling
approaches described in Sections 6.3.3 – 6.3.5 may be needed. The case study (Section 7)
illustrates how a time series estimated from weekly monitoring using geostatistical models
could be incorporated into the dietary exposure assessment.
6.3.2 Comparative Analysis of Monitoring Frequency Performance
To evaluate the relative performance of different monitoring frequencies for estimating
exposure windows of specific durations, the USEPA performed a simulation analysis on
two example sites representing two different watershed areas for which daily or near daily
concentration measurements are available. The AEEMP MO-01 data from 2007 and the
NCWQR Maumee River data from 1995 will be used in example analyses (see Appendix
D.1 for the measured atrazine concentrations for each of these data sets).
From the daily to near daily concentrations measured at from April through August/
September in each year, the USEPA divided the data into sampling intervals of 4, 7, 14, or
28 days and sampled at random from each interval 10,000 times. The resulting compiled
chemographs, derived from the known true profile, represented trials that reproduced
sampling within 4-day, 7-day, 14-day (biweekly), or 28-day (monthly) windows. Intervening
values were estimated for each resulting chemograph by linear interpolation, with daily, 4day, 14-day, 28-day, and 90-day running averages calculated for each sampling. The
Page 120 of 184
maximum value for each of these running averages was compiled into a distribution,
resulting in five distributions of 10,000 maxima for each estimated average. Percentiles of
those distributions of maximum values (Tables 11 and 12) are compared against the site’s
true maximum value for each measurement.
Tables 11 and 12 provide the results for each example site, along with a simple ‘bias’ factor
for assessing the varying average duration estimates (peak, 4-, 14-, 28-, and 90-day
averaging periods) based on the 4-, 7-, 14-, and 28-day sampling intervals. The
multiplicative ‘bias’ factor in the tables is simply the ratio of the true maximum to the 5th
percentile maximum for each estimate derived from the 10,000 runs. For example, with 4day sampling intervals, estimates of the 1-day peak can be nearly 4 times lower than the
true peak concentration. Sampling within a 2-week window can return estimates of the 4day average that are over 10 times lower than the true 4-day average. (Minus signs on the
factors simply note an underestimation of true values.)
The bias factors in Tables 11 and 12 assume that the sampled concentrations reflect an
average across the day. This assumption only holds true for autosampler data collected for
MO-01, which integrated time sips across a 24-hour period. Grab samples collected during
a single time period within a day have additional uncertainty related to intra-day variability
in pesticide concentrations in the water body. This intra-day variability is not reflected in the
analysis below.
The bias factors for estimating daily concentrations run from about 1.5X lower to more than
50X lower for the 2007 MO-01 data, increasing as the sampling interval increases.
Sampling within every 4 or 7 day period underestimates the true average, but not by as
large a factor as with biweekly or monthly sampling intervals.
Page 121 of 184
Table 11: Summary of 10,000 simulations of monitoring estimates at different sampling intervals for
the AEEMP MO-01 site in 2007.
th
th
th
th
Averag
-ing
Period
Sampling
Interval
True
Max
(ppb)
Lowest
Max
(ppb)
5 %ile
Max
(ppb)
10 %ile
Max
(ppb)
Bias
Factor
th
(5 %ile)
50 %ile
Max
(ppb)
95 %ile
Max
(ppb)
Daily
Peak
4 days
7 days
14 days
28 days
4 days
7 days
14 days
28days
4 days
7 days
14 days
28days
4 days
7 days
14 days
28 days
4 days
7 days
14 days
28 days
91.6
91.6
91.6
91.6
59.7
59.7
59.7
59.7
27.0
27.0
27.0
27.0
15.1
15.1
15.1
15.1
8.1
8.1
8.1
8.1
23.2
17.2
2.6
0.97
18.9
15.6
2.5
1.0
11.2
11.4
2.2
1.0
6.7
7.2
1.7
1.0
4.3
4.0
1.3
0.8
23.4
19.0
6.2
1.57
22.8
17.4
5.9
1.6
15.3
12.4
4.8
1.5
8.9
7.7
3.5
1.4
5.5
4.9
2.1
1.2
26.2
23.2
8.1
2.58
23.1
20.3
7.6
2.5
16.6
13.1
6.4
2.4
9.8
8.2
4.8
2.2
5.8
5.1
2.8
1.5
-3.9
-4.8
-14.8
-58
-2.6
-3.4
-10.1
>-37
-1.8
-2.2
-5.6
-18.0
-1.7
-2.0
-4.3
-10.8
-1.5
-1.7
-3.9
-6.7
71.8
33.3
23.2
14.26
57.5
29.4
21.7
14.0
29.6
20.7
17.8
13.2
16.8
13.2
12.6
11.9
7.9
7.2
7.7
5.5
>91
>91
>91
71.8
76.4
82.2
85.0
69.4
41.9
56.0
69.3
63.0
23.1
34.3
46.8
54.3
10.8
14.4
17.9
23.7
4-day
avg.
14day
avg
28day
avg.
90day
avg.
In contrast, data for the Maumee River generally show much lower bias factors compared
with those for MO-01 (Table 12).
Page 122 of 184
Table 12: Summary of 10,000 simulations of monitoring estimates at different sampling intervals for
the NCWQR Maumee River site in 1995.
th
th
th
th
Averag
-ing
Period
Sampling
Interval
True
Max
(ppb)
Lowest
Max
(ppb)
5 %ile
Max
(ppb)
10 %ile
Max
(ppb)
Bias
Factor
th
(5 %ile)
50 %ile
Max
(ppb)
95 %ile
Max
(ppb)
Daily
Peak
4 days
7 days
14 days
28 days
4 days
7 days
14 days
28days
4 days
7 days
14 days
28days
4 days
7 days
14 days
28 days
4 days
7 days
14 days
28 days
14.1
14.1
14.1
14.1
11.7
11.7
11.7
11.7
7.8
7.8
7.8
7.8
6.3
6.3
6.3
6.3
4.4
4.4
4.4
4.4
8.9
5.7
4.6
3.4
7.9
5.3
4.5
3.3
5.7
4.4
4.3
3.1
5.3
4.4
3.9
2.8
3.7
3.1
2.5
1.8
8.9
6.2
4.7
4.4
8.1
6.0
4.7
4.4
6.3
5.4
4.7
4.3
5.7
5.1
4.4
4.1
4.0
3.7
3.3
3.0
8.9
7.4
5.1
4.5
8.3
7.0
5.0
4.5
6.4
5.9
4.9
4.4
5.8
5.3
4.6
4.2
4.1
3.8
3.4
3.1
-1.6
-2.3
-3.0
-3.2
-1.4
-2.0
-2.5
-2.7
-1.2
-1.4
-1.7
-1.8
-1.1
-1.2
-1.4
-1.5
-1.1
-1.2
-1.3
-1.5
10.6
9.9
7.6
5.7
9.8
9.0
7.4
5.6
7.3
7.3
6.9
5.4
6.2
6.3
6.1
5.1
4.4
4.3
4.2
3.9
>14.03
>14.03
>14.0
12.3
>13.3
13.2
13.3
12.0
9.5
10.8
11.3
11.1
7.1
7.9
9.1
9.9
4.7
5.1
5.7
6.4
4-day
avg.
14day
avg
28day
avg.
90day
avg.
In these examples, as in all sites, the differences are directly related to the nature of the
data and the magnitude of difference between what may be a short-lived peak and the
concentrations preceding and following it. Concentrations at Maumee are lower overall,
thus making the magnitude of potential bias smaller with regard to missed peaks. In
addition, if the duration of peak values extends over the course of a week instead of for a
day or two, then sampling within a 4-day window is more likely to ‘pick up’ some portion of
the elevated concentration profile. For averages of longer duration, potentially missed
peaks obviously have less impact on the estimates. These differences in the nature and
magnitude of the profiles can be attributed, at least in part, to differences in watershed size
and hydrology but may also reflect differences in atrazine use intensity in the watersheds.
Based on these two examples only, an uncertainty factor for a 7-day sampling frequency,
as used for the CWS in the AMP, might be in the range of 2-4X for estimating a 4-day
average concentration and 2X for estimating a 14- or 28-day average concentration. Using
these examples for the 90-day average period from the 2003 assessment (USEPA, 2003),
Page 123 of 184
the bias factor would be less than a factor of 2 for 7-day sampling intervals. Differences are
expected to exist for sampling from larger watersheds and from reservoirs, and for other
conditions that a larger, representative sample of watersheds might show. Bias factors
may tend to be slightly lower for reservoirs, for example, based on both differences in size
and on the ‘smoothing out’ we would expect to see as water from streams/rivers reach the
larger static water body. They may be higher where sampling is primarily from small rivers.
The USEPA’s planned analysis of intensive monitoring representative of a range of
watersheds and water bodies will likely result in a smaller uncertainty factor for CWS that
are supplied by reservoirs with a large watershed and a larger uncertainty factor for CWS
supplied by flowing waters with smaller watersheds.
Syngenta has submitted a report using this basic methodology, but covering a larger
sample of sites and years. In that report, Mosquin et al ( 2011b) examined data from three
different sources: (1) one Community Water System (St. Louis/St. Charles, 1993-98 and
2000) with results for treated (“finished”) water; (2) four rivers/streams from the NCWQR
data set (Maumee River, Sandusky River, Honey Creek, and Rock Creek) for the years
1993-2008; and (3) sites from the Atrazine Ecological Exposure Monitoring Program
(AEEMP) with autosampler data (11 sites from 2009, and 23 sites from 2010).
Simulating sampling frequencies from 2 days to 7 days on the near-daily data, Mosquin et
al (2011b) calculated 1-day (peak), 2-day, 3-day, 4-day, 7-day, and 28-day average
concentrations. Performance was measured by examining % error, % bias, and % relative
mean squared error (RMSE). The authors calculated exact measures of performance for
systematic sampling, in addition to performing the stratified random sampling as described
above, running 1,000 simulations of each combination of sampling interval and average
duration.
For 7-day sampling intervals, Mosquin et al (2011b) found up to -77% error for estimating
daily peaks and -67% error for estimating 4-day averages. They did not investigate
sampling periods of longer than 7 days, where performance would be expected to decline.
It is not always clear how the percentiles were compiled, however, and results for the
AEEMP sites have been presented as a group. In the AEEMP, particular sites and years
can have fairly high values that would be missed in certain instances by 7-day grab
samples. Bias factors corresponding to these error estimates, as a result, are expected to
be much greater when individual sites and years are examined.
The USEPA has presented results for two example sites to demonstrate both some general
concepts as well as the difference between results for two particular site/types, and it is
clear that the discussion can be greatly enhanced by the addition of more data.
The Agency believes, however, that a more helpful approach would be to present summary
statistics on the low end percentiles of the simulated runs by site and by year, rather than to
present percentiles of the grouped data. Important differences exist among sites and from
Page 124 of 184
one year to the next at the same site (see Section 6.4). A protective approach will highlight
these differences where they exist.
6.3.3 Geostatistical Analysis and Stochastic Application
The September 2010 SAP noted that combining a deterministic model, such as PRZM or
USGS’s SEAWAVE-Q (used in Sullivan et al, 2009), with a regression-based model, such
as WARP, offers “the most promising approach to deal with sparse data in flowing water”
(FIFRA SAP, 2010b). However, because of the difficulty in implementing these methods,
the SAP suggested that it may be easier to use WARP in combination with a statistical
approach such as kriging (FIFRA SAP, 2010b). This section describes two approaches that
build on geostatistical analysis and stochastic simulations: (1) conditional stochastic
simulations from variography with kriging and (2) using soft (indirect) data when monitoring
data are sparse. This second approach involves regressions with WARP combined with
variography and stochastic simulations. The full analysis can be found in Appendix D.2
(Geostatistical Analysis and Conditional Stochastic Simulation Application).
The geostatistical modeling approach selects a stationary random function model to
describe the relationship of variance as a function of distance or time (Isaaks and
Srivastava, 1989). This relationship can be quantified using a variogram, correlogram, or
covariance function. For a pesticide time series, this nearest-neighbor modeling approach
assumes that concentrations in close proximity in time are more similar than concentrations
farther apart in time. Applying a random function model to pesticide time series generally
requires a condition of stationarity: that is, an estimate has an equal probability of occurring
regardless of location in space and time (Isaaks and Srivastava, 1989). This may not hold
for an entire year of pesticide concentrations due to seasonal trends, but may be applicable
to specific times during the pesticide use season. To address stationarity concerns
associated with seasonality of pesticide concentrations in water, the USEPA targeted the
monitoring analysis from the earliest planting date to approximately 100 days post-planting.
This represents the time with the highest magnitude and frequency of atrazine detections.
The USEPA chose two intensively sampled monitoring datasets – NCWQR Maumee River
monitoring for 1995 and AEEMP monitoring for MO-01 in 2007 – to evaluate the approach
for different years and hydrologic characteristics (watershed size and stream flow
characteristics). We simulated 4-, 7-, 14-, and 28-day sampling intervals with the monitored
time series. Sample sets with less than 15 points – the 14- and 28-day sampling intervals –
provided the poorest variograms for predicting daily pesticide concentrations from kriging.
In those instances, we first used WARP to estimate the percentiles in the time series and
evaluated options for fitting the merged WARP and sampled data in a new time series.
Figure 22 illustrates the analysis strategy used for time series modeling from the sampled
monitoring data.
Page 125 of 184
DETERMINE APPROPRIATE
POINT ESTIMATION
PROCEDURE FOR TIME
SERIES
INFILL TIME SERIES
WITH MODEL
ESTIMATED VALUES
NO
ARE THERE MORE THAN 15
DATA POINTS IN TIME
SERIES?
YES
PROCEED WITH
VARIOGRAM ANALYSIS
PROCEED WITH 1DIMENSIONAL
ORDINARY KRIGING
MERGE
MONITORING
DATA
USE WARP
MODEL TO
ESTIMATE
PERCENTILES
IN TIME SERIES
USE COVARIATE
VARIABLE TO
ESTIMATE
CONCENTRATION
ARRANGE
PERCENTILES
OF FLOW
DATA WITH
MERGE DATA
PERCENTILES
SEQUENTIAL STOCHASTIC
SIMULATION- GENERATE
EQUALLY PROBABLE
REALIZATIONS OF TIME
SERIES
Figure 22: Analysis Strategy for Modeling Pesticide Time Series Concentrations from Monitoring
Data.
In the initial variogram analysis, spherical or gaussian models fit the 1995 Maumee River
and MO-01 2007 atrazine data simulated with 4-, 7-, and 14-day sampling intervals (Table
13). Although the extent of autocorrelation, as depicted by the range, was highly variable
among the case study data sets, longer ranges (52-80 days) were found in the 1995
Maumee River data. This phenomenon may be associated with the larger watershed size
and daily flow characteristics. While kriged time series provided reasonable prediction of
Page 126 of 184
missing data for the 4- and 7-day sample intervals (Figure 23), reliable variograms could
not be estimated for the 14- and 28-day sample intervals from either monitoring dataset.
Table 13: Variogram Models Using the NCWQR Maumee River 1995 and AEEMP MO-01 2007 Time
Series with Different Sampling Frequencies.
Data
Sampling
Interval
Number
of
samples
Maumee
Actual (daily) 100
River, 1995
4-day
25
(days 1007-day
15
200)
14-day
8
28-day
4
6
MO-01, 2007 4-day
27
(days 917-day6
15
6
190),
14-day
8
6
complete
28-day
4
time series5
Nugget1
(µg/L2)
Model2
Sill3
(µg/L2)
0.10
0.00
0.00
0.30
2.2
0.00
0.00
0.50
3.80
Spherical 0.97
Spherical 3.70
Spherical 3.20
Gaussian 3.20
No temporal correlation
Spherical 3.60
Spherical 3.80
Spherical 3.80
No temporal correlation
Range4
(Days)
16
52
61
80
27
27
32
1
The nugget describes the amount of variance resulting from random processes without temporal
correlation; it is associated with sampling error, sampling technique, or inherent variation in daily
pesticide concentrations.
2
Variogram models provide a description of variance as a function of lag distance or lag time
3
intervals. The Spherical model is defined as the variance at lag(h)=C*(1.5*h/a-0.58(h/a) )+nugget.
2 2
The Gaussian model is defined as the variance at lag(h)=C*(1-exp(-(3h) /a )+nugget. This model
does not represent the Gaussian distribution model. Parameters in the variogram model:
h = lag time or lag distance
2
C = sill, the variance for random process (ug/L )
a = range in days or distance for correlation of variance
3
The sill is the variance value at which the variogram levels off (see Figure 23 for an illustration).
4
The range is the lag time at which the variogram reaches the sill. It represents the period in which
monitoring values are autocorrelated.
5
Grab samples collected every 4 days from 4/01/07 to 7/19/07. Complete time series includes
autosampler data collected on most days in between the grab sample dates. Appendix 7.A also
includes an analysis of the MO-01 2007 monitoring using only the 4-day grab samples. The models
based on the daily sampling series had greater means, maximums, and variances than the models
based on the 4-day grab samples, likely because the 4-day samples missed the maximum
concentration detected in the daily samples.
6
Sampling intervals estimated from kriged samples.
Page 127 of 184
Figure 23: Variograms for 4-day and 7-day Sampling Frequency for the Maumee and MO-01 2007
data: a.) 4-day 1995 Maumee; b.) 7-day 1995 Maumee; c.) 4-day grab MO-01 2007; and d.) 7-day
grab MO-01 2007.
We used conditional stochastic simulations to assess the uncertainty associated with
estimated concentrations by running 1,000 equally-probable realizations of time series with
the same temporal autocorrelation and descriptive statistics as the kriged time series. A
subsampling of realizations for the 4-day and 7-day sampling derived time series provide a
reasonable description of temporal behavior and descriptive statistics of atrazine
concentration for the Maumee 1995 site from Julian day 100 to 200 (Figure 24a and b).
Although a variogram was modeled for the 14-day sample data (Figure 24c), insufficient
data precluded providing a reliable estimate of the temporal structure and descriptive
statistic of atrazine at the Maumee River site. Because a variogram could not be
constructed from 4 data points for the 28 day sample data, we tried to use daily flow to
approximate the temporal structure of atrazine concentrations (Figure 24d). As expected,
the estimate of peak concentrations decreased and the uncertainty around the time series
estimations increased as the sampling interval increased; beyond 14-day intervals, the
number of data points was insufficient for reliable estimates.
Page 128 of 184
(a) 4-day intervals
16
Estimate Mean (SD)
Actual Data
14
12
Atrazine Conc (ug/L)
Atrazine Conc (ug/L)
Estimate Mean (SD)
Actual Data
14
12
10
8
6
10
8
6
4
4
2
2
0
0
100
120
140
160
180
200
220
100
(c) 14-day intervals
Julian Day
16
120
140
160
180
200
220
(d) 28-day intervals
Julian Day
16
Estimate Mean (SD)
Actual Data
14
Estimate Mean (SD)
Actual Data
14
12
Atrazine Conc (ug/L)
12
Atrazine Conc (ug/L)
(b) 7-day intervals
16
10
8
6
10
8
6
4
4
2
2
0
0
100
120
140
160
180
200
220
100
120
140
160
180
200
220
Julian Day
Julian Day
Figure 24: Representative realizations of kriged atrazine time series from (a) 4-day, (b) 7-day, (c) 14day, and (d) 28-day sampling of the NCWQR Maumee River 1995 data.
Similar patterns are evident in the conditional simulations for the MO-01 2007 monitoring
data, although the realizations underestimated the actual peak concentrations in the 4- and
7-day sampling simulations (Figure 25). Although the temporal pattern for the 14-day
sampling interval for MO-01-2007 did not exactly match the actual data, the conditional
simulations were capable of identifying the temporal occurrence of the peaks (Figure 25c),
in contrast to the 14-day simulations for the Maumee River data (Figure 24c).
The simulated sampling intervals for the 2007 MO-01 data set did not capture the highest
atrazine peak that occurred on Julian days 152-154 (Figure 25). This results in an
underestimate of the true maximum concentrations for the subsequent conditional
simulations. While a different sampling of the daily concentrations from MO-01 might
capture the true peak, this particular sampling was used in Section 6.3.6 as an evaluation
of how much of an impact missing the true peak concentration might have on estimating
longer duration rolling average concentrations with conditional simulations of the variogram
models.
Page 129 of 184
Figure 25: Representative realizations of kriged atrazine time series from (a) 4-day, (b) 7-day, (c) 14day, and (d) 28-day sampling of the MO-01 2007 data.
With fewer data points resulting from less frequent monitoring samples, the uncertainty in
predicting daily atrazine concentrations increases. In the absence of data, the incorporation
of additional information may assist in predicting a daily time series of pesticide
concentrations. One approach involves the use of a covariate, such as average daily flow,
to estimate atrazine concentrations between monitoring points. Another approach is to
merge monitoring data with WARP estimates of the annual distribution of pesticide
concentrations. This approach requires additional steps to derive a time series from the
distribution.
Appendix D.2 analyzes two covariate approaches – one using a co-occurring pesticide and
one using stream flow data. Covariate approaches using other pesticides will suffer from
the same sampling frequency issues as atrazine monitoring. One option may be to explore
the use of more frequently monitored nutrient concentrations, such as nitrate as a
covariate. While average daily stream flow data may be a useful covariate for estimating
Page 130 of 184
atrazine concentrations in flowing water bodies, the relationship between flow and pesticide
concentrations is also dependent on the timing of application as well as the watershed size.
Analyses of the NCWQR 1995 Maumee River and the AEEMP 2007 MO-O1 monitoring
data sets found significant regressions between atrazine concentrations and average daily
flow rate within certain time segments of the data sets. The timing of atrazine application is
a critical piece of information in determining the correct time interval. The highest
concentrations in a time series are generally found in the first runoff events after pesticide
application (USEPA, 2007, 2009a). Additionally, high average daily flow events in the
absence of atrazine prior to atrazine application or after extensive degradation of atrazine
are expected to impact the slope of the regression equation. Exploiting any relationship of
flow and atrazine concentration requires knowledge of the atrazine application dates within
a watershed. Unfortunately, data on the date of pesticide applications in a watershed are
not readily available. However, since atrazine application dates will likely parallel corn
planting dates in most areas, any variable influencing corn planting dates, such as air
temperature, soil temperature, or soil moisture conditions, may be an appropriate predictor
for estimating atrazine application timing.
Appendix D.2 presents a proof-of-concept approach for integrating sparse monitoring data
with WARP predictions of distribution percentiles into a reliable concentration time series.
WARP provides estimates of the 5th, 10th, 15th, 25th, 50th, 75th, 85th, 90th and 95th
percentiles and maximum concentration across the entire year (Stone and GIlliom, 2009).
The WARP percentile estimates and the available monitoring data are merged into a single
dataset.
The newly generated distribution still needs to be sequenced in a time series. One possible
procedure for sequencing is relating atrazine concentrations to flow. As previously
discussed, a significant regression equation exists for atrazine and flow in the 120 -200 day
samples in the Maumee River data. The flow was weighted to ensure that flow events
immediately after pesticide application have more influence on atrazine runoff. The
weighting factors for flow were derived using the first-order degradation model:
A=A0e-k*time
where
A0 = 1 lb/A
k = degradation rate (0.01152 days-1)
time = days
The actual daily flow data from Day 120 to Day 200 were multiplied by the daily flow
weighting factor.
The flow and atrazine data were separately ranked according to percentiles in the
distribution and then matched to create a time series. The newly sequenced time series
were used for variogram analysis and kriging. In the absence of watershed- and yearPage 131 of 184
specific WARP parameters for the 1995 Maumee River monitoring data, the USEPA used
surrogate inputs from two Ohio sites (OH-01-2004 and OH-03-2005) included in the
AEEMP study to evaluate how well the approach could identify the times of peak
concentrations. The merged WARP data predicted for OH-01-2004 had a much higher
predicted maximum atrazine concentration than OH-03-2005. This difference is mainly
attributed to the difference in May and June precipitation. However, the two simulated time
series illustrate that the temporal occurrence of the atrazine peak mimics the actual time
series. This situation suggests that weighted flow may be a reasonable predictor of
temporal occurrence daily atrazine concentrations for location where flow data is available.
6.3.4 Mechanistic Approach: PRZM Hybrid Model
Syngenta submitted a proposal for developing a PRZM Hybrid model to augment measured
monitoring data by estimating possible runoff events that may result in increased
concentrations on unsampled days (Miller et al, 2011). The model would use local rainfall
and crop planting data for the year of monitoring and watershed-specific soil, cropping, and
hydrologic properties to simulate the environmental fate and transport of atrazine at the
watershed scale. For each watershed, daily atrazine concentrations will be estimated from
PRZM-generated atrazine flux and runoff values for each soil map unit that are then
aggregated to the watershed based on area-weighted map unit concentrations and
adjusted by the percent of corn and sorghum in the watershed (Miller et al, 2011).
Miller et al (2011) propose combining the resulting PRZM time series with monitoring data
to estimate potential concentrations on unsampled dates between measured values. This
approach appears to be similar to a method Syngenta explored to estimate atrazine
concentrations between 4-day sampling events for the ecological exposure monitoring
program (Snyder et al, 2007). The estimation approach may not be suitable for larger
watersheds where the time of travel for pesticide loads is greater than one day without
accounting for the lag time.
Although no results were available in time to be reviewed for this document, this approach
may provide an option for estimating pesticide time series where available monitoring data
are too sparse to estimate daily concentrations with other statistical methods. Miller et al
(2011) plan to explore distribution matching to calibrate PRZM concentrations to measured
data. However, in instances where the monitoring data are sparse, distributions based on
measured data are likely to underestimate the true distribution of pesticide concentrations
in water. An alternate approach may be to match PRZM model distributions with percentile
distributions estimated by WARP.
Page 132 of 184
6.3.5 Watershed Atrazine Analysis Model
In addition to the hybrid PRZM modeling approach, the USEPA briefly evaluated a simple
watershed-scale mass balance model (see Appendix D.3). This mass balance model uses
an iterative fitting method to optimize the parameter estimates of a simplified watershed
mass balance model for watersheds currently monitored for atrazine. Potentially, spatial
variation in watershed parameters from sampled watersheds could be used to predict
parameters for non-sampled watersheds located between the sampled watersheds, which
in turn could be used to predict daily concentrations for those non-sampled watersheds.
If a simple model could be found that fits the data well, it would provide a method based on
mass balance principles for predictions of “non-sampled” dates (i.e., the concentration of
atrazine rises based on the amount of atrazine applied to the watershed and falls based on
the amount degraded and discharged from the watershed). However, the more complex
such a model needs to be, the more data would be needed to provide assurance that
proper calibration has been achieved. Because larger watersheds are generally more
complex than smaller watersheds (greater variation in application dates, rainfall events
differ across a large watershed, greater variation in travel times to deliver atrazine to the
monitoring point), a simpler model may not be possible to provide enough assurance of
proper calibration at CWS with larger catchment areas that have a limited numbers of
samples.
The USEPA conducted a preliminary evaluation of the mass balance approach on selected
AEEMP monitoring sites (Appendix D.3). We used simple mass balance models to
estimate the contribution of atrazine to the watershed from each individual storm event
greater than 0.4 cm rainfall based on the load of atrazine discharged from the watershed.
Discharge was calculated by iteratively fitting daily predictions of atrazine concentration to
the less-frequently measured (monitoring) atrazine concentrations. While the simple
watershed mass balance models were often able to fit the variation in atrazine
concentrations (r2s often exceeding 0.8), the mass of atrazine discharged often differed by
an order of magnitude in either direction from estimates of atrazine applied to the
watershed based on estimates of the watershed area planted in corn and sorghum and
local estimates of atrazine use rates (documented in USEPA, 2009a and 2009b).
The “imbalance” issues may result in part from apparent anomalies or errors in the flow
records. However, the imbalance problem is likely due primarily to the coarseness of the
model temporal resolution; i.e., the time-step (see Appendix D.3 for a discussion on this).
The discrepancy between expected maximum mass applied and expected loads may be
because the watershed effluent flow and atrazine concentrations are quite dynamic in these
small watersheds. Therefore a grab sample with a high concentration in a relatively low
flow condition can be collected early on a day with rising flow conditions and falling
concentrations. Because flow appears in the data set as average daily flow, the high
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concentration paired with a large average daily flow results in a load for a particular day
that exceeds maximum expected mass of atrazine applied to the watershed.
For this method to be useful, the model would need a better method of deriving a flowweighted atrazine concentration. One method would be to tie atrazine concentrations
measured in a particular day to the flow data at the sub-daily intervals. Typically, USGS
collects flow data at automated gages at 15 minute intervals and then calculates an
average daily flow from the 15 minute interval data.
6.3.6 Generating Atrazine Time Series from Weekly CWS Monitoring for Use
in Dietary Exposure Assessments
Sections 6.3.2 – 6.3.5 evaluated approaches that could be applied to weekly monitoring
from the CWS to generate estimates of TCT exposures for comparison to a human health
endpoint of concern with a duration that may range from 4 to 28 days. The analysis of
monitoring frequencies in Section 6.3.2 indicates that the bias factors from 7-day sampling
intervals would likely range from 2 to 4 for a 4-day average duration to 2 for a 28-day
duration.
While the September 2010 SAP recommended using WARP in combination with
geostatistical or deterministic models to estimate atrazine concentrations for drinking water
exposures, the analysis in Section 6.3.3 suggests that a reasonable time series can be
approximated from the 7-day sampling frequencies using only kriging and stochastic
simulations.
We evaluated this for the case study (Section 7), using the variogram models for the 1995
Maumee River and 2007 MO-01 data to generate 1,000 daily time series realizations from
conditional stochastic simulations (Figures 26 and 27). The actual daily time series for each
data set in black. The 95th and 5th percentiles of the stochastic simulations are shown as
red dashes, with the shaded area representing a 90 percent probability range for the time
series concentrations based on the simulations. In the absence of a full daily time series,
the stochastic simulations of weekly monitoring would characterize the likely range in
exposures over time (shown as the grade shading between the red-dashed lines in Figures
26 and 27).
The simulations of the daily time series from the variogram models underestimated the
actual peak concentrations (Figures 26a and 27a), suggesting a bias factor may still be
needed for estimates that require a peak exposure. However, the 95th percentile of the
rolling average concentrations from the simulations provided a reasonable approximation of
the actual rolling-average concentrations (Figures 26b-d and 27b-d).
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16
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14
Atrazine Concentration, ug/L
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Atrazine Concentration, ug/L
14
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Julian Day
Figure 26: Estimates of daily and rolling-average atrazine concentrations from the actual time series
(black line) and 5th and 95th percentiles (dashed red lines) of realizations of kriged time series from
7-day sampling of the Maumee River 1995 data.
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Figure 27: Estimates of daily and rolling-average atrazine concentrations from from the actual time
series (black line) and 5th and 95th percentiles (dashed red lines) of realizations of kriged time series
from 7-day sampling of the MO-01 2007 data.
While the CWS monitoring data for the AMP are sampled at 7-day intervals, other
monitoring data for atrazine and for other pesticides are often sampled less frequently. For
those monitoring data sets with sampling intervals of longer than 7 days, time series and
rolling-average concentration estimations will require a more complicated approach,
combining monitoring data with WARP percentile estimates and using kriging and
conditional simulations to generate a time series for exposure estimates:
•
•
•
•
Generate a WARP-predicted distribution for the site, using watershed data specific
for the CWS or monitoring location
Merge monitoring data with WARP distributions by ranking flow within a determined
time frame in which flow and concentrations are correlated and matching predicted
concentration percentiles with flow percentiles
Generate a variogram of simulated time series with nugget, range, and sill
Use kriging to interpolate the time series
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•
•
Generate stochastic simulations of the time series for uncertainty analysis
Generate realizations of time series and bracket the estimated time series based on
an upper and lower percentile of realizations.
These steps are illustrated in Appendix D.2.
6.4 Year-to-Year Temporal Variability
Uncertainty factor considerations based on monitoring design must account not only for
uncertainty in sampling frequencies in capturing short-term temporal variability, but also for
the year-to-year variability at the same site. During registration review, the USEPA will look
at the multi-year history of CWS monitoring for atrazine to characterize the expected range
in concentrations from year to year.
In any given location, the magnitude and duration of pesticide concentrations in water vary
from year to year, depending on such factors as the magnitude, timing, and frequency of
pesticide application, the timing and intensity of rainfall events post-application, soil
moisture conditions at the time of application and rainfall, and the type, timing, and extent
of land management practices in place (Gilliom et al., 2006). Atrazine occurrence in water
follows a seasonal pattern that corresponds to seasonal usage and weather patterns, with
larger spikes in concentration related to pre-emergent spring applications that are followed
by periods of runoff-producing rainfall.
For the most part, atrazine has had fairly consistent usage (average application rate,
percent of crop treated) over time. Changes in the relative proportion of the watershed in
corn and sorghum as a result of crop rotations and commodity demands may occur,
particularly in smaller watersheds, but can be accounted for with yearly cropping data.
However, much of the year-to-year variations in atrazine concentrations will likely result
from yearly differences in rainfall during the atrazine use season. An analysis of the
influence of rainfall on atrazine concentrations in the AEEMP study suggests that the
relationship between atrazine concentrations in streams and precipitation is a function of
the distribution and timing of the rainfall in relation to application rather than to the amount
of rainfall in a given month or season (USEPA, 2007 and 2009a).
In evaluating a monitoring study, the USEPA has to take into consideration where the
exposure estimates from the time period of sampling fall into the extended period of
pesticide use and how protective the exposure estimates from a monitoring study are in
relation to that longer use period. For atrazine, the key issue in characterizing the potential
year-to-year variability in atrazine concentrations in water relates to how long sampling
should continue at individual CWSs before concluding, with a reasonable certainty of no
harm, that TCT concentrations in drinking water do not exceed the level of toxicological
concern.
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An analysis of the year-to-year variations in TCT concentrations for the CWS included in
the AMP indicates that the ratio of lowest to highest maximum yearly TCT detections
increases with increasing number of years of monitoring (Table 14). This would be
expected since longer study durations increase the likelihood of capturing wider ranges in
likely exposures.
Table 14: Comparison of lowest and highest yearly maximum detections for CWS in the AMP based
on number of years sampled.
No. of Yrs.
Sampled
No. of
CWS
Sampled
Ratio of the Lowest to Highest Annual Maximum
Detections
Minimum
Median
90th
Maximum
Percentile
Total Chloro-Triazines Measured in Source Waters
2-3 years
30
1.1
2.9
5.6
11.3
4-5 years
13
1.7
3.0
11.8
25.4
6 years
102
1.6
4.7
19.9
57.4
Total Chloro-Triazines Measured in Treated Waters
2-3 years
30
1.3
2.9
6.4
15.8
4-5 years
13
2.1
6.1
13.2
15.7
6 years
102
1.5
4.2
13.2
49.0
Mosquin et al (2011a) collected atrazine monitoring data from different datasets at across a
range in years (1993-2009). The authors considered quarterly compliance monitoring at
CWS for the Safe Drinking Water Act (SDWA) as well as more intensive monitoring with up
to weekly sampling for the AMP and earlier atrazine Voluntary Monitoring Program (VMP).
The more intensively sampled AMP and VMP datasets offer the potential to evaluate the
range in maximum yearly atrazine detections over time for those CWS that were included in
both studies. Even with the intensity of sampling in the VMP and AMP datasets (4 per
month), a number of actual peak concentrations are expected to be underestimated for
each CWS sampling year by a factor that depends on the sampling frequency (see Section
6.3.2).
The PRZM Hybrid model described by Miller et al (2011), or a variation of the modeling
approach, may provide an alternative approach to estimating the potential year-to-year
variability in atrazine concentrations in water as a result of variations in rainfall. The
USEPA has used PRZM to generate multiple years of exposure estimates using daily
rainfall and temperature data collected from meteorological stations across the country. A
hybrid modeling approach would entail using site- and year-specific weather, cropping, soil,
hydrology, and pesticide usage information to calibrate the estimated daily PRZM
concentrations with measured monitoring data. Subsequent years of weather data could
then be modeled to characterize the range in potential exposures as a result of variability in
rainfall patterns from year to year. Such an approach would involve substantial data
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collection and model calibration against available monitoring in order to characterize the
year-to-year variability in atrazine concentrations in water.
The USGS WARP model could also be used to characterize the range in year-to-year
variability in atrazine/TCT concentrations. This approach would involve varying the
temporal parameters – May-June precipitation, atrazine use intensity – within expected
ranges to characterize the extent of year-to-year variations. Because atrazine use intensity,
which reflects both application rates and area grown in treated crops in the watershed, is a
major driver in the WARP model (Stone and Gilliom, 2009), the error in estimating this
parameter may be the dominant component in the variability in the WARP output.
6.5 Spatial Variability
The applicability of any pesticide monitoring data beyond the sites for which they are
collected depends on the extent to which we can place the monitoring data in a broader
spatial context. Spatial statistical designs that are tiered by relative watershed vulnerability
assist in placing monitoring results in context with other sites in the pesticide use area (see
USEPA, 2007, for a spatially-balanced design used to select monitoring sites representing
vulnerable watersheds for the AEEMP). In the case of the AMP monitoring study, CWS
were not selected randomly or with a statistical design, but were identified as vulnerable
based on results of quarterly compliance monitoring. Thus, the results are applicable to this
population of CWS. To the extent that we can relate the CWS sites to vulnerable tiers (such
as those estimated with WARP), we may be able to make inferences about other CWS or
about the population of CWS.
While the geographic distribution of pesticide use is the major predictive variable for
pesticide occurrence in surface water on a broad geographic scale (Gilliom et al., 2006;
Stone and Gilliom, 2009), watershed characteristics such as soil susceptibility to runoff
and/or erosion, weather patterns, land management practices, and hydrology can be
important factors driving pesticide occurrence on a local scale (USEPA, 2009a and 2009b;
FIFRA SAP, 2009).
The CWS included in the AMP occur in the more vulnerable watershed tiers identified by
WARP (Figure 28; USEPA, 2010b). While this provides confirmation that the CWS included
in the AMP are located in the most vulnerable atrazine use areas, it does not necessarily
provide a basis from which to draw inferences regarding other CWS within the same
vulnerable areas that did not meet the monitoring threshold. As a part of registration review,
the USEPA will evaluate the entire population of CWS within the most vulnerable atrazine
use area to determine whether additional CWS should be included in the AMP. The USEPA
(2009a and 2009b) watershed-scale vulnerability assessment for atrazine in the AEEMP
focused on headwater watersheds, but may have potential applications for better identifying
vulnerable CWS that should be included for more intensive monitoring.
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Figure 28: Location of CWS in the AMP in relation to vulnerable watersheds identified by WARP
based on atrazine use on corn and sorghum.
6.6 Summary
This section focused on methods for evaluating how well the existing AMP drinking water
monitoring study characterizes the uncertainties in temporal and spatial variability of TCT
concentrations in water. The analysis needs to address uncertainties resulting from (a) how
well the monitoring sampling frequency captures daily and seasonal variations in pesticide
concentrations in surface water, (b) year-to-year variations in magnitude and duration of
concentrations, and (c) spatial patterns of pesticide occurrence in surface water.
Estimating atrazine/TCT concentrations for various durations (ranging from 4 to 28 days) in
a given year at a given CWS involves reconstructing the exposure pattern from weekly
snapshots taken of the actual time series. The degree of uncertainty in those estimates
depends upon the frequency of sampling (uncertainty or bias factors increase with
increasing intervals), the exposure duration of concern (factors decreasing as the
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timeframe of concern increases), the size of the watersheds, and the magnitude of
concentration (Section 6.3.2). An analysis of two intensively sampled monitoring data sets
– those with daily or near-daily sampling during the high use/runoff season – to represent
larger (NCWQR monitoring for Maumee River in OH) and smaller (AEEMP monitoring in
MO-01) suggests that a bias factor of at least 2 to 4X may be needed to estimate maximum
exposures from 7-day sampling intervals. The actual bias factors are likely to vary
depending not only on sampling intervals and exposure durations of concern but also on
watershed size and water body type.
As noted in the previous SAPs on atrazine (USEPA, 2010a and 2010b), a combination of
monitoring and statistical/modeling approaches can provide an approximation of time series
distributions for use in drinking water exposure assessments. While the SAP recommended
a combination of WARP with deterministic models or kriging, the USEPA’s analysis
described in Section 6.3.3 found that a reasonable estimate of 4- to 28-day rolling average
concentrations can be approximated from 7-day sampling frequencies using kriging and
stochastic simulations, with upper and lower percentiles from the conditional simulations
providing a characterization of uncertainty. The USEPA used the 95th and 5th percentiles for
uncertainty bounds in the case study described in Section 7.
For less-frequent sampling (in most instances, 14-day sampling intervals is a more
common lower bound of sampling intervals), the bias factors will be much greater. Where
estimated exposures from monitoring adjusted to account for the bias factor overlap with
the toxicity concentration of concern, exposure approximations become more complicated,
involving not only geostatistical simulations but WARP and/or deterministic modeling.
In determining the uncertainty in monitoring data, the USEPA must also consider year-toyear variability. Approaches described in section 6.4 include extending the analysis of yearto-year variations within the CWS monitoring time frame using a PRZM hybrid approach or
WARP.
The applicability of the monitoring results in the AMP beyond the CWS included in the
study depends on the extent to which we can place the data in a broader spatial context.
For the AMP, CWS were considered vulnerable based on quarterly compliance monitoring
data. The USEPA (2009a and 2009b) watershed-scale vulnerability assessment for
atrazine in the AEEMP may provide additional context in better interpreting the CWS
results and identifying vulnerable CWS that should be included for more intensive
monitoring.
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7.
CASE STUDY: IMPLICATIONS OF MOA & TOXICITY PROFILE ON WATER
MONITORING
7.1
Introduction
In the 2003 human health risk assessment, a duration of 90 days was used for doing
exposure assessment of trichlorotriazines (i.e., atrazine and its metabolites) for comparison
to an administered dose no-observed adverse effect level (NOAEL) for attenuation of the
LH surge in a 6-month rat study (IRED 2003). Syngenta proposed a weekly sampling
frequency of selected CWS to characterize TCT concentrations over the critical exposure
duration of 90 days. The current scientific re-evaluation of the health effects of atrazine has
identified shorter atrazine exposure durations that can potentially result in LH attenuation in
rats. According to the critical study by Cooper et al. 2010, 4 days of repeated once daily
dosing with atrazine (the minimum duration evaluated thus far) beginning on the day of
proestrous is enough to induce a statistically significant attenuation of the LH surge in rats
(Cooper et al. 2010). Since the frequency of drinking water monitoring should be informed
by the temporal relationship between exposure and the toxicological endpoint of concern
(i.e., LH attenuation), shorter durations than 90 days are being considered in the current reevaluation of the health effects associated with atrazine exposure.
Plasma triazines in the rat build up upon repeated once daily dosing with atrazine to reach
pseudo steady state by the fourth day when attenuation of the LH surge is observed. In
essence, based on the information available, LH attenuation is observed when plasma
levels are sustained at a specific range. The time of 4 days to reach steady state in the rat
is in the range of predictions based on linear elimination pharmacokinetics that such time
should take roughly 3-5 half-lives. When the estimated rat plasma elimination rate constant
for triazines is allometrically scaled for a human female body weight of 60 kg, the estimated
time to reach steady state is 21-30 days (plasma half-life of 6-8 days). Similar to rat studies,
this time estimate would be based on a constant dose level and frequency of water
consumption, both of which are known to be variable in the human situation. Thus given the
lack of human specific information that would allow a more precise estimate of the critical
duration of exposure, the Agency proposes a range of exposure durations of 4 - 28 days
relevant to LH attenuation. More specifically, three exposure durations consisting of 4, 14,
and 28 days are being proposed to bracket the most likely human exposure durations that
may result in LH attenuation based on the exposure requirements for LH attenuation in rat
toxicity studies. The 28-day duration is within the range predicted by allometric scaling of
rat plasma elimination pharmacokinetics for humans and is also the average duration of the
human menstrual cycle. Notably, in both rats and humans, the time estimated to reach
steady state coincides with the respective length of their ovarian cycles of 4 and 28 days,
respectively.
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The duration of 14 days is being proposed as a midpoint between the other two durations
that would allow better characterization of an appropriate water monitoring frequency.
Finally, the duration of 4 days was selected on the basis that the limited human information
available from the Davidson 1987 study suggests that the pharmacokinetics of atrazine in
humans may be somewhat similar to that observed in rats. For instance, similar to rat
findings, atrazine and DEA appeared to be short-lived in human blood, not having been
detected at the first time point of 2 hours (Table 7). The estimated human whole blood halflife of 2.8 hours for DIA is in the range of estimated rat plasma half-life values of 1.2 - 3.9
hours at 3, 10, and 50 mg/kg bw atrazine (Table 3). The estimated human half-life for
DACT of about 11-18 hours (Table 7) is also very comparable to the rat plasma value of 89 hours (Table 3). Thus, human pharmacokinetics may not be that much different as
compared to rats and justifies including 4 days as an exposure duration that should be
evaluated.
7.2 Atrazine Time Series Estimates for Use in Dietary Exposure Assessments
In Section 6, the USEPA evaluated methods for characterizing the uncertainty in estimating
4- to 28-day rolling average TCT concentrations in drinking water from weekly samples
from CWS included in the AMP. The Agency determined that a reasonable time series can
be approximated from the 7-day sampling frequencies using geostatistical methods (kriging
and variogram models) and stochastic simulations (see Sections 6.3.3 and 6.3.6). For the
case study, we used the NCWQR 1995 Maumee River and AEEMP 2007 MO-01 data sets
to represent a range in watershed sizes for flowing water bodies and a range in atrazine
concentrations. These data sets do not represent actual CWS but have been used for the
monitoring analysis because they include daily or near-daily sampling during the high
use/runoff season and allow for evaluation of sampling frequencies. The results presented
below are for illustrative purposes only.
Figures 29 and 30 (also shown as Figures 27 and 28 in Section 6) show the actual daily
time series for each data set in black for comparison to the stochastic simulations of the
variogram models for 7-day sampling intervals. The 95th and 5th percentiles of the
stochastic simulations are shown as red dashes, with the shaded area representing a 90
percent probability range for the time series concentrations based on the simulations. The
simulations of variogram models for the daily time series underestimate the actual peak
concentrations (Figures 29a and 30a); for acute toxicity endpoints of concern, estimates of
peak, single-day exposures might require an additional bias factor for characterization.
However, the 95th percentile of the rolling average concentrations from the simulations
provided a reasonable approximation of the 4- to 28-day rolling-average concentrations
(Figures 29b-d and 30b-d).
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Atrazine Concentration, ug/L
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Figure 29: Estimates of daily and rolling-average atrazine concentrations from the actual time series
(black line) and 5th and 95th percentiles (dashed red lines) of realizations of kriged time series from
7-day sampling of the Maumee River 1995 data.
Page 144 of 184
100
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Julian Day
Figure 30: Estimates of daily and rolling-average atrazine concentrations from the actual time series
(black line) and 5th and 95th percentiles (dashed red lines) of realizations of kriged time series from
7-day sampling of the MO-01 2007 data.
7.3 Estimating human plasma average daily AUC from rolling average
atrazine concentrations
The short in vivo half-life of atrazine, primarily due to metabolism and the intrinsic activity of
metabolites, suggest that the link between atrazine exposure and attenuation of the LHsurge can be better characterized through the use of an internal dose metric. At the
September 2010 SAP meeting, the Agency presented AUC estimates of plasma triazines
as the basis for doing an internal dose-response assessment associated with LH
attenuation in the rat. In this report, methodologies based on the pharmacokinetic behavior
of plasma triazines are being proposed by the Agency for estimating the corresponding
human plasma average daily AUC from water exposure estimates that can then be
compared to the PoD plasma AUC for LH attenuation in the rat. The proposed approach
incorporates the best information available with respect to LH attenuation and
pharmacokinetics of plasma triazines. As compared to the approach used in the 2003
human health risk assessment, the proposed water monitoring approach identifies not only
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the magnitude of exceedance but also the time regions in the water chemograph where
those exceedances are more likely to occur. It incorporates an integrated measure of the
dose intake and information on body distribution and plasma clearance.
The process begins by estimating AUCwater for a given duration of concern. As previously
discussed, durations of 4, 14 and 28 days are being proposed based on LH attenuation
studies in rats and human information. AUCwater is calculated based on the trapezoid rule
(see Figure 21). The more frequent the water sampling frequency, the better the estimate
of AUCwater. The resulting AUCwater is used in the estimation of a time-weighted average
daily intake of atrazine through drinking water according to Equation 4 in Section 4. The
predicted AUCplasma can then be calculated through the use of equation 5 in Section 4 that
accounts for dose intake, body distribution, and plasma clearance information. For the 1995
Maumee River and 2007 M0-01 case studies, the rat plasma elimination rate constant (kel)
and volume of distribution (Vdss) were allometrically scaled for a human female body
weight of 60 kg and a water consumption rate of 2 L/day was assumed. A body weight of
60 kg was selected for this comparative analysis on the basis that young females may be a
particular susceptible population to disruptions of LH. However, the proposed approach is
readily amenable to evaluations of distributions of body weights and water consumption
rates.
The results corresponding to the exposure estimates of the 1995 Maumee River and 2007
MO-01 monitoring data sets, sampled at weekly intervals, (Figures 29 and 30) are shown in
Figures 31 and 32, respectively. For comparison purposes, the PoD BMDL rat plasma AUC
with an applied 300X combined uncertainty factor (UF) is depicted in the graphs. The
combined UF is hypothetical at this time but would presumably be reflective of inter- and
intra-species extrapolation factors as well as any contribution for FQPA. As can be seen in
Figure 31, the predicted human AUCplasma profile for the 1995 Maumee River dataset is
well-below the BMDL PoD rat plasma AUC at all time points. The MOEs based on the 95th
percentiles and the rat PoD plasma AUC range from 1424 at the low end to over 64,000 at
the high endpoint. Thus, the exposure levels of the 1995 Maumee River dataset seem to be
below the level of concern based on a 300X combined uncertainty factor.
Page 146 of 184
actual (daily sampling)
Sim 95%ile
0.06
Sim 5%ile
0.04
0.02
0.00
100
110
120
130
140
160
150
180
170
190
200
Julian Day
Maumee 95: 14-d rolling averages
0.10
BMDL AUC/300x
0.08
actual (14-d rolling ave)
Sim95%ile
Sim5%ile
0.06
0.04
0.02
0.00
100
110
120
130
140
150
160
Julian Days
170
180
190
200
Predicted human plasma AUC ((mg/L)*h)
BMDL AUC/300x
0.08
Predicted human plasma AUC ((mg/L)*h)
Predicted human plasma AUC ((mg/L)*h)
Predicted human plasma AUC ((mg/L)*h)
Maumee 95: Daily Sampling
0.10
Maumee 95: 4-day rolling averages
0.10
0.08
0.06
BMDL AUC/300x
actual (4-d rolling ave)
Sim95%ile
Sim5%ile
0.04
0.02
0.00
100
110
120
130
140
150
160
170
180
190
200
Julian Days
Maumee 95: 28-d rolling averages
0.10
actual (28-d rolling ave)
Sim95%ile
0.08
BMDL AUC/300x
Sim5%ile
0.06
0.04
0.02
0.00
100
110
120
130
140
150
160
170
180
190
200
Jilian Days
Figure 31: Estimates of rolling average daily human plasma AUC values corresponding to the actual
time series (black line) and 5th and 95th percentiles (dashed red lines) for the Maumee River 1995
dataset according to Figure 29. The dashed line is for comparison to the rat plasma PoD AUC with a
300X applied uncertainty factor.
Evaluation of the 2007 MO-01 dataset suggests, unlike the Maumee River 1995 dataset,
that there are time periods of exceedance for a 4-day duration of exposure based on a level
of concern of 300. Figure 32 shows that the rat plasma PoD AUC is exceeded for ≈ 5 days
based on the 95th percentile. The time periods, but not necessarily the magnitudes of
exceedance, seem to be adequately captured by 4- but not 14- or 28-day rolling averages.
A 100X combined UF is also included in Figure 32 to show that the resulting human
AUCplasma values would be below the level of concern in such case.
In comparison to the constant dose level and dosing frequency in rat studies, TCT
exposures resulting from consuming drinking water will be variable in nature (see, for
example, Figures 29 and 30). While such variability would likely preclude human plasma
levels from reaching steady state, the equations described in Section provide an average
time-weighted estimate of TCT levels in human plasma representative of sustained levels
of exposure for a given duration that can then be compared to the PoD BMDL rat plasma
AUC. The key comparison, thus, would be TCT plasma levels greater than the PoD BMDL
for extended periods of time, related to a given duration of potential concern.
Page 147 of 184
The proposed approach that estimates internal measures of exposures in the form of
human plasma AUC can provide valuable perspective for evaluating water monitoring
frequencies in the characterization of exposure to pesticides like atrazine.
MO-01: 4-d rollin averages
BMDL AUC/100x
0.25
0.20
0.15
actual (daily sampling)
Sim 95%ile
Sim 5%ile
0.10
BMDL AUC/300x
0.05
0.00
100 110 120 130 140 150 160 170 180 190 200
Predicted human plasma AUC ((mg/L)*h)
Pre dicte d human plasma AUC ((mg/L)*h)
MO-01: daily sampling
BMDL AUC/100x
0.25
0.20
0.15
actual (4-d rolling ave)
Sim 95%ile
Sim 5%ile
0.10
BMDL AUC/300x
0.05
0.00
100 110 120 130 140 150 160 170 180 190 200
Julian Days
Julian Day
MO-01: 28-d rolling averages
BMDL AUC/100x
0.25
actual (14-d rolling ave)
0.20
Sim 95%ile
Sim 5%ile
0.15
0.10
BMDL AUC/300x
0.05
0.00
100
110
120
130
140
150
160
Julian Days
170
180
190
200
Predicted human plasma AUC ((mg/L)*h)
Predicted human plasma AUC ((mg/L)*h)
MO-01: 14-d rolling averages
BMDL AUC/100x
0.25
actual (28-d rolling ave)
0.20
Sim 95%ile
0.15
Sim 5%ile
0.10
BMDL AUC/300x
0.05
0.00
90
100 110 120 130 140 150 160 170 180 190 200
Julian Days
Figure 32: Estimates of daily rolling-average atrazine concentrations from the actual time series
(black line) and 5th and 95th percentiles (dashed red lines) of realizations of kriged time series from
7-day sampling of the MO-01 2007 data.
Page 148 of 184
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