# SCIENTIFIC ISSUES CONCERNING

HEAL TH EFFECTS OF CHLORPYRIFOS:

REVIEW OF CHLORPYRIFOS

BIOMONIT ORING RESEARCH AND

INTERPRETIVE APPROACHES

AARON NIMAN, L

T USPHS

INDUSTRIAL

HYGIENIST

CHESTER RODRIGUEZ, PHD

TOXICOLOGIST

USEP A, OFFICE OF PESTICIDE PROGRAMS



# April 10, 2012 FIFRA S AP

# Outline

 

# Chlorpyrifos Biomarker Overview

 

# TCPy Biomonitoring Study Comparison

 

# Biomonitoring and AChE Inhibition

Risk

- Based Biomonitoring Interpretation **2**



#  

# Risk

- Based Biomonitoring Interpretation

 

# Challenges

 

# Approaches

# Chlorpyrifos Biomonitoring:

Overview

**3**

 

# Chlorpyrifos biomarkers of exposure have been reviewed in the

literature (Barr & Angerer , 2006; Eaton et al., 2008)

 

# Most common chlorpyrifos biomarkers of exposure include urinary

metabolite TCPy and the non

- specific DAP metabolites DEP and

DETP

Although less common, chlorpyrifos has also been monitored  

# Although less common, chlorpyrifos has also been monitored

directly in blood and other biologic media (e.g., Columbia Cohort)

**Biomarker/A nalyte**  **Biologic**

**Matrix Population**  **Group**

3,5,6

- trichloro

- 2

- pyridinol

(TCPy ) Urine All

Meconium Pregnant Females,

Neonates

Diethylphosphate ( DEP) Urine All

Diethylthiophosphate

( DETP) Urine All

Chlorpyrifos Blood All **Common Chlorpyrifos Biomarkers of Exposure** 

# Chlorpyrifos Biomonitoring :

Urinary Biomarkers (DEP/DETP

, TCPy)

**4**



# Specificity

 

# DEP and DETP are non

- specific biomarkers of multiple of OP

pesticides

 

# TCPy is only metabolite of chlorpyrifos

, chlorpyrifos

- methyl,

trichlopyr

 

# Chlorpyrifos and other OP pesticides have been shown to form

DEP , DETP , and TCPy in environmental media and food

 

# May overestimate chlorpyrifos exposure if assumed that urinary

biomarker levels only attributable to chlorpyrifos



# Va **riability & Pharmacokinetic Factors** 

 

# High variability observed in research suggests that spot

- urine

samples cannot accurately quantify pesticide exposure (Barr &

Angerer

, 2006)

 

# However

, spot samples may be suf ficient for assigning exposure

categories in epidemiology studies

# Chlorpyrifos Biomonitoring :

Blood Biomarkers

**5**



#  

# Measurement of chlorpyrifos in blood provides more

direct measure of internal chlorpyrifos dose

 

# Blood measurement is far less common in the literature

than urinary TCPy & DEP/DETP

 

# Requires more sensitive analytical method

 

# May place greater burden on study participants

 

# Blood concentrations represent more narrow period of

exposure relative to urinary biomarkers

# TCPy Biomonitoring Study Comparison

**6**



#  

# Summarized TCPy biomarker levels in the general U.S.

population and targeted study groups

 

# Intended to provide broad comparison of study results

**Key Differences:** 

 

# Ti ming relative to residential phase

- out

 

# Study populations (age, location, setting)

 

# Study design and research objectives

 

# Selected studies intended to represent major monitoring

studies

 

# NHANES

 

# ORD observational studies

 

# Dow AgroSciences observational Studies (High

- Low)

 

# Peer

- Reviewed literature (2008 SAP; Eaton et al., 2008)

 

# Children’ s Health Cohorts

# TCPy Biomonitoring Study Summary

**7** 

# Reference **Study Population**  **Ty pe of Study**  **P opulation**  **Collection Period** 

Hill et al., 1995 General US Population 20

- 59 year olds 1988

- 1992

NHANES General US Population 6

- 11 year olds, 20

- 59 year

olds 1999

- 2000, 2001

- 2002

Egeghy et al. 201 1 ORD

- NHEXES, AZ 5

- 12 year olds 1995

- 1997

ORD

- MNCPES 3

- 13 year olds 1997

ORD

- CTEPP

, NC 2

- 5 year olds 2000

- 2001

ORD

- CTEPP

, OH 2

- 5 year olds 2001

ORD

- JAX 4

- 6 year olds 2001

ORD

- CPP

AES 2

- 4 year olds 1999

- 2000

Lu et al., 2008 CPES

- WA 3

- 11 year olds 2003

-- 2004

Lu et al., 2006 WA C hildren 3

- 11 year olds 2003

Alexander et al., 2006 Farm families 4

- 18 year olds, Adults 2000

- 2001

Fenske et al., 2002 Farm Children ≤ 6 years olds 1995

Curwin et al., 2005 Farmers Adults 2001, Spring

- Summer

Curwin et al., 2007 Farm Families Children and Adults 2001, Spring

- Summer

Berkowitz et al., 2003 Mt. Sinai Pregnant Female 1998

- 2001

Why att et al., 2009 Columbia Pregnant Female 2001

- 2004

Castorina et al., 2010 CHAMACOS Pregnant Female 1999

- 2000

Eskenazi et al., 2007 CHAMACOS Pregnant Female 2000

- 2003

# 8

1

10 100 1000**T C** **P y** **U r** **i n** **a r** **y C** **o n** **c e** **n t** **r a** **t i** **o n** **( μ** **g /** **L )**  

# Referenc

**e Range** 

**GM GM** 



# 0.1NHANES1999-2000(n=481)NHANES2001-2002(n=573)



# CDC, 2009 (6

- 11

years) Egeghy et al, 2011 Lu et al., 2008 (3

- 11 years) Lu et al., 2006 (3

- 11 years) Curwin et al, 2007 Alexander et al.,

2006 (4

- 18 years)



# TCPy **Biomonitoring Study**  **S ummary**  **: Child**  **Study P**  **opulations**

# 9

1

10 100 1000**T C** **P y** **U r** **i n** **a r** **y C** **o n** **c e** **n t** **r a** **t i** **o n** **( μ** **g /** **L )**  

# Referenc

**e Range Maternal Cohorts** 

**GM**

**GM GM** 

**GM**

**GM GM**  **GM**



# TCPy **Biomonitoring Study**  **S ummary**  **: A dult**  **Study P**  **opulations.** 0.1NHANESIII,1988-1992(n=993)NHANES1999-2000(n=832)NHANES2001-2002(n=1,113)ResidentialLow-Crack&Crevice/Spot(n=8)ResidentialHigh-TurfTreatment(n=10)WorkerLow-Re-Entry(n=10)WorkerHigh-Mix/Load/Apply/Clean(n=15)Nonfarmers,chlorpyrifosnotsprayed(n=45)Farmers,chlorpyrifosnotsprayed(n=45)Farmers,chlorpyrifosself-sprayed(n=2)Father,nonfarm(n=23)Father,farm(n=24)Mother,nonfarm(n=24)Mother,farm(n=24)Farmfamilies—Applicatorspre-appl.(n=34)Farmfamilies,Applicatorspost-appl(n=34Farmfamilies,Spousespre-applFarmfamilies,Spousespost-appl(n=34)Mother–childpaircohort1998-2001(n=365)Prenatal1(n=95)Prenatal2(n=83)Prenatal3(n=54)Prenatal4(n=21)Postnatal(n=73)1stPregnancy(n=442)2ndPregnancy(419)Prenatal1(n=538)Prenatal2(n=481)NHANESSubsample1999-2002(n=224)



# Hill et al, 1995;

CDC, 2009 Dow AgroSciences Curwin et al, 2005 Curwin et al, 2007 Alexander et al., 2006 Berkowitz

et al., 2003 Wh yatt et al, 2009 Eskenazi et

al, 2007 Castorina et al,

2010**T C** **P y** **U r** **i n** **a r** **y C** **o n** **c e** **n t** **r a** **t i** **o n** **( μ** **g /** **L )** 



# * NHANES data represents all survey participants aged 20

- 59 years old. *

# 10

0.1

1 10 1001992(n=993)2000(n=832)2002(n=1,113)2002(n=224)Prenatal1(n=538)Prenatal2(n=481)2001(n=365)Prenatal1(n=95)Prenatal2(n=83)Prenatal3(n=54)Prenatal4(n=21)Postnatal(n=73)1stPregnancy(n=442)2ndPregnancy(419)**T C** **P y** **U r** **i n** **a r** **y C** **o n** **c e** **n t** **r a** **t i** **o n** **( μ** **g /** **L )**  

# Reference Range **Maternal Cohorts** NHANESIII,1988-1992(n=993)NHANES1999-2000(n=832)NHANES2001-2002(n=1,113)NHANESSubsample1999-2002(n=224)Prenatal1(n=538)Prenatal2(n=481)Mother–childpaircohort1998-2001(n=365)Prenatal1(n=95)Prenatal2(n=83)Prenatal3(n=54)Prenatal4(n=21)Postnatal(n=73)1stPregnancy(n=442)2ndPregnancy(419)



# Hill et al, 1995; CDC, 2009 Castorina et al, 2010 Berkowitz

et al., 2003 Wh yatt et al, 2009 Eskenazi et al, 2007 

# *



# TCPy **Biomonitoring Study**  **S ummary**  **: NHA**  **NES and Children’**  **s Health**  **Cohort Results** 

* NHANES data represents all survey participants aged 20

- 59 years old.

# TCPy Comparison Results: Findings

**11**



#  

# TCPy levels of mothers in the CHAMACOS, Columbia,

and Mount Sinai cohorts varied in comparison with

TCPy levels observed in NHANES

 

# Median TCPy levels higher in CHAMACOS and Mount Sinai

Cohorts ( Eskanazi et al., 2007; Castorina et al., 2010) Cohorts Eskanazi et al., 2007; Castorina et al., 2010)

 

# Median TCPy levels lower in Columbia Cohort (Whyatt et al.,

2009)

However

, in contrast to the other cohorts, data were collected, in

part, during a time period of rapid decline in use of chlorpyrifos

# TCPy Biomonitoring and AChE

Inhibition

**12**



# AChE Monitoring **Overview**

 

# Red Blood Cell (RBC) AChE and Plasma BuChE most

common biomarkers of ChE activity

 

# RBC AChE inhibition considered an early marker of

potential chlorpyrifos toxicity

 

# Plasma BuChE has been shown to be inhibited at lower

levels of exposure than RBC AChE

 

# As a result, Plasma BuChE may be a more sensitive

biomarker for monitoring potential physiological change.

# TCPy Biomonitoring and AChE

Inhibition

**13**



# Previous **Agency Findings** 

 

# Agency previously evaluated relationship between TCPy

and RBC/Plasma AChE activity at 2008 SAP

 

# RBC/Plasma AChE inhibition observed in some study

participants of registrant studies

 

# However , large variability in response to the

administered dose of chlorpyrifos

 

# As a result, dif

ficult to draw definitive conclusions about

the relationship between urinary TCPy levels and

RBC/Plasma AChE inhibition.

# TCPy Biomonitoring and AChE

Inhibition

**14**



# Recent Study **Findings (Post**

**- 2008 SAP)** 

 

# Tw o observations studies have been published that

further evaluate the relationship between TCPy on a

creatinine

- adjusted basis and RBC/Plasma AChE

inhibition

 

# Garabrant **et al. (2009)**

Evaluated group of exposed workers at a chlorpyrifos

manufacturing facilities and a reference group of

chemical manufacturing workers

 

# Farahat et al. (201 **1)**

Evaluated cotton field workers in Egypt following

chlorpyrifos application to cotton fields

# Comparison **of Creatinine**

**- Adjusted**  **Urinary T CPy levels (μg /**  **g creatinine) reported**  **by Gar**  **abr ant**  **et al. (2009)**

**and Fa**  **ra hat**  **et al. (2011) with the**  **Gener al**  **U.S. Population.** 

# TCPy Biomonitoring and AChE

Inhibition

**15**



# Po **pulation Group**  **Time Pe**  **riod n**  **TC Py (μg /**  **g creatinine)**

**Ave ra**  **ge Median**  **(95% CI) 95**  **th** 

# Pe **rcentile**

**(95% CI)**

**NHANES (CDC, 2009)**

General P opulation 1999

- 2000 1,994 NR 1.5 (1.2

- 1.7) 8.4(6.3

- 11.6)

2001

- 2002 2,508 NR 1.9 (1.6

- 2.2) 9.2 (6.9

- 12.3)

**Gar abr**  **ant et al, 2009** 

NR

– Not reported 

# Gar **abr ant**  **et al, 2009**

Exposure Group Fa ll 1999 53 NR 37.5 432.6

Spring 2000 53 NR 40.7 436.3

Tu rnaround 50 NR 59.2 7,403.8

Fa ll 2000 52 NR 51.5 987.2

Reference Group Fa ll 1999 60 NR 3.2 13.9

Spring 2000 57 NR 4.8 11.2

Tu rnaround 57 NR 6.6 13.0

Fa ll 2000 58 NR 6.3 15.5

**Fa ra**  **hat et al, 2011** 

Applicators

1

- to 2

- week

Exposure P eriod 14 6,437 NR NR

Te chnicians 12 184 NR NR

Engineers 12 157 NR NR

# Garabrant et al. (2009) Results:

TCPy  AChE Relationship

**RBC AChE**  **Plasma BuChE**  **16**

# Farahat et al. (201 1) Results:

TCPy  AChE relationship

**17**



# RBC **AChE**

# TCPy Biomonitoring and AChE

Inhibition

**18**



# Garabrant **et al. (2009)** 

 

# No association observed between TCPy and RBC AChE

 

# Statistically significant association observed between TCPy and

Plasma BuChE levels above a cutpoint of 1 10 μg TCPy /g creatinine

**Farahat et al. (201**  **1)**

 

# Identified an inflection point representing a significant decrease in

RBC AChE activity at TCPy levels greater than 3,148 μg TCPy /g

creatinine

 

# Reported that both studies observed similar inflection point of

inhibition of Plasma BuChE activity

 

# Garabrant et al. (2009) 11 0 μg TCPy/g creatinine

 

# Farahat et al. (201 1) 11 4 μg TCPy /g creatinine

# Risk

**- Based**  **Biomonitoring**

**Interpretation**

**19**



# Qualitative

**NAS 2006 Biomarker Interpretation**  **Framew ork**  

# Semi

**- Quantitative**  **Quantitative**

# Biomonitoring Interpretation:

Challenges

**Specificity**

 

# Direct exposure to metabolites in the environment ( TCPy

, DEP/DETP)

**Longitudinal Va**  **riability**

 

# Dif ficult to relate single biomarker measurement **20**

 

# Highly variable depending on sampling time/method,

pharmacokinetic behavior , biomarker specificity , exposure routes,

etc.

**To xicological**  **Relevance**

 

# Not directly comparable to regulatory screening value

 

# Not always aligned with a toxicologically

- relevant internal dose

metric

 

# TCPy formation reflective of detoxification and can occur in

absence of chlorpyrifos

- oxon

TCPy  AChE relationship unclear (at environmental exposure

# Biomonitoring Interpretation:

Qualitative Approaches

Evaluate biomonitoring data without estimating

chlorpyrifos exposure

 

# Non

**- Comparative**  **21**



#  

# Urinary TCPy data for dif ferent time periods may be compared for

temporal trends in exposure (e.g., pre

- versus post

- mitigation)

 

# No link to chlorpyrifos, exposure routes, or health outcomes

 

# Comparative

 

# Comparison to controlled human studies

 

# Primary comparison is to Nolan et al.,1984: measured urinary

TCPy in human volunteers (males) following a single oral dose of

0.5 mg/kg bw chlorpyrifos

# Biomonitoring Interpretation:

Semi

- Quantitative Approaches

Estimate biomarker levels likely associated with

regulatory exposure guidelines

 

# Biomonitoring Equivalents

 

# Screening level approach for analyzing biomonitoring data **22**



#  

# Uses pharmacokinetic information and forward dosimetry

approaches to convert an exposure guideline value (e.g., cRfD )

to a biomarker level that can be compared to biomonitoring data

Hays et al., 2007 and

2008



#  

# Considerations

 

# Biomarker Specificity: approach highly dependent on biomarker specificity

 

# Direct exposures to TCPy take place

 

# To xicological Relevance: biomarker must be aligned with a toxicologically

relevant internal dose metric

 

# TCPy is reflective of detoxification of chlorpyrifos and can form in the absence of

chlorpyrifos

- oxon

# Biomonitoring Interpretation:

Semi

- Quantitative Approaches

Estimate chlorpyrifos exposures from biomarker

levels

 

# Semi

- Quantitative Reverse Dosimetry

 

# Utilizes creatinine

- normalized urinary levels of biomarker;

lifestage

- changes may be incorporated **23**



#  

# Utilizes creatinine

- normalized urinary levels of biomarker;

lifestage

- changes may be incorporated

 

# Corrects for incomplete clearance of parent chemical as

reflected by biomarker

Mage et al. 2004



#  

# Considerations

 

# Estimates represent net absorbed doses without route

specific information

 

# Biomarker Specificity; assumes biomarker is derived from

parent chemical

 

# Direct exposures to TCPy take place

# Quantitatively evaluate risk using exposure linked

physiologically based pharmacokinetic (PBPK)

modeling **24** 

# Biomonitoring Interpretation:

Quantitative Approaches

Dow AgroSciences (2011). Source

- to

- Outcome Modeling Physiologically Based Pharmacokinetic/ Pharmacodynamic (PBPK/PD) Model

Linked to a Dietary Exposure Model: Chlorpyrifos as a Case Study. FIFRA Scientific Advisory Panel, Washington, DC, February 15

- 18, 2011.

# Biomonitoring Interpretation:

Quantitative Approaches

**25**



# Chlorpy **rifos PBPK Model** 

 

# Several groups working on chlorpyrifos PBPK modeling

 

# Ti mchalk et al. (2002) PBPK Model

 

# Refined since originally developed in 2002

Evaluated at 201 1 FIFRA SAP  

# Evaluated

 

# Limitations

 

# Based on the “typical” adult human; does not incorporate life

- stage

related changes (e.g., pregnancy or childhood)

 

# Can only simulate oral exposures to chlorpyrifos wi th particular

emphasis on dietary exposures

 

# Several ef

forts to expand the model to other life

- stages have yet

to produce a model robust enough to inform human health risk

assessment.

# 26 

# Summary

 

# Several approaches can be used to analyze the

biomonitoring data associated with chlorpyrifos

exposure

 

# Each approach has limitations that need to be carefully

considered for any type of interpretation

 

# Agency soliciting recommendations from the SAP Panel

regarding strengths and limitations of approaches, as

well as alternative approaches not yet considered by the

Agency

# Questions **27**