SCIENTIFIC ISSUES CONCERNING 

HEALTH EFFECTS OF CHLORPYRIFOS:

REVIEW OF CHLORPYRIFOS 

BIOMONITORING RESEARCH AND 

INTERPRETIVE APPROACHES 

AARON NIMAN, LT USPHS

INDUSTRIAL HYGIENIST

CHESTER RODRIGUEZ, PHD

TOXICOLOGIST

USEPA, OFFICE OF PESTICIDE PROGRAMS

FIFRA SAP April 10, 2012
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 Although less common, less common, chlorpyrifos chlorpyrifos has also been has also been monitored monitored 

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

Biomarker/Analyte 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

ChlifBld 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

Variability & 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 sufficient 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:

 Timing 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 Type of Study Population 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. 2011 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 - CPPAES 2-4 year olds 1999-2000

Lu et al., 2008 CPES-WA 3-11 year olds 2003--2004

Lu et al., 2006 WA Children 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

Whyatt 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

Urin

ary

C

o

n

c

e

ntratio

n (μ

g/L)

Referenc

e Range

GM GM

0.1

N

H

A

NES 1999-2000 (n=481)

N

H

A

NES 2001-2002 (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 Summary: Child Study Populations
9

1

10

100

1000

T

C

P

y

Urin

ary

C

o

n

c

e

ntratio

n (μ

g/L)

Referenc

e Range

Maternal Cohorts

GM

GM

GM

GM

GM GM

GM

TCPy Biomonitoring Study Summary: Adult Study Populations.

0.1

N

H

A

NES III, 1988-1992 (n= 993)

N

H

A

NES 1999-2000 (n=832)

N

H

A

NES 2001-2002 (n=1,113)

Residential Low- Crack &

Crevice/Spot (n=8)

Residential High- Turf Treatment (n=10)

Worker Low- Re-Entry (n=10)

Worker High- Mix/Load/Apply/Clean (n=15)

Nonfarmers, chlorpyrifos not sprayed (n= 45)

Farmers, chlorpyrifos not sprayed (n=45)

Farmers, chlorpyrifos self-sprayed (n=2)

Father, nonfarm (n=23)

Father, farm (n=24)

Mother, nonfarm (n=24)

Mother, farm (n=24)

Farm families—Applicators pre-appl. (n=34)

Farm families, Applicators post-appl (n=34

Farm families, Spouses pre-appl

Farm families, Spouses post-appl (n=34)

Mother–child pair cohort 1998-2001 (n=365)

Prenatal 1 (n=95)Prenatal 2 (n=83)Prenatal 3 (n=54)Prenatal 4 (n=21)

Postnatal (n=73)

1st Pregnancy (n=442)

2nd Pregnancy (419)

Prenatal 1 (n=538)Prenatal 2 (n=481)

N

H

A

NES

Subsample 1999-2002 (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

Whyatt et al, 2009 Eskenazi et 

al, 2007

Castorina et al, 

2010 

T

C

P

y

Urin

ary

C

o

n

c

e

ntratio

n (μ

g/L)

* NHANES data represents all survey participants aged 20-59 years old.

*
10

0.1

1

10

100

1992 (n= 993)

2000 (n=832)

2002 (n=1,113)

2002 (n=224)

Prenatal 1 (n=538)Prenatal 2 (n=481)

2001 (n=365)

Prenatal 1 (n=95)Prenatal 2 (n=83)Prenatal 3 (n=54)Prenatal 4 (n=21)

Postnatal (n=73)

1st Pregnancy (n=442)

2nd Pregnancy (419)

T

C

P

y

Urin

ary

C

o

n

c

e

ntratio

n (μ

g/L)

Reference Range Maternal Cohorts

N

H

A

N

E

S III, 1988-1992 (n= 993)

N

H

A

N

E

S 1999-2000 (n=832)

N

H

A

N

E

S 2001-2002 (n=1,113)

N

H

A

N

E

S

Subsample 1999-2002 (n=224)

Prenatal 1 (n=538)Prenatal 2 (n=481)

Mother–child pair cohort 1998-2001 (n=365)

Prenatal 1 (n=95)Prenatal 2 (n=83)Prenatal 3 (n=54)Prenatal 4 (n=21)

Postnatal (n=73)

1st Pregnancy (n=442)

2nd Pregnancy (419)

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

et al., 2003

Whyatt et al, 2009 Eskenazi et al, 2007 *

TCPy Biomonitoring Study Summary: NHANES 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 Cohorts (Eskanazi Eskanazi et al., 2007; et al., 2007; Castorina Castorina et al., 2010) 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, difficult 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)

 Two 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. (2011)

Evaluated cotton field workers in Egypt following 

chlorpyrifos application to cotton fields
Comparison of Creatinine-Adjusted Urinary TCPy levels (μg /g creatinine) reported by Garabrant et al. (2009) 

and Farahat et al. (2011) with the General U.S. Population.

TCPy Biomonitoring and AChE 

Inhibition

15

Population Group Time Period n

TCPy (μg /g creatinine)

Average Median (95% CI) 95th Percentile 

(95% CI)

NHANES (CDC, 2009)

General Population 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)

Garabrant et al, 2009

NR – Not reported

Garabrant et al, 2009

Exposure Group

Fall 1999 53 NR 37.5 432.6

Spring 2000 53 NR 40.7 436.3

Turnaround 50 NR 59.2 7,403.8

Fall 2000 52 NR 51.5 987.2

Reference Group

Fall 1999 60 NR 3.2 13.9

Spring 2000 57 NR 4.8 11.2

Turnaround 57 NR 6.6 13.0

Fall 2000 58 NR 6.3 15.5

Farahat et al, 2011

Applicators

1- to 2- week 

Exposure Period

14 6,437 NR NR

Technicians 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. (2011) 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 110 μg TCPy/g creatinine

Farahat et al. (2011)

 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) 110 μg TCPy/g creatinine

 Farahat et al. (2011) 114 μg TCPy/g creatinine
Risk-Based Biomonitoring 

Interpretation

19

Qualitative 

NAS 2006 Biomarker Interpretation Framework

Semi-Quantitative Quantitative 
Biomonitoring Interpretation: 

Challenges

Specificity

 Direct exposure to metabolites in the environment (TCPy, 

DEP/DETP)

Longitudinal Variability

 Difficult to relate single biomarker measurement

20

 Highly variable depending on sampling time/method, 

pharmacokinetic behavior, biomarker specificity, exposure routes, 

etc.

Toxicological 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 
Biomonitoring Interpretation: 

Qualitative Approaches

Evaluate biomonitoring data without estimating 

chlorpyrifos exposure

 Non-Comparative

21

 Urinary TCPy data for different 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

 Toxicological Relevance: biomarker must be aligned with a toxicologicallyrelevant 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 routespecific 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

Chlorpyrifos PBPK Model

 Several groups working on chlorpyrifos PBPK modeling

 Timchalk et al. (2002) PBPK Model

 Refined since originally developed in 2002

 Evaluated Evaluated at 2011 FIFRA SAP

 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 with particular 

emphasis on dietary exposures

 Several efforts 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
27 Questions