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PBPK/PD Modeling of 

Chlorpyrifos

EPA Scientific Advisory Panel, Washington, DC, 

February 15-18, 2011

Torka Poet, PhD

Biological Monitoring & Modeling

Pacific Northwest National Laboratory

torka.poet@pnl.gov
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Information in this presentation 

addresses:

• Chapters in the report

3. PBPK/PD modeling in typical humans of various ages

• Charge Questions

1. Physiologically Based Pharmacokinetic/Pharmacodynamic 

(PBPK/PD) Modeling

3. Model Calibration and Evaluation with Direct Dosing Human 

Studies

5. Sensitivity Analyses, Variability, and Uncertainty

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011
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Chlorpyrifos (CPF) PBPK/PD Model 

Development (history)

• Core Team

• Charles Timchalk, PhD DABT

• Paul Hinderliter, PhD DABT

• Ahmed Kousba, PhD

• Funding

• Dow AgroSciences

• EPA-STAR Grant R828608

• CDC/NIOSH Grant R01OH03629

Selected Publications of Related Models

• CPF Rat & Human. Timchalk et al., (2002) Tox. Sci.

• Monte Carlo PON1. Timchalk et al., (2002) Toxicol. Lett.

• Diazinon (DZN) Rat & Human. Poet et al., (2004) NeuroTox.

• Age-Dependent Preweanling Rat. Timchalk et al., (2007) Tox. Sci.

• Binary CPF & DZN Rat. Timchalk & Poet, (2008) NeuroTox.

• Pregnant Rat. Lowe et al., (2009), Tox Sci. 

• Human Agricultural Exposures. Garabrant et al (2009) J Exp Sci Env Epid.

• Rat CPF & Nicotine. Lee et al, (2010), Chemico-Biol Interact.

• Human Childrens TCPy. Lu et al., (2010), EHP.

• Sookwang Lee, PhD

• Torka S. Poet, PhD

• Jordan N. Smith, PhD
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PBPK Model Structure

Chlorpyrifos

Liver

Qc

Venous Blood

Arterial Blood

Qs

Qr

Qd

Qb Cvb

Ql

Cvs

Cvr

Cvd

Cvl

Qf Cvf

KsI

KaI

Oral Exposure

Fa

Brain

Diaphragm

Slowly Perfused

Fat

Rapid Perfused

Stomach

Intestine

PON1

CYP450

Urinary 

Clearance

TCPy

Volume of 

Distribution

Synthesis of New Esterase

“Free”

CPF-Oxon +

Degradation of Esterase Released TCPy Metabolite

(TCPy model)

Regeneration

hr-1

B-Esterase (B-EST) Inhibition

(shaded compartments)

Inhibition

μm hr-1

Free Esterase Oxon Esterase Aged Complex hr-1

hr-1

μmole hr-1

CPF-Oxon

Cao Cvo

Liver

Qs

Qr

Qd

Qb Cvbo

Ql

Cvso

Cvro

Cvdo

Cvlo

Cvfo

Brain

Diaphragm

Slowly Perfused

Fat

Rapid Perfused

Qf

Blood

CYP450

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011
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Evolution of Model Since 2002

• Extrahepatic metabolism

• Enterocytes (rat, extrapolated to human based on tissue size)

• Brain (rat TCPy, extrapolated to human based on tissue size)

• Measured Human Metabolism In Vitro

• PON1 metabolism of CPFoxon, liver and plasma = ratio very 

different from rat

• Plasma 13 d 75 yr

• Liver 3 d 43 yr

• CYP450 metabolism of CPF to CPFoxon or TCPy

• Liver 3 d 43 yr

• Partition Coefficients (Lowe et al., 2009)

• Directly measured bimolecular inhibition rate constant 

(AChE and BuChE) inhibition dynamics in rat plasma, 

brain (Kousba et al., 2004; Kousba et al., 2007).

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011
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LifeStage Model

• Approach

• Changes in Body Weight

• Growth of Tissues

• Ontogeny of Metabolic Processes

• Body Weight Age

• Body Weight Tissues

• Tissue Blood Flow

• Gender

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011
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NHANES

U.S. population 2007-2008

NHANES

U.S. population 2007-2008

A generalized, age- and gender-specific 

Gompertz function used to calculate body 

weight (Luecke et al., 2007)

Age-Specific Body and Tissue Weights

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011
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Valentin, 2002 (blue), or 

Young et al., 2009 (black) 

Lafortuna, 2005, 

Butte, 2000; or 

Valentin, 2002

Data from literature 

Showing tissue 

volume in young to 

adults

1-6 order polynomial 

equations were fit to 

the data. (Similar to 

Luecke et al., (2007) 

and Young et al., 

(2009))

Tissue Growth 

Data from literature 

describing tissue 

weights/body weight 

in young to adult 

individuals

Comparison to default, 

non-growth model 

estimates, substantial 

improvements

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011
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Metabolic Ontogeny: CYP450

CPF

CPF-oxon TCPy

CYP450

LiverVolume Hepatic Protein VmaxInVivo= InVitro max 

L

V

In Vitro, CYP450-mediated metabolism of CPF was not age dependent, on a per 

mg basis. But, the liver volume increases with age. The age-dependence shown 

here is based on Liver growth

Liver

Tissue, and therefore activity

14x difference with Age

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011
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Metabolic Ontogeny: PON1

CPF-oxon

TCPy

PON1

While hepatic PON1 was not 

age-dependent, 

Plasma activity was.

50x difference with age.

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011
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Controlled Human Studies: Nolan et 

al., 1984: TCPy PK

Most parameters in the model have 

been measured in vitro or 

extrapolated from the rich animal 

data

Fitted parameters:

• Absorption of CPF from the 

stomach to the intestines

• Absorption of CPF from intestines 

into the body

•The volume of distribution for TCPy

• Urinary elimination rate of TCPy

0.5 mg/kg CPF on lactose tablet

6 male volunteers
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Controlled Human Studies: Kisicki 

et al. 1999: TCPy PK

Evaluation of the model against 

data from Kisicki et al, (reported in 

Timchalk et al., 2002)

• 0.5, 1.0, and 2.0 mg/kg target 

doses

• 12 male and 12 female volunteers
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Evaluation of the model against data from 

Kisicki et al, 1999 (reported in Timchalk et 

al., 2002) and Nolan et al., 1984

• 0.5, 1.0, and 2.0 mg/kg target doses

• RBC AChE inhibition measured in 1 

subject at the 2.0 mg/kg dose

Controlled Human Studies:

Plasma and RBC Inhibition

Plasma BuChE

Nolan et al., 1984

RBC BuChE

Kisicki et al., 1999
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Timchalk et al., 2002

Comparison to 2002 Model Output

Predicted ChE Inhibition

• Plasma ChE inhibition 

similar

•range of Nolan et al., 1984 

data.

• RBC inhibition more 

sensitive

• new metabolic parameters

• Brain inhibition more 

sensitive

• new metabolic parameters

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011

Lifestage
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LifeStage Timchalk et al., 2002

Comparison to 2002 Model Output

Predicted Plasma ChE vs TCPy in Urine

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011
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Predictions of RBC Inhibition for 6 

mo, 3 yo, and 30 yo Individuals

• Peak Inhibition of RBC AChE

• Single CPF doses ranging from 

0.1 to 50 mg/kg

• 3 orders of magnitude above 

dietary exposures (presented 

later

• Exposures below ~1 mg/kg will 

not be experimentally observed

Expand model-predicted RBC 

inhibition at 0.5 and 1 mg/kg to 

compare ages

• @ 5 mg/kg 6 mo old show ~ 1% 

more inhibition than adults (92. 5% of 

control compared to 93.8%) at peak ~ 

9.5 hr

• @ 1 mg/kg 6 mo old exhibit ~ 10% 

more inhibition than adults, and 3 yr 

olds show ~ 5% (62.7% of control, vs 

68.5 compared to 73.1 %) at peak ~ 

9.5 hr

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011
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Summary

• LifeStage Model has evolved from 2002 PBPK model

• Has been applied to several studies

• Strong publication history

• LifeStage Model based on body weight and tissue growth 

changes from birth to adulthood

• New rodent and in vitro human studies used to 

parameterize the LifeStage Model

• Few parameters have been fit to data (TCPy compartmental 

model)

• Predictions of LifeStage model fit available controlled 

human exposure studies

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011
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Summary Continued

• Predictions of LifeStage model very similar to the 2002 

model

• Plasma BuChE ~ especially with inhibition less than 40%. With 

increased doses, the LifeStage model predicts > inhibition

• RBC AChE inhibition more sensitive to CPF exposures

• Brain AChE inhibition is more sensitive at doses > 0.5 mg/kg

• @ 1 mg/kg infants exhibited RBC AChE peak inhibition 

rates <2x higher than adults

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011
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PBPK Models

• For this project, the clearest and most relevant PD endpoint 

and PK dose metrics have been investigated 

• PBPK models, like this LifeStage model are uniquely capable 

of evaluating

• Age and gender differences

• Physiological state (i.e. pregnancy)

• Dose metrics

• Parent and metabolite in any tissue

• Cmax or AUC

• Cumulative internal dose over days, weeks, or months

• Dosing scenarios

• Biomonitoring data

• Run to Steady State

• Parent or metabolite in blood or urine

• Extrapolating from in vivo and in vitro studies to put experimental 

dose regimes into perspective