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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.gov2 

# 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, 20113 

# 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, PhD4



# 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 Aged Complex Oxon Esterase hr- 1 hr- 1 μmole hr- 1 CPF - Oxon

Cvo Cao 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, 20115 

# 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, 20116 

# 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, 20117 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, 20118

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, 20119



# Metabolic Ontogeny: CYP450 CPF CPF - oxon TCPy 

### CYP450 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, 201110 

# 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, 201111 

# 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 volunteers12 

# 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 volunteers13 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., 199914



### 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 

### Lifestage15 

### 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, 201116 

# 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, 201117



# 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, 201118 

# 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, 201119



# 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