Paul S. Price

The Dow Chemical Company

Source-to-Outcome Modeling 
Information in this presentation 

addresses: 

 Chapters in the report 

5. Dietary exposure modeling

6. Linking exposure models

7. Longitudinal exposures

8. Using model-to-measurement comparisons to evaluate 

model predictions

 Charge questions 

2. Longitudinal dietary exposure assessment

4. Comparison of model predictions with human 

monitoring data 

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 2
Paul Price

Modeling Oral Doses from 

Dietary Exposures
Exposure modeling

 Single day dietary exposure modeling has been performed since 

the late 1980s using the same basic approach

 Multiple models have been developed and evaluated by the SAP

 Conclusion from multiple model reviews 

– While models differ in certain details their results are similar 

for single day exposures

 Longitudinal (multiple consecutive days for one individual) is a 

greater challenge 

– Limited empirical data on longitudinal exposures 

– Multiple methods proposed to simulate longitudinal 

exposures 

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 4
Exposure modeling in this project

 The PBPK/PD model is not directly linked to the 

exposure model - exposure data from any dietary 

model can be used

 Models used in this project:

– CARES 3.0 and LifeLine 4.1 

– DEEMTM 2.16 used in model comparison

 Residue data

– Reflect recent dietary intakes (2006-2008)

– Developed following existing EPA regulatory policies and public 

examples

– Details given in Attachment C “2010 Update of the Acute Dietary Risk 

Assessment for Chlorpyrifos”

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 5
Results

 Models give similar estimates of daily doses

– Within a factor of 2-3 at the 99.9th percentiles

 Doses 

– Highest in 3 year olds 

– 10 - 20% lower in infants

– 50% lower in adults

 Consistent with age-related differences in 

exposures to residues of other pesticides 

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 6
Paul Price

Linking Dietary Exposure and 

PBPK/PD Models
Defining the individual

 The exposure modeling precedes the PBPK/PD 

modeling 

 The PBPK model assumptions on physiology and 

metabolism need to be consistent with dose 

estimates and an oral pathway of exposure

 Exposure models define age, gender, and 

bodyweight

 Output of model is therefore:

– Age, gender, and bodyweight

– Longitudinal dose history

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 8
Moving data between the models

 Data from dietary model is converted to an 

Excel file;

 The PBPK/PD model reads this file as an input

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

Age (y)

Sex 

0=M, 

1=F

Weight 

(kg)

Dose 

Day 1 

(mg/kg)

Dose 

Day 2 

(mg/kg)

Dose 

Day 3 

(mg/kg)

Dose 

Day 4 

(mg/kg)

Dose 

Day 5 

(mg/kg)

0.5 1 6.8 3.97E-06 4.39E-07 1.12E-05 1.04E-05 1.65E-06

0.5 1 6.4 3.31E-06 9.61E-07 1.12E-05 6.78E-05 1.65E-06

9
Models view doses differently

 Exposure models define dose as:

– Mass of residue in daily diet on a bodyweight basis 

(mg/kg/d)

– Timing of dose within a 24 hour period typically not 

considered

 PBPK/PD model defines dose as:

– Mass entering into the intestine compartment

– Rate is not constant over time

 Mass entering intestine is driven by:

– Timing of meals and fraction of dose in each meal

– Rate of stomach emptying 

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 10
Converting dietary doses to intestine 

loading rates

 Dietary doses: 

– Occur during multiple eating events in a day; but

– Top 2% of population receive 75% of daily dose from one 

food

– Assumed total daily dose occurs at a single meal

– Assumption is conservative (increases estimate of peak levels 

in blood) 

 Stomach emptying rates: 

– Time to empty first half of a meal reported as 30-90 minutes

– A value of 90 minutes is used in the model 

– Model not sensitive for rate of transfer into the intestine

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 11
Modeling Longitudinal 

Exposures
Challenge of modeling 

longitudinal exposures

 Current dietary surveys do not collect data on an 

individual’s dietary intakes on consecutive days 

 This requires exposure modelers to simulate 

longitudinal dietary exposures

– Multiple approaches have been proposed

 This introduces uncertainty in risk predictions for 

exposures longer than one day

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 13
Approach used 

1. Continuous exposures to constant doses

2. Different approaches for simulating longitudinal 

exposures

– LifeLine seasonal, LifeLine daily, and CARES

3. Investigate impact of longitudinal exposures on 

adults with doses at or above 99.9th percentile 

dose

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 14
LifeLine random day and CARES

No correlation between doses on consecutive days. 

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

Moderate correlation

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 16
Investigating impacts of longitudinal 

exposures: bounding the issue 

All sets of data can be placed on a common scale

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

Perfect 

Correlation, 

No Variation

No 

Correlation

Maximum 

Variation

Moderate 

correlation,

Limited

Variation

Constant 

dose 

LifeLine 

Seasonal

CARES 

and 

LifeLine 

Daily

17
Impact of a constant dose 

 Continuous exposures to constant dose were modeled 

for typical adult and infant over 30 days 

 Five doses (0.5- 0.0001 mg/kg)

– Doses that cause significant AChE inhibition (0.5 mg/kg) 

– Doses that occur on one day to the top 1% of 3 year olds 

(0.0001 mg/kg)

 Determined 

– Build up of CPF and CPF-oxon 

– Increasing inhibition of AChE

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

levels in blood

• Levels of CPF increase 

slightly 

• Approximately 16% 

increase in peak level 

on day 30 vs. day 1

• Increase not dose 

dependent 

• Levels of CPF-oxon 

increase in a dose 

dependent manor:

• 18 fold increase at 0.5 

mg/kg

• 1% increase at 0.0001 

mg/kg

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 19
Persistence
RBC and Brain 

AChE Inhibition

• RBC AChE inhibition 

increases in a dose and 

age dependent manner:

• 0.1 mg/kg adults had a 20 

fold increase and infants a 

12 fold increase

• At 0.0001 mg/kg there 

was a 2.5 fold increase for 

both ages

• Brain AChE inhibition 

increases similar for adults and 

infants 

• Increases ranged from 4 

fold at high dose to 3 fold 

at low dose

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 21
Predictions of the three longitudinal 

approaches for CPF and CPF-oxon

 All three approaches give similar results

 Consistent with the finding that CPF and CPFoxon formed on one day do not carry over to the 

following days

– If there is no “carry over” then differences in 

assumptions about exposures on prior days should 

have no impact 

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 22
Predictions of the three 

approaches for AChE inhibition

Approaches differ at low exposures but agree for high 

exposures (top one third) 

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 23

CARES LifeLine Daily LifeLine Seasonal
Why are responses in high-dose individuals 

independent of modeling assumptions?

 Asymmetrical effects of high and low doses on 

subsequent days

– A high dose (1000) will affect the next day even if only 10% 

carries over if the next day’s dose is small (10)

– 1000 *0.1 +10= 110 versus 10 (a 11 fold change)

– In contrast, if the days are reversed 

– 10*0.1 + 1000 = 1001 versus 1000 (a 0.1% change)

 Therefore individuals receiving smaller dose are more 

sensitive to doses on prior days than individuals 

receiving high doses

– More affected by differences in the longitudinal models

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 24
Impact of longitudinal exposures on AChE 

inhibition in highly-exposed individuals

 Procedure:

– Dietary dose histories (365 days) were created for a large number of 

adults using CARES

– Individuals were identified who had a “high” daily exposure (in the top 

0.1% of the daily exposures received by adults) on one of the 365 days

– The doses on the four days prior, and subsequent, to the high exposure 

day were identified

» Resulting in nine days of exposure with the high daily exposure 

occurring on day five

 The Variation model was used to determine impact of dietary 

exposures on peak RBC and brain AChE inhibition for:

– The peak day alone; and 

– The nine days of exposures

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 25
0.001

0.01

0.1

-3 -2 -1 0 1 2 3

Peak RBC inhibtion (Percent)

z-Score

High exposure day plus 4 prior 

and 4 subsequent days 

High exposure day only

Impact of longitudinal exposures 

on high exposure days

Prior an subsequent exposures increased RBC 

AChE inhibition by an average of 10%

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 26
Findings on Longitudinal 

Exposures

 The findings of the various analyses lead to the same 

conclusions on the impacts of longitudinal dietary exposures

– CPF and CPF-oxon do not accumulate over time at dietary doses 

– AChE inhibition does increase but the impact is minimal (10%) at the 

99.9th percentile

 AChE effects from dietary exposures 

– Highly exposed individuals can be predicted based on single daily doses

– Typical- and low-exposed individuals require consideration of 

longitudinal exposures

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 27
Evaluating the Source-toOutcome Model by Using 

Human Monitoring Data 
Model-to-Measurement

 Source-to-outcome models and measured data

– Recognizing the challenges of model evaluation

 Key strategy in model-to-measurement 

comparisons

 Comparisons to blood levels of CPF

 Comparisons to urinary measurements of TCPy

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 29
No one set of monitoring data can evaluate 

the entire source-to-outcome model

PBPK 

Modeling

PBPD 

Modeling

AChE 

Predictions

Daily 

Dietary 

Exposures

PBPK 

Modeling

PBPD 

Modeling

Model of Human 

Variance (Adults)

Model of Human 

Models of Variance (Children)

Longitudinal 

Exposures

AChE 

Predictions

Comparisons of 

estimates of dietary 

intakes in three 

dietary studies 

(Model-to-Model)

Dietary 

Residue 

Data

Comparisons of blood 

levels across three 

models of longitudinal 

exposure (Model-toModel)

Comparison of Blood levels of Chlorpyrifos and Urine levels of 

TCPy (Model-to-Measurements)

Comparison of TCPy and effects in chlorpyrifos 

manufacturing workers (Model-to-Measurement)

Variation in calibration studies

(Model-to-Measurement)

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 30
Strategy

 Measured data is produced by surveys with 

“characteristics” that affect their findings

– Ages surveyed

– Time since last exposure

– Pregnancy

 To facilitate model-to-measurement the source-tooutcome model needs to predict results as the 

survey would measure them

– Mimic survey characteristics 

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 31
Comparison to survey data on 

blood levels of CPF 

 Current levels are available from two surveys Whyatt

et al. and Barr et al. 

– Data collected from mothers at the time of delivery

– Exposures believed to be limited to dietary exposures

– The time since last meal is not reported 

 Compare to model results

– Variation across adults

– Levels in blood of adults at any time between 0 and 24 hours 

following a dietary exposure

– Adjusted to reflect pregnancy 

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 32
Model-to-Measurement Results

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 33

0.007

0.05

0.09

0.2

0.4

0.7

1

7

0.001

0.01

0.1

1

10

100

Min 10% 25% 50% 75% 90% 95% Max

Blood Concentrations ng/l

Model

Model

Detection limit 0.75 ng/l
Model-to-Measurement Results

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 34

0.007

0.05

0.09

0.2

0.4

0.7

1

7

2.3 2.5

16

0.001

0.01

0.1

1

10

100

Min 10% 25% 50% 75% 90% 95% Max

Blood Concentrations ng/l

Model Whyatt et al.

Detection limit 0.75 ng/l
Model-to-Measurement Results

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 35

0.007

0.05

0.09

0.2

0.4

0.7

1

7

2.3 2.5

16

1.3

1.7 1.8

10

0.001

0.01

0.1

1

10

100

Min 10% 25% 50% 75% 90% 95% Max

Blood Concentrations ng/l

Model Whyatt et al. Barr et al.

Detection limit 0.75 ng/l
Comparison to TCPy data

 NHANES data:

– Data collected in 2002 limited to 12yr and older

– Spot urine sample

 NHANES data on TCPy reflects:

– Dietary intakes of TCPy, CPF, and methyl-CPF

– TCPy produced by metabolism of CPF residues in diet is 

believed to be between 5 and 20% of total TCPy 

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 36
Comparison to NHANES data 

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 37

of
Comparison to NHANES data 

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 38

of
Comparison to NHANES data 

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

-3 -2 -1 0 1 2 3

0.1

1

10

100

Distribution of total TCPy

for portion if the population

with detectable levels

Predicted distribution from

Source-to-outcome model

}

Range of predictions of

TCPY from metabolism of

chlorpyrifos (5-20% of

total)

Portion of the

population with non

detectable levels of

TCPy

Z-Score

TCPy Concentration

in Urine (μg/L)

39
Comparison of observed levels of TCPy 

in urine and model predictions

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 40

4

10

0.1

1

10

100

Adults Children

Concentration of TCPy in Urine (μg/l)

Model (5% Assumption)
Comparison of observed levels of TCPy 

in urine and model predictions

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 41

4

10

1

2.5

0.1

1

10

100

Adults Children

Concentration of TCPy in Urine (μg/l)

Model (5% Assumption)

Model (20% Assumption)
Comparison of observed levels of TCPy 

in urine and model predictions

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 42

4

10

1

2.5

11.5

16

0.1

1

10

100

Adults Children

Concentration of TCPy in Urine (μg/l)

Model (5% Assumption)

Model (20% Assumption)

Curwin et al. 2007
Comparison of observed levels of TCPy 

in urine and model predictions

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 43

4

10

1

2.5

11.5

16

5.1

0.1

1

10

100

Adults Children

Concentration of TCPy in Urine (μg/l)

Model (5% Assumption) Model (20% Assumption)

Curwin et al. 2007 Lu et al. 2008
Conclusions

 The portions of source-to-outcome model have 

been evaluated by comparison to human volunteer 

studies 

– Measure of central tendency for groups of individuals 

(LifeStage model)

– Measure variation across individuals (Variation model)

– Variation model over predicts response

 The predictions of the exposure and PBPK portions 

of the model matched the monitoring data

EPA Scientific Advisory Panel, Washington, DC, February 15-18, 2011 44