A Framework for High Throughp

Exposure (HTE) Estimation

A Framework for High Throughp

Exposure (HTE) Estimation

Presented by John Wambaugh

Office of Research and Development

National Center for Computational Toxicology

Presented by John Wambaugh

Office of Research and Development

National Center for Computational Toxicology
Presentation Roadma

Introduction

High Throughput Exposure

(HTE) Estimation

High Throughput Toxicokinetics

(HTTK) and Reverse

Toxicokinetics (RTK)

Ongoing Research

• Framework overview• HTE Forward Data and Models• HTE Reverse Data and Models

• SEEM Analysis and HTE Estimates

Future Appli
Introduction

 The timely characterization of the human and ecological risk po

thousands of existing and emerging commercial chemicals is a c

challenge facing EPA in its mission to protect public health and t

environment Environmental 

Consumer Use an

 ExpoCast is an initiative to develop the

necessary approaches and tools for

rapidly predicting exposure for

thousands of chemicals (Cohen‐Hubal,

et al., 2010)

 Proof of Concept (First Generation

Analysis): Used off‐the‐shelf high

throughput exposure models and

evaluated predictions with

biomonitoring data to characterize

uncertainty (Wambaugh et al., 2013)

“All cases are unique, and very similar to others.”― T.S. El
Exposure Science in the 21st C

 2012 NRC re

 N

f

p

ctt

 N

eamn

 B

qpvn

Figure from Egeghy et al. (2012), “The exposure data landscape for manufactured chemicals”
How to Make Good Forec

1) Think probabilistically (especially, Bayesian): We use an appro

evaluates model performance systematically across as many c

(and chemistries) as possible

2) Forecasts change: Today’s forecast reflects the best available d

but we must accept that new data and new models will cause 

to be revised

Orrin Pilkey &Olinda Pilkey‐Jarvis (2007) Nate Sil

3) Look for consensus: We evaluate

as many models and predictors/

predictions as possible
Exposure Forecasting (Exp

 Develop the tools and data necessary to rapidly qu

human and ecological exposure potential of chem

 Focus is distinct from many existing exposure tools 

support either screening level assessments on a pe

chemical basis or full regulatory risk assessment

In Nate Silver’s terminology:

a prediction is a specific statement

a forecast is a probabilistic statement

Wikipedia (statistics): “when information is transferred across time, often 

points in time, the process is known as forecasting”
Goals for High Throughput Ex

• Incorporate multiple models into consensus predictio

1000s of chemicals

• Evaluate/calibrate predictions with available measure

data across many chemical classes

• Empirically estimate uncertainty in predictions

• Analogous efforts for both human and ecological exp

but human is more developed to date
Why Bayesian Statistic

 If we observe

three yellow

cars, what dowe infer

about thecolor of other

cars?
Why Bayesian Statistic

 We could infer th

are yellow, BUT:

• This conflicts wi

prior experience

• Is not that much 

likely than otheexplanations (it happened by ch
Why Bayesian Statistic

 Bayesian analysis is a rigorous

statistical methodology

 Attempt to considers all likely

explanations for data, rather than

focusing on determining just the

single most likely explanation

 Proper mathematical combination of

prior expectations and newly

obtained data in order to obtain a

new distribution of expected values
Exposure Space

Chemical Manufacture

Consumer

Building Materials

Products, Articles,

Building Materials Environmen

Release

Air, Dust, Surfaces Food 

Human

Direct Use

(e.g., lotion)

Residential Use(e.g. ,flooring)

MONITORING

DATA

RECEPTORS

MEDIA

Biomarkers

of Exposure

Media Samples

Waste
Exposure Pathways

Chemical Manufacture

Consumer

Building Materials

Products, Articles,

Building Materials Environmen

Release

Air, Dust, Surfaces Food 

Near‐Field

Direct

Near‐Field

Indirect

Human

Dietary Far‐Field

Direct Use

(e.g., lotion)

Residential Use(e.g. ,flooring)

MONITORING

DATA

RECEPTORS

MEDIA

EXPOSURE

PATHWAY

(MEDIA + RECEPTOR)

Biomarkers

of Exposure

Media Samples

Waste
Forward Prediction of Exp

Chemical Manufacture

Consumer

Building Materials

Products, Articles,

Building Materials Environmen

Release

Air, Dust, Surfaces Food 

Near‐Field

Direct

Near‐Field

Indirect

Human

Dietary Far‐Field

Direct Use

(e.g., lotion)

Residential Use(e.g. ,flooring)

MONITORING

DATA

RECEPTORS

MEDIA

EXPOSURE

PATHWAY

(MEDIA + RECEPTOR)

Biomarkers

of Exposure

Media Samples

Waste

Data and

Models

HTE ForwardData and ModelsPeter Egeghy
Forward Prediction

 Treat different models like related high‐throughput assays – co

USEtox RAIDAR

United Nations Environment Program and

Society for Environmental Toxicology and

Chemistry toxicity model Version 1.01

Rosenbaum et al. 2008

Risk Assessment IDent

And Ranking model Ve

Arnot et al. 200
Forward Prediction

 Different HTE models provide different, but hopefully related, chemical‐specifi

 We look for consensus since some models may perform better for different ch

chemicals classes)

7834 EDSP Chemicals

USEtox or RAIDAR

Predictions (kg / kg emitted)
Reverse Inference of Expo

Chemical Manufacture

Consumer

Building Materials

Products, Articles,

Building Materials Environmen

Release

Air, Dust, Surfaces Food 

Near‐Field

Direct

Near‐Field

Indirect

Human

Dietary Far‐Field

Direct Use

(e.g., lotion)

Residential Use(e.g. ,flooring)

MONITORING

DATA

RECEPTORS

MEDIA

EXPOSURE

PATHWAY

(MEDIA + RECEPTOR)

Biomarkers

of Exposure

Media Samples

Waste

• Biomarkers reflect all pathways, not just those in forward m

pathways contribute to estimated uncertainty

Data and

Models

HTE ReverseData and ModelsWoodrow Setzer
Reverse Inference

 Different HTE models

provide different

chemical‐specificpredictions

 Some models may do a

better job than others

overall, so we want to

evaluate performanceagainst monitoring

data

 Separate evaluations

can be done for various

demographics CDC, Fourth National Expos
Systematic Empirical Evaluat

Models (SEEM)

 There are four basic steps in the SEEM framework

1. Forward prediction of exposures, which involves model c

parameterization

2. Inference of exposures from monitoring data

3. Systematic evaluation and calibration of the predictions a

inferred exposures

4. Extrapolation of the calibrated model predictions and est

uncertainty to chemicals with no monitoring data.

 To achieve these aims the SEEM framework used B

formalism and multivariate, linear regression for

demonstrating and evaluating predictive ability
Illustration of the SEEM Fram

EstimateUncertainty

EDSPChemicalsQSARs andHTE Data

Biomonitoring

Data

Inferred (Reverse) Exposure

Model 1Model 2

...

Cm

Apply calibration and uncertainty to

other chemicals

Evaluate Model Performa

and Refine Models

Forward Predictions

Exposure

Inference

Dataset 1Dataset 2

...
Better Models and Data Sh

Reduce Uncertainty

Image from Little et al. (2012)

Uncertainty/Variability of NHANES Biomonitoring

~10% Far field

(Industrial) Releases

~60% Indoor /

Consumer Use

Indirect Exposure Direct Expo

• Consolida

Activities D

• ACToR Us

CPcatDB• SHEDS‐HT
SEEM Evolution – 1st and 2nd Gener

Near‐field Human Exposure

Model and Predictors

• Existing com

transport mo

correlation to exposures• Near field fimportant

• Simple, rea

• Better corremeasured ex

• Similar pred

demographic

1st Gen

Calibration/Evaluation Data SEEM C

2nd Gen

USEtox

RAIDAR

Near Field / Far Field

NHANES

Urine

Data

Production Volume

Use Categories

Production Volume

Phys‐Chem Properties

NHANES

Urine

Data

3rd Gen

• Analysis yet 

SHEDS‐HT

Literature

Models

CPcat Database

NHANES

Urine

Data

Toward a 3rdGeneration SAnalysis: NewData and MoKristin Isaacs
Model and Predictors Calibration/Evaluation Data SEEM C

USEtox

RAIDAR

SHEDS‐HT Down the drain

EPA/USGS

water

monitoring

data and

fish conc.

data

Production Volume

SEEM Evolution – Far‐field W

Eco (fish) and Human Exposure

HT EXAMS‐

KABAM‐BASS

Far‐field Water• Analysis yet 

High

Throughput

Ecological

Exposure

Modeling

Craig Barber

Evaluation D

St

Framew

Ecological E

Pre

Woodrow 
Summary

 High throughput computational model predictions 

exposure is possible

 SEEM compares the predictions of relevant model

exposures inferred from monitoring data to develo

calibrated prediction and empirical uncertainty est

 The SEEM calibration and uncertainty can then be 

to other chemicals

 We have completed two generations of the SEEM 

exposure analysis and are developing a third gene

human analysis and a first generation ecological ex

analyais

“As far as the laws of mathematics refer to reality, they are not certain; and as far as t

certain, they do not refer to reality.”

Albert Einstein, quoted in J R Newman, The World of Mathematics (1956)
Presentation Roadma

Introduction

High Throughput Exposure

(HTE) Estimation

High Throughput Toxicokinetics

(HTTK) and Reverse

Toxicokinetics (RTK)

Ongoing Research

• Framework overview• HTE Forward Data and Models• HTE Reverse Data and Models

• SEEM Analysis and HTE Estimates

Future Appli