High Throughput

Forward Prediction of

Human Exposure

High Throughput

Forward Prediction of

Human Exposure

Presented by Peter P. Egeghy, Ph.D. M.P.H.

National Exposure Research Laboratory

Office of Research and Development

Presented by Peter P. Egeghy, Ph.D. M.P.H.

National Exposure Research Laboratory

Office of Research and Development
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
 Provide background on SEEM analysis Present the exposure data “landscape”

 Describe our process for identifying and

evaluating HTE models

 Explain how chemical use profiling data 

obtained and integrated into SEEM ana

Objectives
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
Available Data: “Exposure 

Exposure Potential

Chemical

Properties

Life Cycle

Properties

Emissions

Characteristics 

Log Kow

Log Koa

VP

MW

Production

Volume

Product

Formulation

Use

Categories

Mass

Emitted

Receptor

Media

Contact

Media

J. Mitchell et al. / PLoS one 8.8 (2013) e70911

P.P. Egeghy et al. / Science of the Total Environm
Tabular Data,Links to Web

Resources

Chemical ID,Structure Chemical

InternetSearches

ACToR API

ToxRefDB

http://actor.epa.gov/

ToxCastDB ExpoCastDB

In Vivo Data

(ORD, OPP)

ToxCast Data(ORD,Collaborators)

Exposure and

Biomonitoring Data(ORD)

ACToR Core 

ACToR: EPA’s Aggregate

Computational Toxicology Re
 The vast majority of

chemicals:

• Unmeasured in the

environment

• Unknown

environmental fate and

exposure potential

 Limited resources

available to increase

measurements

 Need for high

throughput exposuremodeling using broad

surrogates

Exposure Information

Limited Chemical Covera

P.P. Egeghy et al. / Science of the Total Environment 4

“EPA study reveals barren exposure data landscape”

‐EDF Chemicals & Nanomaterials Blog, 
 Goal: Collaboratively develop capacity for rapid determin

exposure potential for thousands of chemicals

 Identify existing approaches

• Community of Practice: 2008‐2010

 Enlist broad‐based support of the scientific community th

of four ExpoCast Workshops

• Surveyed mandates for chemical prioritization and related 

science needs

• Systematically evaluated approaches on 15 relevant factors

• Publication: “Exposure‐based prioritization of chemicals fo

assessment,” ES&P, 2011

• Fostered engagement in demonstrating the application of e

tools to HT assessment of exposure potential

Leveraging Existing Tools for 

Determination of Exposure Po
High Throughput Exposu

Model Challenge

9 Mitchell et al., Sci Total Environ, 2013
 Four key criteria for HTE models:

• Parameterized from minimal data sets

• Broadly applicable across many chemicals

chemical classes

• Operated in an automated (batch) manne

• Capable of making quantitative of human

exposure predictions (e.g., mg/kg‐BW/day

Evaluation of Models aga

Key Criteria
Model Full Name

RAIDAR Risk Assessment IDentification And Ranking Yes 

USEtoxTM UNEP‐SETAC Toxicity Model Yes 

EFAST2 Exposure and Fate Assessment Screening Tool v2.0 Almost 

GExFRAME/Scibin Generalized Exposure Framework Yes PRoTEGE Prioritization/Ranking of Toxic Exposures with GIS Extension 

3MRA Multimedia, Multipathway, and Multireceptor Exposure and Risk Assessment 

APEX Air Pollutants Exposure Model 

ChemSTEER Chemical Screening Tool For Exposures & Environmental Releases 

DEEMTM Dietary Exposure Evaluation Model 

EMSOFT Exposure Model for Soil‐Organic Fate and Transport Yes 

FIRST FQPA Index Reservoir Screening ToolHAPEM Hazardous Air Pollutant Exposure Model 

HEM Human Exposure Model 

LifeLineTM Model The Lifeline Group LifeLineTM Model 

MCCEM Multi‐Chamber Concentration And Exposure Model Almost 

PIRAT Pesticide Inert Risk Assessment Tool 

PRZM and EXAMS Pesticide Root Zone Model and Exposure Analysis Modeling System

ReachScan ReachScan 

SHEDS Stochastic Human Exposure and Dose Simulation 

SOFEA and FEMS Soil Fumigant Exposure Assessment and Fumigant Exposure Modeling System 

SOPs Standard Operating Procedures for Residential Pesticide Exposure Assessment 

SRD Source Ranking Database 

STIR Screening Tool for Inhalation Risk Yes 

SWIMODEL Swimmer Exposure Assessment Model Yes 

TRIM.FaTE Total Risk Integrated Methodology 

WPEM Wall Paint Exposure Assessment Model 

Evaluation of Models agains

Key Criteria
High Throughput Exposure M

Models meeting all four criteria for high throughput exposure asse
CASRN Category 1 Category 2 ... Category 1265277‐42‐10 1 ...0

50‐41‐9 1 1 ...0

... ... ... ......

CASRN Category 1 Category 2 ... Category 1265277‐42‐1 0 10 ... 1

50‐41‐9 31 7 ... 3

... ... ... ......

Binary matrix

Table: Hits per use category for a given chemical

Work by Alicia Frame, Kathie D

Dionisio et al. ma

Chemical Use Categories:

• 15 distinct chemical use categories

derived from ACToR

• Data sources (“assays”) manually

assigned to categories

• Chemicals in data sources

computationally assigned to categories

Chemical Us

Antimicrobials 

Chemical Warfare 

Consumer Use 

Dyes and Colorants 

Fertilizers 

Flame Retardants 

Food Additive 

Fragrances 

Heuristics for Chemical U

Creation of ACToR UseD
 Vast majority of chemicals do not have ex

related information available

 Systematic evaluation of “off‐the‐shelf” ex

models found few are capable of HTE estim

 Only Fate & Transport models are suitable 

purposes, but ignore residential sources

 Use profiling information has been curate

supplement F&T models with “near field” 

information

Summary
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

...