New High-throughput Methods to Estimate Chemical Exposure 1 7/8/2014

Exposure SAP White Paper

New High-throughput Methods to 

Estimate Chemical Exposure

Scientific Advisory Panel Meeting, July 2014
New High-throughput Methods to Estimate Chemical Exposure 2 7/8/2014

Table of Contents

List of Acronyms and Abbreviations.............................................................................................. 3

Preface............................................................................................................................................. 4

1. Introduction................................................................................................................................. 7

2. Exposure Prioritization with ExpoCast Research..................................................................... 12

2a. Introduction......................................................................................................................... 12

2b. Data and Models for Forward Prediction of Human Exposure .......................................... 17

2c. Data and Models for Inference (Reverse Prediction) of Human Exposure ........................ 36

2d. Systematic Evaluation of Forward and Reverse Prediction across Many Chemicals ........ 40

2e. First Generation SEEM Analysis: (Far-Field HTE with Near Field Indicator).................. 44

2f. Second Generation SEEM Analysis: Heuristics of Exposure ............................................. 53

2g. Limitations of Approach ..................................................................................................... 64

2h. Conclusions......................................................................................................................... 66

3. High-throughput Toxicokinetics (HTTK) and Reverse Toxicokinetics (RTK) ....................... 68

3a. Introduction......................................................................................................................... 68

3b. HTTK for Environmental Chemicals.................................................................................. 70

3c. Evaluation and Domain of Applicability of HTTK/RTK ................................................... 73

3d. Limitations of Approach ..................................................................................................... 84

3e. Conclusions......................................................................................................................... 86

4. Ongoing Research..................................................................................................................... 90

4a. Third Generation SEEM Analysis ...................................................................................... 90

4ai. Introduction.................................................................................................................... 90

4aii. New Data for Forward Prediction................................................................................. 91

4aiii. SHEDS-HT................................................................................................................ 104

4aiv. Conclusions................................................................................................................ 112

4b. Ecological Exposure ......................................................................................................... 113

4bi. Overview...................................................................................................................... 113

4bi. Limitations of Approach.............................................................................................. 121

4c. New Data for HTTK ......................................................................................................... 124

5. Future Applications................................................................................................................. 126

EDSP Integrated Activity and Exposure Based Prioritization and Screening........................ 126

TSCA – Prioritization and Assessment................................................................................... 127

Acknowledgements..................................................................................................................... 129

Supplemental Material................................................................................................................ 129
New High-throughput Methods to Estimate Chemical Exposure 3 7/8/2014

References................................................................................................................................... 130

List of Acronyms and Abbreviations

ACToR: EPA’s Aggregated Computational Toxicology 

Repository

AER: RTK bio-Active dose to Exposure dose Ratio

AIC: Akaike Information Criterion

AOP: Adverse Outcome Pathway

BASS: EPA’s Bioaccumulation and Aquatic System 

Simulator

BCF: BioConcentration Factor

BW: Body weight

CAS-RN: Chemical Abstracts Service Registry Number 

(chemical identification number)

CCL 3: the third Contaminant Candidate List

CDC: the United States Centers for Disease Control

CDR: EPA Chemical Data Reporting (formerly IUR)

CPCat: Chemical-Product Categorization database

CPCPdb: Consumer Product Chemical Profiles database

CPRI: Crop Protection Research Institute

Css: plasma Concentration after Steady-State is reached due

to repeated exposures

CTCP: Clinical Toxicology of Commercial Products

DSSTox: Distributed Structure-Searchable Toxicity 

Database

EBC: Expected Biological Concentrations 

EDSP: the Endocrine Disrupter Screening Program

EDSTAC: Endocrine Disruptors Screening and Testing 

Advisory Committee

EEC: Expected Environmental Concentration

E-FAST2: Exposure and Fate Assessment Screening Tool 

v2.0

EPA: the United States Environmental Protection Agency

EPI Suite: EPA’s Estimation Program Interface Suite

ExpoCast: EPA’s Exposure foreCast prioritization research 

program

EXAMS: EPA’s Exposure Analysis Modeling System

FDA: the United States Food and Drug Administration

FFDCA: Federal Food, Drug, and Cosmetic Act

FIFRA: Federal Insecticide, Fungicide, and Rodenticide 

Act

fup: fraction of chemical that is unbound in plasma when in 

the presence of endogenous levels of plasma 

protein

HEDS: EPA’s Human Exposure Data System

HPDB: Household Product Database

HPV: High Production Volume

HT: High-throughput

HTS: High-throughput screening

HUC: Hydrologic Unit Code 

IEMS: Integrated Exposure Modeling System 

IUR: EPA’s Inventory Update Reporting and Chemical 

Data Reporting list (now CDR)

IVIVE: in vitro-in vivo extrapolation

KABAM: EPA’s Kow (based) Aquatic BioAccumulation 

Model

KOW: Octanol-Water partition coefficient (also called 

lipophilicity or hydrophobicity)

LOD: Limit of detection

MC: Monte Carlo simulation

MCMC: Markov Chain Monte Carlo simulation (for 

Bayesian analysis)

MSDS: Material Safety Data Sheet

MySQL An open-source relational database management 

system that can be interfaced via a structure 

query language or “SQL

NCGC: the NIH National Chemical Genomics Center

NHANES: National Health and Nutrition Examination 

Survey

NIH: the United States National Institutes of Health

NIOSH: the CDC National Institute for Occupational 

Safety and Health

NLM: the United States National Library of Medicine

NPDWR: National Primary Drinking Water Regulation

NRC: National Research Council

NTP: the NIH National Toxicology Program

OPPT: EPA Office of Pollution Prevention and Toxics

ORD: EPA Office of Research and Development

OSHA: the United States Occupational Safety and Health 

Administration

PBTK: Physiologically-Based Toxicokinetics

PCA: Principal Components Analysis

PDP: the USDA Pesticide Data Program

PRZM: EPA’s Pesticide Root Zone Model

PWS: Public Water System

QSAR: Quantitative Structure Activity Relationship

RAIDAR: Risk Assessment IDentification, And Ranking 

model

RED: Rapid Equilibrium Dialysis

RTK: Reverse Toxicokinetics

SAP: Scientific Advisory Panel

SDWA: Safe Drinking Water Act

SEEM: Systematic Empirical Evaluation of Models 

framework

SMILES: Simplified Molecular Input Line Entry System 

TK: Toxicokinetic

ToxCast: EPA’s Toxicity foreCast prioritization research 

program

Tox21: Toxicology in the 21st Century – the 

NTP/NCGC/EPA/FDA consortium for chemical 

hazard HTS

TSCA: the Toxic Substance Control Act

USDA: the United States Department of Agriculture

USGS: the United States Geological Service

USEtox: United Nations Environment Program and Society 

for Environmental Toxicology and Chemistry 

toxicity model
New High-throughput Methods to Estimate Chemical Exposure 4 7/8/2014

Preface

The risk posed by any of the thousands of untested man-made chemicals in our environment is a 

function of both the hazard presented by the chemical and the extent of exposure. However, 

relatively few chemicals have estimates of exposure intake or toxicokinetics (TK), limiting the 

understanding of risks. For the thousands of chemicals with minimal information, we believe that 

mathematical models will allow basic prioritization of these chemicals for further research.

The complexity of mathematical models for regulatory science must match the available data and 

application in order for their assumptions and conclusions to be defensible. In other words, the 

models must be fit for purpose. If the current resources and tools permit in depth study of only a 

handful of chemicals annually, then new tools requiring fewer resources must be developed to 

prioritize the thousands of untested chemicals in order to identify those chemicals most likely to 

be in need of investigation with more traditional methods. One such new tool for high throughput 

exposure (HTE) prediction is the focus of the science to be reviewed by this Scientific Advisory 

Panel (SAP).

Here we consider two distinct but related types of mathematical modeling: mechanistic and 

empirical modeling. Mechanistic models are hypotheses about the actual mechanisms that have 

been characterized by a measured process. For example, a mechanistic model might describe the 

flow of a chemical through the environment until its concentration is measured in environmental 

media or biomonitoring samples. Empirical models are statistically-derived relationships within

measured data. When constructed from first principles, mechanistic models do not necessarily 

have to be empirical models, for example when considering the fundamental physics of subatomic particles within an atom. However, an engineer intending to build a bridge does not 

model the vast numbers of atoms within each piece of steel, rather he or she uses empiricallydetermined constants for strength and flexibility of each steel bridge element in order to 

mechanistically model how the bridge will distribute weight. Similarly, mechanistic models used 

by the EPA are intended to model specific exposure pathways as characterized using 

empirically-measured parameters such as chemical emissivity from consumer products and 

articles.

Complex systems science makes it clear that many real world systems are particularly sensitive 

to certain perturbations. Under the right circumstances, the impact of these seemingly small 

differences can grow exponentially until two things that at first seemed very similar are now 

widely divergent. This is why engineers include large uncertainty factors when building bridges, 

and it is why we must endeavor to empirically evaluate the predictive ability of any models we 

use. However, we prefer to use empirically-evaluated mechanistic models rather than purely 

empirical models because mechanistic models allow some estimation of the applicability of a 

model to new situations. If we can show that mechanistic assumptions are predictive and we 

believe that those same assumptions are applicable to other cases, then we can use an 

empirically-calibrated mechanistic model for extrapolation. Here we report on early results of 

applying a Systematic Empirical Evaluation of Models (SEEM) framework to extrapolate the 

predictions and uncertainty of mechanistic and empirical model predictions for exposure and TK 

to thousands of chemicals with no existing estimates.
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In the context of HTE modeling we expect to have only minimal data to differentiate one 

chemical from the next. Just as we must carefully examine any model, we must closely examine

the data that we use. For high throughput data we must accept the possibility of imperfections in 

the input data and so it is critical that we analyze the performance of that chemical-specific 

information to predict exposure before we use it as a basis for prioritization. In the second 

generation SEEM analysis, we report that some of the basic chemical-specific information that 

we can obtain for thousands of chemicals does correlate with exposure in a meaningful manner.

As a general approach, model predictions of exposure and TK are calculated for all chemicals 

using the same degree (usually minimal) of chemical information that is available for all 

chemicals. Model uncertainty is estimated using biomonitoring and ecological monitoring 

information that is available for a subset of chemicals. Assuming the subset of chemicals with 

monitoring data is representative, the model predictions and associated uncertainty is then 

applied to all chemicals

Most of the analyses reported here were conducted within a Bayesian statistical framework. 

Bayesian statistics is a rigorous statistical methodology that considers all likely explanations for 

data, rather than focusing on determining just the single most likely explanation. Bayesian 

statistics involves the proper mathematical combination of prior expectations and newly obtained 

data in order to obtain a new distribution of expected values. For instance, a Bayesian statistician 

who expects a coin is fair would be unsurprised if, after ten flips, six heads were obtained to four 

tails. This is statistically almost as likely as getting a five heads (or six tails) from a fair coin. If, 

after ten thousand tosses, roughly six thousand had been heads, a Bayesian statistician would 

then say that the data had overcome the prior assumption and that the coin appears to slightly

favor heads. Similarly if, after only ten tosses, heads was obtained ten times, the Bayesian might 

again say that the data outweighed the prior assumption of a fair coin because that event was 

already so unlikely for a fair coin.

A balance must be struck between model complexity, availability of data, and acceptable levels 

of uncertainty. Here, we use Bayesian methods to estimate both unknown model parameters and 

to estimate the predictive ability of these models. We have to estimate many unknown 

parameters because the exposure events we want to predict can only be inferred from biological 

or ecological monitoring data. The prior assumptions used in the analysis are conservative in that 

we do not assume that the models predict the data. Only when the data overwhelms our prior 

assumptions do we state that we believe that the models have predictive ability. For all of the 

models reported here, we find that the data demonstrates that the models have some predictive 

ability, albeit with large uncertainty on those predictions. We find that this uncertainty still 

allows meaningful exposure-based chemical prioritization.

Bayesian statistics are combined with model evaluation tools to try to develop a parsimonious 

model – the simplest predictive explanation is considered to be the best. The error in the models 

predictions is assumed to be due to random processes. By examining the distribution of errors in 

our model predictions we can determine whether they appear random, building confidence that 

our models are more appropriate for extrapolation to other circumstances; that is, they do not just 

describe the noise in a particular data set.
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In addition to exposure, similar approaches are applied to in vitro methods to assess TK. TK 

describes how, after receiving a dose of chemical, the amount of that chemical in different parts 

of the body changes with time due to blood flow, metabolism, and excretion. In vitro high 

throughput TK (HTTK) allows high throughput bioactivity data to be converted into putative 

hazardous doses that can be compared with high throughput exposure predictions to support

prioritization and initial screening for the thousands of chemicals with no other information.

HTTK methods were originally intended as a preliminary step for clinical trials where it was 

presumed that collection of TK data from actual test subjects would eventually provide more 

definitive information. Because we cannot collect human in vivo TK data for the entire 

Endocrine Disruptors Screening Program (EDSP) universe of chemicals, we must evaluate the 

predictive ability of HTTK methods more rigorously. We find that HTTK predictions for some 

quantities that characterize tissue dose do seem to correlate with in vivo animal measurements. 

We further find that we can predict some chemicals for which HTTK appear to work best.

Model evaluation combined with Bayesian methods provide the tools for evaluating the 

predictions of high throughput models parameterized with minimal data sources. We can 

characterize the expected uncertainty in these predictions, allowing exposure-based 

prioritization. HTE prediction and HTTK, when taken together with in vitro bioactivity profiling 

as a surrogate for hazard, will allow for rapid prioritization and initial screening of chemicals in 

the EDSP universe and beyond.
New High-throughput Methods to Estimate Chemical Exposure 7 7/8/2014

1. Introduction

While many recent advances have been made by the EPA in high-throughput bioactivity 

profiling, concurrent advances in rapid, quantitative prediction of human and ecological 

exposures have been lacking, despite the importance of both measures for an integrated approach 

to the prioritizing and screening of chemicals. A recent report by the National Research Council 

of the National Academies, “Exposure Science in the 21st Century: a Vision and a Strategy” [1], 

described a number of examples of both chemical toxicity and risk evaluation in critical need of 

quantitative exposure predictions. These examples included the screening and prioritization of 

chemicals for targeted toxicity testing, focused exposure assessments or monitoring studies, and 

quantification of population vulnerability. This SAP will review new approaches to rapidly 

estimate potential chemical exposures and toxicokinetics as tools for the US EPA Endocrine 

Disruptor Screening Program (EDSP) universe of chemicals. These approaches include: (1) a 

new framework for evaluating and extrapolating high-throughput exposure (HTE) predictions for 

thousands of chemicals with limited exposure information; (2) high-throughput toxicokinetic 

(HTTK) methods to relate in vitro bioactivity to human exposures; and (3) ongoing research to 

refine and expand the applicability of these methods, including to ecological endpoints.

The EPA developed the Endocrine Disruptor Screening Program (EDSP) in response to the 

statutory mandate in the Federal Food, Drug, and Cosmetic Act (FFDCA) to "develop a 

screening program...to determine whether certain substances may have an effect in humans that 

is similar to an effect produced by a naturally occurring estrogen, or such other endocrine effects 

as the Administrator may designate." As part of the EDSP, the statute also provides the EPA 

with authority to "provide for the testing of any other substance that may have an effect that is 

cumulative to an effect of a pesticide chemical if the Administrator determines that a substantial 

population may be exposed to such a substance." In addition to FFDCA, the Safe Drinking Water 

Act (SDWA) provides the EPA with authority to provide for testing "of any other substances that 

may be found in sources of drinking water if the Administrator determines that a substantial 

population may be exposed to such substance." Beyond testing and determining endocrine 

effects, FFDCA also authorizes the EPA to take action: "In the case of any substance that is 

found...to have an endocrine effect...the Administrator shall, as appropriate, take action under 

such statutory authority as is available to the Administrator...to ensure the protection of public 

health."

To begin meeting this statutory mandate, the EPA in 1996 chartered a Federal Advisory 

Committee to address endocrine disruption: the Endocrine Disruptor Screening and Testing 

Advisory Committee (EDSTAC). The EPA largely adopted the EDSTAC recommendations, and 

in an August 1998 Federal Register notice established the EDSP. In its final report, EDSTAC 

made several key recommendations to:

• Address both potential human and ecological effects from chemical exposures

• Examine effects of these chemicals on estrogen, androgen and thyroid hormone-related 

processes

• Include pesticide and non-pesticide chemicals, contaminants, and (after evaluating single 

chemicals) mixtures
New High-throughput Methods to Estimate Chemical Exposure 8 7/8/2014

• Develop a two-tiered screening and testing strategy, now known as the Endocrine 

Disruptor Screening Program

The consideration of chemical exposure has been an integral component of the EDSP from its 

beginning. In 1998, the EDSTAC recommended four types of exposure-related information to be 

used as the foundation for priority setting in the EDSP: “(a) biological sampling data for humans 

and other biota; (b) environmental, occupational, consumer product, and food-related data; (c) 

data on environmental releases; and (d) data on production volume.” EDSTAC viewed these 

categories as a “hierarchy or spectrum in an exposure chain” ranging from actual detection in 

human and animal tissues to production volumes that indicate only potential for release and 

exposure. 

To identify the first chemicals to undergo screening in the EDSP (known as List 1), EPA 

considered public comments and the recommendations from a joint SAP – Science Advisory 

Board (SAB) consultation on the EDSTAC final report. Based on these recommendations, EPA 

selected between 50 and 100 chemicals, drawn from pesticide active ingredients and pesticide 

inert ingredients that are also considered high production volume chemicals (HPV). The number 

of chemicals (50 to 100) was thought to be a reasonable undertaking to allow the nascent EDSP 

to gain experience with the test systems before proceeding through the larger universe of 

chemicals. To identify the specific chemicals that would comprise List 1, EPA generally applied 

the EDSTAC exposure recommendations, focusing on four pathways of exposure to pesticide 

active ingredients: (1) consumption of food containing pesticide residues (food pathway); (2) 

consumption of drinking water containing pesticide residues (water pathway); (3) residential use 

of pesticide products (residential use pathway); and/or (4) occupational contact with pesticide–

treated surfaces (occupational exposure pathway). For HPV pesticide inert ingredients, the EPA 

focused on: (1) human biological samples (human biological monitoring pathway); (2) 

ecological tissues that have human food uses (ecological biological pathway); (3) drinking water 

(drinking water pathway); and/or (4) indoor air (indoor air pathway). Based on these exposure 

considerations and public review, the EPA issued a list of 67 pesticide chemicals (58 active 

ingredients and 9 HPV inert ingredients) for EDSP screening in 2009, referred to as List 1.

In developing the second list of chemicals for EDSP screening (List 2), EPA focused on 

continuing to address pesticide chemicals and beginning to address drinking water contaminants. 

Pesticide chemicals on List 2 represent pesticides that were scheduled for Registration Review 

during fiscal years 2007 and 2008. EPA identified drinking water contaminants for EDSP List 2 

from chemicals that were regulated under the national primary drinking water regulation 

(NPDWR) (40 CFR part 141) or were unregulated contaminants that are listed on the third 

Contaminant Candidate List (CCL 3) [2]. Based on these considerations, the final EDSP List 2 

consists of 109 chemicals (41 pesticide active ingredients and 68 SDWA chemicals).

In November 2012, EPA published the document entitled “Endocrine Disruptor Screening 

Program Universe of Chemicals and General Validation Principles” identifying a universe of 

approximately 10,000 chemicals for EDSP prioritization and screening [3]. This estimate of the 

universe of chemicals for consideration under EDSP is based on the statutory authorities of the 

Federal Food, Drug, and Cosmetic Act (FFDCA) and SDWA. As portrayed in Figure 1, the 

historical approach to listing chemicals based primarily on exposure considerations has only 
New High-throughput Methods to Estimate Chemical Exposure 9 7/8/2014

addressed a small fraction of the universe for screening. Even for the 176 chemicals on Lists 1 

and 2, EPA has been careful to note that the public should not presume EPA currently suspects 

that such chemicals interfere with the endocrine systems of humans or other species. Other 

chemicals could have higher priority for EDSP screening and testing if hazard considerations are 

joined with exposure considerations in an integrated approach to prioritization and screening. 

Such an approach is consistent with the 1998 EDSTAC recommendation: “Data resulting from 

[High Through Put Screening] HTPS will be combined with exposure-related information, and 

with any other effects-related information that is available, for each chemical for the purpose of 

setting priorities for [Tier 1 Screening] T1S.”

EPA’s EDSP is actively pursuing the application of computational toxicology and exposure 

methods to create a more efficient and robust screening program. This is in reflection of recent 

advances in computational toxicology and exposure methods that are heralding an accelerated 

pace for chemical screening, allowing simultaneous testing of thousands of compounds in highthroughput methods. The EDSP is collaborating with EPA's Office of Research and 

Development, and our federal Tox21 partners at National Institute of Health and the Food and 

Drug Administration, to bring these new, cutting-edge, computational exposure and risk based 

prioritization methods into the program. This FIFRA SAP review is a continuation of a series of 

reviews focused on the application of these high throughput tools for prioritization and screening 

of the universe of EDSP chemicals.

Figure 1: Only a small fraction of the 10,000 chemical EDSP universe has been addressed

to date.

10000

67

109

176

EDSP Universe List 1 List 2
New High-throughput Methods to Estimate Chemical Exposure 10 7/8/2014

ExpoCast is an EPA Office of Research and Development (ORD) initiative to develop the 

necessary approaches and tools for rapidly prioritizing and screening thousands of chemicals 

based on the potential for human and ecological exposure [4]. This focus for ExpoCast is distinct 

from many existing exposure tools that support either screening level assessments on a per 

chemical basis or full regulatory risk assessment. These traditional exposure tools are lower 

throughput, requiring considerable data to make predictions of sufficient precision for a full risk 

assessment [5]. ExpoCast efforts have focused on empirically assessing the uncertainty in HTE 

predictions by evaluation and calibration with available monitoring (e.g., biomonitoring or water 

concentration) data. By using rigorous statistical techniques to reconcile HTE estimates based on 

the limited data that is available, the uncertainty in the HTE predictions are quantified. We refer 

to this framework as SEEM. We find that in some cases, even highly uncertain predictions may 

be useful for chemical prioritization [6]. This SAP will review the use of the SEEM framework 

for HTE predictions to rapidly estimate potential chemical exposures for prioritization of the 

EDSP universe of chemicals. The focus will be on estimating the upper confidence limits of 

human chemical exposure values [6]. 

In addition to prediction of exposure intake dose (e.g., absorbed dose in mg/kg bodyweight 

(BW)/day), this SAP will review HTTK methods to extrapolate from in vitro concentration data 

to an in vivo dose (in vitro to in vivo extrapolation or IVIVE) for chemicals that have been run 

through a battery of high-throughput endocrine screening assays (e.g., ToxCast; not to be 

confused with the EDSP Tier 1 screening battery) [7]. HTTK provides a bridge between 

bioactivity in the high-throughput screening (HTS) assays and exposure by either predicting 

tissue concentrations due to exposure or inferring administered doses that would be needed to 

cause tissue bioactive concentrations in vivo (i.e., reverse toxicokinetics or RTK) [8]. 

For human health risk prioritization, RTK can be used to estimate the daily administered dose 

(mg/kg BW/day) necessary to produce steady-state in vivo blood concentrations equivalent to 

concentrations showing biological activity in the in vitro HTS assays (μM) [8]. The putative 

bioactive doses can then be directly compared with predicted exposures (mg/kg BW/day). 

Without the time and resources necessary to generate in vivo toxicokinetic data for the thousands 

of chemicals relevant to the EDSP, HTTK using in vitro assays coupled with IVIVE modeling 

can serve as a useful surrogate. HTTK methods were developed for pharmaceutical compounds 

to estimate therapeutic doses for clinical use. HTTK approaches have been effective for 

pharmaceutical compounds and predicted concentrations are typically on the order of the 

measured in vivo concentrations [9]. For non-therapeutic compounds, human TK data is typically 

not available, and as such, it is essential to carefully characterize the predictive ability of these 

models and define the domain of applicability. A systematic evaluation of the appropriateness of 

HTTK for environmental compounds is presented in this paper.

This SAP will also review how HTE methods can be applied to ecological endpoints. The HTE 

models used for human exposure include environmental fate and transport components, such as

prediction of concentrations of chemicals in the aquatic environment. If a sufficient data set of 

environmental chemical concentrations from water monitoring can be developed (e.g., United 

States Geological Service and EPA data), the SEEM framework could be used to predict 

concentrations of EDSP chemicals in the aquatic environment. Additionally, there are existing 

data and predictive models for relating tissue concentrations in aquatic biota (fish) to their 
New High-throughput Methods to Estimate Chemical Exposure 11 7/8/2014

immediate environment (e.g., water), with the ratio of these two values being the 

Bioconcentration Factor (BCF) [10]. Just as HTTK relates exposure intake doses to plasma 

concentrations for humans, the BCF relates the environmental water concentration to a wholebody concentration for the fish. If an in vitro concentration is identified as bioactive, the BCF 

allows the prediction of the water concentration needed to produce that bioactive concentration

in a fish.

HTE prediction and HTTK allow for an exposure-based, rapid prioritization of chemicals in the 

EDSP universe and beyond.
New High-throughput Methods to Estimate Chemical Exposure 12 7/8/2014

2. Exposure Prioritization with ExpoCast Research 

2a. Introduction

There are currently more than 10,000 chemicals in wide commercial use and 700 to 1000 

chemicals entering commerce every year [11-15]. For the majority of these chemicals in 

commerce, the exposure data necessary for prioritization are lacking [1, 8, 16]. In order to 

provide the necessary exposure science for these thousands of chemicals, the EPA and National 

Academies have developed a long-range vision for exposure science in the 21st century, and a 

strategy for implementing this vision over the next twenty years [1, 17-19]. Furthermore, there is 

a need to assess potential exposure to chemicals before approval for commercial production [12, 

15, 16]. To meet these needs, efficient exposure screening methods should be amenable to rapid 

implementation, be based on basic chemical properties, and provide quantitative estimates for 

hundreds to thousands of chemicals [5, 6]. The EPA’s ExpoCast initiative aims to provide these 

capabilities [4].

A key development within ExpoCast is the SEEM framework, which we have applied to 

exposure prioritization. In order to prioritize thousands of chemicals for the EDSP, we must use

limited data and computational models based upon broad assumptions while also carefully 

characterizing the uncertainty in our approach. These important components are provided by the 

SEEM framework illustrated in Figure 2. SEEM is designed to integrate the predictions of 

multiple models and empirically evaluate model performance systematically, over as many 

chemicals as possible [20, 21]. SEEM makes predictions of human exposure and weights

contributing HTE model components appropriately, all with an appropriate characterization of 

uncertainty. The SEEM framework is intended to be sufficiently flexible to make use of all 

extant models capable of high-throughput exposure prediction [5], and incorporate new models 

as they become available. Within this framework, new and revised models can be evaluated for 

improved predictive ability, and ability to decrease uncertainty, while new data can be 

incorporated to better characterize model performance.

Through the SEEM framework, we have identified a method to evaluate and develop predictive

tools for human exposures. We believe that this method meets the mandate of the National 

Research Council report for an objective, standardized, and transparent approach to HTE

modeling [1]. The value of future exposure prioritization work can now be quantitatively 

demonstrated by reducing the large uncertainties currently associated with predicting human 

exposure to environmental chemicals.

Wambaugh et al. ([6]) demonstrated the application of the SEEM framework for rapid,

exposure-based prioritization of chemicals using minimal information that estimates uncertainty 

using monitoring data. As described in the section “First Generation SEEM Analysis,” the results 

were primarily appropriate for identifying areas of future research vital to providing sufficient 

HTE assessment.
New High-throughput Methods to Estimate Chemical Exposure 13 7/8/2014

Figure 2: Illustration of the SEEM framework applied to high-throughput exposure 

modeling. Exposures inferred from biomonitoring data (y-axis) are jointly regressed on 

predicted exposures for those chemicals using multiple HTE models (x-axis) to evaluate the 

predictive ability of the models. The regression weights allow evaluation of each exposure 

models (a weight of zero would indicate no predictive ability). The regression also serves as 

a calibration and estimation of uncertainty for extrapolating to other chemicals.
New High-throughput Methods to Estimate Chemical Exposure 14 7/8/2014

Characterizing exposure for large numbers of chemicals requires appropriate estimates of 

uncertainty and variability. In order to evaluate the HTE predictions, “actual exposure” must be 

determined, for instance via monitoring environmental chemicals in the immediate vicinity of a 

population and identifying biomarkers of exposure. Collecting such data is expensive and labor 

intensive [22, 23]. Figure 3 illustrates a portion human and ecological chemical exposure space 

that we have considered. Within this space, there are multiple exposure opportunities for both 

human and ecological receptors via different media. An exposure pathway includes interaction 

with one or more media and one receptor. For human exposure, the pathways that can be 

identified given the available biomonitoring data are primarily limited to residential, nonoccupational environments. Within this environment, two broad classes of exposure are 

identified: near-field (i.e., indoor, proximate sources) and far-field (e.g., dietary, air, and drinking 

water) sources. Near-field exposure is further subdivided into two categories: (1) direct exposure 

to chemicals in consumer products during use (including personal care products and cleaning 

products); and (2) indirect residential exposures (including inhalation of contaminated air and 

contact with contaminated surfaces) to chemicals intentionally applied by individuals in the 

home, or emitted from consumer articles or building materials. Far-field pathways (including the 

dietary pathway) consist of industrial, agricultural, or other chemicals not used by the exposed 

individuals, but that may be present in their environments (e.g., ambient air, food, and water due 

to chemical use, release, and transport in the physical environment). 

The types of data critical to any characterization or evaluation of exposure are also represented in

Figure 3. Unfortunately, the actual exposure events are both difficult to monitor and confounded 

by the inherent complexities of human behavior. However, we can obtain data that could be used 

to characterize potential sources (i.e., upstream) of exposure such as composition of consumer 

products, characterization of environmental releases, or monitoring data on media 

concentrations. We can also obtain exposure indicator data (i.e., downstream), particularly 

biomarkers of exposure (e.g., chemical concentrations in urine or plasma). Since the exposure 

event itself is difficult to observe, we develop models to either estimate exposure from upstream 

sources (forward modeling), or infer exposure from downstream sources (reverse modeling). 

Both forward and reverse modeling have drawbacks, and recent advances have been made to 

compare the results of both approaches to establish model effectiveness – that is, predictive 

ability of models for chemicals covered by biomonitoring and ecological monitoring data [6].
New High-throughput Methods to Estimate Chemical Exposure 15 7/8/2014

Figure 3: A graphical representation of human and ecological exposure space in relation to exposure pathways, receptors, and 

monitoring data.
New High-throughput Methods to Estimate Chemical Exposure 16 7/8/2014

One program that contributes to monitoring of environmental chemicals is the National Health 

and Nutrition Examination Survey (NHANES). NHANES is designed to assess the health and 

nutritional status of adults and children in the U.S. (http://www.cdc.goc/nchs/nhanes.htm). As 

part of NHANES, the CDC conducts ongoing assessments of collected human samples (e.g., 

blood, urine) in order to quantify the level of chemical compounds present in the average U.S. 

resident, and regularly presents findings from these studies in The National Report on Human 

Exposure to Environmental Chemicals [24]. NHANES covers a few hundred of the thousands of 

environmental chemicals and potential metabolites for which data are needed [25].

The NHANES data reflect aggregate human exposures across all pathways for the general 

population. Thus, while the forward prediction models may only partially cover the exposure 

pathways in play, the estimated uncertainty will reflect all exposures that resulted in the chemical 

being present in a biomonitoring sample. The HTE model calibration performed with SEEM 

framework is designed to account for pathways not currently covered by the HTE models. 

However, the impact of exposure routes not reflected by NHANES monitoring, including 

occupational exposure, cannot be assessed in this manner. 

In the section “Second Generation SEEM Analysis” we describe several HTE descriptors that 

can be obtained for tens of thousands of chemicals and appear to explain a significant portion of 

the chemical-to-chemical variance in exposures indicated by NHANES. We find five factors that 

are indicators of whether biomarkers of a chemical are likely or unlikely to be present in 

NHANES biomonitoring samples. We further show that the same five factors explain a similar 

portion of the variance among various demographics characterized by NHANES, including 

women of child-bearing age and children aged 6-11. We use this model to prioritize 7785 

chemicals with respect to the upper 95% predicted exposure in (mg/kg BW/day).

If we have HTE models, data to parameterize those models in order to make predictions, and 

data to evaluate the predictions for at least a subset of the chemicals, then we can apply the 

SEEM framework to estimate human exposure for thousands of chemicals.
New High-throughput Methods to Estimate Chemical Exposure 17 7/8/2014

2b. Data and Models for Forward Prediction of Human Exposure

Available Data

The assessment of available data for the forward prediction of exposure can best be considered in 

the context of “exposure space”. Whereas the identification of key regions of the analogous 

“chemical space” (e.g., molecular mass, octanol-water partition coefficient) has been 

instrumental to drug discovery [26], key aspects of “exposure space” may be particularly 

relevant to exposure prediction. These key aspects include physicochemical properties, product 

characteristics, emissions characteristics, and exposure pathways.

Physicochemical Properties: Inherent properties of chemicals drive potential for human and 

ecological exposure in the context of a given system. Chemical substances have been classified 

with respect to exposure potential (independently of quantity released to the environment)

according to their persistence, bioaccumulation, and potential for long-range atmospheric 

transport [27]. Persistence, the propensity to remain in the environment for long periods of time, 

and bioaccumulation, accumulation in primary aquatic living organisms via the food chain, are 

often key factors used in conducting exposure-based prioritizations [28] and are currently used 

by EPA to evaluate new chemicals before permitting their entry into the marketplace [29].

While persistence and bioaccumulation can be determined experimentally in the laboratory under 

highly controlled conditions, they are more often estimated computationally using a relatively 

small number of physicochemical properties. However, model selection and the assumptions 

chosen in their application may result in vastly different rankings of human exposure potential 

[30, 31]. Chemical characteristics of persistent organic pollutants are well established; namely, 

low water solubility, high lipid solubility, semi-volatility, and high molecular mass. Persistence 

and bioaccumulation are useful for assessing exposure via contact with the surrounding natural 

outdoor environment and chronic exposure through certain types of food (e.g., certain species of 

fish), but estimates of persistence in the outdoor environmental may provide little information on 

exposure in the indoor environment. To date, the effort applied toward the identification of 

physical and chemical properties that most directly influence indoor persistence has been 

modest. Weschler and Nazaroff [32] identified molecular weight, melting point, saturation vapor 

pressure, Henry’s law constant, water solubility, octanol–water partition coefficient, and 

octanol–air partition coefficient as directly influencing the partitioning and transport of semivolatile organic compounds in indoor environments. These properties and others are integrated 

into the fugacity-based, indoor source-to-concentration module of EPA’s new SHEDS-HT model 

(described later in Section 4, Ongoing Research).

The models analyzed here were, in part, parameterized using EPA’s Estimation Program Interface 

(EPI) Suite (4.11, November, 2012, http://www.epa.gov/opptintr/exposure/pubs/episuite.htm). EPI 

Suite quantitative structure activity relationships (QSARs) were used to estimate physicochemical properties if Simplified Molecular Input Line Entry System (SMILES) descriptions 

[33] of chemical structure were available and the QSARs did not fail for that structure. In 

addition to QSAR model estimates, EPI Suite contains a database of experimentally obtained 

physico-chemical properties that were used in place of estimated values when available.
New High-throughput Methods to Estimate Chemical Exposure 18 7/8/2014

Of the 2127 chemicals considered for the first generation SEEM analysis, the physical-chemical 

data required for model parameterization could be found or calculated for only 1950 of these 

chemicals, leaving 167 without complete sets of model parameters. Experimental data were used 

for only six of the model parameters and only 4.8% of the chemicals had data for all six. While 

50.7% of the chemicals had measured KOW and 54.3% had measured water solubility, half-lives 

were determined only from QSARs calibrated to the results of a 17 member expert panel that 

categorized 200 chemicals into semi-quantitative time categories (hours, days, weeks, months, 

and longer than months) [34].

The predictions of EPI Suite QSARs for different properties can depend both on the same 

structural features of compounds and the predictions of other EPI Suite QSARs (e.g., KOW); thus, 

the predictions tend to be highly correlated. Principal components analysis (PCA) is a 

mathematical technique that attempts to reduce a large number of correlated variables into a 

smaller number of uncorrelated variables (the “principal components”). The principal 

components are then ordered such that they explain a decreasing amount of the variance; i.e. the 

first principal component describes the factors that most distinguish the set, the second principal 

component explains less variance than the first, and so on. For the model parameters describing 

the 1950 chemicals, the half-lives in environmental media and the BCF most distinguished one 

compound from another (Figure 4). 

Product Use Characteristics: The general population is typically exposed to chemicals indirectly 

through the environment or directly through use and contact with consumer products. As such, 

potential for human exposure to a given chemical is a function of the chemical/product lifecycle. 

Since the exposure pathways for the majority of chemicals are not monitored, environmental 

concentration and use data is sporadic. Thus, production volume is often relied on as a primary 

surrogate for potential exposure. Though many legacy environmental contaminants were high 

production volume chemicals, the assumption that human exposure is strongly correlated with 

production volume has been questioned [35]. 

Direct exposure to chemicals in consumer products typically exceeds environmental exposure,

[36], but is likely to be less than that experienced in occupational environments. Health Canada’s 

legislatively mandated Domestic Substance List categorization exercise highlighted the 

importance of direct consumer exposure relative to indirect environmental exposure. 

Specifically, use category was identified as an important surrogate for exposure-based 

prioritization, with use profiling proving far more influential than either production volume or 

persistence and bioaccumulation in determining exposure rankings [28]. At a more fundamental 

level, inhalation exposures to chemicals in products are commonly thought to be a function of 

the vapor pressure of the chemical and the characteristics and use scenarios associated with a 

given product. The SHEDS-HT model (described later in Section 4) can produce aggregate 

estimated chemical exposures from both near-field direct and near-field indirect sources based on 

a library of exposure scenarios. The near-field direct scenarios reflect exposure during the use of 

a consumer product, whereas indirect exposures result from incidental or accidental contact after 

the original usage event (post-application). Since information on product composition and 

consumer product use is required as inputs to these models, there is a pressing need for product 

formulation and product use information. Our progress in developing such formulation and usage 
New High-throughput Methods to Estimate Chemical Exposure 19 7/8/2014

information is described in detail in Section 4 (under New Data for Forward Prediction).

Figure 4: Principal components analysis of the physico-chemical properties for the 

chemicals evaluated for the first generation SEEM. Principal component one was 

dominated by half-life in soil, water, and sediment, Principal component two was largely 

comprised of the bioconcentration factor. Principal component three was a mix of the 

molecular weight and the water solubility. The compounds indicated with larger (red) 

spheres are those for which NHANES data were available. Figure from Wambaugh et al.

([6])

Principal 

Component 2

Principal 

Component 1

Principal 

Component 3
New High-throughput Methods to Estimate Chemical Exposure 20 7/8/2014

Chemical Emissions Characteristics: Emission of the chemical into the system of interest (e.g., 

the residential environment) is necessary to bridge information on use classification with 

potential for human exposure. Emission data are required at the relevant scale for exposure 

analysis. Either a compound is released far-field from the receptor individual/population and 

information on environmental fate and transport is required, or the compound is released nearfield and information on the fate and transport in the immediate (usually indoor) environment is 

required. In either case, data on the mass of chemical emitted, media into which the chemical is 

emitted, and the media from which the chemical is available for contact are required [37]. As 

described in more detail later in this Section, production volume can be used as a gross measure 

of the amount of compound released into the environment for prioritization based on exposure 

from far-field sources. Recently published methods [38] hold the promise of rapid estimation of 

emissions of semi-volatile organic compounds in the indoor environment.

Exposure Routes and Pathways: Data on chemical levels in environmental media (e.g., air, dust, 

water, food) combined with information on the movement of chemical among various media 

provide critical information for estimating potential human exposure. While data on chemical 

levels in biological media (e.g., blood, urine, fingernails) provide a direct indication of human 

exposure, data on levels in environmental media (e.g., air, water, soil) and in commercial and 

industrial products can serve to identify the relative contribution of different exposure pathways.

Exposure Data Resources: EPA’s Aggregated Computational Toxicology Resource (ACToR) 

contains publicly available data on chemicals in the environment, including information on

hazard, exposure, risk assessment, regulations, and production [39]. Only about one fifth of all 

chemicals with hazard information in ACToR have exposure information. All exposure-related 

data in ACToR have been organized into hierarchical categories, with the relationship among the 

taxonomy categories displayed in the network diagram in Figure 5. All publicly available 

exposure-related data for chemicals listed by Chemical Abstracts Service Registry Number 

(CAS-RN) are brought into the system, but no proprietary data are included as this information is 

protected under law. The types of exposure data available can be classified into the following 

categories: (a) production/import volumes and processes; (b) consumer product ingredients and 

usage; (c) environmental release; (d) environmental media concentrations; and (e) human 

exposure/biological monitoring. The following is a partial listing of the key data sources. A 

complete listing of data sources can be found in the “Data Collections” section of ACToR.

Based on the Toxic Substances Control Act (TSCA), EPA determined in 1977 to compile and 

keep current an inventory of chemical substances in commerce in the U.S. This inventory is 

called the TSCA Chemical Substance Inventory (TSCA Inventory) and is updated by the EPA 

Inventory Update Reporting (IUR) program, which is now known as the Chemical Data 

Reporting (CDR) program (http://www.epa.gov/oppt/cdr/pubs/guidance/basic.html). Information 

on production and import volumes in ACToR includes all non-confidential information from the 

IUR, which collects screening-level, exposure-related information on chemical substances 

produced or imported at any single site at 25,000 lb or more. Information is also captured from 

the EPA High Production Volume Information System. Additional production process 

information can be accessed through links provided to the ATSDR Toxicological Profiles. 

Industrial use and product ingredient information comes from, among other sources:
New High-throughput Methods to Estimate Chemical Exposure 21 7/8/2014

 the U.S. Food and Drug Administration (FDA) Everything Added to Food in the United 

States inventory

 FDA’s Approved Drug Products

 Health Canada’s Substances in Cosmetics and Personal Care Products Regulated Under 

the Food and Drugs Act listing

More recent efforts to extract detailed information on industrial uses, product categories, and 

chemical ingredients are detailed in Section 4 (Ongoing Research).

Environmental release data comes from a number of sources including:

 EPA’s Toxics Release Inventory and National Emissions Inventory

 EPA Pesticide Industry Sales and Usage Market Estimates

 Great Lakes Commission Toxic Air Emissions Inventory

 Health Canada National Pollutant Release Inventory
New High-throughput Methods to Estimate Chemical Exposure 22 7/8/2014

Figure 5: Network diagram of exposure taxonomy used in ACToR. Figure from Egeghy et al. ([25]).
New High-throughput Methods to Estimate Chemical Exposure 23 7/8/2014

Environmental fate information comes from the:

 EPA Toxic Substance Control Act Test Submission Database

 EPA Inert Ingredients in Pesticide Products tolerance reassessment decisions

 U.S. National Oceanic and Atmospheric Administration Pharmaceuticals in the 

Environment Information for Assessing Risk database

 EPA ECOTOX database.

Sources of measured environmental concentrations include:

 EPA’s National Contaminant Occurrence Database for unregulated contaminants in 

public water systems

 EPA Air Data for ambient air monitoring

 EPA National Air Toxics Assessment for modeled ambient data

 UN Intergovernmental Panel on Climate Change Greenhouse Gas Inventories

 U.S. Department of Agriculture (USDA) Pesticide Data Program (PDP) database of 

pesticide residues in the U.S. food supply

 Summaries of the FDA Total Diet Study market basket survey of contaminants in food

 EPA Human Exposure Data System (HEDS) for exposure-related media concentrations 

from observational studies

 Occupational exposure limits from the Occupational Safety and Health Administration 

(OSHA) and the CDC National Institute for Occupational Safety and Health (NIOSH)

Human Exposure Monitoring data includes personal air, dermal, and biomarker values from 

observational studies contained in EPA HEDS, and biological monitoring from the CDC 

NHANES.

The number of unique chemicals in ACToR with information on production volume, use 

category, chemical release, and measurement of concentrations in food, water, soil, air, and 

biological fluids is presented in Figure 6.

Production volume information is available for 14,591 chemicals and use categories for 

approximately 7722 chemicals. Only a much smaller fraction has readily accessible measured 

concentrations in exposure-related media. Water is the medium with the largest number of 

measured chemicals with over 1150 unique compounds. Measurement of chemicals in water is 

performed routinely by two separate agencies (EPA and U.S. Geological Survey). Nearly 800 

substances have available measurements in soil, 720 are measured in food, and 670 are measured 

in air. Biomarker measurements are available for nearly 400 compounds, with over 200 

measured in NHANES alone.
New High-throughput Methods to Estimate Chemical Exposure 24 7/8/2014

Figure 6: The number of unique chemicals by exposure category in ACToR. A complete 

listing of data sources can be found in the “Data Collections” section at 

http://www.actor.epa.gov. Figure from Egeghy et al. ([25]). 

Of the initial set of 2127 chemicals analyzed in the first generation SEEM framework, 1936 

chemicals had sufficient physico-chemical properties available to parameterize fugacity-based,

environmental partitioning fate and transport models (described below). Production/release data 

were available for 975 of these chemicals. Urinary metabolites (or unchanged parent 

compounds) were available in NHANES for 82 parent chemicals, but production volumes of less 

than 25,000 lbs/year had to be assumed for about half of these chemicals because they were not 

on the IUR or Crop Protection Research Institute (CPRI) lists. A Venn diagram showing the 

overlap between chemicals of interest, chemicals that can be modeled, chemicals with available 

production volumes, and chemicals available for model calibration and evaluation is presented in 

Figure 7.
New High-throughput Methods to Estimate Chemical Exposure 25 7/8/2014

Figure 7: Data availability for the chemicals evaluated in the first generation SEEM

analysis. From an initial list of 2127 chemicals, 1936 chemicals had sufficient use data and 

physico-chemical properties available to parameterize fate and transport models. 

Production/release data were available for 975 of these chemicals. Out of 82 parent 

chemical exposures that can be inferred from the NHANES data set, 43 of these chemicals 

had to be assumed to be produced at less than 25,000 lbs/year because they were not on the 

IUR or CPRI lists. Figure from Wambaugh et al. ([6]).

Two sets of data were used to provide surrogates for chemical release to the environment. 

Information on chemical production volume was obtained from the 2006 U.S. EPA IUR 

(http://www.epa.gov/oppt/iur/index.htm) and used as a gross measure of the amount of 

compound released into the environment. Note that production volumes are provided in coarse 

bands (e.g., 1M-10M lb/year). For pesticides, publicly-available CPRI 2002 data on application 

levels by state and crop were aggregated, and resulting national application levels used as a 

substitute for overall production volume. Compounds which were not covered by CPRI and were 

not on the IUR were assumed to be produced between 0 and 25K lb/year, the minimum 

requirement for being listed in the IUR [6].

Creation of ACToR UseDB: Chemical use information was compiled from the ACToR database 

in order to create the ACToR UseDB. EPA ORD used an automated informatics approach to 

mine the ACToR database and assign chemicals to a small number of high-level chemical use 

categories based primarily on federal, state, or international regulatory or trade association 

listings of chemicals (e.g., U.S. EPA Antimicrobials Reregistration, The International Fragrance 

Association’s List of Fragrance Ingredients). These data were assigned to various use categories; 

based on these listings, we derived 15 distinct chemical use categories within ACToR UseDB 

(e.g., antimicrobials, colorants/dyes, flame retardants, personal care products). Chemicals were 

Chemicals that Could 

be Modeled

(1936) Production / Release 

Data

IUR (6759)

CPRI (242)

NHANES

Evaluation 

Chemicals

Chemicals of 

Interest (2127)

31 51

inorganic
New High-throughput Methods to Estimate Chemical Exposure 26 7/8/2014

checked against the list of uses; if a chemical was listed as used for a particular purpose, then the 

associated category would be assigned to that chemical. As such, a single chemical could fall 

into multiple use categories.

To reduce incorrect category assignments, a few adjustments were made after calculating the 

number of hits per category. Because pesticide residues from raw products carried over into 

processed foods were at one time considered food additives by the FDA (see the FDA’s 1958 

Food Additives Amendment), the majority of chemicals assigned to the pesticide category were 

also marked for food use. To address this, any chemical that was known to be a pesticide was 

excluded from the food use category.

To provide a weight of evidence for assigning use categories to individual chemicals, the number 

of assays per use category which included a given chemical (the number of ‘hits’) was tabulated; 

more ‘hits’ per use category indicated stronger support for that category assignment for that 

chemical. A threshold (3 instances) was used to make a Boolean classification of whether a 

chemical was in a category. Chemicals above the threshold were automatically assigned to the 

category, chemicals with no hits were automatically not assigned to the category, and chemicals 

with fewer hits than the threshold were manually curated. Manual adjustments were made to 

reduce incorrect category assignments, expert opinion was solicited to identify any chemicals 

that were miscategorized, and any chemicals with fewer than three hits for a given use category 

were scrutinized. 

Given the preliminary nature of the ACToR UseDB when the first generation SEEM analysis 

was performed, five categories – personal care products, consumer use, fragrance, 

pharmaceutical, and food additive – were aggregated into a single “near field” indicator variable 

(i.e., having a value of one if some near field use exists, and zero otherwise).

High throughput chemical descriptors for the Tox21 Library

The inter-agency Tox21 consortium involving the EPA, the National Institutes of Health, and the 

Food and Drug Administration has evaluated over 8000 chemicals using a broad range of in vitro 

high-throughput screening (HTS) assays to identify potential hazards through interactions with 

proteins, pathways, and cellular processes [17, 40]. The second generation SEEM analysis 

examined the 7785 Tox21 chemicals for which high-throughput chemical descriptors could be 

obtained.

We considered only potential exposure-related chemical descriptors that could be obtained 

readily for thousands of chemicals (e.g., determined from QSAR or available in databases). 

Factors chosen for inclusion were: (1) use category annotations from the ACToR UseDB; (2) a 

mixture of measured and predicted physico-chemical properties from EPI Suite; and (3) total 

chemicals production from the IUR HPV List. Chemicals not present on the HPV list were 

assumed to be produced at less than 25,000 lbs/year (the criteria for inclusion on the HPV list).

Of the 15 chemical use categories in the ACToR UseDB, petrochemicals and fertilizers were not 

present among the NHANES chemicals. Modified categories were constructed from the 

“Consumer use” and “Chemical/Industrial Process use” ACToR categories, and from the 

“Pesticide use” and “Inert Ingredients in Pesticides use” ACToR categories. For the initial best 
New High-throughput Methods to Estimate Chemical Exposure 27 7/8/2014

subsets analysis, an additional category “Other” was created for chemicals with no use indicated 

by ACToR.

Figure 8 demonstrates a heat map of the high-throughput chemical descriptors used for the 

evaluation (NHANES) chemicals in the second generation SEEM analysis. The rows in Figure 8

are the chemical descriptors (i.e., potential predictors) and the columns are individual chemicals. 

The rows and the columns are permuted to place similar values near each other according to 

hierarchical clustering by Euclidean distance. The horizontal side bar at the top annotates the 

chemicals to indicate the chemical classes assigned by NHANES. For example, the parabens are 

clustered together. No strong correlations between the use category predictors are observed, 

however this was achieved by performing logical operations on some of the ACToR categories. 

The “Consumer use” and “Chemical/Industrial Process use” ACToR categories were 

transformed into three modified categories (chemicals with both uses, and chemicals with 

exclusively one or the other use), and the “Pesticide use” (which may include both active and 

inert pesticide ingredients) and “Inert Ingredients in Pesticides use” ACToR UseDB categories 

were transformed to create two mutually exclusive modified categories: Pesticide Active use and 

Pesticide Inert use. The high-throughput chemical descriptors for the entire 7785 chemical 

library evaluated are available in Figure 9. Note that the use descriptors for the overall chemicals 

dataset differ from the NHANES dataset, with a much smaller fraction of chemicals being 

pesticide actives, and the majority of chemicals having no use identified by ACToR. Figure 9

shows these data for the entire chemical library.
New High-throughput Methods to Estimate Chemical Exposure 28 7/8/2014

Figure 8: Databases and predictions from chemical structure provide high-throughput descriptors 

that were used to investigate correlation with environmental chemical exposure to humans. Here 

we show the data for the 106 NHANES chemicals used in the second generation SEEM analysis. 

Each column of the grid in the middle corresponds to a chemical, whose class as identified by the 

CDC is annotated by the color bar at the top of the heat map and the legend at the bottom. Each 

row corresponds to a high-throughput descriptors, and the intensity of the color at each grid point 

indicate shte value of the descriptor for the chemical. The descriptors consist of Boolean (0/1) use 

category annotations from the ACToR database, experimental and/or predicted physical-chemical 

properties, and chemical production volume data. Continuous variables have been scaled to be 

between zero and one. The dendrograms at the top and left-hand size indicate how chemicals and 

categories have respectively been clustered hierarchically using the complete linkage method [41]. 
New High-throughput Methods to Estimate Chemical Exposure 29 7/8/2014

Figure 9: Physical chemical properties, production data, and chemical use categories used 

as input to the HTE heuristic model developed by the second generation SEEM analysis.

The rows correspond to the potential high-throughput exposure heuristics, consisting of 

Boolean (0/1) use category annotations from the ACToR, experimental and/or predicted 

physical-chemical properties, and production data (continuous variables have been scaled 

to be between zero and one). Two use categories with no representation among the 

NHANES chemicals (Fertilizer and Petrochemical) are included. Chemicals with no use 

indicated by ACToR are categorized as “Other”. 
New High-throughput Methods to Estimate Chemical Exposure 30 7/8/2014

Available Models

When the ExpoCast initiative was launched in 2009 [4], one of the first activities was a review of 

available models for making high-throughput exposure predictions [5]. In order to match the 

purpose of screening thousands of chemicals with minimal information, four key criteria were 

considered: models must be (1) capable of being parameterized from minimal data sets; (2) 

broadly applicable across many chemicals and chemistries (i.e., chemical classes); (3) operated 

in an automated (batch) manner; and (4) capable of making quantitative (mg/kg BW/day) 

predictions of human exposure. 

Although many screening level and other exposure models exist (see Table 1), most do not meet 

all of the above criteria. For example, two EPA tools that do not meet all of the criteria are the 

EPA Standard Operating Procedures for Residential Pesticide Exposure Assessment tool and the 

Exposure and Fate Assessment Screening Tool v2.0 (E-FAST). The EPA Standard Operating 

Procedures for Residential Pesticide Exposure Assessment tool is a Microsoft Excel-based 

application for screening level exposure predictions. In addition to requiring basic physicochemical descriptors, it requires user-provided information about chemical-specific application 

rates, amounts of area treated, average residues, and percent spray of the proposed product. 

Although databases and assumptions could be developed to provide this information, as it stands 

this model does not meet the first criteria. Since this model only covers application of pesticides, 

it also does not meet the second criteria. Since a user must navigate through Excel to manually 

enter the chemical-specific information, it also does not meet the third criteria. However, the 

model does make quantitative predictions, so it does meet the fourth criteria. 

E-FAST is a screening-level model to estimate chemical concentrations in water to which 

aquatic life may be exposed, as well as generate human inhalation, drinking water ingestion, and 

fish ingestion exposures resulting from chemical releases to air, water, and land. EFAST2 was 

developed to support EPA assessments of potential exposures to chemicals [42]. The model is 

characterized by EPA as a “screening model” that predicts exposure for humans and the 

environment through various release and exposure scenarios. EFAST2 has been successfully 

applied in a regulatory setting for many years as a screening-level tool for the EPA Office of 

Pollution Prevention and Toxics (OPPT) pre-manufacturing notification program [43]. Where 

USEtox and RAIDAR assume steady state releases, the EFAST2 model can evaluate exposures 

from several different temporal release patterns [5]. Like the other two models, EFAST2 does 

not assess occupational exposure to workers. Since EFAST2 was developed for screening level 

estimates, the predicted exposures are intended to be highly conservative (e.g., likely to be at the 

high end of or higher than concentrations that might actually be occurring in a real-world setting) 

and rely on many default values [5, 42]. One of the main challenges of using EFAST2 lies in 

obtaining input parameters that would accurately determine the exact chemical releases and fate 

and transport properties in the environment [43]. Some required chemical-specific input 

parameters are not readily available and therefore human interaction is needed to make 

judgments for each chemical [43]. This limitation precludes using EFAST2 as a high-throughput

exposure model in this study [5]. 

Table 1 provides a model-by-model evaluation of the suitability of screening-level exposure 

models for use in HTE.
New High-throughput Methods to Estimate Chemical Exposure 31 7/8/2014

Although we require HTE models to make quantitative predictions, to date we have not focused 

on harmonizing the exposure scenarios used between the models. Because all the models used 

have been linear (i.e., changes in exposure scenario would have the same, multiplicative impact 

on all chemicals), we have left the model predictions unscaled. For the purposes of the linear 

regression within SEEM, the scaling factor is subsumed by the intercept term of the linear 

regression [6]. Because the predictions are quantitative, the differences between the predictions 

are quantitative, even if the overall scaling is unknown. For example, ten chemicals could be all 

clustered near 1 mg/kg BW/day, and a second group of ten chemicals could be clustered near 1 

μg/kg BW/day. The differences between chemicals ranked one through ten is small, as are the 

differences between chemicals ranked eleven through twenty, but the difference between the two 

groups (such as between the chemicals ranked ten and eleven) are important. This will be true 

even if there is an unknown scaling factor x, such that group one should be clustered around x

mg/kg BW/day and group two is around x μ/kg BW/day – the thousand-fold difference between 

the two groups is preserved. This is in contrast to models that predict ranks only (e.g., the 

difference between the nth and the (n+1)th ranked exposure is unknown – it could be perhaps be 

negligible or even very large).

An exposure-based prioritization model challenge was issued publicly in 2010 

(http://www.epa.gov/ncct/expocast/exposure_based_challenge.html) to elicit existing 

prioritization schemes or models for rapid estimation of exposure potential. Challenge 

participants were asked to use existing approaches to prioritize a set of 52 compounds based on 

potential for exposure. The results of this review of screening models was reported in Mitchell et 

al. [5]. Of five models reviewed, including EPA tools, only two were found to meet all four 

criteria for HT exposure models.

The two models found meeting the requirements were the United Nations Environment Program 

and Society for Environmental Toxicology and Chemistry toxicity model (USETox) Version 

1.01 [44] and the Risk Assessment IDentification And Ranking (RAIDAR) Version 2.01 [45]

multimedia mass balance model. Both models were necessarily capable of running in highthroughput mode and making quantitative exposure predictions [5]. Based upon the compoundspecific partitioning (e.g., fugacity) and degradation (i.e., media half-life) properties and the 

assumption of steady-state (i.e., for a constant emission rate, sufficient time has passed that the 

concentration in each media is constant), the models predict chemical fate and distribution in 

representative environmental media (e.g., air, water, soil, sediment, biota) [6].

The USEtox model makes predictions for partitioning into 11 well-mixed compartments – air, 

agricultural soil, natural soil, freshwater, and seawater for global and continental scales as well 

as urban air – due to environmental release into up to 6 different media – urban air, rural air, 

agricultural soil, natural soil, sea water, and fresh water. From these media concentrations, 

exposure factors are used to predict human chemical exposure via the following pathways: air, 

drinking water, pesticide exposed produce, unexposed produce, meat, dairy products, and fish. 

To approximately reflect North America, USEtox assumes a continent of size 2.47*107km2and 

a population of 9.98x108, corresponding to a population density of ~40 people / km2. To predict 

individual exposure, the population intake fraction (kg/kg) was divided by 9.98x10 8for the 

continental population, converted to mg, and divided by 70 kg for bodyweight [6]. 
New High-throughput Methods to Estimate Chemical Exposure 32 7/8/2014

The RAIDAR model simulates chemical partitioning into air, water, soil, sediment and 

representative biota as a result of environmental releases into air, water, soil. From these media 

concentrations, exposure factors are used to predict human chemical exposure via the following 

pathways: air, water, foliage, root, fish, meat, and milk. RAIDAR predicts human intake 

fractions on a per individual basis, whereas USEtox predicts a population intake fraction. Only 

for comparing the two models on the basis of intake fraction (Figure 10) was the RAIDAR intake 

fraction was multiplied by 9.98 x108to match USEtox's population density and scaled by 

9.01x106/105to account for the difference in the size of the regions simulated by the two models. 

In Wambaugh et al. ([6]), chemicals were identified as pesticides based upon their presence on 

the CPRI list, all other chemicals were assumed to be industrial compounds. Two broad release 

profiles (pesticide and industrial) were assumed and, depending on the fate and transport model 

used, were characterized in slightly different manners. The models that were initially used, 

RAIDAR [45] and USEtox [44] (described below), are far-field models. For pesticides,

application was assumed to be equally into soil and air (50% soil, 50% air for RAIDAR and 50% 

continental agricultural soil, 50% continental air for USEtox). To assess environmental impact 

for chemicals handled under TSCA, it is typical to assume either release or emissions into the air 

or water; as other releases onto land, water, or public treatment works are effectively releases 

into water [46, 47]. Since the vast majority of the chemicals of interest here are those amenable 

to in vitro HTS (i.e., non-volatile and soluble in DMSO), all chemicals other than pesticides were 

assumed to largely be released to water (80% water, 10% soil, 10% air) for RAIDAR and 

continental freshwater (75% continental freshwater, 5% to each of continental natural soil, 

agricultural soil, air, and seawater and urban air) for USEtox [46].

Having predicted the concentrations per unit emission in various environmental media, both 

models make additional assumptions to predict multiple human exposure pathways (i.e., 

inhalation, water ingestion, various food ingestion) to calculate an overall population intake 

fraction (i.e., kg absorbed per kg emitted) [48]. Similar quantities can be predicted for ecological 

endpoints. Figure 11 clusters chemicals based on USEtox and RAIDAR predictions of 

partitioning into various environmental media. The continental USEtox media predictions and 

the RAIDAR predictions are similar (USEtox also predicts the global concentrations resulting 

from the same continental release) [6].

Using the environmental partitioning results that are shown in Figure 11, both RAIDAR and 

USEtox make assumptions about human exposure pathways that allow human exposure metrics 

to be calculated, including an overall population intake in units of kg exposed to population per 

kg emitted (i.e., intake fraction) [48]. Figure 10 shows general agreement between the predicted 

intake fractions for the two models, except for select chemicals with relatively low 

hydrophobicity (log KOW < 1). This discrepancy is largely due to differences in the way the 

models simulate chemical accumulation in vegetation as a function of whether non-hydrophobic 

compounds will eventually reach the plants at steady-state. Triangles plotted in Figure 10 show

compounds that would be considered to be "likely bioaccumulators" (log KOW > 4.5 and a 

bioaccumulation factor (BAF) > 1000) [49]. The most likely bioaccumulators predicted, as 

shown in Figure 10, have high intake fractions [6].
New High-throughput Methods to Estimate Chemical Exposure 33 7/8/2014

Table 1: Screening Level Exposure Models (Including EPA-Expo-Box Models)

Model Full Name

Minimal Data Sets

Parameterized from 

Broad Applicability 

Across Chemicals

Mode) Operation

Automated (Batch 

Predictions 

Quantitative 

(mg/kg BW/day)

EPA Screening Tool

Evaluated in 

Mitchell et al. (2013)

RAIDAR Risk Assessment IDentification And Ranking Yes Yes Yes Yes No Yes

USEtoxTM UNEP-SETAC Toxicity Model Yes Yes Yes Yes No Yes

EFAST2 Exposure and Fate Assessment Screening Tool v2.0 Almost Yes No Yes Yes Yes

GExFRAME/Scibin Generalized Exposure Framework Yes Yes No No No Yes

PRoTEGE Prioritization/Ranking of Toxic Exposures with GIS Extension No Yes No Yes No Yes

3MRA Multimedia, Multipathway, and Multireceptor Exposure and Risk 

Assessment

No Yes Yes Yes Yes No

APEX Air Pollutants Exposure Model No No Yes Yes Yes No

ChemSTEER Chemical Screening Tool For Exposures & Environmental Releases No Yes No Yes Yes No

DEEMTM Dietary Exposure Evaluation Model No No Yes Yes Yes No

EMSOFT Exposure Model for Soil-Organic Fate and Transport Yes Yes No No Yes No

FIRST FQPA Index Reservoir Screening Tool No No No No Yes No

HAPEM Hazardous Air Pollutant Exposure Model No No Yes Yes Yes No

HEM Human Exposure Model No No No Yes Yes No

LifeLineTM Model The Lifeline Group LifeLineTM Model No Yes No Yes Yes No

MCCEM Multi-Chamber Concentration And Exposure Model Almost Yes No Yes Yes No

PIRAT Pesticide Inert Risk Assessment Tool No No No Yes Yes No

PRZM and EXAMS Pesticide Root Zone Model and Exposure Analysis Modeling System Yes No

ReachScan ReachScan No Yes No No Yes No

SHEDS Stochastic Human Exposure and Dose Simulation No Yes Yes Yes No No

SOFEA and FEMS Soil Fumigant Exposure Assessment and Fumigant Exposure Modeling 

System 

No No No Yes Yes No

SOPs Standard Operating Procedures for Residential Pesticide Exposure 

Assessment

No No No Yes Yes No

SRD Source Ranking Database No Yes Yes Yes Yes No

STIR Screening Tool for Inhalation Risk Yes No No Yes Yes No

SWIMODEL Swimmer Exposure Assessment Model Yes No No Yes Yes No

TRIM.FaTE Total Risk Integrated Methodology No No Yes Yes Yes No

WPEM Wall Paint Exposure Assessment Model No No No Yes Yes No
New High-throughput Methods to Estimate Chemical Exposure Page 34 7/8/2014

Figure 10: Comparison of USEtox and RAIDAR predicted intake fraction (kg exposed to 

population per kg emitted) via exposure factors that translate predicted environmental 

media concentrations into human exposure metrics. Of particular interest are putative 

bioaccumulators (indicated by triangles), compounds with log KOW < 1 (squares), and the 

NHANES chemicals (open symbols). A convex hull of the NHANES chemicals indicates 

reasonable coverage of the predicted exposure space by those chemicals. Approximately 

88% of the chemicals are predicted by both models to have intake fractions below 10-3. 

Figure from Wambaugh et al. ([6]).
New High-throughput Methods to Estimate Chemical Exposure Page 35 7/8/2014

Figure 11: Far field models predict the distribution of chemicals into environmental media, 

from which human exposure can be inferred via exposure factors. For the first generation 

SEEM predictions from USEtox and RAIDAR were used for 1936 chemicals, which have 

been clustered here based upon the amount (kg) of compound predicted to be present in 

each environmental medium as the result of a unit emission (1 kg/day). Each column of the 

grid in the middle corresponds to a environmental medium prediction from either UDEtox 

or RAIDAR. Each row corresponds a chemical, and the intensity of the color at each grid 

point indicates the value for the predicted amount for the chemical in that medium. The 

two-way clustering used Euclidean distance of the logarithm of the partition fraction and 

complete linkage (pair with maximum distance used to compare clusters). Figure from 

Wambaugh et al. ([6]).
New High-throughput Methods to Estimate Chemical Exposure Page 36 7/8/2014

2c. Data and Models for Inference (Reverse Prediction) of Human Exposure

Available Data

The NHANES survey is an invaluable data source, but addresses only a few hundred of the 

thousands of chemicals for which data are needed, often through measurement of potential 

metabolites [47]. Still, the NHANES data would seem to provide the best available dataset to test 

the HTE models. However, we must recognize that the NHANES sample data (from the blood 

and urine of survey participants) reflects only a snapshot drawn from a lifetime of exposure to 

environmental chemicals, and there are presumably many possible exposure scenarios (with 

respect to e.g., route, magnitude, and duration) that would be consistent with that snapshot. 

These limitations reduce the ability to evaluate exposure model predictions and increase the 

estimated uncertainty in those evaluations. 

We are limited by the available monitoring data and cannot address exposures that occur in 

scenarios not covered by that data. In the case of NHANES, this includes children under the age 

of 6, and indeterminate occupational exposures. However, as additional monitoring data sets 

become available, we can perform new model evaluations and calibrations with that data to 

reflect new scenarios and chemicals/chemistries. 

Exposures were inferred via RTK from the NHANES data set for 82 chemicals in the first 

generation SEEM analysis and 106 chemicals in the second generation SEEM analysis (more 

NHANES data became available and more parent-metabolite connections were identified

between the two analyses). The inferences for the NHANES chemicals alone allow for model 

evaluation: the inferred exposures provide the data set for comparison to exposure model 

predictions, and allow calibration and an empirical determination of predictive uncertainty. Some 

of the inferred exposures have large uncertainty themselves, likely due to complex parent 

compound exposure and urine metabolite relationships, or urine concentrations that were below 

the limit of detection.

The NHANES study comprises approximately 10,000 individuals, with chemical data available 

from about 2000 individuals. Note that the sample size for chemical analyses somewhat varies 

depending on the NHANES cycle and chemical being evaluated. While the first generation 

SEEM analysis focused on the total U.S. population, in the second generation analysis 

demographic subsets with sufficient statistical power were constructed by considering one factor 

(e.g., gender or age). Typically, we assumed that two factor subsets from the NHANES 

chemicals dataset were too small, though an exception was made for the women of child-bearing 

age demographic (about 590 subjects) due to the importance of exposure for this subgroup. One 

hundred six (106) parent chemical compound exposures were inferred from 68 urine metabolites 

identified in the NHANES biomonitoring data. 

The domain of applicability of the NHANES chemicals used for model calibration and 

evaluation can be, in part, assessed by the convex hull of the intake for the NHANES chemicals 

(shown by a polygon in Figure 10). Although it does not completely cover the range of values 

predicted by USEtox and RAIDAR for the 1936 chemicals in the first generation SEEM 

analysis, the 82 NHANES chemicals do cover a wide region of predicted intake fractions. 
New High-throughput Methods to Estimate Chemical Exposure Page 37 7/8/2014

Chemicals with high intake fractions appear to be slightly underrepresented by NHANES, with 

only 4.2% of the NHANES chemicals having intake fractions greater than 10-3for both models, 

while this occurs for ~12% of the overall chemicals [6].

The attributes of the 106 NHANES chemicals that could be inferred for the second generation 

application of SEEM relative to the 7785 chemicals that are prioritized with that model are 

discussed in the section “Second Generation SEEM Analysis”.
New High-throughput Methods to Estimate Chemical Exposure Page 38 7/8/2014

Available Models

While RTK [50, 51] is relatively straightforward for a metabolite that is related to only one 

parent compound, many metabolites can be derived from multiple parent compounds and many 

parent compounds have multiple metabolites. A mapping, shown in Figure 12, of parent

chemicals to metabolites derived from the NHANES reports was derived in order to identify 

relationships, and potential non-identifiability of parent compounds from a given metabolite [24, 

52]. A stoichiometry of one parent molecule per one metabolite molecule was assumed unless 

known otherwise, and, wherever SMILES descriptors were available for parent and metabolite, 

confirmed by manual inspection [6]. The proportions of different product molecules that result 

from a parent molecule was generally unknown, and treated as such in the estimation process, 

preserving mass balance.

In the first generation SEEM analysis, a steady-state RTK approach was used to infer exposure 

from NHANES biomonitoring data for creatinine adjusted urine concentrations. Assumptions 

similar to those of Mage et al. [50] were made – chiefly that the individuals were at steady-state 

due to a constant rate of exposure. An average daily creatinine excretion of 122.6 mg/dL [53], 

the body weight measured by NHANES [53], and an average daily urine volume of 1.4 L [54]

were used along with a mapping between parent and metabolite compounds (including the 

relative molecular weights) to convert urine concentration to an exposure in units of mg/kg/day.

The second generation SEEM analysis made use of subject-specific data. In 2010, NHANES 

began reporting urine flow data along with chemical monitoring data. This data was modeled to 

determine distributions appropriate for each demographic analyzed, and the modeled values were 

used for chemicals without urine flow data. The urine flow rate along with the subject’s body 

weight, as reported by NHANES were used along with the parent and metabolite mapping to 

convert urine concentration (e.g., μg/L) to an exposure in units of mg/kg bodyweight/day.

Bayesian methodology was a natural choice to allow use of estimates that fell below the limit of 

detection. It was assumed that the population distribution of compounds in urine was lognormal. 
New High-throughput Methods to Estimate Chemical Exposure Page 39 7/8/2014

Figure 12: Mapping of inferred parent chemicals (dark/red) and metabolites reported in 

urine by NHANES (light/blue). Although in some cases there is a single relationship 

between a parent and a urine metabolite, for many chemicals there are multiple parents 

and multiple urine metabolites, confounding reverse toxicokinetic estimation of exposure. 

Figure from Wambaugh et al. ([6]).
New High-throughput Methods to Estimate Chemical Exposure Page 40 7/8/2014

2d. Systematic Evaluation of Forward and Reverse Prediction across Many 

Chemicals

The ultimate goal is to develop a framework for evaluating the performance of high-throughput

exposure models based on exposures inferred from biomonitoring data, and apply the resulting 

calibration to other chemicals with no monitoring data. 

There are four basic aims of the SEEM framework (Figure 2): (1) forward prediction of 

exposures, which involves model curation and parameterization; (2) inference of exposures from 

monitoring data; (3) systematic evaluation and calibration of the predictions against the inferred 

exposures; and (4) extrapolation of the calibrated model predictions and estimated uncertainty to 

chemicals with no monitoring data. To achieve these aims the SEEM framework used Bayesian 

formalism and linear regression for demonstrating and evaluating predictive power in exposure 

prioritization. 

Bayesian formalism is a rigorous statistical methodology for incorporating uncertainty into 

mathematical models using probability distributions. Every parameter in a model can be 

characterized by a distribution with a mean and standard deviation – the better we know the 

parameter the smaller the standard deviation. Prior assumptions about the value of parameters are 

informed by the addition of new data to produce posterior distributions for the parameters that 

reflect both the prior assumptions and the data. We use Bayesian methods in two ways. First, to 

make forward prediction or reverse inference of exposure where there are parameters that are not 

directly known and must be estimated from available data [6, 55, 56]. Second, to estimate the

relative predictive power of the forward models (i.e., weights) [6].

Linear regression is used to evaluate the predictive power of multiple exposure models by 

comparison to chemicals with known (or in this case inferred) exposure from biomonitoring data, 

e.g., exposures inferred from empirical data for a subset of the chemicals in order to both 

establish the physical relevance of each model and to estimate the uncertainty. Multivariate 

regression on the set of available high-throughput models provides regression coefficients for 

each of the models and the regression coefficients act both as weights for a single calibrated 

predictor for the data set used to test the model (i.e., inferred exposures from biomonitoring data)

and as an evaluation of model performance (a weight of zero indicates lack of model predictive 

ability for the data set in question). The variance of the data about the calibrated predictor 

provides an empirical estimate of the uncertainty. The calibrated predictor and its uncertainty are 

then extrapolated to the remainder of the chemicals for which there were no biomonitoring data.

Statistical methods were used to compare, calibrate, and assess the uncertainty of the predictions 

of the exposure models using the biomonitoring data. A Bayesian framework in particular was 

assumed to address two separate problems. First, for many of the predicted chemicals the 

biomonitoring data were below the limit of detection. Second, to enforce mass balance a large 

number of unknown fractional allocations of various parent compounds to various metabolites 

are required. Bayesian analysis provides a formal framework for explicitly stating the 

assumptions about the parameters used and judiciously combining those assumptions with 

available data to assess uncertainty.
New High-throughput Methods to Estimate Chemical Exposure Page 41 7/8/2014

The Bayesian implementation of the SEEM framework for ExpoCast analysis proceeds in four 

stages:

Analysis Stage 1: Steady-state RTK for estimating the ‘data’, parent exposures, from urine 

metabolite or product concentrations.

Since NHANES reports metabolite (‘product’) concentrations, while the exposure models 

utilized in this analysis predict parent chemical exposures, the first step is to estimate parent 

chemical exposure values that are consistent with the observed distribution of metabolite 

concentrations. There are at least two challenges associated with this. First, there is uncertainty 

about the stoichiometry relating parent to multiple metabolites (i.e., when a parent molecule can 

be metabolized to one of several product molecules, the proportion that quantify the probabilities 

of each path from parent to product are not always known). Second, multiple parents may be 

metabolized to the same product molecules. We ultimately want to relate median parent 

concentrations to model predictions and other predictors. To accommodate the various uncertain 

steps linking product to parent, we found it most convenient to use Bayesian methodology for 

this analysis. The result is a probability distribution (i.e., a series of samples from that 

distribution or chain) that characterizes the uncertainty in median exposure for each parent 

chemical.

The relationships hypothesized in the data are described by a statistical model. Parameters of this 

model that are known imperfectly are assigned probability distributions that characterize the 

degree of uncertainty. Often these will be very broad, serving primarily to help regularize the 

resulting computations but adding little real information, but sometimes these priors convey real 

information that is inconvenient to include in the model in any other way. Using Bayes’s rule 

from probability theory allows this model to define a joint probability distribution for the model 

parameters (for both statistical and more deterministic parts of the model). This is the posterior

distribution, and summarizes all the information in the data, the prior information, and the model 

posited for the data.

In this analysis, the priors used assumed that the exposure values are log-normally distributed 

around a mean lPmu with standard deviation sd.V, lPmu is normally distributed with mean 0 and 

variance 1000, the proportions of the metabolites from a parent compound are Dirichletdistributed αi set to 1.

Samples from the posterior distribution are generated via Markov Chain Monte Carlo (MCMC) 

[57]. In principle this is no different from generating pseudo-random samples from a 

conventional probability distribution using a computer, but in practice is much more complex. 

For other than the simplest statistical models, the consecutive values in the sample are not 

independent from each other, and the distribution of the random sample only converges to the 

desired posterior distribution as the number of individual sampled values increases.

Stage 1 concludes by testing the samples for convergence. This is done in two ways, by testing 

whether earlier and later samples have similar enough distributions, and by checking that 

different chains converge to the same distribution.
New High-throughput Methods to Estimate Chemical Exposure Page 42 7/8/2014

Markov Chain Monte Carlo for log geometric mean population exposures to parent compounds 

was performed via JAGS v3.1.0 [58]. For the second generation SEEM analysis, separate 

inferences were performed for the various demographic subsets of the NHANES considered 

(e.g., children aged 6-11).

Analysis Stage 2: Sample from the posterior distribution of production volumes

Production volumes are crudely estimated or reported. We have two sources, each with their own 

error characteristics. The CPRI data are estimated from reported use data, as point estimates, 

while the EPA IUR data are reported in predetermined bins. We assume that the overall 

distribution of use/production is lognormal. The IUR data are then interval-censored, with 

irregularly-spaced intervals, while each CPRI estimate is sampled from a lognormal distribution 

with unknown median and coefficient of variation assumed to be about 20%. We further assume 

that the true CPRI values are distributed among chemicals following the same distribution as the 

IUR values.

Analysis Stage 3: Find a model to explain the median parental exposure

The logic of this stage is to carry out linear regressions of the parental median exposures on 

various combinations of predictors in order to select the most parsimonious model relating 

median parental exposure to the model predictions. 

For the first generation SEEM analysis, the following procedure was used to examine RAIDAR 

and USEtox predictions, production volumes, and an indicator for near- versus far-field 

classification:

As listed in Table 2, multiple models were fit, and the best model selected taking into account 

the number of models fit and the complexity of the models. The Akaike information criterion

(AIC) [32] was used to select among the models, backed up by significance testing of individual 

terms using likelihood ratio tests.

Both the ‘dependent variables’ in the regression, the estimates of parental exposure, and one of 

the ‘independent variables’ the production volume, have known uncertainties (see stages 1 and 2) 

that should be taken into account in deciding on models for the data. Model selection and 

evaluation was performed on samples from the two sets of posteriors. All the regression models 

were fit for each sample, keeping track of AIC values for each model. Finally, decisions were 

made about best models based on the average AIC across all replicates.

The estimates of parental exposure values that come from the estimation process are not 

independent, and regression models need to take that dependence into account. Half of the 

available samples were used to estimate the covariances of the joint posterior distribution, 

identified as Σ. Although it is an estimate, we treat Σ as a known quantity. Then, the following 

likelihood is maximized:

 

     

1 1 1 2

2

2 2 2 2

n

n n e

s

p s





     

 

ly xβ Σ I ly xβ

Σ I
New High-throughput Methods to Estimate Chemical Exposure Page 43 7/8/2014

where n is the number of parent chemicals (80 in this analysis), ly is the vector of logtransformed population medians of the parental exposures, x is an n×p matrix of values for p

predictor variables, such as an indicator of near- versus far-field, log RAIDAR- or USEtoxpredicted exposure, and/or log production volume; β is the vector of regression coefficients, and 

σ2 is the scalar variance that characterizes the residual variance of the log population median 

exposures after the predictors in x are taken into account.

The procedure for selecting the most parsimonious model was replaced in the second generation 

SEEM analysis with the method of best subsets [59], as is described in the section “Second 

Generation SEEM Analysis.”

Analysis Stage 4: Estimate posteriors for the parameters for the target model

Finally, MCMC is again used, this time to reanalyze all three steps at once to determine the joint 

posterior distribution for the model selected in stage 3, the RTK model from step 1, and the 

production volume sampling from step 2. The models from are combined to relate parental 

exposures to the desired linear combination of predictors. This Bayesian analysis establishes the 

regression weights (relative importance of) the various predictors for the inferred ranges of 

exposures that are consistent with biomarkers measured in urine samples and reported by 

NHANES. The posterior distributions established using NHANES are then used to make 

predictions for the full set of chemicals

We selected this Bayesian approach because we wish to consider the full range of plausible 

exposures indicated by a given biomarker (e.g., multiple chemicals can produce the same 

biomarker compound) and because biomonitoring data is often confounded by limits of detection 

(i.e., the amount of biomarker is only known to be below a certain limit). Bayesian analysis 

allowed to specify uncertainty in our inputs, estimate uncertainty in our regression coefficients, 

and to propagate that uncertainty into our predictions.
New High-throughput Methods to Estimate Chemical Exposure Page 44 7/8/2014

2e. First Generation SEEM Analysis: (Far-Field HTE with Near Field Indicator)

A null model (one value for all chemicals) and 22 nested linear models were examined for fitness 

based on the analysis of the 82 parent chemical exposures inferred from NHANES (Table 2) [6]. 

The 22 models related log geometric mean population exposures to predictors consisting of 

subsets of log RAIDAR and USEtox unit emissions, log production volumes, near field contrast, 

and the interaction effects of near field contrast and the log unit emission estimates and log 

production volume. Each model was fit to repeated samples of the posterior distribution of the 

log geometric mean inferred parent exposure concentrations from stage 1. Production volumes 

were drawn from a distribution reflecting the uncertainty for that chemical (e.g., 1M-10M 

lb/year). Models were selected by comparing mean AIC values, and the model selection checked 

using ten-fold cross validation.

In this analysis, summary data reported by the CDC on chemicals in urine was used in the form 

of estimates of population quantiles. Using the data in this form obviates the need for more 

complex analysis that takes the NHANES clustered sampling into account, but introduces the 

need to account for the uncertainty of the estimates, reported as confidence intervals, and values 

that are reported as falling below the limit of detection.

Table 2 shows that near field use was the single most predictive chemical aspect (p-value 0.01 

without any other factors). On average, those chemicals with indoor/consumer use yield 

significantly greater NHANES-inferred exposures. Neither USEtox nor RAIDAR total predicted 

exposures (i.e., predicted unit emission multiplied by production volume) alone are significantly 

associated with the inferred exposures from the 82 NHANES chemicals (p-values 0.077 and 

0.194, respectively). Generally, chemicals above the limit of detection in NHANES had 

consumer/indoor use, while those with only the far field sources for which the fate and transport 

models were designed were below the limit of detection. Ten-fold cross validation diagnostics 

did not indicate that the correlations were driven by specific chemicals.

A calibrated model with intercept and regression coefficients for the unit USEtox and RAIDAR 

predictions for the near field chemicals only, and a separate intercept only for the far field 

chemicals (model 16 in Table 2), was selected from among the five most parsimonious linear 

regression models. This model was selected for including both RAIDAR and USEtox predictions 

and having a low AIC, which is a statistical measure of model parsimony [60]. The AIC value 

for this model was found to be stable with respect to ten-fold cross validation. A model 

excluding USEtox was slightly more parsimonious, but the difference was not statistically 

significant.

The selected model was fit to the NHANES samples using Bayesian methods. Predictive 

intervals for exposure estimates were computed as quantiles of the resulting posterior predictive 

distributions. The ability of this model to predict potential exposure is evaluated in Figure 

13,where the empirically calibrated, optimal predictor based on RAIDAR, USEtox, and the near 

field indicator is compared to the geometric mean of the U.S. population exposures inferred from 

NHANES urine metabolites. Despite the large scatter, the joint model predictor appears to be 

without obvious bias (i.e., does not under/over-predict). Taking no correlation as a null 

hypothesis, the p-value for the calibrated predictor is 0.017.
New High-throughput Methods to Estimate Chemical Exposure Page 45 7/8/2014

Table 2: Calibration Model Selection by AIC.

Model 

Number

Description Mean 

AIC

Normalize

d AIC

RsquaredMean Log 

Likelihoo

d

0 intercept 481.98 8.74 0.00 -238.99

1 intercept + NearField 474.98 1.74 0.10 -234.49

2 intercept + NearField + lV 475.67 2.43 0.12 -233.83

3 intercept + NearField + ltR 476.27 3.03 0.11 -234.13

4 intercept + NearField + luR + lV 476.41 3.17 0.14 -233.20

5 intercept + NearField + ltU 475.12 1.88 0.13 -233.56

6 intercept + NearField + luU + lV 477.08 3.85 0.13 -233.54

7 intercept + NearField + luR + luU 

+ lV

477.24 4.00 0.15 -232.62

8 intercept + NearField + ltR + ltU 475.84 2.61 0.15 -232.92

9 intercept + NearField + NearField 

* luR + luU + lV

476.11 2.88 0.18 -231.06

10 intercept + NearField + luR + 

NearField * luU + lV

478.09 4.85 0.16 -232.05

11 intercept + NearField + luR + luU 

+ NearField*lV

478.51 5.27 0.16 -232.25

12 intercept + NearField + NearField 

* luR + NearField * luU + 

NearField * lV

479.07 5.83 0.19 -230.53

13 intercept + NearField + 

NearField*luR + NearField*luU + 

lV

477.61 4.37 0.18 -230.80

14 intercept + NearField + luR + 

NearField*luU + NearField*lV

479.58 6.34 0.16 -231.79

15 intercept + NearField + 

NearField*luR + luU + 

NearField*lV

477.54 4.31 0.19 -230.77

16 intercept + NearField + 

NearField:luR + NearField:luU + 

NearField:lV

475.33 2.09 0.17 -231.67

17 intercept + NearField + 

NearField:luR + NearField:luU

474.91 1.67 0.15 -232.45

18 intercept + NearField + 

NearField:luR + NearField:lV

473.99 0.75 0.16 -231.99

19 intercept + NearField + 

NearField:luU + NearField:lV

477.04 3.81 0.13 -233.52

20 intercept + NearField + 

NearField:luR

473.24 0.00 0.14 -232.62

21 intercept + ltR 481.51 8.27 0.04 -237.76

22 intercept + ltU 480.10 6.86 0.06 -237.05
New High-throughput Methods to Estimate Chemical Exposure Page 46 7/8/2014

lV = ln(Production Volume), ltR = ln(Total RAIDAR), luR = ln(Unit RAIDAR), ltU = ln(Total 

USEtox), luU = ln(Unit USEtox), NearField = 0 for far-field chems, 1 for near-field, NearField * 

luX = separate slopes for luX, NearField : luX = slope only for NearField = 1, ltR = luR + lV, 

ltU = luU + lV
New High-throughput Methods to Estimate Chemical Exposure Page 47 7/8/2014

The relative uncertainty in the inferred exposures using reverse toxicokinetics (y-axis) is 

indicated by the vertical confidence interval in Figure 13. Those chemicals with a large 

uncertainty in the inferred exposure estimate likely have complex parent exposure-urine product 

relationships (e.g., the metabolite dimethylphosphate can result from exposure to 17 different 

parent organophosphate pesticides, see Figure 12), or were below the limit of detection. Note 

that the vertical placement of plot symbols in Figure 13 indicates the median inferred exposure. 

For chemicals where many or all NHANES urine products were below the limit of detection, the 

median is no more likely to be the true exposure value than any other value within the vertical 

confidence interval.

Generally, the chemical properties (Figure 4), chemical specific model predictions (Figure 10), 

and production volume data (Figure 15, Figure 16) for the 1850 chemicals without NHANES 

data appear to be within the range of the 82 chemicals with NHANES data, indicating that most 

chemicals may be within the domain of applicability of the empirical calibration.

Given the predictions of the empirically calibrated consensus model, ranking on the basis of 

predicted exposure was conducted for all 1936 chemicals, based on the upper limit of the 95% 

confidence interval from the empirical calibration to the NHANES data. However, as shown in 

Figure 14, the large uncertainty associated with the far field models and the coarse near field 

indicator predictor, indicated by the horizontal error bars at each plot point, should lead to 

skepticism of specific predicted values. Despite this uncertainty as to the specific exposure dose 

for the general public within the confidence interval, Figure 14 indicates that for all but the top 

100 chemicals there is 95% confidence that exposures will be less than 0.29 mg/kg BW/day. For 

the bottom 1000 chemicals, there is 95% confidence that exposures are less than 0.27 μg/kg 

BW/day.

In this first generation SEEM analysis, a determination of whether a chemical was a “pesticide” 

was solely based upon whether or not the chemical was on a list compiled by the CPRI. This list 

is not complete, and points to the need for more reliable sources of information for large 

numbers of chemicals. For those chemicals covered by CPRI, data on the kg applied 

agriculturally were available. For all other chemicals, production volume data were used as a 

crude surrogate for actual chemical release. This simplification is a key issue for models like 

USEtox and RAIDAR – the volume released into the environment is a multiplicative factor and 

results are extremely sensitive to errors in the emission characterization [61]. Although EPA IUR

production volume category data are available for many chemicals, these categories can span an 

order of magnitude and they are not directly linked to intended use, e.g., environmental vs. 

indoor releases. However, as shown in Figure 7, many NHANES chemicals were produced at 

levels less than 25K lb/year and their presence in the urine of the general population was driven 

by near field use rather than these traditional metrics of production and far field environmental 

release.
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Figure 13: Correlation of inferred exposures and consensus exposure predictions from the 

first generation SEEM analysis. Exposures inferred from NHANES biomonitoring data 

were linearly regressed on the unit emission predictions of RAIDAR, USEtox, and a near 

field use indicator variable in order to create a calibrated predictor for predicted exposure 

(mg/kg/day). The solid line indicates the 1:1 line (perfect predictor). Bayesian analysis was 

used to distribute urine products using mass-balance, giving the 95% confidence intervals 

(light lines) and medians (solid triangles for compounds with near field use, open circles for 

far field only). The uncertainty of the inferred exposures (width of the vertical confidence 

interval) is strongly dependent on the number of general population quantiles with 

concentrations below the limit of detection in NHANES. The correlation was statistically 

significant (p = 0.017). Figure from Wambaugh et al. ([6]).
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Figure 14: Predicted human exposures (mg/kg/day) for 1936 chemicals using the model 

calibrated in the first generation SEEM analysis with exposures inferred from NHANES. 

The uncertainty in each prediction is indicated by the horizontal 95% confidence interval. 

NHANES chemicals used for the calibration are indicated by the lighter confidence interval 

bars. The black tick marks on the left-hand side indicate compounds with only far field 

uses. Figure from Wambaugh et al. ([6]).
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Figure 15: We evaluated the domain of applicability of NHANES chemicals in the first 

generation SEEM analysis in part by comparing the exposure (mg/kg/BW/day per kg/day 

emitted) predictions from RAIDAR and the production volume (kg/day) used for all 

chemicals. A convex hull (solid line) indicates the domain of 82 NHANES chemicals. Figure 

from Wambaugh et al. ([6]).
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Figure 16: We also evaluated the domain of applicability of the NHANES chemicals in the 

first generation SEEM analysis by comparing the exposure (mg/kg/BW/day per kg/day 

emitted) predictions from USEtox and the production volume (kg/day) used for all 

chemicals. A convex hull (solid line) indicates the domain of 82 NHANES chemicals. Figure 

from Wambaugh et al. ([6]).
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Physico-chemical properties inherent to a given compound (e.g., octanol-water partition 

coefficients) and properties of those compounds in environmental media (e.g., degradation halflives in soil) have previously been used to make high-throughput estimates of potential chemical 

exposure [46, 49, 62, 63]. Those prioritizations were on a relative rather than an absolute (i.e., 

mg/kg bodyweight/day) scale. Environmental fate and transport and multimedia exposure 

models have been developed to model distribution and degradation in various environmental 

media. These mass balance models can be used to make predictions of human exposure based 

upon “exposure factors,” such as assumptions of human interactions with environmental media 

and derivation of food from the environment [44, 45]. Minimally parameterized by predictions 

from chemical structure and release volumes, these far field exposure models can be used to 

make high-throughput exposure estimates [5, 64]. When other, near field sources of exposure 

also exist (e.g., indoor emissions or direct contact with chemicals in products) this contribution 

to overall exposure can dominate far field sources [65].

Both environmental half-lives and physico-chemical properties have long been considered 

drivers of environmental fate and potential exposure [46, 62-64]. However, positive correlation 

between exposure prioritizations based on these properties and other measures of exposure have 

been elusive [62, 66], and many of these prioritizations have been on a relative rather than an 

absolute (i.e., mg/kg bodyweight/day) scale. Careful consideration of the confidence in these 

predictions (i.e., uncertainty) is essential, and comparison of model predictions to real world 

exposure data is highly desirable [1, 20, 21].

This first generation application of the SEEM framework demonstrates that it is possible to 

generate rapid exposure estimates using minimal information and estimate uncertainty using 

monitoring data. In Wetmore et al. [8], human oral equivalent doses (mg/kg/day) estimated to 

correspond to in vitro bioactivities were calculated. The oral equivalent doses for the vast 

majority of chemicals in Wetmore et al. [8] were in excess of 10-4 mg/kg/day, while we find in 

Figure 14 that even with large estimated uncertainty, that the upper-limit of the 95% confidence 

intervals for the 668 chemicals with the lowest exposure predictions are below this level.
New High-throughput Methods to Estimate Chemical Exposure Page 53 7/8/2014

2f. Second Generation SEEM Analysis: Heuristics of Exposure

In the first generation SEEM analysis, we evaluated 1936 chemicals using far-field mass balance 

human exposure models and a yes/no indicator for near field (e.g., consumer product in the 

home) chemical use [6]. Information on chemical use was found to be most predictive; 

generally, chemicals above the limit of detection in NHANES had indoor/consumer product use. 

This simple (i.e., yes/no) near field indicator variable had greater predictive power than the more 

complicated environmental exposure models. 

The ability of a simple heuristic – whether or not chemicals were present in the home – to 

forecast exposure in the prior study raises the question of what other simple heuristics might be 

used to classify chemicals. Given a variety of rapidly obtained data (putative chemicals use 

categories and physico-chemical properties largely obtained from QSAR), which factors best 

explain exposures inferred from the available NHANES biomonitoring data? Further, if near 

field sources of exposure tend to be the predominate source of chemical exposures indicated by 

biomonitoring, what data and models exist to address near field exposure for the thousands of 

chemicals with limited exposure data?

Though the first generation model calibrated existing models with a relatively limited number of 

chemicals inferred from NHANES urine data (N=82) [6], in this research we aim to build a new 

model by identifying more detailed heuristics from a slightly larger set of chemicals (N=106). 

Caveats to this approach include the danger of over-fitting, and we aim to determine the simplest 

model (i.e., least number of factors) that best describes the data without fitting noise in that data. 

To assess model parsimony, we rely on the AIC [60]. The minimal predictive subset of factors 

we identify in this manner we describe as the heuristics of environmental chemical exposure.

In this analysis, we explored various demographic groups identified by NHANES. 

The converged Markov Chain of parent chemical exposures for each demographic group 

provided the samples investigated via the method of best subsets [59]. A total of 19 potential 

predictors were considered, comprised of production volume, 13 chemical use categories, 3 

physico-chemical properties, and two randomly generated descriptors. For each subset size and 

sample, best subset selection was performed using complete enumeration [59]. The average AIC 

across all samples indicated the optimal number of factor. The frequency of occurrence of the 

factors among the best subset size was used to determine the optimal model. The subset size and 

best factors were consistent across all demographics.
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AIC depends on the number of factors (subset size) and the success of the best combination of 

that number of factors for describing the sample. In the top-half of Figure 17 the distribution of 

the relative AIC across all samples is plotted for each subset size. Lower AIC scores indicate a 

more parsimonious model – a model that explains more variance with fewer predictive factors. 

The plot points at each subset size in Figure 17 indicate the median AIC across all samples, while 

the 97.5 percentile and the 2.5 percentile are indicated by the vertical line at each size of subset. 

The horizontal dotted line indicates the minimum average relative AIC, and the vertical dotted 

line indicates that the minimum average relative AIC occurs when the number of predictive 

factors in the subset is five. Therefore, a five-factor model is suggested by AIC in the top-half of

Figure 17.

Because the samples represent different combinations of chemical exposures that would be 

consistent with the urine metabolites identified by NHANES, different factors may be better for 

different samples. In the bottom-half of Figure 17, the predictors involved in the best model with 

higher frequencies are represented by darker circles, and those with lower frequencies by lighter 

circles. For the five factor subset suggested by AIC, the factors with highest frequencies are 

“Production volume” and four modified use categories from ACToR: “Consumer & Industrial” 

use, “Pesticide Active” use, “Pesticide Inert” use, and “Industrial no Consumer” use, where 

“Industrial” indicates use in chemical/industrial processes.

Using the factors identified by best subsets, a Bayesian regression was performed to jointly infer 

the regression coefficients, stoichiometric relationships, and the parent exposures from the 

NHANES subject-specific urine samples for the total NHANES sample and each demographic 

subset. For this analysis, Hamiltonian Monte Carlo was performed via Stan (http://mc-stan.org). 

As is shown in Figure 18, the correlations obtained are much improved over the previous model 

(Figure 13). The calibrated model has an R2of approximately 0.5.

Figure 19 shows the median and the upper limit of the 95% credible interval for predicted 

exposures (mg/kg BW/day) derived from the heuristics in Table 3, for the entire 7785 chemical 

library under study. The highest overall upper 95% limit for the general populations was for 

diethyl phthalate, a chemical monitored by the NHANES program. In Figure 19, we highlight 

two groups of chemicals. First, there are 8 chemicals for which the upper limit of the 95% 

credible interval is higher than that of the highest exposure for methyl paraben, the next highest 

chemical currently monitored by NHANES. These are Tox21 chemicals that have similar use 

profiles to the highest exposure NHANES chemicals, but may have larger production volumes. 

Second, the 4182 Tox21 chemicals grouped together in the middle of the rankings in Figure 19. 

These chemicals all have no use indicated among the four significant use category heuristics, 

although some have other known uses within ACToR. The predictions for these chemicals are 

therefore drawn from the six NHANES chemicals without a predicted use in the ACToR.

Also plotted in Figure 19 are the median and upper 95% predicted exposures for children aged 6-

11 (the youngest group with NHANES urine biomonitoring data). The exposures for children are 

predicted to be systematically slightly higher than for the total population.
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Figure 17: Determination of the number and most informative parameters in the second 

generation SEEM analysis. For the 106 chemicals inferred from NHANES biomonitoring,

five chemical descriptors were optimal for predicting exposures without over-fitting (e.g., 

fitting noise). The top part of the figure shows the average relative AIC (smaller is better) 

for models made with different numbers of parameters for explaining 1500 different 

combinations of chemical exposures. The 97.5% percentile and the 2.5% percentile from 

the relative AIC of the samples are indicated by the vertical line at each size of subset. The 

horizontal dotted line indicates the minimum average relative AIC, and the vertical dotted 

line indicates its corresponding size of subset. The predictors involved in the optimal model 

with higher frequencies are represented by darker circles, and those with lower frequencies 

by lighter circles. To control for potential noise in the modeling approach, two random 

variables generated from binomial distribution with probability 50% and 10% of obtaining 

1 were included in parallel. Neither are not selected as optimal descriptors in the five factor 

model. 
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Figure 18: Correlation of inferred exposures and predicted exposures based on the 

consensus model determined by the second generation SEEM analysis. Exposures inferred 

from NHANES biomonitoring data were linearly regressed on high-throughput descriptors 

(Figure 9) in order to create a calibrated predictor for predicted exposure (mg/kg/day). The 

solid line indicates the 1:1 line (perfect predictor). Bayesian analysis was used to distribute 

urine products using mass-balance, giving the 95% confidence intervals (light lines) and 

medians. The uncertainty of the inferred exposures (width of the vertical confidence 

interval) is strongly dependent on the number of general population quantiles with 

concentrations below the limit of detection in NHANES. The consensus model shows

predictive power (R2 ~0.5). Compare withFigure 13 to see the improvement of the new, 

heuristic model.
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Figure 19: Prioritization of 7785 chemicals with respect to the upper 95% predicted 

exposure (mg/kg/day) with the model from the second generation SEEM analysis for the 

total U.S. population is included together with the predictions for children aged 6-11. For 

each chemical the lower circle indicates the median and the upper circle indicates the 

upper limit of the 95% credible interval for predicted exposures (mg/kg BW/day) derived 

from the heuristics in Table 3. Arrows indicate the chemicals inferred from the NHANES 

data, which were used to determine the linear regression models Figure 20. Demographicspecific predictions for the 7785 chemicals are extrapolated from these NHANES 

chemicals. The grey box indicates the range of the 4182 chemicals with no use indicated by 

ACToR for any of the four significant use category heuristics.
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Although the heuristics in Table 3 seem plausible in retrospect, additional data may diminish or 

eliminate their significance. Similarly, plausible heuristics not supported by this analysis (e.g., 

vapor pressure) may be significant for chemicals not included in the current set of monitoring 

data. Although quantitative predictions are useful for prioritization, the statistical analysis here 

may be even more useful as a means to organize and prioritize the types of data needed for 

predicting human exposure to the thousands of chemicals in our environment [20, 21]. For 

chemicals that are similar to chemicals known to have high measured concentrations in the urine 

of the U.S. population, we need additional exposure information, such as biomonitoring and 

environmental sampling. New biomonitoring data will also be needed for the chemicals whose 

properties are most different from those currently being studied in the NHANES and by the U.S. 

EPA. In this manner, factors such as vapor pressure and other environmental factors may be 

shown to be important exposure heuristics. Chemicals without monitoring data, but that are 

similar heuristically to those chemicals with low exposure indicated by monitoring data, become 

lower priorities for further evaluation with respect to exposure.

Note that two of the predictive factors are modified ACToR categories “Consumer use and 

Chemical/Industrial Process use” (defined as the intersection of the two ACToR categories) and 

“Chemical/Industrial Process use, no Consumer use.” The final two columns of Table 3 illustrate 

the difference between the NHANES chemicals and those chemicals that make up the U.S. 

government Tox21 library – although there are a significant number of chemicals in Tox21 that 

have similar uses to the NHANES chemicals, the largest fraction of the total (4182 out of 7785) 

chemicals have no use identified by ACToR.

With respect to the five predictive factors in the optimal model, the ten demographic groups do 

not appear to vary substantially (Figure 20). When the optimal model with five predictive factors 

determined for the general population is run using data from ten demographic groups, resultant 

regression coefficients are very similar across demographic groups. 

The distribution of values for the regression coefficient in Figure 20 indicates the trend 

corresponding to each heuristic. For all demographic groups, exposures are estimated to be 

higher if: (1) both consumer use and chemical/industrial process use is indicated; (2) if pesticidal 

inert use is indicated; and (3) if production volume is high. For all demographic groups, 

exposures are estimated to be lower if chemical/industrial process use without consumer use, or 

pesticidal active use, is indicated. The difference between pesticidal inerts and actives is 

interesting. Most pesticide active ingredients monitored by NHANES are found to be below the 

limit of detection in the urine of most Americans, while the chemicals indicated as pesticidal 

inerts (e.g., bisphenol a, methyl paraben) are known to be inert ingredients in other classes of 

products that do not conform to the strict ingredient reporting standards of pesticides.
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Figure 20: Regression coefficients for the model from the second generation SEEM analysis 

across different demographic groups monitored by NHANES studies (abbreviations 

include Reproductive Age Females, children aged 6-11, children aged 12-19, adults aged 

20-65, adults 66 and older, and people with body mass indices less or greater than 30). 

Separate linear regressions were made of the inferred exposures on the five optimal 

descriptors for different demographic groups monitored by the CDC NHANES studies. 

The posterior distribution of standardized regression coefficients for the top five factors.

Large positive coefficients indicate correlation with increased exposures, while large 

negative coefficients correspond to decreased exposures. Among the demographic groups 

considered there is little variation in the significance of those factors for the chemicals we 

have examined.
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Figure 21: Regression intercepts of the model from the second generation SEEM analysis 

across different demographic groups monitored by NHANES studies (abbreviations 

include Reproductive Age Females, children aged 6-11, children aged 12-19, adults aged 

20-65, adults 66 and older, and people with body mass indices less or greater than 30). The 

distribution of intercepts (i.e., the contribution from sources not explained by the top five 

factors), does appear to vary between demographics.
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Figure 22 demonstrates that for all demographics, using two or more descriptors explained 

roughly 50% of the variance. This indicates a substantial improvement over the explanatory 

power of first generation model (R2~0.15, Figure 13). Shown in Figure 21 is the variation 

between demographics of the intercept term in the linear regression – this corresponds to the 

average value of the exposure that is unexplained by the five heuristics of exposure. We note that 

this value does appear to differ between some demographics. The largest average exposure is for 

children aged 6-11 years (the youngest age for which NHANES urine monitoring data is 

available), and decreases with age. This indicates that although the heuristics of exposure appear 

to describe a substantial fraction of the chemical-to-chemical variation in exposure, those aspects 

of exposure that drive the differences between demographics are not described by these 

heuristics.

In the absence of any other exposure data, the heuristics of exposure in Table 3 provide a 

reasonable basis for initial prioritization of those chemicals most in need of additional study. 

These heuristics meet the need identified by the NRC for predictive tools that will enable 

development of exposure information on thousands of chemicals that are now in widespread use, 

and enable informed prioritization of those chemicals [1]. Although the state of knowledge on 

human exposure is not ideal, some easily obtained information correlates with the exposure as 

inferred from biomonitoring data for 106 environmental chemicals. For the total U.S. population, 

as well as various demographic groups, including children aged 6-11 and women of child 

bearing age, our high-throughput heuristic model is able to explain roughly half of the variance 

in the inferred exposures. Out of the thousands of other chemicals without publicly accessible 

biomonitoring data, those with properties and uses that are most like the chemicals to which 

people are known to be highly exposed are logical targets for further investigation by more 

traditional exposure science methods.
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Figure 22: Amount of variance (R2) explained by the second generation SEEM model as a 

function of number of factors. The amount of variance in the exposures inferred from the 

NHANES chemicals is roughly independent of the number of parameters, which is why the 

method of best subsets (Figure 17) was used to identify the appropriate number of 

parameters. The vertical lines indicate the 95% credible interval across the 1500 different 

exposure scenarios inferred from the NHANES urine data.
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Table 3: High-throughput exposure heuristics 

Number of Chemicals

Heuristic Description

Inferred 

NHANES 

Chemical 

Exposure

s

(106)

Full 

Chemical 

Library 

(7785)

ACToR 

“Consumer use &

Chemical/Industr

ial Process use”

Chemical substances in consumer 

products (e.g., toys, personal care 

products, clothes, furniture, and homecare products) that are also used in 

industrial manufacturing processes. 

Does not include food or 

pharmaceuticals.

37 683

ACToR 

“Chemical/Indust

rial Process use 

with no 

Consumer use”

Chemical substances and products in 

industrial manufacturing processes that 

are not used in consumer products. 

Does not include food or 

pharmaceuticals

14 282

ACToR UseDB

“Pesticide Inert 

use”

Secondary (i.e., non-active) ingredients 

in a pesticide which serve a purpose 

other than repelling pests. Pesticide use 

of these ingredients is known due to 

more stringent reporting standards for 

pesticide ingredients, but many of these 

chemicals appear to be also used in 

consumer products (see Figure 8).

16 816

ACToR 

“Pesticide Active 

use”

Active ingredients in products designed 

to prevent, destroy, repel, or reduce 

pests (e.g., insect repellants, weed 

killers, and disinfectants).

76 877

TSCA IUR 2006 

Total Production 

Volume

Sum total (kg/year) of production of the 

chemical from all sites that produced 

the chemical in quantities of 25,000 

pounds or more per year. If 

information for a chemical is not 

available, it is assumed to be produced 

at <25,000 pounds per year.

106 7785
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2g. Limitations of Approach

The EPA has identified over 10,000 chemicals in the EDSP universe that are currently in 

commercial use, of which only a fourth may have been adequately assessed for potential hazard

and even fewer for exposure [11-15]. The EPA needs to prioritize these chemicals on the basis of 

exposure [14].

Many screening-level exposure models exist for assessing one chemical at a time (see Table 1), 

and many tools exist to assist for making reasonable decisions about use and potential exposure 

to chemicals from the minimum legally required information provided by a manufacturer [e.g.,

see the EPA’s EXPOsure toolBOX (EPA-Expo-Box) website at 

http://www.epa.gov/risk/expobox for examples regarding residential SOPs and occupational data 

for conventional pesticides and antimicrobial pesticides]. The data available for running these 

models can vary greatly depending on the applicable law, with pesticides typically having the 

most available data whereas other chemicals may only have structure-derived physico-chemical 

property predictions. As reported in Mitchell et al. [5], most of these screening tools cannot be 

automated to make predictions for thousands of chemicals at once (what is known as “batch 

mode”). If, for example, a model without batch mode only required 5 minutes to both enter the 

chemical-specific parameters and run the simulation to make predictions, running 8000 

chemicals through a model would take 17 full work weeks. Every time new data became 

available or the model was refined, an additional 17 weeks would be required to update the 

predictions. This is not an efficient or cost effective approach to the evaluation of potential 

human exposure.

In contrast, Wambaugh et al. ([6]) demonstrated a method for generating rapid, exposure

estimates using minimal information and estimates uncertainty using monitoring data. The 

methods described in Wambaugh et al. ([6]) make use of the available models that are capable of 

making potential exposure predictions for thousands of chemicals in minutes. In order to achieve 

these efficiencies, the various HTE models make assumptions, particularly with respect to the 

granularity of space and time [44, 45]. We attempt to use compare exposure predictions to 

exposures inferred from monitoring data, and we do so systematically across as many chemicals 

(and chemistries) as possible to quantify the impact of these assumptions.

Using Bayesian methods and monitoring data we get a calibrated prediction of exposure for 

thousands of chemicals, and just as importantly, we get an empirical estimate of our uncertainty. 

Any approximations, omissions, or mistakes should increase the estimated uncertainty when 

evaluated systematically across chemicals over time. This uncertainty reflects contributions from 

at least the following four factors: (1) incomplete/inappropriate chemical descriptors; (2) 

imperfect model assumptions; (3) measurement uncertainty and other sampling issues for the 

monitoring data; and (4) imperfect inferences from the monitoring data. This approach can be 

applied in any case where there are HTE models that predict values that should be consistent 

with a source of monitoring data.

For example, the high-throughput fate and transport models that were evaluated by Wambaugh et 

al. ([6]) describe a mean human population exposure and mean water concentration for large, 

geographically homogenous regions composed of a few, homogenous types of environmental 
New High-throughput Methods to Estimate Chemical Exposure Page 65 7/8/2014

media (e.g., air, water, soil) that are at steady-state with respect to constant environmental 

releases of chemical. Similarly, human exposure inference from biomonitoring data assumes that 

biomarkers of exposure reflect a steady-state with respect to constant exposures 

Exposure inference from the NHANES data was also conducted under the assumption of steadystate conditions [50]. This is a significant assumption when trying to relate human tissue and 

fluid sample chemical composition metrics to real world environmental exposures across various 

media (e.g., air, water, soil), which may be sporadic and/or seasonal, over the course of a 

lifetime. At this time, however, these assumptions are necessary for chemicals where there are no 

data to do otherwise [5, 25] and these assumptions are a key contributor to the overall 

uncertainty estimated in Wambaugh et al. ([6]). The benefit of these assumptions is that the 

evaluation and calibration of exposure model predictions for thousands of environmental 

chemicals takes a few days at most. As HTTK data becomes available (Section 3), it may 

eventually be possible to characterize the confidence in these inferences with respect to the 

drivers of variability (e.g., exposure frequency, duration, and chemical metabolism/excretion 

half-life). As finer grained chemical use information becomes available, it might be used jointly 

with basic PK data to eliminate the need for the steady-state assumption altogether. At this time, 

the steady-state assumption appears to be the best available for HTE methods, and is a 

contributor to the overall uncertainty estimated in Wambaugh et al. ([6]).

As described in Section 4, ORD has undertaken multiple research activities to address many of 

the limitations identified by Wambaugh et al. ([6]). These include better chemical use 

descriptions via the Consumer Product Chemical Profiles database (CPCPdb) [67] and 

Chemical-Product Categorization (CPCat) database [68], characterization of population 

variability by analysis of exposure biomarkers for different groups characterized by the CDC 

NHANES study, and more dynamic near-field exposure scenarios using a newly created highthroughput version of the Stochastic Human Exposure and Dose Simulation (SHEDS-HT).

It is not expected that HTE predictions will recapitulate the results of running a more detailed, 

higher tier exposure model. However, we do expect that more detailed model predictions should 

be within the uncertainty bound of the high-throughput prediction. Generally, if one has the time 

and data to run a higher tier model, then it is reasonable to expect that the higher tier model 

would outperform HTE estimates. However, HTE predictions offer a means to determine which 

of the thousands of chemicals with little associated data should be evaluated first. 
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2h. Conclusions

In the NHANES data, chemicals with near field sources are generally above the limit of 

detection, whereas those with far field only sources were generally below, providing only 

minimal information for evaluating far field exposure routes. Most of the NHANES compounds 

with products in urine above the limit of detection have significant and diverse use in the home 

that could produce near field releases. For these chemicals, near field sources are a much more 

significant driver than the diffuse, continent-wide sources of far field models.

As demonstrated by the second-generation model, easily obtained heuristics of near field 

exposure can explain more than half the variability in parent chemical exposures inferred from 

CDC NHANES biomonitoring data. However, the remaining unexplained variance appears to be 

associated with the well documented differences in exposures between demographic groups. 

More troubling are the thousands of chemicals of interest to the EPA and other federal agencies

that are not used (as indicated by use categories) in ways similar to those chemicals within the 

CDC NHANES dataset. Additional biomonitoring data is needed to develop empirical exposure 

heuristics for these chemicals. Ultimately, the ability to apply knowledge gained from the extant 

monitoring data to thousands of chemicals with no other source of information is critical 

information for determining the potential for human exposure to environmental chemicals. 

As additional data sources like the CPCPdb and CPcat become available for ever larger groups 

of chemicals, new high-throughput heuristics may be identified and more detailed models may 

be possible. These heuristics, however, are surrogates for the underlying exposure factors (e.g., 

presence or absence of a chemical in lieu of knowledge of what fraction of a product’s weight is 

made up by the same chemical). Heuristics of exposure may therefore be thought of as 

necessary, but not sufficient, for the occurrence of actual human exposure. Though a large 

portion of the variance in exposures inferred from NHANES can be explained with these simple 

heuristics, it seems plausible that more mechanistic models covering pathways (e.g., inhalation, 

dermal absorption) and exposure events (e.g., duration of contact, emissivity of chemical) in 

greater detail should be able to explain some of the remaining unexplained variability from 

chemical to chemical, and allow for determination of varying levels of uncertainty for different 

chemical classes.

Many proposed near field models would benefit from improved physico-chemical data and 

predictions [38, 69]. EPI Suite could not predict all necessary properties for 167 of the chemicals 

initially considered. HTS for physico-chemical properties [70] could reduce or eliminate the 

need for some QSARs to predict these properties, while allowing other QSAR methods to 

expand into new regions of chemical space. Assays amenable to HTS exist for physico-chemical 

properties [70] as well as for half-lives in environmental media [71]. In vitro assays to estimate 

biotransformation half-lives may be especially valuable [66]. The rate limiting step in half-life 

assays is typically the chemical specific analytical chemistry methods, which are available for 

many ToxCast chemicals [8].

Expanded monitoring data are needed to better characterize actual exposures. For the majority of 

chemicals, where resources such as NHANES data are not available, new and more flexible 

approaches are needed to quantify population-level chemical exposures. HTS techniques are 
New High-throughput Methods to Estimate Chemical Exposure Page 67 7/8/2014

becoming available that can simultaneously screen for thousands of xenobiotic chemicals as well 

as endogenous markers of biological response and exposure in plasma [72].

For the thousands of chemicals with no other data on human exposure, the SEEM framework can 

determine a calibrated and empirical uncertainty estimate for predicting human exposure and 

provide a means to prioritize chemicals within the EDSP universe based on exposure.
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3. High-throughput Toxicokinetics (HTTK) and Reverse 

Toxicokinetics (RTK) 

3a. Introduction

HTS methods have the potential to prioritize environmental chemicals in order to identify those 

that are most in need of further evaluation [1, 8, 17, 73-78]. HTS methods exist for identifying 

likely chemical-induced changes to human biology in vitro [7, 40] as well as for predicting 

ranges of plausible human exposure (HTE, [6]). TK provides a bridge between in vitro HTS and 

HTE by either predicting tissue concentrations due to exposure or inferring exposure doses

(mg/kg BW/day) that would be needed to cause tissue bioactive concentrations in vivo (i.e., 

reverse TK or RTK) [8, 51, 73-77, 79-83].

For pharmaceutical compounds, successful methods have been developed for determining some 

aspects of TK (e.g., hepatic metabolic clearance and steady-state behavior) from limited in vitro

methods and chemical structure-derived property predictions [84-86]. Without the time and 

resources to generate in vivo TK data for the thousands of chemicals that require TK data, these 

HTTK methods for pharmaceuticals may be useful for environmental chemicals [8, 78]. 

HTTK methods can estimate the dose (mg/kg BW/day) needed to produce steady-state plasma

concentrations (Css, in units of μM) equivalent to the activation concentrations observed in vitro

(this is RTK) [8, 73, 87]. In drug development, these HTTK methods allow an initial 

determination of the therapeutic doses for clinical studies. This approach is considered effective 

for pharmaceutical compounds and it is claimed that the predicted concentrations are typically on 

the order of the actual, in vivo concentrations [9]. 

To date, the TK models used for environmental chemicals have been relatively simple, making 

three key assumptions: (1) that the whole body is at the same concentration (i.e., plasma); (2) 

that the environmental exposure is constant and uniform (i.e., constant infusion), and (3) that 

enough time has passed that the plasma concentration is at steady-state with respect to the 

environment [8]. Dynamic simulation (e.g., more realistic, sporadic dosing) of physiologicallybased toxicokinetic (PBTK) models allows the prediction of chemical concentration in specific 

tissues at different times. PBTK models are more complicated and are typically considered 

higher tier models [77, 81, 88]. However, it is possible to rapidly parameterize generic PBTK 

models. These high throughput PBTK (HTPBTK) models use QSARs for tissue-specific 

properties (e.g., Schmitt [89] or Poulin and Thiel [90]) and attempt to make conservative (i.e.,

human health protective) assumptions about unknown dynamic processes, such as absorption 

[76, 79].

Since the goal of HTTK modeling of pharmaceuticals is to determine efficacious doses for 

follow-on human clinical trials in which in vivo TK will be measured [84], the degree of 

confidence needed in these methods is different for environmental compounds, for which both 

ethical and resource considerations likely preclude human testing. Unfortunately, HTTK 

approaches have demonstrated only mixed success for environmental chemicals when compared 

to models developed with in vivo data [8]. Further, a general barrier to the use of TK, especially 

PBTK, methodology in assessing environmental chemical risk to humans has been complexity 
New High-throughput Methods to Estimate Chemical Exposure Page 69 7/8/2014

and perceived lack of transparency [88, 91]. If HTTK methods are to be used to determine risk, 

their uncertainty must be well characterized ideally with rigorous statistical methodology. 

Here we report results for analysis of HTTK predictions for 396 environmental chemicals using 

an empirical TK model (similar to what was used by Wetmore et al. [8] via SimCyp). For 292 of 

these chemicals, we also analyze HTPBTK predictions. We determine when the assumptions 

necessary to build these HTTK models using the available data will lead to conservative results 

(i.e., errors that would underestimate the difference between bioactive and environmental doses); 

and when and for which chemicals we should collect additional data, potentially by more 

traditional TK methods. In keeping with the SEEM approach, we empirically determine our 

confidence by statistical analysis of model predictions for these chemicals. 

We perform both simulation experiments and compare model predictions from in vitro data with 

human and rat in vivo measurements. We test the assumptions of previous work, particularly 

with respect to the measurement of key parameters and the time needed to approach steady-state, 

and analyze the discrepancies between predictions and measurements to characterize those 

properties of environmental chemicals that correlate with poor predictive ability. 

We describe a multi-step TK triage system, based on our analysis, to allow thousands of 

environmental chemicals to be characterized with respect to their potential risk to human health. 

We use the term triage in part because of the urgency for screening level risk evaluation for 

thousands of chemicals, and in part because provisional models can be made for most 

environmental chemicals with HTTK methods, but some require much more attention. We 

believe that this framework provides for both informing evaluation of new environmental 

chemicals through comparison to similar chemicals with extant TK knowledge, and for 

expanding the base of knowledge through new experimental measurements that can be targeted 

to maximum impact.
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3b. HTTK for Environmental Chemicals

Toxicokinetic Models Used

We used two general TK models. The first, a “three-compartment” model (Figure 23) for 

determining plasma concentrations did not determine tissue partitioning, and featured 

elimination due to metabolism of the parent compound in the liver and renal elimination by 

passive glomerular filtration, adjusted for plasma protein binding (i.e., the product of glomerular 

filtration rate and fup). We modeled hepatic metabolism using the “well-stirred” approximation 

[92, 93] from the flow into the liver and the in vitro measured values for fraction unbound 

intrinsic hepatic clearance and the density of hepatocytes in liver (1.1 x 108hepatocytes per gram 

liver and a liver density of 1.05 g/mL). 

Figure 23: The general, three compartment TK model, used by SimCYP for RTK [8].

The second general TK model is physiologically-based (PBTK) with separate tissue 

compartments for the gut, liver, lung (unused for dosing in this research), kidneys, and arterial 

and venous blood, as shown in Figure 24. The rest of the tissues in the body were aggregated into 

a single compartment termed “Rest”. Tissue descriptors, including volume; blood flow; cellular 

fraction of total volume; water, lipid and protein fraction of cellular volume; and distribution of 

types of lipid were taken from the corrected table in Schmitt [89]. Tissue flows and volumes 

were scaled to cardiac output and body weights from Davies and Morris [54], which also 

provided values for glomerular filtration rate and hematocrit. Absorption from the gut lumen into 

gut tissue was modeled as a first order process with a rate of 1/h. Venous blood from the gut and 

arterial blood fed separately into the liver, where metabolism was again modeled with the “wellstirred” approximation

Tissue to unbound fraction in plasma partition coefficients were predicted using Schmitt’s 
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method [89] from the in vitro fup, the predicted hydrophobicity (EPI Suite), and any acid/base 

dissociation constants available in the literature. We assumed tissues were perfusion-limited (i.e., 

tissue, red blood cells, and plasma come to equilibrium rapidly with respect to the flow of 

blood). The same, constant ratio of blood to plasma concentration was used throughout the body 

and was predicted using the hematocrit and the predicted partitioning between red blood cells 

and plasma. With these assumptions, the chemical concentration in any tissue divided by that 

tissue’s partition coefficient was equal to the unbound fraction of chemical in the plasma of that 

tissue. The rest of body compartment was created by weighting the partition coefficients for all 

tissues described in Schmitt [89] by their volumes.

Figure 24: The general, physiologically-based TK (PBTK) model used to investigate the 

impact of steady-state assumptions.

Determination of steady state

An analytic solution for liver plasma concentration was derived from the differential equations 

describing the three-compartment model by assuming constant infusion dosing. This analytic 

solution is the equation used for steady state plasma concentrations (Css) in previous work [8, 73, 

87, 92]. For the PBTK model, a series of repeated, every eight-hour oral doses were simulated 

until the venous concentration just before dosing was within a small tolerance (1%) of the 

venous concentration eight hours earlier.

In vitro chemical data

Two types of in vitro data were used to parameterize the TK models: (1) CLint, the intrinsic 

metabolic clearance of the parent compound by primary hepatocytes (substrate depletion 

approach) in wells of a multi-compound plate [94]; and (2) fup, the fraction of chemical unbound 
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in the presence of plasma protein as assessed using rapid equilibrium dialysis (RED) in which 

two wells are separated by a membrane that is permeable by smaller molecules, but prevents the 

plasma protein added to one well from migrating to the other well (the relative chemical 

concentration in the two linked wells gives the free fraction of chemical) [95]. For environmental 

chemicals, data on these two parameters were obtained primarily from Wetmore et al. [8] and 

Tonnelier et al. [76]. For pharmaceutical compounds these values were compiled from Obach et 

al. [96], Jones et al. [97], Naritomi et al. [98], Ito and Houston [99], Riley et al. [100], Schmitt 

[89], and Obach et al. [101]. Where available, acid/base dissociation constants were curated from 

this literature; otherwise chemicals were treated as neutral/unionized regardless of tissue pH. 

Molecular weight and structure were determined from the Distributed Structure-Searchable 

Toxicity Database (DSSTox) (http://www.epa.gov/dsstox) and hydrophobicity was predicted for 

all compounds using EPI Suite.

Monte Carlo simulation and parameter distributions

For comparison with the results in Wetmore et al. [8] obtained with the SimCYP population 

simulator [84], Monte Carlo was performed to simulate human variability. Normal distributions, 

truncated to ensure positive values, were used with mean values drawn from the references 

above and a coefficient of variation of 0.2. Body weight, liver volume, blood flow to the liver, 

and CLint were all varied in this manner. The fup was drawn from a censored distribution with 

identical properties to the other distributions if the mean was above the limit of detection 

(assumed to be 1% unbound) and a uniform distribution between 0% and 1% unbound otherwise. 

For each chemical, 1000 different combinations of parameters were used to determine Css, 

allowing estimation of the 5%, median, and 95% quantiles.

In vivo chemical data

Human in vivo Css for a dose of 1 mg/kg BW/day were identified for 11 environmental 

compounds (two chemicals having two different values) by Wetmore et al. [8]. For 

pharmaceutical compounds, the measured human clearance (L/day) and volume of distribution 

(L) in Obach et al. [101] was used to was used to determine Css for the same dose. 

Rat in vivo data for pharmaceutical compounds were curated by the Netherlands Organisation 

for Applied Scientific Research.
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3c. Evaluation and Domain of Applicability of HTTK/RTK

The implementation of the vision outlined in Toxicity Testing in the 21st Century has benefited 

greatly from technologies pioneered for drug discovery [17]. These methods include both in vitro

tools for identifying similarities between chemicals with known hazard and previously

unexamined environmental chemicals (e.g., ToxCast [7] and methods to extrapolate from 

laboratory conditions to simulated human in vivo conditions [80]. 

HTTK methods for anticipating human TK (primarily distribution, metabolism, and excretion 

with very limited success for absorption [102]) allow the prediction of the equivalent dose 

(mg/kg BW/day) needed to produce steady-state plasma concentrations (Css, in units of μM) 

equivalent to the activation concentrations observed in vitro [84-86]). The ratio of lowest activity 

equivalent dose to the highest predicted exposure, the AER, provides an integrated approach for 

prioritizing and initial screening of chemicals [8, 78].

We have examined three key assumptions from previous HTTK research. We found that it is 

possible to parameterize a HTPBTK model to allow tissue-specific concentrations to be 

predicted and more complicated exposure scenarios to be simulated except when the protein 

binding assay (for fup) fails. With these HTPBTK models, we simulated more realistic dosing 

scenario of three daily doses (e.g., at meal time or from consumer product use) and found that 

there were two broad classes of environmental chemicals: for most chemicals (240 of 292) 

steady-state was reached in a few weeks, indicating that the steady-state assumption is 

reasonable. However, for a second class of chemicals steady-state was not reached in hundreds 

of years. Therefore, the first element of our proposed triage process is to determine the 

appropriateness of the steady-state assumption, which can be done from in vitro data and QSAR, 

as in Figure 25, where the total elimination rate (total clearance divided by volume of 

distribution) is used to separate the two classes of chemicals.

With our HTPBTK models we are also able to compare peak concentrations with the Css derived 

from the three compartment model at steady-state. Using a default value for absorption rate 

(kabsorb = 1/h) we again simulated three daily doses (totaling 1 mg/kg BW/day) until steady-state 

was reached for most chemicals. As shown in Figure 26, we find that the peak concentration for 

repeated dosing is generally similar to the Css predictions for the constant infusion exposure that 

was used previously. 
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Figure 25: A histogram of number of chemicals compared with the number of days it takes 

to effectively reach steady-state . This analysis evaluates the plausibility of the steady-state 

dosing assumption. Using HTPBPK and assuming three daily doses (every eight hours), we 

find that the majority of chemicals reach steady state in roughly two weeks, while a second 

population of chemicals never reach steady state. 

We note two cases where the values are significantly different: first, for rapidly eliminated 

chemicals with low predicted Css, we find that the Css predicted for an infusion scenario 

overestimates the peak concentration. In this situation, the chemical is cleared in less than eight 

hours and essentially each new exposure is a separate exposure so that there is no accumulation 

of exposure. The second case where HTPBPK peak predictions vary from the constant infusion 

Css is for highly accumulative compounds – in this case, even after tens of thousands of days the 

chemicals do not reach steady-state, making the HTPBPK predictions less than Css since steadstate is never reached. Since the constant infusion exposure predictions overestimate the more 

complicated simulated exposures, both of these discrepancies are health-protective.
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Figure 26: Peak plasma concentrations from the HTPBPK model are compared against the 

steady-state concentration predicted by the three compartment model for a constant 

infusion exposure (as in Wetmore et al. [8]). The dashed, identity (1:1) line indicates that 

for most compounds the peak concentrations are very similar to Css.

SimCYP allows Monte Carlo (MC) simulation of variability in parameters (e.g., liver volume, 

cardiac output) in order to simulate biological variability [84]. In the Wetmore et al. [8] human 

dosimetry paper, MC was used to calculate a 5th, median, and upper 95th percentile Css for the 

same fixed dose. In Figure 27, we used our own MC simulation to show that we can generally 

recreate the previous result, but also that a few key changes can make the predictions generally 

more health protective.

In both the Wetmore et al. papers [8, 87], the RED assay for measuring protein binding fails in 

some cases because the amount of free chemical is below the limit of detection. For those 

chemicals, a default value of 0.5% free was used. In Figure 27, the default value is replaced with 

random draws from a uniform distribution from 0 to 1%. Using this distribution, we find that the 

lower 5th and median Css values are not impacted, but that the upper 95th(i.e., the most sensitive) 

are significantly higher than were estimated using the default assumption. Because the free 

chemical being below the limit of detection is a common occurrence (92 times out of 294 

experiments in human and rat), this change impacts many chemicals.
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Also in the Wetmore et al. papers [8, 87], a p-value threshold of 0.10 was used for systematic 

clearance of parent chemical vs. no disappearance of chemical in the hepatic clearance assay 

(i.e., systematic clearance of parent chemical) to determine if the assay had performed well. For 

the results shown in Figure 27, we have required that p-values be less than 0.05. If the hepatic 

assay is considered to have failed we use a default value of 0 (i.e., no hepatic clearance), which 

acts to increase the estimated plasma concentration for a fixed dose and is therefore health 

protective. Those chemicals in that are far from the identity line are the chemicals where the pvalue for systematic clearance was between 0.5 and 0.1. 

One of the drawbacks of hepatocyte suspensions in clearance detection is that they are not 

sensitive in measuring clearance for lower turnover compounds. Many of the compounds in the 

Wetmore et al. papers [8, 87] for which no or not-quite significant clearance was measured

might be expected to yield significant clearance when using plated hepatocytes over a longer 

time course [103]. Follow-up measurement of clearance rates using a plated system (e.g., Smith,

et. al [103]) may be of value at higher tiers to ensure that more accurate, rather than 

conservative, values are obtained.

We analyzed in vivo TK data on Css for 11 environmental chemicals [8] and 74 pharmaceuticals 

[101]. This data serves as our for model calibration and evaluation as it allows us to evaluate 

how well HTTK (as in Figure 27, but without MC simulation) does for predicting actual human 

Css. In Figure 28, we compare our HTTK Css predictions from in vitro data (as in Figure 27, but 

without MC simulation) with Css values inferred from human in vivo studies. We observe poor 

(R2 ~0.19), but mostly conservative correlation (i.e., the in vitro predicted plasma concentrations 

are higher than the in vivo data indicates, thus reducing the AER). 
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Figure 27: Correlation between previously predicted Css values [8] and the new method. In 

the Rotroff et al. [73] and Wetmore et al. [8] papers, SimCYP was used to predict 

distributions of Css from in vitro data. We recreated the aspects of the SimCYP TK MC 

simulation software used to predict Css resulting from a steady-state infusion of 1 mg/kg 

BW/day, and then changed a key assumption to better reflect screening for environmental 

chemicals. Any one chemical’s median and quantiles are connected by a dotted line. Rat 

predictions from Wetmore et al. [87] are also reproduced by this model.
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Figure 28: Comparison of Css predicted from in vitro HTTK with in vivo Css values 

determined from the literature. The dashed line indicates the identity (perfect predictor) 

line. Limited correlation was observed (R2 ~0.19). We note that the predicted value is an 

underestimate for 23 predictions and overestimated for 65 predictions. The white lines 

indicate the discrepancy between measured and predicted values (the residual) which we 

model in Figure 30.
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Figure 29: The recursive partitioning regression tree [104] for the discrepancy between the 

Css predicted from in vitro data and the Css measured in vivo [101].
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Figure 30: Relationship between actual residuals and predicted residuals. A recursive 

partition regression tree (Figure 23) was constructed for the residuals (the difference 

between the observation and the prediction) to predict the error as a chemical-specific 

estimate of the accuracy of HTTK.
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Using the method of recursive partitioning [104], a decision tree (Figure 29) was constructed 

using physico-chemical properties, in vitro data, and QSAR for likely drivers of discrepancies 

(e.g., transporter substrates and perfluorinated chemistry). Chemicals with low predicted 

residuals are within the domain of applicability of HTTK approaches. We find that the tree relies 

predominantly on fraction unbound, intrinsic clearance rate, hydrophobicity, and acid/base 

dissociation, but does include predictions for the ATP‐binding cassette transporter MDR1, which 

removes xenobiotics from cellular environments [105]. The performance of the model 

demonstrates that we can predict the errors associated with the HTTK predictions (R2 ~0.69) 

better than we can predict the Css values (Figure 28). For those chemicals in Figure 30 where the

predicted residual is less than 0.1, the actual residuals are less than 1 for all but one chemical, so 

that for these chemicals we are confident that the Css values predicted by HTTK is conservative.

In Figure 31 and Figure 32, we compare in vivo time course data for rats with HTPBTK 

predictions based on in vitro data. For a given chemical, the amount dosed and route of 

administration, as well as any biological or other variability, produce different plasma 

concentration kinetics which can be summarized by the time-integrated plasma concentration 

(Area Under the Curve or AUC). Neglecting route of administration, we can show correlation 

between in vivo data and HTPBTK predictions in Figure 31 (R2 ~ 0.58). However, when oral 

doses are contrasted against intravenous and subcutaneous dose routes, we find greater 

correlation (R2 ~ 0.65) since we systematically over-predict AUC for chemicals administered 

orally. The contrast between oral and intravenous doses is ~3.6 (AUCs from oral doses are 

roughly 3.6 times lower than predicted). This result supports the conclusion that the assumptions 

used for HTPBTK (i.e., 100% bioavailability and rapid absorption) are conservative. Given that 

oral routes of exposure are assumed for all chemicals in Wetmore et al. [8], the HTTK approach 

appears to be health protective in this regard. 

In Figure 32, we show that peak plasma concentration (Cmax) appears to be well described by the 

HTPBPK model (R2 ~0.69) and that there is a systematic bias toward over-prediction 

(conservative), although the handful of cases where HTPBPK under-predicts Cmax are all for oral 

administration.

The in vivo data in Figure 31 and Figure 32 are drawn from various treatments of 15 

pharmaceutical chemicals and six environmental chemicals, thus we cannot yet make final 

assessments of the HTPBPK methodology, but it appears that the effort to organize and collect 

additional in vivo data on environmental compounds is justified.
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Figure 31: Comparison of in vitro HTPBTK predictions of AUC with in vivo measurements 

from the literature. A total of 15 pharmaceutical chemicals and six environmental 

chemicals are shown. The predictions and in vivo studies are for the rat and each chemical 

may have more than one study with potentially different doses and routes. The predicted 

AUC for orally administered doses (po – triangle) generally overestimates tissue dose with 

respect to intravenous (iv – circle) or subcutaneous doses (sc – square) which were modeled 

as iv doses. The upper dotted line is the linear regression for iv doses, while the lower 

dotted line is for the oral doses; the trend is the same for both, the contrast between the 

lines indicates the over-prediction of AUC for oral doses (R2 ~ 0.58).
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Figure 32: Comparison of in vitro HTPBTK predictions for Cmax (maximum plasma

concentration) with in vivo measurements from the literature. A total of 15 pharmaceutical 

chemicals and six environmental chemicals are shown. The predictions and in vivo studies 

are for the rat and each chemical may have more than one study with potentially different 

doses and routes. The literature data include three routes of administration: oral doses (po 

– triangle), intravenous (iv – circle), and subcutaneous (sc – square) which was modeled as 

iv doses. The Cmax appears to be well described by the HTPBPK model (R2 ~0.69).
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3d. Limitations of Approach

Recall that the EPA must consider thousands of chemicals and has limited resources to assess 

exposure to human populations and the environment [14]. We are exposed to low levels of 

thousands of these chemicals through our environment [11, 12, 15, 72]. TK is needed to compare

in vitro HTS [7, 40] and HTE [6] by either predicting tissue concentrations due to exposure or 

inferring exposure doses that would be needed to cause tissue bioactive concentrations in vivo

(i.e., reverse TK or RTK) [8, 73-76, 79-83]. 

Approaches to address TK uncertainty and variability must be able to handle censored (limit of 

detection) measurements, as we do with our new methods. We find that measurement limitations 

such as non-detects (i.e., highly bound chemicals) for the protein binding assay may result in an 

artifactual underestimation of Css values, which in turn may overestimate the margin between 

bioactive doses and estimated exposures (i.e., overestimate the AER). However, comparison with 

in vivo data indicates that HTTK in vitro methods generally over estimate Css. 

For the metabolic clearance assay, a stricter limit on determining whether there is systematic 

disappearance of the compound (i.e., setting metabolic elimination to zero for p-value < 0.05 

rather than 0.10) is more conservative since it decreases AER. 

We used simulation study via HTPBTK to evaluate the steady-state assumption, determining that 

there is a broad class of chemicals that reach steady-state rapidly and that for most chemicals the 

peak concentration from three daily doses is within an order of magnitude of the Css.

Availability of data is a key issue for a statistical analysis of HTTK of environmental 

compounds. For pharmaceuticals, there is both in vitro and in vivo data available for hundreds of 

compounds. However, although in vitro TK data is available for most of the 309 ToxCast Phase I 

environmental chemicals, only 13 in vivo values were available for those same compounds [8]. 

Drawing from pharmaceutical literature, human in vivo values for Css was available for 87 

chemicals overall. As we move to HTPBPK, the data are even more limited (only 22 total 

chemicals).

Pharmaceutical compounds are typically small molecules designed to be well-absorbed, and 

targeted to cause specific biochemical effects [106, 107]. The chemicals we are exposed to in our 

environment can, however, be much more structurally diverse [108]. Individuals may be exposed 

to these chemicals from near field sources: either from direct application (e.g., cosmetics) or 

indirect proximate sources (e.g., out-gassing from furniture upholstery or unintended presence in 

food due to packaging materials) or far-field sources (e.g., agricultural pesticides or industrial 

pollution) [16]. Given the diversity of intended applications for environmental chemicals, their 

properties can vary greatly from those of pharmaceuticals.

Although we used MC simulation to characterize some aspects of human variability (e.g., body 

weight), the assumptions we made of how to model these variations can influence the results of 

the predictions, especially for the extremes (e.g., the highest 95th percentile) [91]. Further, any 

key determinants of variability that are not included in our simulation cannot be assessed for 

impact. 
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We recognize that the HTPBTK methods we are using do not include prediction of important 

aspects of TK such as bioavailability and absorption rate, but we can make conservative 

assumptions for these values and test these assumptions against in vivo data.

There are many ways in which HTTK models in general might omit key drivers of TK, 

including: extra hepatic metabolism, secretion in bile/enterohepatic recirculation, unusual 

binding affinity for one tissue, substrates for transporters in renal, hepatic, and other tissues, and 

poor absorption [73]. Until in vitro assays are developed for these processes, QSAR provides the 

best way forward. Unfortunately, QSAR is not yet capable of providing rate constants; rather it 

allows estimation of how likely or unlikely a process is to occur. Thus we have used QSAR to 

classify the prediction errors determined by comparing with in vivo data. However, we must 

worry about structure outside the domain of applicability of QSAR. Therefore, another tier of 

HTTK triage is developing data to both expand this domain of applicability by measuring new 

chemistries, and by targeting those chemicals most likely to be subject to these processes as 

indicated by large predicted errors.

The perfusion-limited assumption necessary to predict chemical partitioning into tissue is most 

likely incorrect for chemicals with slow passive transport through lipid membranes and for 

chemicals that are substrates for transporters in various tissues. QSAR models can offer 

qualitative prediction of whether or not these processes are at work, thus indicating greater or 

lesser confidence in these model assumptions, but again, there are currently no existing methods 

for predicting the quantitative rates that would be necessary for including these effects in the 

PBTK model.

In order to prioritize thousands of chemicals with limited data using models based upon broad 

assumptions, we must carefully characterize the uncertainty in our approach. Here we use direct 

comparison to in vivo data in order to get an empirical estimate of our uncertainty. Any 

approximations, omissions, or mistakes should work to increase the estimated uncertainty when 

evaluated systematically across chemicals. Once estimated, the uncertainty allows appropriate 

weighting of HTTK predictions in prioritization.
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3e. Conclusions

For chemicals with limited data, the ratio of lowest putative bioactive dose to the highest 

possible exposure (AER) has been proposed as a means for an integrated approach to prioritizing 

and initial screening of chemicals [8, 74, 78]. Since the determination of bioactive dose relies on 

IVIVE, the methods used must be shown to be health protective (conservative) in the sense that 

the plasma concentrations resulting from a known dose are either accurate or over-estimated. We 

have evaluated HTTK approaches to use in vitro data to predict steady-state plasma

concentration (Css) resulting from constant exposures. We used simulation studies via HTPBPK 

models to test key assumptions, finding that for most chemicals the peak plasma concentrations 

are either very similar or less than the Css calculated using more simple models. We have also 

used simulation studies to examine key assumptions about the in vitro measurements, identifying 

some refinements. Finally, we have compared in vitro predicted Css with in vivo measured Css for 

some chemicals. We observed poor (R2 ~0.19), but usually conservative correlation. However, 

we can model the difference between in vivo measurements and HTTK predictions (i.e., the 

residuals or errors), achieving a much greater ability to predict errors (R2 ~0.69) than to make 

overall predictions. For those chemicals where the predicted residual is less than 0.1, the actual 

residuals are less than 1 for all but one chemical, so that for these chemicals we are confident 

that the Css values predicted by HTTK is conservative.

If a sufficient in vivo TK data set can be developed, then a statistically defensible TK triage 

system can better identify those chemicals for which simple in vitro characterization is sufficient, 

and those chemicals for which more substantial research efforts (e.g., tissue-specific partitioning, 

accumulation, and transport) are warranted.

For non-therapeutic compounds, it is unlikely that there will ever be controlled human TK data.

If these methods are to be the only TK data for some compounds, it is essential to carefully 

characterize their predictive ability and domain of applicability. Though some in vivo TK data 

exists to allow statistical assessment of HTTK predictions, these data are predominantly for 

pharmaceutical compounds (Figure 28). However, rat HTTK data have been developed for 

approximately fifty environmentally-relevant compounds (from ToxCast phase I) that have 

undergone toxicity testing in rat [87]. If in vivo TK data were collected for some or all of these 

compounds, then the HTTK could be much better evaluated for environmentally-relevant 

compounds. In vivo TK data provides a combination of both evaluation data for properties that 

can predicted in vitro and with QSAR (e.g., clearance and volume of distribution) as well as the 

only way to obtain data on other key processes (e.g., absorption rates, extrahepatic metabolism). 

For example, although CACO2 membrane permeability assays exist, it is currently difficult to 

translate this data into quantitative bioavailability [102]. Thus, if in vivo data could be collected 

for both oral and intravenous dosing, the relative concentrations could give an approximate 

assessment of bioavailability that might then be extrapolated to humans.

We have examined three key assumptions from previous HTTK research both through direct 

comparison of predictions to in vivo data and with simulation studies. We found that it is 

possible to parameterize a HTPBTK model to allow tissue-specific concentrations to be 

predicted and more complicated exposure scenarios to be simulated except when the protein 

binding assay (for fup) fails. Drawing from the result of our analysis, we organize the tiers of 
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HTTK testing into a toxicokinetic triage framework for environmental chemicals, presented in

Table 4.

TK Data

TK 

Prediction/Consequence Confidence

Domain of 

Applicability

Targeted 

Chemicals

1

In vitro

clearance of 

parent 

compound by 

hepatocytes 

and binding 

to plasma 

protein

Plasma Css from constant 

infusion exposure

If comparison 

with predicted 

exposure 

indicates large 

AER then 

infusion Css

should be 

sufficient 

Soluble non- or 

semi-volatile 

(HTS 

appropriate) 

compounds 

that reach 

steady state 

(Figure 25)

HTS 

compounds

1a CYP-specific 

in vitro data

Population variability, lifestage variability

Human 

variation in 

CYP 

expression are 

well 

characterized

HTS 

compounds

Chemicals with 

AER near one 

for general 

population

2

Physicochemical 

properties 

(measured or 

QSAR) 

Prediction of partition 

coefficients allows 

HTPBPK steady-state 

predictions for more 

realistic exposure 

scenarios. 

If comparison 

with predicted 

exposure 

indicates large 

AER then 

HTPBTK 

should be 

sufficient (i.e., 

conservative 

due to 100% 

absorption)

QSAR-defined 

(e.g., training 

set,

extrapolation 

methodology)

All chemicals 

with successful 

binding in vitro

assay

3

QSARs for 

likely 

transporter 

substrates

Assumptions of perfusionlimited tissue partitioning 

and passive renal excretion 

by glomerular filtration 

may be questionable

Can identify 

those 

chemicals 

where the 

impact is 

negligible 

(Figure 30).

QSAR-defined 

All chemicals 

with successful 

binding in vitro

assay

3b

Transporterspecific in 

vitro data

Tissue-specific 

accumulation, active 

secretion/resorption in 

kidney

Well 

characterized 

for non highthroughput 

applications

HTS 

compounds

Chemicals 

indicated by 

QSAR

4

In vivo oral 

and iv dose 

TK study, 

plasma only

Oral bioavailability, 

absorption rate, volume of 

distribution, and clearance 

(refined PBTK model)

If volume of 

distribution 

from HTPBTK 

model and in 

vitro estimate 

of clearance 

hold, then

refined PBTK 

model should 

Any compound

Diverse 

members of 

descriptor 

space to 

sufficient to 

evaluate

predictions 

from in vitro

methodology, 
New High-throughput Methods to Estimate Chemical Exposure Page 88 7/8/2014

Experimentally, limit of detection issues with the protein binding assay might be addressed by 

many methods, such as measuring free chemical using multiple plasma protein concentrations to 

develop a curve describing free fraction as a function of protein binding, allowing extrapolation 

to physiological protein binding concentrations [109] or by running many more replicates, 

pooling the replicates, and then concentrating the sample to get chemical levels above detection.

At each triage stage, we attempt to identify those chemicals for which the HTTK methods appear 

to be useful, and those chemicals for which the methods do not currently appear to apply. The 

first element of our proposed triage process is to determine the appropriateness of the steadystate assumption, which can be done from in vitro data and QSAR, as in Figure 25. Using 

HTPBTK models, we simulated more realistic dosing scenarios and we found that there were 

two broad classes of environmental chemicals: for most chemicals steady-state was reached in a 

few weeks, indicating that the steady-state assumption is reasonable. However, for a second class 

of chemicals steady-state was not reached in hundreds of years. 

The application of in vitro methods to TK is subtly different for pharmaceutical development and 

environmental chemical screening. For instance, screening level assays for protein binding 

accept that some assays results will be beneath the limit of detection (i.e., highly bound to 

protein) of the screening-level analytical chemistry [8]. Tools developed for pharmaceutical 

research (e.g., SimCYP) are built assuming that follow-up experiments would be performed on 

promising chemicals, whereas in environmental chemical screening the resources may not exist 

for immediate follow-up and the data must be used as is. We found that typical assumptions such 

as half the limit of detection ([8]) may result in an artifactual underestimation of Css values, 

which in turn may overestimate the margin between bioactive doses and estimated exposures 

(AER). 

We have shown that provisional HTTK models can be developed from in vitro data for many 

environmental chemicals to allow comparison of HTS bioactivity data and HTS exposure 

predictions. We have used statistical evaluation of the performance of these models relative to in 

vivo data to identify when they are health protective and when additional, targeted measurements 

may be necessary. While we expect that these methods will be refined and replaced as greater 

knowledge becomes available, we provide the most informed methodology given the data 

available today.

be excellent use QSAR for 

rest
New High-throughput Methods to Estimate Chemical Exposure Page 89 7/8/2014

Table 4: HTTK Triage: Tiered Testing of Environmental Chemicals

TK Data TK Prediction/Consequence Confidence

Domain of 

Applicability Targeted Chemicals

1

In vitro clearance 

of parent 

compound by 

hepatocytes and 

binding to plasma 

protein

Plasma Css from constant infusion 

exposure

If comparison with 

predicted exposure 

indicates large AER then 

infusion Css should be 

sufficient 

Soluble non- or semivolatile (HTS 

appropriate) 

compounds that reach 

steady state (Figure 

25)

HTS compounds

1a CYP-specific in 

vitro data

Population variability, life-stage 

variability

Human variation in CYP 

expression are well 

characterized

HTS compounds

Chemicals with AER 

near one for general 

population

2

Physico-chemical 

properties 

(measured or 

QSAR) 

Prediction of partition coefficients 

allows HTPBPK steady-state 

predictions for more realistic 

exposure scenarios. 

If comparison with 

predicted exposure 

indicates large AER then 

HTPBTK should be 

sufficient (i.e., 

conservative due to 100% 

absorption)

QSAR-defined (e.g., 

training set,

extrapolation 

methodology)

All chemicals with 

successful binding in 

vitro assay

3

QSARs for likely 

transporter 

substrates

Assumptions of perfusion-limited 

tissue partitioning and passive renal 

excretion by glomerular filtration 

may be questionable

Can identify those 

chemicals where the 

impact is negligible 

(Figure 30).

QSAR-defined 

All chemicals with 

successful binding in 

vitro assay

3b

Transporterspecific in vitro 

data

Tissue-specific accumulation, 

active secretion/resorption in 

kidney

Well characterized for 

non high-throughput 

applications

HTS compounds Chemicals indicated by 

QSAR

4

In vivo oral and iv

dose TK study, 

plasma only

Oral bioavailability, absorption 

rate, volume of distribution, and 

clearance (refined PBTK model)

If volume of distribution 

from HTPBTK model and 

in vitro estimate of 

clearance hold, then

refined PBTK model 

should be excellent

Any compound

Diverse members of 

descriptor space to 

sufficient to evaluate

predictions from in vitro

methodology, use 

QSAR for rest
New High-throughput Methods to Estimate Chemical Exposure Page 90 7/8/2014

4. Ongoing Research

4a. Third Generation SEEM Analysis

4ai. Introduction

Here we describe new data sources for better characterizing near field chemicals exposure for 

thousands of chemicals. These new data sources allow the parameterization of a mechanistic 

model, SHEDS-HT, which can simulate variability in human exposure, including multiple 

exposure pathways and dynamic (i.e., not stead-state) scenarios. These new data and model 

should allow a third generation SEEM prioritization model to be developed that will both allow 

greater insight into human exposure for chemicals with biomonitoring data and, due the 

mechanistic nature of SHEDS-HT, better extrapolation to chemicals without biomonitoring data.

We continue to hold any new exposure prediction efforts to the same standards as described in 

Mitchell et al. ([5]). Models must be: (1) capable of being parameterized from minimal data sets;

(2) broadly applicable across many chemicals and chemistries (i.e., chemical classes); (3) 

operated in an automated (batch) manner; and (4) capable of making quantitative (mg/kg 

BW/day) predictions of human exposure. New data sources must be similarly supportive of these 

goals.

New data sources, such as CPCPdb and CPCat allow better characterization of potential human 

interactions with chemicals. Other similar sources can and will be used as they become available. 

Unfortunately, protecting confidential business information may limit the use of some potential 

sources in peer reviewed scientific publications.

As we broaden our modeling approaches and data sources we should expect to both increase our 

predictive ability and be susceptible to a wider range of model errors and data quality issues. 

These aspects should be reflected in our empirical estimate of uncertainty. Recall that this is 

what is provided by the model evaluation framework described in Wambaugh et al. ([6]) and 

illustrated in Figure 2. We attempt to compare exposure predictions to exposures inferred from 

monitoring data, and we do so systematically across as many chemicals (and chemistries) as 

possible. Using Bayesian methods and monitoring data, we both get a calibrated prediction of 

exposure for thousands of chemicals, but just as importantly we get an empirical estimate of our 

uncertainty. Any approximations, omissions, or mistakes incorporated in the approach should 

increase the estimated uncertainty when evaluated systematically across chemicals. 
New High-throughput Methods to Estimate Chemical Exposure Page 91 7/8/2014

4aii. New Data for Forward Prediction

4aiia. Consumer Product Chemical Profiles database (CPCPdb)

Consumer product use is an important determinant for human exposure to a broad range of 

chemicals, and information about the chemicals and their concentrations within products is an 

important consideration. Importantly, there are a few potential sources for such information. One 

is Material Safety Data Sheets (MSDSs) that are required under the Occupational Safety and 

Health Administration (OSHA) Hazard Communication Standard to include product ingredients 

with known toxicity. The MSDS is intended to inform workers and emergency response 

personnel of hazards and the safe management of chemicals and personnel. All hazardous 

components in excess of 1% (0.1% for carcinogens) are required to be disclosed through the 

MSDS. Although OSHA does not require MSDSs to be provided to consumers, many 

manufacturers and retailers are providing MSDSs as a public service. For example, Wal-Mart 

has made the MSDS inventory of their products available online for more than 10 years. Another 

source is the Clinical Toxicology of Commercial Products (CTCP), which is a compendium of 

consumer product ingredients dating back three decades. The CTCP database includes 14,000 

products and over 900 generic product formulations [110]. More recent efforts by the National 

Library of Medicine (NLM) is the Household Product Database (HPDB), which exists in a webaccessible format indexed by consumer product category. It includes the MSDS listing and 

chemical percent composition by weight when available and is soft-linked to multiple NLM 

informatics resources. However, there remains no comprehensive resource to categorize 

chemicals in order to perform a high-throughput exposure evaluation based on chemical use in 

consumer products. 

Accordingly, the current research focused on organizing consumer product ingredient 

information is motivated by the need for high-throughput evaluation of exposure as a 

fundamental component of risk screening. What has been lacking is the methodology for 

capturing the available data in a form that supports HTE screening for rapid risk analysis. In 

developing the Consumer Product Chemical Profiles database (CPCPdb) [67], we relied on 

available data sources for collecting a structured set of product compositions that can be used to 

inform exposure assessment and to generate generic product formulations similar to those used 

in regulatory exposure assessment models (e.g., E-FAST or the Netherlands National Institute 

for Public Health and the Environment’s ConsExpo). The database was not developed 

specifically for the EDSP, but instead was developed as a tool for exposure-based chemical 

prioritization. Rather than focusing first on the chemicals that are known to have toxic effects 

and asking if there is a likelihood of exposure (i.e., hazard-centric risk assessment), the CPCPdb 

can be used to identify those chemicals with which the general population is expected to have

greatest contact, so that data obtained from both in vitro and in vivo toxicity testing can be used 

more judiciously. Exposure-based prioritization can be used to focus hazard identification and 

assessment efforts to a subset of relevant chemicals, streamlining the risk assessment process by 

reducing the dimensionality of possible chemical exposures to the probable set of chemicals 

requiring our immediate attention. Please note that the construction of a more general database of 

chemical use information, the chemical-product categorization (CPCat) database, is described in 

the next section.
New High-throughput Methods to Estimate Chemical Exposure Page 92 7/8/2014

Figure 33: Consumer Product Chemical Profile Data curation/annotation workflow: 

Approximately 10,000 MSDSs were identified, parsed, and manually annotated through a 

custom-built curation system. Figure from Goldsmith et al. ([67]).
New High-throughput Methods to Estimate Chemical Exposure Page 93 7/8/2014

Database construction: The methodology for building the CPCPdb can be broken down into 

three major steps: (1) building and curating a database for consumer product ingredients and 

percent compositions using available MSDSs; (2) identifying and annotating product use 

categories for all products in the database; and (3) evaluating data quality. Descriptions of each 

of these efforts are captured in Figure 33.

The first step in the formation of the database was collecting consumer product MSDSs from a 

publicly available source. Wal-Mart was chosen because of its diverse consumer product 

portfolio, high-market share product inventory with high consumer coverage, and publicly 

available database of MSDSs. MSDS files were downloaded and programmatic tools were used 

to extract the ingredient information contained in each MSDS to a MySQL database. MySQL is 

an open-source relational database management system that can be interfaced via a structure 

query language or “SQL.” Roughly 70% of the MSDSs (in Adobe PDF) were embedded with 

text that was successfully parsed using custom scripts. The remaining PDF documents contained 

scanned images and were extracted using Tesseract-OCR (optical character recognition) [111]. 

Entries for all identified ingredients in a given MSDS were crafted to document the MSDS in 

which it was found with chemical name and/or chemical registry number. 

To ensure that the extraction of data in the CPCPdb had a level of quality that is commensurate 

with the needs of both the EDSP prioritization and potential risk assessment activities, a custom 

web-enabled interface was developed for manually curating every entry in the MySQL database. 

The interface, developed in the PHP and Perl computer languages, provided annotators with a 

means of opening a given MSDS, verifying the chemical names and associated registry numbers, 

and manually entering the “Min” and “Max” percent composition when available in the MSDS. 

A detailed standard operating procedure was drafted and updated regularly to ensure that 

curators were following the same guidelines during mining and curation. An additional feature of 

this custom interface was its ability to identify products that contained identical chemical 

compositions or identical MSDSs (e.g., different size bottles of the same shampoo). The curator 

could then verify the level of congruence between the different MSDSs. If the MSDSs were 

identical, a “soft-link” between them was created. If chemical compositions in different MSDSs 

were similar, but not identical, the curator could copy curated information to reduce the amount 

of manual input needed, thereby reducing the overall workload of curation considerably. 

Chemical name verification: Chemical identifiers (e.g., names and registry numbers) selected 

either algorithmically or via the manual curation interface were cross-referenced with chemicals 

listed in ACToR. Linking with ACToR allowed access to chemical structures so that additional 

parameters could be derived that could also support a number of risk assessment objectives (e.g.,

QSAR-based calculation of pharmacokinetic properties and physico-chemical properties). To 

ensure that the link was made correctly, the interface provided both the ACToR name and MSDS 

name that were then verified using appropriate secondary chemical name data sources, such as 

the Royal Society of Chemistry’s ChemSpider (www.chemspider.com), or the DSStox master 

list (http://www.epa.gov/ncct/dsstox/). For example, an MSDS listing of “cetearyl alcohol” 

combined with an ACToR entry of “Alcohols, C16-18” resulted in a search of secondary 

sources. 
New High-throughput Methods to Estimate Chemical Exposure Page 94 7/8/2014

Product category annotation: The product categorization scheme employed on the retailer’s 

online shopping website was retained. This categorization scheme, with a top-level category 

(e.g., “beauty”) and a subcategory (e.g., “shampoos”), provided a convenient method for product 

organization. A freely available data extraction program, ScreenScraper (http://www.screenscraper.com/), was used to retrieve product categories from the retailer’s website. Following the 

automated category retrieval, multiple manual checks were used to solve specific issues such as 

misspellings, special characters in names, and same product in multiple categories. Curation 

experts evaluated every categorization to make sure that it was both consistent with assigned 

categories for similar products within our database and logical based on personal experience.

Numbers of products and chemicals: In total, about 22,000 MSDSs (representing 22,000 

products) were retrieved from the retailer’s public website. Initial extraction with natural 

language processing scripts yielded over 80,000 unique MSDS/CAS-RN pairings. Improvement 

of extraction scripts netted an additional 15,000 MSDS/CAS-RN pairings, producing a database 

with about 95,000 unique MSDS/CAS-RN pairs. Manual curation identified 13,000 duplicative 

entries among the 22,000 MSDS entries. (i.e., entries that have identical composition information 

as another MSDS entry) representing 5000 unique products. These duplicates represent MSDSs 

for different packaging of the same brand of products (e.g., different sizes of the same 

formulation of toothpaste), for “flanker brands” (the same brand sold as a unique product 

because of a specific fragrance, additive, or other distinguishing feature), and for competing 

brands of similar products (e.g., multiple rubber cement products with the same composition 

information). Removing duplicated entries reduced the number of unique MSDSs by 8000 and 

the MSDS/CAS-RN pairings by 27,000. Matching product names to product categories, we were 

able to categorize over 22,000 product names (representing about 8900 unique products) 

containing nearly 1800 unique chemicals into 15 top level product categories, and 353 

subcategories.

Data quality: Some 59,000 of the pairings were confirmed to be correct by manual analysis (a 

specificity of roughly 87%). An additional 4500 MSDS/CAS-RN pairings that were missed by 

the automated procedure were also identified (indicating a sensitivity of 93% in automated CASRN extraction). Mapping to existing chemicals in the ACToR database reduced the MSDS 

entries to about 9800.

Upon the completion of manual data curation and entry, we randomly selected 130 MSDSs to 

check the accuracy of the process. Entries were compared to the MSDS, and omissions and 

transcriptional errors within the database for names, CAS-RN, and percent composition were 

tabulated. In total, 97% (587/608) of the CAS-RNs in the 130 product subsample were properly 

annotated. While CAS-RNs were typically found in the tabulated ingredient section in the 

MSDS, chemical names were found in nearly all sections of the MSDS making omission more 

likely. Upon detailed review of the 130 selected MSDSs, 88% (114/130) were found to have no 

missed chemical names. For chemical names, 824 of 914 names listed on the 130 MSDSs were 

captured. While the collection of names was more prone to error than CAS-RN identification, the 

specificity of ingredient identification was high (i.e., all names listed were in fact in the MSDS). 

This high specificity is key since improperly labeling a product as containing a chemical is more 

problematic than missing an ingredient that does occur (note that not all chemicals are listed due 

to the limitations of legal requirements of MSDS reporting). 
New High-throughput Methods to Estimate Chemical Exposure Page 95 7/8/2014

Ingredient diversity: We observed wide variability in chemical ingredients across products, as 

evident in the network diagram presented in Figure 34. In the diagram, the large nodes represent 

top-level product categories and the small nodes represent unique chemical species. Edges are 

drawn if a chemical is present within a product in that product category. Three distinct classes 

are apparent: chemicals present in only a single product class (in green), those present in a small 

number of classes (in orange), and those in a large number of product classes (in red). Chemicals 

that are only present in a single product category (e.g., lithium hydroxide in beauty products) 

may be used as source tracers for a specific product category. Such chemical markers provide 

information about product usage in the home as an alternative to surveying mechanisms. In 

contrast, those chemicals found in the most product categories (in red) are of no use as tracers 

but have the greatest exposure potential. 

The similarity or diversity of ingredient profiles in different product classes is also evident in 

Figure 35 (see NHANES overlap below). Products with similar ingredients cluster closer 

together. This could be important in refining the categorization scheme. If ingredient profiles are 

similar for multiple product classes, it may be possible to consolidate those classes under a single 

category, thereby simplifying the exposure modeling effort and reducing the parameterization 

burden.

NHANES overlap: Only 47 of the 210 chemicals monitored in NHANES appear in the CPCPdb; 

however, a chemical ingredient is captured in over 70% of the unique chemical classes (14 of 20 

classes). Parabens, phthalates, and volatile organic compounds are in the largest number of 

product categories. Beyond these frequently used chemicals, Figure 35 shows that most 

chemicals are in products with only a handful of uses, indicating that the presence of biomarkers 

for exposure to these chemicals may correlate with specific consumer product-related activities. 

Bisphenol A, flame retardants (polybrominated and diphenyl ethers) and perfluorinated 

compounds are not found, though they are known to be either in packaging or used in the 

manufacture of many products in the home.
New High-throughput Methods to Estimate Chemical Exposure Page 96 7/8/2014

Figure 34: The chemical space of consumer products. Network diagram of 15 top level product use categories nodes (yellow) 

connected to ~1.6K chemicals and summary table of the CPCPdb product and chemical landscape. [Legend: BY = beauty, 

BB=baby, HL = health, Pet = Pet care, Gr = Grocery, HI = home improvement, PC = photo center, Hm = home, Ele = 

electronics, LG = gardening, Ap = Apparel, AT = automotive and tires, CPS = crafts and party supplies, Ty = Toy, Sports and 

outdoor hidden under AT, Ele, LG] Figure from Goldsmith et al. ([67]).
New High-throughput Methods to Estimate Chemical Exposure Page 97 7/8/2014

Figure 35: Product categories for NHANES Chemicals that are in the CPCPdb. Dark color indicates the presence of these 

chemicals (rows) in a “specific” product (lower) category. The ribbon at the top indicates the corresponding upper product 

category (14 of 15 categories). Chemicals and products were clustered using Euclidean distance. Figure from Goldsmith et al.

([67]).
New High-throughput Methods to Estimate Chemical Exposure Page 98 7/8/2014

4aiib. Chemical-Product Categorization (CPCat) Database

Resources for identifying chemical presence (and quantity) in various types of consumer 

products, including the CPCPdb [67], are described in the previous section. There remains, 

however, the need for a comprehensive resource to categorize chemicals broadly by use and 

function in order to perform high-throughput exposure prioritization. To fill this gap in 

knowledge, U.S. EPA has developed the chemical-product categorization (CPCat) database, the 

result of a large-scale effort to catalog and consolidate relatively disparate data sources in order 

to make chemical use information publically available and in a form consistent with facilitating 

high-throughput exposure modeling [68]. The CPCat database is available for public download 

and as an online searchable website at http://actor.epa.gov/cpcat. Moreover, the database is 

directly applicable to examining the potential uses of the chemicals implicated with a specific 

adverse outcome pathway in the EDSP.

The U.S. EPA’s ACToR database is a compilation of publically available data on chemical 

toxicity for more than 550,000 unique chemicals (http://actor.epa.gov) [39]. ACToR includes, 

but is not limited to, high and medium production volume chemicals, pesticides, industrial 

chemicals, and ground and drinking water contaminants. Just over 36,000 of these chemicals had 

sufficient information to be mapped to a use category within CPCat. 

Various sources were used to construct the CPCat database. Table 5 details the class of chemical 

categories provided by each source, and the number of categories and chemicals associated with 

each source. Across the considered data sources there are 2,687 chemical categories. These 

specific categories may be combined into 5 general classes: (1) general product-use: the 

chemical is used in a general product type (e.g., cleaning and washing); (2) specific product-use: 

the chemical is used in a specific consumer product (e.g., oven cleaner); (3) therapeutic-use: the 

chemical is used as an ingredient in a pharmaceutical with a biological target or to treat a specific 

disease (e.g., antibiotic); (4) functional-use: the chemical has a functional role according to its 

properties (e.g., solvent); and (5) industrial sector-use: the chemical is used in an industrial sector 

(e.g., mining). Those chemicals with a variety of documented uses and functions will show up in 

multiple classes and categories.
New High-throughput Methods to Estimate Chemical Exposure Page 99 7/8/2014

Table 5: Summary of data sources used to construct the CPCat database. 

1Class of category used for chemical categorization in the original data source.

2Number of unique chemical categories in the original data source.

3Cassettes are comprised of two or more CPCat terms separated by spaces that must be interpreted 

together.

4Chemicals in ACToR, mapped to one or more use categories.

Original data source Class of category 1 Original 

categories 2

CPCat 

cassettes 3 Chemicals 4

EPA ACToR Assays and 

Lists Use categories 137 180 436,062

EPA ACToR UseDB Use categories 15 15 31,622

EPA Chemical Data 

Reporting (CDR) 2012:

Consumer Use categories 34 36 3,321

Industrial Function Functional categories 34 27 5,023

Industrial Sector Industrial sector categories 42 43 5,226

EPA CPCPcat Product categories 359 191 2,778

EPA Design for the 

Environment (DfE) Functional categories 11 9 444

EPA Inventory Update 

Reporting (IUR) 2006 Use categories 19 24 1,152

Dow Chemical Company Functional categories 19 18 104

University of Alberta 

DrugBank Therapeutic categories 582 463 1,754

Swedish Chemicals Agency 

(KemI) Functional categories 61 31 876

Australian National 

Industrial Chemicals 

Notification and 

Assessment Scheme 

(NICNAS)

Use categories 17 17 177

Substances in Products in 

the Nordic Countries 

(SPIN):

Detpcat Use categories 781 284 6,491

Industrial Sector Industrial sector categories 580 221 4,603

NACE Industrial sector categories 57 52 7,745

UC62 Use categories 61 59 9,059

Environmental Working 

Group Human Toxome 

Project

Functional categories 16 16 442
New High-throughput Methods to Estimate Chemical Exposure Page 100 7/8/2014

CPCat terms: The use descriptions within each database listed in Table 5 were mapped to CPCat 

terms. CPCat terms were selected by aggregating all category names from each different source 

taxonomy, taking care to eliminate redundancies or superfluous information. To aid data 

processing, chemical category taxonomies from each source were translated into a common 

format before entry into the CPCat database. Terms were also combined into “cassettes;” terms 

within a cassette must be interpreted together. Any unique set of CPCat terms can be combined 

to create a CPCat cassette; however, while some combinations are common, others never occur. 

An underscore between two words indicates a compound word (e.g., automotive_care) and 

should be considered the same as a single unique CPCat term. A data dictionary including a list 

of all unique CPCat terms and cassettes is included with the database. While no specific 

hierarchy was defined, CPCat terms often refer to different levels of detail, with distinct levels of 

specificity when reading from left to right (e.g., “manufacturing building_material wood” 

describes a chemical used in the manufacture of building materials that are wooden).

Number of chemicals: A total of 43,596 unique chemicals from the U.S. EPA’s ACToR database 

mapped to at least one CPCat cassette. There are 838 unique CPCat cassettes, excluding those 

including the “drug” term, and 1,307 unique CPCat cassettes total. The cassettes are 

permutations of 385 unique CPCat terms, excluding those with only drug-related uses, and 835 

CPCat terms total. 

Potential exposure routes for chemicals subject to the EDSP: The U.S. EPA’s EDSP is mandated 

to identify and analyze chemicals for their potential to interact with and disrupt specified 

endocrine pathways (estrogen, androgen, thyroid and steroidogenesis). The two main classes of 

chemicals covered in the EDSP are pesticide ingredients (active and inert) and chemicals with 

the potential to be found in sources of drinking water. Potential drinking water contaminants are 

only subject to testing after a determination of substantial population exposure, as required by 

SDWA [112].

In this example, we focus on a set of 5,251 SDWA compounds that have not been eliminated 

from future EDSP screening by a set of filters proposed by the U.S. EPA. While the CPCat 

cassettes do not provide any direct, quantitative measure of exposure, they can be used as one 

input to a prioritization scheme. Ultimately, exposure information obtained from the CPCat 

categories would be included with hazard identification information (e.g., the results of running 

high-throughput endocrine-pathway laboratory assays to determine whether exposure is likely to 

increase the incidence of adverse health effects in humans). However, the first step in 

prioritization could be to rank the SDWA chemicals by their likely exposure potential, with 

exposure potential based on the number of consumer-use related CPCat cassettes the chemical is 

associated with (i.e., the number of consumer-use related “hits”). Theoretically, the more 

consumer-use related CPCat cassettes that a chemical is associated with would translate to a 

larger number of potential exposure pathways for an individual. Of all unique CPCat cassettes, 

237 were selected as being broadly related to consumer exposure (including exposures from 

food), described generally by the example cassettes presented in Table 6. The 237 consumer 

exposure related CPCat cassettes are associated with 19,623 unique chemicals.

Of the 5,251 SDWA compounds, 4,189 are present in CPCat, and 3,518 map to at least one of 

the consumer-use related CPCat cassettes. 
New High-throughput Methods to Estimate Chemical Exposure Page 101 7/8/2014

Table 6: Example CPCat cassettes that were selected as being broadly related to consumer 

exposure. A space separates unique terms within a cassette, and an asterisk indicates 

combined cassettes.

CPCat Cassette CPCat Cassette CPCat Cassette

adhesive consumer_use * drinking_water * lubricant consumer_use *

air_fresheners consumer_use 

*

electronics * paper consumer_use *

air_treatment consumer_use explosives consumer_use personal_care *

apparel * extermination consumer_use pesticide consumer_use

apparel_care * fertilizer consumer_use pet

appliance consumer_use * flame_retardant polish apparel_care footwear

arts_crafts* food * solvent consumer_use

automotive_care 

consumer_use

food_additive * sports_equipment *

automotive_component 

consumer_use *

food_contact * stoves consumer_use

baby_use * food_residue * surface_treatment 

consumer_use

batteries consumer_use fragrance consumer_use tea_coffee

beverage * fuel automotive textile consumer_use *

building_material 

consumer_use *

fuel consumer_use toilets baby_use

child_use * fungicide consumer_use tools consumer_use *

cleaning_washing 

consumer_use *

furniture * tools lawn_garden

colorant consumer_use * heating * tools personal_care *

consumer_use hunting toys *

consumer_use_ACToRUseDB impregnation consumer_use 

detected

water_treatment 

consumer_use

décor * lawn_garden consumer_use writing*

dietary_supplement leather consumer_use
New High-throughput Methods to Estimate Chemical Exposure Page 102 7/8/2014

Table 7 lists the number of different consumer-related CPCat cassettes for each of the SDWA 

chemicals with ≥60 hits. These chemicals might be placed higher on the priority list, due to their 

association with many different CPCat cassettes, while those SDWA compounds which are 

associated with <5 CPCat cassettes (2,496 compounds) might be given a lower priority for 

further evaluation. It is important to again note that the number of “hits” should not be taken as a 

quantitative surrogate for exposure measurement. Rather, the number of hits can be used to more 

generally group chemicals with more or less exposure potential, by having more or fewer 

exposure pathways accessible to it. In addition, hits on specific groups of CPCat cassettes could 

be prioritized based on their exposure potential, for example if chemicals with fewer hits are 

included cassettes with a high exposure potential (e.g., food related CPCat cassettes), those 

chemicals could be prioritized over chemicals with more hits on cassettes with a lower exposure 

potential (e.g., cassettes related to apparel).

The list of chemicals in Table 7 may be reduced by eliminating chemicals that are common food 

substances (e.g., ethanol, sodium chloride, citric acid) or are otherwise widely used and 

considered safe (e.g., sodium hydroxide, talc). However, as a preliminary result, we see that 

prioritizing based on the number of consumer-use related CPCat cassette hits does highlight 

certain phenol compounds that, in their parent or metabolite form, may interact with the estrogen 

receptor (e.g., propyl 4-hydroxybenzoate, methyl 4-hydroxybenzoate).

As CPCat is expanded with other sources of exposure information, such as further chemical-toproduct mappings, the utility of the CPCat database will be enhanced by including quantitative 

information on the amount of chemicals included in various products. We believe that this 

publicly available database will be a valuable resource for regulators, risk assessors and exposure 

scientists with a need to identify, elucidate, and take stock of exposure pathways. This utility 

initially will be demonstrated through application in the EDSP.
New High-throughput Methods to Estimate Chemical Exposure Page 103 7/8/2014

Table 7: Number of consumer-use related CPCat cassettes associated with EDSP chemicals. 

Chemicals associated with less than 60 consumer related CPCat cassettes are omitted. 

CAS RN Name CPCat cassette 

hits

57-55-6 1,2-Propanediol 122

64-17-5 Ethanol 116

56-81-5 Glycerol 111

67-63-0 Isopropyl alcohol 92

99-76-3 methyl 4-hydroxybenzoate 86

1310-73-2 sodium hydroxide 85

77-92-9 citric acid 85

13463-67-7 titanium dioxide 83

7647-14-5 sodium chloride 81

102-71-6 2,2,2-nitrilotriethanol 78

106-97-8 Butane 75

128-37-0 2,6-di-tert-butyl-p-cresol 74

75-28-5 Isobutane 73

94-13-3 propyl 4-hydroxybenzoate 72

122-99-6 ethylene glycol monophenyl ether 65

2682-20-4 2-methyl-4-isothiazolin-3-one 65

1934-21-0

trisodium 5-hydroxy-1-(4-sulphophenyl)-4-(4-

sulphophenylazo)pyrazole-3-carboxylate 64

3844-45-9

dihydrogen (ethyl)[4-[4-[ethyl(3-

sulphonatobenzyl)]amino]-2-

sulphonatobenzhydrylidene]cyclohexa-2,5-dien-1-

ylidene](3-sulphonatobenzyl)ammonium, disodium salt 64

67-64-1 Acetone 64

14807-96-6 talc (Mg3H2(SiO3)4) 63

100-51-6 benzyl alcohol 61

57-11-4 stearic acid 60
New High-throughput Methods to Estimate Chemical Exposure Page 104 7/8/2014

4aiii. SHEDS-HT

Over the past 13 years, EPA has developed a series of mechanistic, probabilistic exposure 

models using the Stochastic Human Exposure and Dose Simulation (SHEDS) framework. Most 

of the SHEDS models are high-tier, state of the science vehicles, which use many inputs to 

characterize in detail the variability and uncertainty in population exposures. The SHEDS 

models, which can examine both residential and dietary sources of exposure, have been 

successfully developed and applied for various chemicals, including organophosphate and 

pyrethroid pesticides [113-117] arsenic [118-120] , and methyl mercury [121]. We have now 

developed a leaner, more versatile model, named SHEDS-High-throughput or SHEDS-HT, 

suitable for high-throughput modeling of exposures to hundreds to thousands of chemicals 

(Figure 36). This model has a lower input burden, faster execution speed, and an increased 

number of exposure scenarios compared to higher-tier SHEDS models. The model simulates 

population exposures to chemicals in consumer products, foods, and drinking water via multiple 

pathways, scenarios, and routes (Figure 36) by utilizing datasets either assembled previously for 

SHEDS (e.g., census, human activity, food consumption, and pesticide residue data) or 

developed recently by EPA (e.g., consumer product chemical composition and product use data) 

[67]. Required chemical property information can be obtained from QSAR-based tools such as 

EPI Suite. The model contains a fugacity-based, source-to-concentration module for estimation 

of post-application air and surface chemical concentrations resulting from consumer product use 

within the home, as well as methods for estimating exposures resulting from direct product use. 

As in the higher-tier SHEDS models, SHEDS-HT can make use of monitoring-based 

distributions of air and surface concentrations for individual chemicals (representing aggregate 

concentrations from all residential uses), if such data are available.
New High-throughput Methods to Estimate Chemical Exposure Page 105 7/8/2014

Figure 36: SHEDS-HT input data streams and modules.

SHEDS-HT 

Probabilistic 

Exposure 

Model
New High-throughput Methods to Estimate Chemical Exposure Page 106 7/8/2014

Table 8: SHEDS-HT exposure pathways, scenarios, and routes with required input data.

Field Scenarios Routes Required Data Streams

Nearfield 

direct

Dermal exposure (either via 

direct application of 

consumer products to body 

or incidentally during 

household use of consumer 

products such as cleaning 

products

Inhalation of vapor during 

use of consumer products

Inhalation of aerosol mass 

during use of consumer 

products

Dermal

Inhalation 

Ingestion (direct 

ingestion plus 

incidental nondietary ingestion 

via hand-tomouth transfer)

Consumer product chemical 

composition

Consumer product use 

patterns

Chemical properties

Time-activity data 

Various exposure factor 

distributions 

Nearfield 

indirect

Application of consumer 

products to household 

surfaces, air, or pets 

Emission of chemicals from 

consumer articles or building 

products

Dermal

Inhalation

Ingestion (object 

to mouth transfer 

plus incidental 

non-dietary 

ingestion via 

hand-to-mouth 

transfer)

Consumer product chemical 

composition

Chemical emission rates from 

articles or building materials

Consumer product use 

patterns

Consumer product article or 

building material use patterns

Chemical fate and transport 

information 

Time-activity data 

Various exposure factor 

distributions 

Dietary Consumption of 

contaminated food or 

drinking water (e.g., from 

agricultural chemical use or 

leaching from food 

packaging)

Dietary ingestion Chemical concentrations in 

foods and drinking water

Population food and water 

intakes
New High-throughput Methods to Estimate Chemical Exposure Page 107 7/8/2014

4aiiia. SHEDS-HT Exposure Methods for Direct, Indirect, and Dietary Pathways

SHEDS-HT can predict direct exposures (exposures obtained during direct use of consumer 

products) for inhalation, dermal, and ingestion routes via inhalation of vapor, inhalation of 

aerosol mass, intentional or unintentional dermal application, and non-dietary ingestion. In 

general, these exposures are parameterized similarly to other available equations for these 

pathways, such as those available in ConsExpo [122] and EFAST’s consumer exposure module 

[123]. Probabilistically-predicted exposures for all routes are dependent on category-specific 

frequency of consumer product use, population prevalence, mass of product used, and product 

chemical composition. Dermal exposures also consider the fraction of product retained on the 

skin post-use, while ingestion exposures include a factor indicating the percent of product used 

that is ingested. Inhalation exposure to vapor is dependent on the vapor pressure of the chemical 

being modeled; inhalation exposure via aerosol mass depends on the airborne fraction of the 

consumer product and the volume of the aerosol cloud. Dermal exposures via direct pathways 

are subsequently available for removal by various processes (such as bathing or washing) and for 

transfer to the mouth, thereby predicting exposures resulting from non-dietary ingestion 

pathways. Key SHEDS-HT input data are summarized in Table 9.

Exposures via the indirect pathway result from simulated individuals breathing indoor air or 

touching contaminated surfaces. The SHEDS-HT fugacity module is used to model the fate and 

transport of chemical within a home, predicting concentrations in air and on surfaces as functions 

of time for each chemical, based on the mass and frequency of use of consumer products in the 

home. Products that are used more frequently or in large amounts will have higher media 

concentrations. The simulated individual is then exposed to these concentrations via inhalation, 

dermal, object-to-mouth, and hand-to-mouth routes. 

Dietary exposures in SHEDS-HT are calculated by determining the total mass of a chemical 

present in the food eaten by a simulated person within a day. The full SHEDS-Multimedia 

(SHEDS-MM) dietary model [124, 125] considers on the order of 465 different food or raw 

agricultural commodities (RACs) that may contain chemical, and requires a distribution of 

chemical residues for each. SHEDS-HT has simplified this approach by considering larger food 

groups, specifically a set of 41 crop groups defined by EPA and used for establishing pesticide 

tolerances. This allows the user to input fewer chemical residue distributions while still 

maintaining variability in residues across food types due to differences in agricultural treatments 

or packaging. The default SHEDS-HT food diaries were constructed by aggregating National 

Health and Nutrition Examination Survey-What We Eat in America (NHANES-WWEIA) 1999-

2006 two-day food intake diaries [126]. Concentration distributions for each group for each 

chemical with a dietary pathway are included as SHEDS-HT input. For each simulated 

individual, a food diary matching age and gender cohort is probabilistically selected, as is a 

concentration for each chemical present in a food group. The dietary exposure is the sum over

food groups of the product of chemical concentration and mass of food consumed. This method 

is consistent with the SHEDS-MM approach, applied to aggregated food groups.

Screening-level estimates of the chemical doses absorbed via dermal, inhalation, and ingestion 

pathways are included in SHEDS-HT. These doses are calculated using distributions of pathwayspecific absorption fractions. Dermal absorption fraction distributions are chemical-specific, as 
New High-throughput Methods to Estimate Chemical Exposure Page 108 7/8/2014

they are scaled via predicted dermal permeability coefficients across chemicals. Currently, the 

distributions absorption fractions for the inhalation and ingestion pathways are constant across 

chemicals.

Table 9: SHEDS-HT default input data with sources and key assumptions.

Database/Parameter 

Description

Information Contained Source(s) Comments

Physiological Information

Body mass, height, and basal 

metabolic rate distributions 

or regressions by age/gender 

cohort

Developed for SHEDSMultimedia from NHANES 

[53] (weights/heights) and 

available literature (BMR) 

[127]

Activity Diaries

Daily location (time spent in 

microenvironments) and 

activity (physical activity 

index -PAI, a time-averaged 

metabolic equivalent) 

information for each age- and 

gender-specific CHAD diary

Calculated from EPA’s 

Consolidated Human Activity 

Database (US EPA) [128]

Food Diaries

Daily mass of food group 

(crop group) and direct and 

indirect drinking water 

consumed by individuals

Calculated from NHANESWWEIA food diaries [126]Model is compatible with 

alternate/refined food groups 

if data are available for 

specific chemicals/classes

Population Data

Number of individuals in US 

in each age age/gender 

cohort 

2000 US Census 

Chemical properties Chemical-specific property 

information 

Derived from EPI Suite Measured data to be used 

where available

Consumer Product 

Chemical Compositions

Probability distributions of 

mass fraction for each by 

consumer product category

Consumer product ingredient 

databases: HPDB [129] and 

CPCPdb [67]

Assumption currently is that a 

chemical is present in all 

products (brands; 

formulations) within a 

category

Scenario Information

Active exposure scenarios 

for each consumer product 

category 

Assigned using best judgment

Consumer Product Use 

Information

Population prevalence and

frequency of use; 

distributions of mass applied; 

other scenario-specific 

parameters (such as fraction 

retained on skin or fraction 

ingested, aerosol cloud 

volumes, airborne fractions)

Consensus values derived 

from field studies, default 

values from other consumer 

product screening models

Assumed using best judgment 

when no data or existing 

defaults available

Exposure Factors

Dermal transfer coefficient, 

hand-to-mouth frequency, 

bathing probabilities, handto-mouth and object-tomouth information, bathing 

and hand washing removals

Distributions developed for 

SHEDS-Multimedia [127]

Appropriate daily-level 

values for some parameters 

determined from fitting 

results from SHEDSMultimedia runs

Residential Media 

Concentration

Information: Source-toConcentration Module 

Inputs

Particle information 

(loadings, composition); 

cleaning rates; air exchange 

rates, housing properties

Derived from Bennett and 

Furtaw [130] with added 

reasonable variability; 

housing data derived from 

available surveys for SHEDSMultimedia [127]

Measured values (or other 

modeled values) for air and 

surface concentrations can be 

used if available
New High-throughput Methods to Estimate Chemical Exposure Page 109 7/8/2014

4aiiib. Chemical Domain 

The SHEDS-HT model was applied to produce exposure dose predictions for a set of chemicals 

found in consumer products, foods, and drinking water. The chemical composition data used in 

the case study was obtained from two existing databases of chemical ingredients in consumer 

products. The first database was EPA’s CPCPdb [67]. This database contains information on 

1797 chemicals found in 8921 consumer products derived from retailer-provided material safety 

data sheets (MSDS). The second database was the National Library of Medicine’s Household 

Products Database [129] which is also based on collated MSDS data. In both cases, chemical 

composition data (i.e., mass percent) could be obtained for a subset of chemical-product pairs. 

This case study focused on “consumable” products (i.e., those used in the home and replenished 

periodically, such as personal care products and cleaning products) and thus products such as 

pharmaceuticals and articles were excluded from both databases. In total, usable data were 

extracted for approximately 1700 consumer product chemicals. Distributions of residues in foods 

and drinking water were obtained for 432 pesticides from the USDA’s PDP databases [131]. 

These databases contain measurements of chemical concentrations in a variety of agricultural 

commodities collected from 1997-2011.

Exposure Predictions for Chemicals in Consumer Products and Pesticides in Foods

SHEDS-HT exposure dose predictions for the U.S. general population for 2111 chemicals 

examined are provided in Figure 37. The predictions were generated for simulated populations of 

10,000 individuals. The range of the predicted exposures for these chemicals spans 4 to 11 orders 

of magnitude, due to significant impact of high variability and uncertainty in the model inputs 

and parameters, but the results indicate also that a good discrimination among chemicals for the 

purpose of prioritization remains feasible. The distributions of dose are highly skewed, with the 

mean dose falling between the 75th and the 95th percentile. Distributions of predicted intake 

doses for the different cohorts simulated are also illustrated in Figure 37, ranked by exposures for 

children age 0-5. In general, greater exposure doses were seen in females than in men, which is 

associated with lower mean body weights and higher prevalence of use of personal care products 

by women. Children age 0-5 had the highest exposures, due to having lowest body weight and 

highest hand-to-mouth activity. The chemicals at the far right of the panel are those to which no 

children were exposed in the 10,000 person simulation, due to low (or 0) prevalence of use of 

corresponding consumer products.
New High-throughput Methods to Estimate Chemical Exposure Page 110 7/8/2014

Figure 37: SHEDS-HT results for over 2000 chemicals with near-field sources. Top: 

Percentiles of total exposure dose. Bottom: Median total exposure dose by age/gender 

cohort, sorted by median values for children.
New High-throughput Methods to Estimate Chemical Exposure Page 111 7/8/2014

4aiiic. Evaluation with NHANES Inferences

Median predicted SHEDS exposure doses are plotted against values for 39 chemicals estimated 

from NHANES biomarker data [6] in Figure 38. The median SHEDS exposure doses were 

significantly correlated with predicted median oral equivalent doses (p<0.0001, R2=0.39, 

R2=0.47 when the observed outlier for chlorpyrifos-methyl is ignored). Overall, the SHEDS 

distributions were higher than the NHANES values; this overestimation of exposure is not 

unexpected, as the screening-level estimates from SHEDS-HT are by design conservative. 

Assumptions that contribute to this overestimation may include (1) that all products within a 

category contain a particular chemical if any product in the consumer database contains the 

chemical; (2) that all mass used in indirect scenarios is initially retained on indoor surfaces postapplication (not wiped up); and (3) that decay rates on indoor surfaces are very low. However, 

even with these assumptions, a significant portion of the variance in the predicted NHANES 

medians can be explained by the SHEDS-HT results. In addition, the NHANES-based exposure 

numbers are also model estimates and thus subject to variability and uncertainty. We are 

currently refining the key model inputs and to the extent possible reducing the uncertainty or 

conservatism built into some of our modeling assumptions. In addition, we are performing a 

more detailed evaluation of these results within the SEEM framework [6] which will allow for 

iterative evaluation of the model, quantification of value added of model refinements, and 

combination with exposure results from other near-field and far-field exposure models to 

produce consensus exposure predictions for large numbers of chemicals.

Figure 38: SHEDS-HT median exposure dose versus biomonitoring-based predicted 

median intakes. The median SHEDS exposure doses were significantly correlated with 

predicted median oral equivalent doses (p<0.0001, R2=0.39, R2=0.47 when the observed 

outlier for chlorpyrifos-methyl is ignored).
New High-throughput Methods to Estimate Chemical Exposure Page 112 7/8/2014

4aiv. Conclusions

The SHEDS-HT framework described herein provides an efficient platform for high-throughput, 

screening level simulations of exposure to chemicals via multiple scenarios and routes. The 

unique advantage of this computational exposure model for both research and regulatory purpose 

applications, is its ability to identify and quantify the major sources, pathways and assumptions 

that influence reliable prediction of chemical-specific exposures or dose. Most of the other 

similar models either use empirical assumptions or calibrated factors that do not lend themselves 

readily for mechanistic evaluation and diagnostic assessment of their results. Moreover, SHEDSHT relies upon real-world human use, application and physical transport/transformation and 

contact processes that frequently have stochastic, environmental and chemical-specific 

characteristics. The model attributes lend itself to sub-modular evaluation of the full model and 

thereby facilitate flexible and quick updates to input data streams used, model parameters chosen 

and refinements to exposure algorithms currently employed. The modular design of the model 

also allows for expansion beyond the current exposure scenarios (including incorporation of 

algorithms, required inputs, and variability/uncertainty) without any major restructuring of the 

model code. The text based nature of the inputs allows for easy simulation of other consumer 

product or food group categorizations. However, the consumer product category and usage data 

developed for the case study described here will allow for the rapid simulation of exposure for 

additional existing or new chemicals once the types of products they are likely to be in are 

known. 

SHEDS-HT has potential application to both high-throughput screening-level prioritization of 

large numbers of EDSP chemicals and medium-throughput exposure predictions for highactivity, high-exposure chemicals. A total of 519 EDSP chemicals were included in the initial 

application of SHEDS-HT described above. A number of further refinements to the current 

model are already on-going or planned to widen the current chemical domain of applicability of 

SHEDS-HT. Recent efforts by ORD researchers have developed databases of chemical use 

categorizations from various sources. These data, in combination with the consumer product 

databases described here, can form the basis for predictive models of chemical use in consumer 

products based on functional use and chemical properties (such as molecular descriptors), 

allowing for the parameterization of SHEDS-HT from chemical structures. SHEDS-HT 

predictions will be included in consensus screening-level exposure predictions for any chemicals 

for which SHEDS can be defensibly parameterized. 

Although SHEDS-HT is designed as a high-throughput model, it has been shown to reproduce 

the results of the higher-tier SHEDS-Multimedia for select chemicals, when the inputs to the two 

models are harmonized. Therefore, SHEDS-HT may be a reasonable selection of a medium-tier 

model for application to identified high-activity high-exposure EDSP chemicals. In this case, 

improved exposure estimates can be achieved through an increase in the amount and specificity 

of SHEDS-HT input data for different exposure scenarios, foods, or consumer products for 

individual chemicals. The current daily-level SHEDS-HT exposure metrics are also suitable for 

use in construction of longitudinal patterns of exposures (via characterization of within- and 

between-individual variability) required for robust mid-tier assessments. Implementation of such 

longitudinal methods are currently planned.
New High-throughput Methods to Estimate Chemical Exposure Page 113 7/8/2014

4b. Ecological Exposure 

4bi. Overview

In order to prioritize thousands of chemicals on the basis of exposure, both human and ecological 

exposure must be considered [1]. To date the majority of ExpoCast research has focused on 

predicting human exposure. However, here we propose applying the SEEM framework to 

ecological exposure models, initially using water concentration data as a proof of concept. 

Relative to the other research described, this new research initiative is in its infancy, but holds 

great promise.

Both ecological and human exposure prioritizations of chemicals regulated by the EPA should be 

well defined and as scientifically comprehensive as needed to make decisions concerning their 

use [132, 133]. Such prioritizations also should be tiered in ways that are conceptually and 

technically dovetailed with one another to enable the Agency to evaluate expected environmental 

exposures and hazards posed by chemicals in a logically consistent manner [133, 134]. Each tier 

should enable the EPA to predict expected environmental concentrations (EECs) which are used 

not only to assess dermal, drinking water, and/or inhalation hazards to exposed biota using 

traditional external toxicity benchmarks (e.g., EC50, LC50, NOEL, etc.), but also to predict 

expected biological concentrations (EBCs) in exposed biota. In turn, resulting EBCs can then be 

used to assess dietary hazards to biota and humans consuming contaminated biota to evaluate the 

accumulative hazards posed by chemicals using residue-based toxicity benchmarks that integrate 

all exposure pathways whose duration and magnitude can vary over time, space, and lifetime of 

the exposed recipient. Many of the components needed to develop and perform integrated risk 

assessments for environmental chemicals already exist and have been used in a piecemeal 

fashion to support the EPA’s environmental decisions concerning chemical registration, control,

and permitting. 

The ExpoCast SEEM framework can be applied to ecological exposure, with minimal 

modification. As shown in Figure 39, because the environmental media to which fauna are 

exposed can be directly assayed (e.g., water sampling), the inference or reverse modeling 

necessary for human exposure is not needed. This should allow greater precision in ecological 

exposure estimates.

The human HTE models include environmental fate and transport components, including 

prediction for ecological water concentrations. If a sufficient data set of ecological water 

monitoring concentrations can be developed (e.g., the USGS and EPA data), then we can use the 

SEEM framework to forecast ecological concentrations for the EDSP chemicals. Additionally, 

there are existing data and predictive models for relating the concentrations in aquatic biota (e.g., 

fish) to their immediate aquatic environment: the ratio of these two values is the 

BioConcentration Factor (BCF) [10]. The BCF serves as an analog to RTK for humans – if an in 

vitro concentration can be identified as bioactive, then the BCF allows the prediction of the 

environmental concentration needed to produce that in vitro bioactive concentration in vivo.
New High-throughput Methods to Estimate Chemical Exposure Page 114 7/8/2014

Figure 39: Application of the SEEM framework to high-throughput ecological exposure 

modeling. Ecological modeling is different from human modeling (Figure 2) in that the 

exposures (concentration in environmental media) can be directly assayed rather than 

indirectly inferred from biomonitoring (e.g., urine) data.

Both of the far field fate and transport models that were used to make human exposure 

predictions do so by first predicting chemical concentrations per unit emission in various 

environmental media (Figure 11). The USEtox model makes predictions for partitioning into 11 

different well-mixed compartments – air, agricultural soil, natural soil, freshwater, and seawater 

for global and continental scales as well as urban air – due to environmental release into up to 6 

different media – urban air, rural air, agricultural soil, natural soil, sea water, and fresh water. 

The RAIDAR model simulates chemical partitioning into air, water, soil, sediment and 

representative biota as a result of environmental releases into air, water, soil.

In Figure 40, we show the USEtox and RAIDAR predictions for the distribution of 7834 EDSP 

chemicals into environmental media. We see that generally the amount of chemical predicted to 

be in water is higher than in other environmental media, but we note that the sensitivity to the 

assumptions about chemical release have not yet been evaluated. Chemicals were identified as 

pesticides using the Pesticide Active designation in the ACTOR UseDB – all other chemicals 

were assumed to be industrial compounds. Two broad release profiles (pesticidal and industrial) 

were assumed and, depending on the fate and transport model used, were characterized in 
New High-throughput Methods to Estimate Chemical Exposure Page 115 7/8/2014

slightly different manners. As with the human exposure modeling, application of pesticides was 

assumed to be equally into soil and air (50% soil, 50% air for RAIDAR and 50% continental 

agricultural soil, 50% continental air for USEtox), i.e., spraying. To assess environmental impact 

for chemicals handled under TSCA it is typical to assume either release or emissions into the air 

or water. Since the Tox21 chemicals must be non-volatile to be suitable for HTS, all chemicals 

other than pesticides were assumed to largely be released to water (80% water, 10% soil, 10% air 

for RAIDAR; and for USEtox 75% continental freshwater, and 5% to each of continental natural 

soil, agricultural soil, air, and seawater and urban air) [46, 47].

Information on chemical production volume was obtained from the 2006 U.S. EPA Inventory 

Update Reporting/Chemical Data Reporting (IUR) and used as a surrogate for the amount of 

chemical released to the environment. Note that the IUR production volumes are provided in 

coarse bands (e.g., 1M-10M lb/year). Compounds that were not on the IUR were assumed to be 

produced between 0 and 25K lb/year, the minimum requirement for being listed in the IUR.

Since both models make continent-wide predictions of water concentrations, evaluation and 

calibration must be performed with data on a similar scale. We propose using water sampling 

data from the USGS and EPA as organized by National Water Quality Monitoring Council. As 

shown in Figure 41, this data source provides chemical concentration in water data from 358,863 

surface water sites. There are between ~700 and ~1300 individual chemicals (currently 

ambiguous due to lack of standardized chemical identification) along with location, date, and 

time stamps as well as the analytical chemistry limit of detection (LOD).
New High-throughput Methods to Estimate Chemical Exposure Page 116 7/8/2014

Figure 40: Fate and transport model predictions for the distribution of 7834 EDSP 

chemicals into environmental media. Each row corresponds to a different chemical and 

each column is a different media prediction. The models, USEtox and RAIDAR, predict the 

amount (kg) of compound present in each environmental medium as the result of a unit 

emission (1 kg/day). Chemicals (row) and environmental media (columns) are clustered 

based on similarity calculated using Euclidean distance of the logarithm of the predicted kg 

in each media and complete linkage.
New High-throughput Methods to Estimate Chemical Exposure Page 117 7/8/2014

Figure 41: National Water Quality Monitoring Council USGS/EPA portal: Map of data for

358,863 surface water sites and chemical concentration in water for ~ 700 to ~ 1300 

individual chemicals. Surface water quality data includes GPS location, date, time stamps, 

and LOD indication.

There are a number of issues with the USGS/EPA water data set that can be addressed with 

Bayesian analysis. As with the NHANES data, the concentration of the assayed chemicals in 

many of the samples are below the LOD. Sampling intensity can be quite heterogeneous, because 

sites were established for different purposes. Siting is generally not random. The selection 

methods here are not equivalent to NHANES probability sampling. Instead, sites are established 

to evaluate compliance and are often deliberately downstream of release points. The distributions 

of measurements are not stationary over space, so spatial variation of sampling intensity can 

cause bias in estimates (e.g., there are much more sample data from New Jersey than Wyoming). 

However, the waterways sampled are hierarchically organized by hydrologic unit codes (HUCs) 

that allow disparate samples of the same waterway to be aggregated into multiple samples of the 

same HUC.

We will use Bayesian methods to estimate national–level concentrations including geometric 

means and higher quantiles, as well as estimate spatial variance using a hierarchical random 

effects model for effects at multiple HUC levels. We propose to select chemicals with extensive 

geographic and temporal samples. We estimate this to be ~300 evaluation chemicals for 

comparison with model predictions. We will split data into temporally more homogeneous 

subgroups (e.g., summer, fall, winter, spring) analyze each temporal grouping separately. As 

with the NHANES data, we will handle LODs with censored likelihoods. 

We can then use weighted regression to evaluate relationship between predictions from 

ecological HTE models and national-level geometric means, and these regression weights will 

provide an empirical estimation of model performance and a calibration for extrapolation to 

other chemicals. The estimation process yields uncertainty distributions for all the parameters, 
New High-throughput Methods to Estimate Chemical Exposure Page 118 7/8/2014

e.g., posterior samples, asymptotic normality of MLE estimates.

In addition to USEtox and RAIDAR, EPA has developed an integrated exposure modeling 

system (IEMS) to forecast and analyze the steady-state and dynamic fate, transport, and 

bioaccumulation of Persistent Bioaccumulative Toxic chemicals (PBTs) in wadeable streams and 

large rivers. Importantly, this prototype IEMS can be modified straightforwardly for use within 

EDSP. In its steady-state mode, this IEMS uses EXAMS [135] to forecast steady-state EECs that

are used to parameterize the KABAM bioaccumulation model [136] which forecasts steady-state 

chemical concentrations in trophic level 2, 3, and 4 fish. In its dynamic mode, EXAMS is used to 

simulate time-varying EECs that are used to parameterize the BASS bioaccumulation model 

[137, 138] which simulates time-varying chemical concentrations in multiple age classes of 

specific fish species. In either mode, model outputs are post-processed to construct semicolon 

separated values (ssv) files for input and analysis within Excel and other statistical software. 

Figure 42 presents a schematic of the data flow and model interaction within this IEMS. These 

models were selected for this IEMS because they have all been peer reviewed and used by the 

EPA Office of Pesticide Programs and ORD to assess the environmental and ecological 

exposures within aquatic ecosystems.

Figure 42: Schematic diagram of ORD’s EXAMS-KABAM-BASS modeling system for the 

prioritization of PBTs and EDSP chemicals. The components identified within the 

trapezoidal boxes will be developed specifically for the EDSP. All other components are 

currently functional in ORD’s proof-of-concept prototype for EDSP. 

To demonstrate the use of this software for addressing EDSP prioritization needs, we made

EFS

EXAMS

Project 

Builders

(EXAMS_prj_e

dsp21.exe)

Creates batch 

files for project 

execution

Environments Chemical 

Properties

Kow

HLC

MP

Sw

BAF DB 

Excel 

data 

table of 

Projects

Combinations 

of

environs, 

Output

Builder

(EXAMS_report_ss

v.

exe)

Generates a 

semicolon 

separated value 

EXAMS 

UDB

Chem.ex

a

Environment

Builder

(EXAMS_river_env

ironments_v5_EDPBT.xlsx)

Facilitates 

creation of EXAMS 

Loading

Generator

Dietary &

Drinking Water

Assessment

SHEDS-HT

BASS KABAM
New High-throughput Methods to Estimate Chemical Exposure Page 119 7/8/2014

preliminary exposure predictions for 74 chemicals from EDSP List 2 [139] with a log Kow > 3. 

Nine EXAMS river environments were developed and loading rates to these rivers were 

estimated for 27 of the 74 selected chemicals using mean down-the-drain discharges (DDD 

g/person-day) by: (1) SHEDS-HT, (2) POTW (publicly owned treatment works) removal 

efficiencies estimated by EPI Suite, and (3) the assumption that an “average” POTW services 

approximately 10,500 people (e.g., http://css.snre.umich.edu/css_doc/CSS04-14.pdf). The 

resulting loading rates varied from 2.23x10-6to 1.65 kg/hr (mean = 0.153; SD = 0.419; 

median=4.1810-4). Using the maximum, minimum, mean, and median of these loading rates, 

2664 (i.e., 74 chemicals x 9 environments x 4 loading rates) EXAMS projects were constructed 

to forecast chemical concentrations under both steady-state and time-varying climatic and 

hydrologic conditions. Predicted dissolved water concentrations (Cwd mg/L), total water 

concentrations (Cwt mg/L), total sediment concentrations (Cst mg/kg dry wt), and whole-body 

fish concentrations (Cf mg/kg wet wt) were then fitted to the log linear regression model:

10 0 1 10 2 10 3 10 OW 4 10 5 10 log log log log log HLC log C a a Q a L a K a a S      w

where C is the concentration of interest, Q (m3/hr) is the river’s mean annual stream flow, L

(kg/hr) is the river’s mean annual loading rate, KOW is the chemical’s octanol-water partition 

coefficient, HLC (atmm3 mol-1) is the chemicals’s Henry’s Law Constant, and (Sw mg/L) is the 

chemical’s aqueous solubility. These physicochemical properties were selected as predictor 

variables because these are the primary variables determining chemical partitioning to suspended 

solids and benthic sediment, and their volatilization from surface waters.

Significant findings of these analyses are:

B1.EECs and EBCs are strongly dependent on the hydrologic and climatic characteristics 

of the rivers of interest since they are essentially inversely dependent on river 

streamflow.

B2.EECs and EBCs are essentially linearly dependent on estimated loading rates.

B3.EECs for water and sediment have significantly different log Kow dependencies.

B4.Given the same chemical loading rates, dynamic EECs are significantly higher than 

their steady-state counterparts. In this analysis, for example, the ratio of dynamic 

EECs to their corresponding steady-state counterparts averaged 1.36 (median=1.30; 

SD=0.233; min=1.06, max=2.06).

B5.Calculated regression models can be used 

 as screening tools for other chemicals, rivers, and loading scenarios whose 

parameters are bounded by those used in the current prototype,

 to back-calculate more refined loading rates from observed field data (e.g., fish or 

sediment concentrations); this capability would be particularly useful for 

chemicals having low aqueous solubilities and/or low release rates, and

 to estimate more informed priors for the Bayesian analyses discussed above.

There are four primary modifications or enhancements of the existing EXAMS-KABAM-BASS 

IEMS that will be undertaken for its integration into the EDSP high-throughput framework. 

These are:

1. development of an EDSP Loading Generator,
New High-throughput Methods to Estimate Chemical Exposure Page 120 7/8/2014

2. development of EXAMS-SHEDS and BASS-SHEDS output modules to configure 

EXAMS, KABAM, and BASS outputs into SHEDS inputs files to facilitate drinking 

water and dietary exposure pathways ,

3. development of EXAMS modeling environments for populations/POTWs of concern 

using the D4EM (Data for Environmental Modeling) data retrieval software, and

4. development of a pchem property database to parameterize EXAMS, KABAM, and 

BASS for chemicals of concern.
New High-throughput Methods to Estimate Chemical Exposure Page 121 7/8/2014

4bi. Limitations of Approach

As with human exposure, we do not expect that ecological HTE predictions will recapitulate the 

results of running a more detailed model. However, if we have properly estimated uncertainty 

then we do expect that more detailed model predictions should be appropriately within the 

confidence interval of the HTE prediction. Generally – if one has the time and data to run a 

higher tier model, then it is reasonable to expect that that model would outperform highthroughput exposure. However, high-throughput exposure offers a means to determine which of 

the thousands of chemicals should be run first.

The goal for ecological exposure is to demonstrate predictive ability using models based on 

minimal chemical data sets, and to quantify the uncertainty in these predictions by comparison to 

available monitoring data. For environmental media concentrations, we do not need to model 

TK, which eliminates some sources of uncertainty, but we still must have three key elements: (1) 

HTE models for predicting environmental media concentrations; (2) monitoring data for 

systematically evaluating the predictions of the HTE models; and (3) data sufficient to 

parameterize the HTE models for the thousands of EDSP chemicals. The data needs may be 

further subdivided into three categories: (3i) physico-chemical information (e.g., Kow); (3ii) 

chemical-specific loadings (e.g., kg/day releases to water); and (3iii) environmental and 

geographic descriptors for specific environments (e.g., distribution of rainfall, relative fraction of 

land to water).

The granularity of the models, evaluation data, and parameterization data all must be comparable 

in order to use SEEM to demonstrate predictive ability. For the human exposure modeling [6],

the U.S. nationally-distributed CDC NHANES data matched the available surrogate data for 

chemical loading (i.e., the IUR U.S. national chemical production list).

As with human exposure prediction, the SEEM approach is to use existing monitoring data to 

both evaluate models and quantify prediction uncertainty using the deviations of model from 

data. One obvious data set for evaluating the models for surface water and sediment 

concentrations comes from the National Water Quality Monitoring Council (NWQMC) data 

portal, which aggregates data collected under several programs by both the USGS and the EPA. 

This data set well characterizes rivers and streams (lotic water bodies), but unfortunately not 

lakes and ponds (lentic water bodies). It seems plausible that more intense exposure occurs in the 

latter, lentic water bodies since the water residence time is higher. Estimation of exposure only in 

lotic water bodies may result in shorter, less intense exposure estimates. If additional monitoring 

data on lentic water bodies becomes available, it can be incorporated into the SEEM framework 

for ecological exposure.

With respect to HTE models, an early key development has been the creation of a linearized 

EXAMS-KABAM model that is capable of running in a high-throughput manner for fixed 

chemical loadings (i.e., the rate at which chemicals are introduced into aquatic systems). In 

contrast to RAIDAR and USEtox, which can predict average water concentrations on large 

geographic scales, this HT EXAMS-KABAM provides a forward predictive model capable of 

making more spatially and seasonally specific predictions for thousands of chemicals. However, 

the KABAM tool is specifically parameterized for a lentic habitat (farm pond) and modification
New High-throughput Methods to Estimate Chemical Exposure Page 122 7/8/2014

will be needed to adapt its use for modeling lotic systems (flowing water systems such as streams 

and springs). Similarly, EXAMS is a single water-body model and it has been validated for large 

lakes and rivers, but not for entire watersheds. As with human exposure, these models can be 

evaluated and subsequently revised in a process guided by the SEEM framework.

It is important to note that all linear exposure models, including the three models proposed for 

use here (RAIDAR and USEtox in addition to EXAMS-KABAM), are extremely sensitive to 

uncertainty in chemical loadings, which are an overall multiplicative factor [140]. Aquatic 

exposure predictions require quantifying the combination of variability of chemical loadings, the 

variability of features (such as flow rates and branching) among aquatic systems, as well as 

differences among chemical properties. For the human exposure evaluation, loadings had to be 

determined from the IUR U.S. national chemical production list. In reality, the loadings for many 

chemical are sporadic because they are not used at a set point in time (for example, pre-planting, 

at-planting, or at a specific growth stage). Rather, active ingredients such as insecticides and 

fungicides may be used in response to pest pressure, so their application timing is variable from 

year to year. Estimations of seasonal exposure across multiple years may contribute to a

downward (non-conservative) bias in exposure estimates if insecticide applications have 

occurred at varying times or intervals in different years.

We can evaluate the sensitivity of predicted EECs to assumptions about loadings (e.g., national 

production volumes vs. spatially-specific releases) wherever both monitoring data and data on 

releases are available. Chemical use characteristics, such as down-the-drain rates and the nature 

of storage and transportation, may be useful for estimating aspects of loadings, analogously to 

how chemical use is helpful in parameterizing SHEDS-HT. Any evaluation of ecological 

exposure models needs to take into account the structure of the monitoring sampling data. While 

real concentrations exhibit a great deal of spatial structure, for screening purposes it should be 

sufficient to quantify the likelihood that a sample concentration would exceed a given level. 

Ideally, extreme quantiles would be predicted and evaluated to determine appropriate calibration 

and accompanying uncertainty intervals. Whereas the samples for a given chemical in NHANES 

come from roughly 2000 individuals, the NWQMC data features more than two million sites, 

potentially greatly increasing the statistical power and allowing, among other things, estimation 

of more extreme quantiles than the median (e.g., 95th percentile and potentially even higher). It 

may be that the NWQMC data set is sufficiently large to allow region specific (i.e., spatiallyvarying) estimates of exposures, albeit with uncertainty that reflects the statistical power of the 

sampling in that region. It is possible that there are systematic biases in the NWQMC data – if 

these have been quantified then they can be easily incorporated into the SEEM framework.

There are many biases inherent in the use of ambient water monitoring data (such as is collected 

by USGS) to estimate exposure to pesticides. These biases include spatial, temporal, and 

sampling aspects: 

The distribution of concentrations in water might be presumed to be described by a skewed 

distribution with short periods of high concentration and long durations of low or no measureable 

concentration. Ambient monitoring data with sampling frequencies less often than weekly may 

not capture peak concentration; thus exposure predictions calibrated to this data may be 
New High-throughput Methods to Estimate Chemical Exposure Page 123 7/8/2014

underestimates. This also biases time-averaged concentrations downward, on longer time scales 

(such as seasonal). Chemical-induced effects might be expected to be associated with the 

relatively infrequent high concentrations. Ideally, screening exposure models need to focus on 

predicting peak concentrations.

Unlike NHANES, the NWQMC data come from heterogeneous sampling designs and 

heterogeneous media. This heterogeneity needs to be considered when comparing model 

predictions to data. A calibration to NWQMC based on ambient monitoring data could provide 

some geographic variation, while division of the calibration into four seasons provides some 

temporal specificity. However, for any chemical that has a geographically-limited use pattern the 

non-detects in other regions may act to reduce mean estimated concentrations.

For example, pesticides used only on cotton, which is grown only in the South, might be 

analyzed in water samples taken in Minnesota. There is no reasonable expectation that such a 

pesticide would be found in that sample. Inclusion of non-detects for cotton pesticides in 

Minnesota water samples will tend to underestimate the intensity of exposure if the results are 

averaged to a national or even regional scale. Thus, we must aim to make spatially-varying 

estimates of extreme concentrations.

The proposed methods for estimating wildlife exposure are presented as a proof of concept using 

water as the exposure medium, and calibrating estimates to water monitoring data. If other 

exposure routes are deemed to be important (e.g., dermal, inhalation, dietary exposure via 

consumption of terrestrial invertebrates, dietary exposure by consumption of pesticide granules 

or baits, direct exposure by spraying) or other receptors are considered (non-aquatic associated 

birds and mammals) then different exposure estimation methods will need to be used, perhaps in 

combination with TK models for chemical concentrations in biota for comparison with 

monitored concentrations in those same biota.

The approach we describe is for exposure-based prioritization. In the case where local sources of 

chemical release are known, then an entirely different class of models is appropriate. EPA has 

many tools for modeling scenarios that represent ‘high end’ exposure on a local scale (e.g., a 

treated field) on a per chemical basis. Here, we are sifting through thousands of chemicals in a 

search for exposures that are so promiscuous and sufficiently large that they potentially cause 

wide-spread bioactivity. 
New High-throughput Methods to Estimate Chemical Exposure Page 124 7/8/2014

4c. New Data for HTTK

To date, in vitro PK data on ToxCast chemicals has been collected by researchers at the Hamner 

Institutes in collaboration with the EPA [8, 73, 87]. As shown in Figure 43, data on ~250 

chemicals has been published on human [8], and an additional rat in vitro PK data set has been 

developed for 53 ToxCast chemicals [87]. Publication by the Hamner Institutes of an additional 

data set of 250 ToxCast chemicals is anticipated in 2014. After that publication, future HTTK 

work is expected to continue at the EPA as part of the ToxCast project, and as part of the EDSP.

Figure 43: Overview of the existing and anticipated HTTK data

A new laboratory is testing an initial list of 150 additional chemicals is being screened as of 

spring 2014. If the testing works as anticipated, this will bring the library of environmental 

chemicals with HTTK data to ~650 by the end of 2014. Refinements are being made to the 

testing protocol, in particular to address LOD issues in the protein binding assay. 

Rapid equilibrium dialysis (RED) on a micro-well plate uses wells joined by a membrane that is 

permeable to the chemical being investigated but not to the human plasma protein that is added 

to one well only [95]. The fraction of chemical unbound is determined by the ratio of the 

chemical concentration in the well without plasma protein to the concentration in the well with 

plasma protein – for highly bound chemicals the free concentration may be below the LOD [8, 

73, 87]. Rather than only test for binding at a relatively physiological concentration of human 

plasma protein as was done previously, two additional plasma preparations will be made with 

diluted (reduced) concentrations of plasma protein. This modification will hopefully reduce the 

bound chemical to the point that free chemical is above LOD for all chemicals. For those 

chemicals where we can measure multiple free concentrations, we can then model the binding of 

that chemical to protein [109].
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To better assess the performance of HTTK for environmental chemicals, two separate activities 

are underway to generate a larger data set of in vivo TK data for evaluation (as in Figure 28). In 

the first, approximately 500 legacy TK studies from the National Toxicology Program (NTP)

will be curated into a database of in vivo TK information. In the second, chemicals with rat 

HTTK data will be investigated in vivo. As shown in Figure 44, data from these in vivo

experiments will greatly expand the number of chemicals available for systematic evaluation of 

HTTK predicted half-life and volume of distribution. By using contrasting oral and intravenous 

doses, the in vivo studies will also estimate bioavailability and absorption rate – two quantities 

that cannot be well estimated in vitro [102].

Figure 44: Overview of the existing and anticipated in vivo data for evaluating HTTK 

predictions from in vitro data.

As these datasets continue to develop, the performance of HTTK will be continuously 

reevaluated, prompting refinements of methods and targeting of chemistries most in need of 

additional data in order to better inform TK needs.

HTTK data is collected more slowly than many other types of in vitro testing data, in part due to 

the need to develop specific analytical chemistry methods for each chemical testing in order to 

quantify the concentration of that chemical [8]. For this reason, a lag exists and is anticipated to 

continue to exist between those chemicals that have been profiled for hazard in vitro (e.g., 

ToxCast) and those for which HTTK data has been collected. QSAR is a possibility for 

addressing this gap – successful models for predicting plasma protein binding exist that have 

been specifically trained to the environmental chemicals of interest [141]. Unfortunately, no such 

tools yet exist for quantitatively predicting hepatic metabolism, although as the data sets 

characterizing metabolism grow, new QSAR for metabolism may become possible.
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5. Future Applications

EDSP Integrated Activity and Exposure Based Prioritization and Screening

This whitepaper provides an overview of the use of the SEEM framework for HTE to rapidly 

estimate potential exposures for the EDSP universe of chemicals. In addition to exposure, this 

whitepaper also provides an overview of HTTK and RTK methods to provide the bridge between 

HTE and high-throughput screening (HTS) bioactivity assays [7]. HTE and HTTK/RTK 

represent critical components for an integrated activity and exposure based approach to 

prioritization and initial screening of the EDSP chemical universe.

Recently, Judson et al. [74] described a high-throughput, integrated activity and exposure based 

approach to chemical prioritization, illustrated in Figure 45: (1) in vitro chemical dose-response 

HTS bioactivity data are used to identify potential biological targets; (2) HTTK methods are then 

employed to determine the human dose needed for each chemical to activate these targets in 

vivo; (3) putative bioactive doses are then directly compared to HTE predictions to estimate 

likelihood of exposures that cause bioactive doses. Chemicals where the putative human 

bioactive dose is comparable to HTE predictions become targets for further investigation [8, 74]. 

The EDSP integrated activity and exposure based approach to prioritization and initial screening

will include a number of datasets and considerations to represent critical points along the 

Adverse Outcome Pathways (AOP) for endocrine modalities. Inherent physicochemical 

properties will help identify those chemicals in the EDSP universe that are not anticipated to be 

available for exposure. HTE information (e.g., ExpoCast) will be used to estimate the upper 

confidence limits of chemical exposure values for people and the environment, providing a 

means to rank the thousands of chemicals in the EDSP universe based on exposure 

considerations. HTS assay data and Quantitative Structure-Activity Relationship models (QSAR) 

will probe critical points along the AOP to characterize and rank the thousands of EDSP 

chemicals based on their potential endocrine activity. Incorporating the output of these sources of 

information in an integrated activity and exposure based approach to prioritization and initial 

screening will be the subject of further development within the EDSP.

EDSP will continue to actively pursue the application of computational toxicology and exposure 

methods to create a more efficient and robust prioritization and screening program. The integrated 

activity and exposure approach to prioritization and screening within the EDSP will be the subject of 

future FIFRA SAP reviews.
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Figure 45: Chemical prioritization, as described in Judson et al. [74], can be conducted by 

using HTTK methods to translate high-throughput bioactivity data into putative human 

doses of concern (mg/kg BW/day) and then comparing those doses to HTE predictions.

TSCA – Prioritization and Assessment 

The Office of Pollution Prevention and Toxics (OPPT) implements the Toxic Substance Control 

Act (TSCA). TSCA covers only industrial chemicals (e.g., solvents, polymers, adhesives, 

coatings, plastics, pigments, detergents) and not pesticides, pharmaceuticals, food, etc. TSCA 

requires the assessment and, if necessary, regulation of all phases of the life cycle of industrial 

chemicals including manufacturing, processing, use, and disposal. TSCA regulates two broad 

categories of industrial chemicals: (1) existing chemicals in commerce on the TSCA Chemical 

Substances Inventory; and (2) new chemicals that are not on this inventory. Hence, OPPT 

conducts prospective evaluation (and risk assessment) of new industrial chemicals and the 

retrospective assessment of an inventory of existing chemicals. 

OPPT evaluates approximately 1000 new substance per year. In addition, the Office has recently 

identified a “work plan” of over 80 existing chemicals to assess within the next three years, with 

the intention of assessing additional existing chemicals at some pace in years thereafter –

demonstrating our clearly defined need to prioritize industrial chemicals of concern for future 
New High-throughput Methods to Estimate Chemical Exposure Page 128 7/8/2014

evaluation. A great challenge to OPPT for all TSCA chemicals is that assessments are generally 

based entirely on existing and available data, which for new substances in particular may be next 

to nil. Therefore, OPPT has for many decades relied on analog and read-across analysis, category 

approaches and predictive modeling (e.g, QSAR and expert systems) to assess the risks of both 

new and existing chemicals. 

Given our charge and data limited environment, the OPPT supports many of the HTS and 

ExpoCast approaches being developed by ORD to prioritize chemicals for future evaluation. If 

conducted and expanded with OPPT needs in mind, they will be useful to broadly improve 

exposure science generally and expand OPPT’s available exposure modeling and predictive 

capabilities. Particular future applications of keen interest to OPPT include:

Development of AOPs and High Throughput In Vitro Assays – Clearly higher throughput, 

less data intensive approaches and methods would aid OPPT in its data limited environment. 

OPPT sees this research as a key component for advancing human health and ecological risk 

assessment methods, and in particular, for establishing quantitative linkages between 

toxicological responses across levels of biological organization. In addition, this kind of 

information is crucial to the development of chemical categories and for establishing 

scientifically defensible read-across on which OPPT so critically relies in a data limited 

environment. Collaboration between OPPT and ORD to date has generated HTS data and 

potential fingerprints or clues to important AOPs for several of OPPT’s HPV Chemical 

categories and certain flame retardants. This analysis could be further extended to run the HPV 

and flame retardant chemicals through Expocast to examine the feasibility, applicability, and as 

proof of concept. As mentioned previously, even highly uncertain HTE predictions may be 

useful for prioritization and initial screening. Given the very large number of chemicals in the 

TSCA universe, these approaches could serve OPPT in prioritizing our assessment focus for 

existing chemicals.

HTTK and RTK – OPPT sees application of HTTK approaches and methods as having future 

application in TSCA assessments. As described, OPPT often used modeling and predictive 

approaches in evaluating data limited chemicals. In order for OPPT to apply HTS toxicity 

information into our assessment (e.g., new chemicals assessments), HTTK methods for 

extrapolating such in vitro data into in vivo doses would be critically needed to provide the 

bridge between HTS bioactivity and exposure estimates.

Near-Field and SEEMs Approaches – OPPT routinely assesses chemicals that are or may be 

used in a wide array of products. Hence, the Near-Field Exposure and SEEM Evaluation 

approaches intended to link environmental monitoring and biomonitoring to sources could be 

particularly useful to OPPT in the future, especially in prioritizing the thousands of existing 

chemicals for evaluation. In particular, OPPT especially sees the potential value of determining 

the applicability of the more dynamic near-field exposure scenarios using a newly created high 

throughput version of the Human Exposure and Dose Simulation (SHEDS-HT) and on-going 

work focused on consumer products to TSCA chemicals of interest to OPPT.
New High-throughput Methods to Estimate Chemical Exposure Page 129 7/8/2014

Acknowledgements

The research presented here draws from draft and published research papers from the authors of 

this document; however, we appreciate the significant contributions of the coauthors of those 

research papers. First and foremost we recognize the contributions of Elaine Cohen Hubal, the 

initiator of the ExpoCast project. We also appreciate contributions from Richard Judson, Rocky 

Goldsmith, Barbara Wetmore, Russell Thomas, Kathie Dionisio, Anran Wang, Jon Arnot, 

Deborah Bennett, Sieto Bosgra, Raina Brooks, Timothy Buckley, Tommy Cathey, Daniel Chang, 

Surajit Dasgupta, Curtis Dary, Ryan Edwards, Hisham El-Masri, Alexi Ernstoff, Peter Fantke, 

Alicia Frame, Sumit Gangwal, Panos Georgopoulos, Graham Glen, Chris Grulke, Kathleen 

Holm, Sastry Isukapalli, Jeffre Johnson, Olivier Jolliet, Thomas Knudson, Alan Liddell, Jade 

Mitchell, Shad Mosher, Chantel Nicolas, Halûk Özkaynak, Muhilan Pandian, Robert Pearce, 

Linda Phillips, Martin Phillips, James Rabinowitz, Madeline Reich, David Reif, Alexander 

Sedykh, Imran Shah, James Sluka, Doris Smith, Luther Smith, Cory Strope, Cecilia, Tan, Tom 

Transue, Alex Tropsha, Rogelio Tornero-Velez, James Vail, and Daniel Vallero. We appreciate

the technical editing provided by Ron Hardman.

Supplemental Material

1) Computer code used for SEEM Analysis of Human Exposure Predictions (NHANESSEEM-code.docx)

2) Manual for the vLiverPBPK package for HTTK (vLiverPBPK.pdf)

3) Manual (SHED_HT_Supplement.docx) and code (SHEDS-HT.zip) for the SHEDS-HT 

model

4) Manual and code for the HT EXAMS-KABAM model
New High-throughput Methods to Estimate Chemical Exposure Page 130 7/8/2014

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