New High

- throughput Methods to Estimate Chemical Ex posure 1 7/8/2014 Exposure SAP White Paper 

# New High

- throughput Methods to Estimate Chemical Exposure

Scientific Advisory Panel Meeting

, July 2014New High

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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 Infe rence (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 Resea rch

................................ ................................ ................................ ..................... 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. Limitatio ns 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

................................ ................................ ................................ ................ 129New High

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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 Aq uatic System

Simulator BCF: BioConcentration Factor BW: Body weight CAS

- RN: Chemical Abstracts Service Registry Number

(chemical identifica tion 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 C ss : plasma Concentration after Steady

- S tate is reached due to repeated exposure s CTCP: Clinical Toxicology of Commercial Products DSSTox: Distributed Structure

- Searchable Toxicity Database EBC: E xpected Biological C oncentrations EDSP: the Endocrine Disrupter Screening Program EDSTAC: Endocrine Disruptors Screening and Testing

Advisory Committee EEC: E xpected Environmental C oncentration 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 f up : 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: Ho usehold Product Database HPV: H igh P roduction Volume HT: High

- throughput HTS: High

- throughput screening HUC: H ydrologic U nit C ode IEMS: I ntegrated E xposure M odeling S ystem IUR: EPA ’s Inventory Update Reporting and Chemical Data Reporting list (now CDR) IVIVE: in vitro

- in vivo extrapolation KABAM: EPA’s K ow (based) Aquatic BioAccumulation Model K OW : 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: Natio nal 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 T oxicokinetics SAP: Scientific Advisory Panel SDWA: Safe Drinking Water Act SEEM: Systematic Empirical Evaluation of Models framework SMILES: Simplified Molecular Input Line Ent ry System TK: Toxicokinetic ToxCast: EPA’s Toxicity foreCast prioritization research
program Tox21: Toxicology in the 21 st Century

– the NTP/NCGC/EPA/FDA consortium for chemical hazard HTS TSCA: the Toxic Substance Control Act USDA: the United States Depa rtment of Agriculture USGS: the United States Geological Service USEtox: United Nations Environment Program and Society

for Environmental Toxicology and Chemistry toxicity modelNew High

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## Preface

The risk posed by an y of the thousands of untested man

- ma de 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, t he 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 fewe r 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 i s 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. M

echanistic model s are hypothes e s 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

. E mpirical mod els are statis tically

- 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 sub

atomic 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 empirically

determined constants for strength and flexibility of each steel bridge element in order to

mec hanistically 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 produc ts 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 thing s 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 pre fer to use empirically

- evaluated mechanistic models rather than purely empirical models because mechanistic models allow some estimation of the applicability of a model to n ew situations. If we can show that mechanistic assumptions are predictive and we be lieve 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 extr apolate the predictions and uncertainty of mechanistic and empirical model predictions for exposure and TK to thousands of chemicals with no existing estimates.New High

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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 an y model, we must closely exami n e the data that we use. For high throughput data we must accept the possibility of i mperfections in the input data and so i t 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 informatio n 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

M ost of the analyses reported here were conducted within a Bayesian statistical framework. Bayesian statistics is a rigorous statistical methodology that considers all likely explanat ions 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 val ues. For instance, a Bayesian stati sti cian who expects a coin is fair would be unsurprised if, after ten flips, six heads were obtained to four
ta il s. 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 a ssumption and that the coin appears to slightly favor heads. Similarly if

, after only ten tosses

, head s 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 fr om 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 mod els 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 exp osure

- 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. T he error in the models predictions is assumed to be due to random processes . B y examining the distribution of errors in our model predictions we can determine whether they appear random

, build ing 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.New High

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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 predicti ons to support prioritization and initial screening f or 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 comb ined 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 prioritizati on. HTE pr ediction 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

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## 1. Introduction

While m any 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 a n integrated approach to the prioritizin g 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 m onitoring 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 chem icals. 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 vit ro 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 effec ts 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 Administ rator 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 dri nking 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 ha ve 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 charte red 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 E DSP. 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) mixturesNew High

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• Develop a two

- tiered screening and testing strategy, now known as the Endocrine Disruptor Screening Program

The consideration of chemical exposure has bee n 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 vol umes 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 pro duction 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 sp ecific chemicals that would comprise List 1, EPA generally applied the EDSTAC exposure recommendations, focusing on four pathways of exposure to pesticide active ingredients: (1) c onsumption of food containing pesticide residues (food pathway); (2)
consump tion 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 in ert ingredients, the EPA focused on: (1) h uman 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 (i ndoor 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 l ist 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 duri ng fiscal years 2007 and 2008. EPA identified drinking water contaminants for EDSP List 2 from chemicals that we re regulated under the national primary drinking water regulation (NPDWR) (40 CFR part 141) or we re unregulated contaminants that are listed on the third Cont aminant 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 Nove mber 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 a uthorities 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 expo sure considerations has onlyNew High

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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 chemical s interfere with th e 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 a n integrated approach to prioritization and screening. Such an ap proach is consistent with the 1998 EDSTAC recommendation: “D ata 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 high

throughput 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 co ntinuation 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 2New High

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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 exposur e 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 i s 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 f or 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 T ier 1 screening battery) [ 7 ] . HTTK provides a bridge be tween 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 prioriti zation, 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 devel oped 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 predicti ve 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 endpoi nts. 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 mode ls for relating tissue concentrations in aquatic biota (fish) to theirNew High

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immediate environment ( e.g ., water ) , with t he ratio of these two values being the Bio c oncentration Factor (BCF) [ 10 ] . Just as HTTK relates exposure intake doses to plasma concentrations for humans, the BCF relates the environmental water concentration to a whole

body co ncentration for the fish. I f an in vitro concentration is identified as bioactive, the BCF allows the predic tion of the water concentration needed to produce that bioactive concentration in a fish

.

HTE prediction and HTTK allow for a n exposure

- based, rapid prioritization of chemicals in the EDSP universe and beyond.New High

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## 2. Exposure Prioritization with ExpoCast Research

2a

. Introduction

There are currently more than 10,000 chemicals i n wide commercial use and 700 to 1000 chemicals entering commerce every year [ 11

- 15 ] . For the ma jority of these chemicals in

commerce , the exposure data necessary for prioritization are la cking [ 1

, 8

, 16 ]

. In order to provide the n ecessary 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 th e s e need s , 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 assumption s 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 make s predictions of human exposure and weight s contributing HTE model components appropriately, all with an appropriate characterization of uncertainty. The SEE M 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, w e have identified a method to evaluate and develop predictive tools for human exposures . We believe that this method meets the mandate of the National Re search Council report for an objective, standardized, and transparent approach to HTE modeling [ 1 ]

. T he 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 ] ) de monstrated 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 ,” t he results

wer e primarily appropriate for identifying areas of future research vital to providing sufficient HTE assessment.New High

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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 predict ive 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 e xtrapolating to other chemica ls

.New High

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C haracterizing exposure for large numbers of chemicals require s 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
t hat we have considered

. Within this space, t here are multiple exposure opportunities for both human and ecological receptor s 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, non

occupational e nvironments. 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. N ear

- 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, w e can obtain data that could be used to characteriz e 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 exposur e 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 (forwa rd 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, predictiv e ability of models for chemicals covered by biomonitoring and ecological monitoring data [ 6 ]

.New High

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

.New High

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One program that contributes to monitoring of environmental chemicals is the National Health and Nutrition Exami nation 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 exposure s across all pathways for the general population. T hus

, while the forward prediction models may only partially cover the exposure pathways in play, the estimated uncertainty will reflect all exposures th at 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 b y 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 th at the same five factor s 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 hum an exposure for thousands of chemicals.New High

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### 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 co efficient ) 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 Prop erties : 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 environme nt for long periods of time, and bioaccumulation, accumulation in primary aquatic living organisms via the food chain, are often key factors used in conduct ing 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 co mputationally using a relatively small number of physicochemical properties . H owever, 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

a nd 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 coef ficient, and octanol

– air partition coefficient as directly influencing the par titioning and transport of semi

volatile organic compounds in indoor environments. These properties and others are integrated into the fugacity

- based, indoor source

- to

- concentrat ion 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 physico

chemical properties if Simplified Molecular Input Line Entry System (SMILES) descriptions [33] of chemical structure were available and the Q SARs 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

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Of the 2127 chemicals considered for the f irst g eneration 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 onl y 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 K OW 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 mo nths) [ 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. , K OW ); thus, the predictions tend to be highly correlated. P rincipal 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 firs t 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, th e half

- lives in environmental media and th e 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 chemi cals 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 D omestic S ubstance L ist 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 persiste nce 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 estimate d chemical exposures from both near

- field direct and near

- field indirect sources based on a library of exposure scenarios. The near

- f ield 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 pr oduct 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 usageNew High

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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 compo unds 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 3New High

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Chemical Emission s Characteristics : Emiss ion 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 near

field and information on the fate and transport in the immediate (usually indoor) envi ronment 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 g ross 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 lev els 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, finger nails) 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 path ways.

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 th e 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) p roduction/import volumes and processes ; (b) c onsumer product ingredients a nd
usage ; (c) e nvironmental release ; (d) e nvironmental media concentrations ; and (e) h uman 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. T his inventory is called the TSCA Chemical Substance Inventory (TSCA Invent ory) 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.h tml ). 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 informa tion comes from, among other sources:New High

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 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 F ood 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 InventoryNew High

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Figure 5 : Network diagram of exposure taxonomy used in ACToR. Figure from Egeghy et al . ( [ 25 ] ).New High

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Environmental fate informatio n 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 th e 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 Administrati on (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 fr action 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 age ncies (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

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Figure 6 : The number of unique chemicals by exposure category in ACToR. A complete listing of data sources can be found in the “D ata Collections” section at http://www.actor.epa.gov

. Figu re from Egeghy et al. ( [ 25 ] ).

Of the initial set of 2127 chemicals analyzed in the first gener ation 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 chemic als. 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 Prot ection 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

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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 prov ide 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 enviro nment. 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 substitu te 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 a n 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 Fragranc e Association’s List of Fragrance Ingredients). These data were ass igned to various use categories; b ased 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)



## 51 31

inorganicNew High

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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 a ssignment 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 n ot 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, f

ive 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, p athways, and cellular processes [ 17

, 40 ]

. The second generation SEEM anal ysis examined the 7785 Tox21 chemicals for which high

- throughput chemica l 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 QS AR 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 a mong the NHANES chemicals. M odified 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 bestNew High

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su bsets 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, t he 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

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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 descripto rs, 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 propert ies, 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 comple te linkage method [ 41 ]

.New High

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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

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Available Models

When the ExpoCast initiative was launched in 2009 [ 4 ]

, one of the first activitie s 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 chem icals 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 scree ning level exposure predictions. In addition to requiring basic physico

chemical descriptors, it requires user

- provided information about chemical

- specific application rates, amounts of area treated, average resid u es, and percent spray of the proposed prod uct. 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 use r 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 s upport 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

- leve l tool for the EPA Office of Pollution Prevention and Toxics (OPPT) pre

- manufacturing notification program [ 43 ]

. Where USEtox a nd 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 determin e 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 l imitation precludes using EFAST2 as a high

- throughput exposure model in th is study [ 5 ]

.

Table 1 provides a model

- by

- model evaluation of the suitability of screening

- level exposure models for use in HTE.New High

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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 che micals), we have left the model predictions unscaled. For the purposes of the linear regression within SEEM, the scaling factor i s subsumed by the intercept term of the linear regression [ 6 ]

. Because the predictions are quantitative, the differenc e s between the predictions

are quantitative, even if the overall scaling is un known. F or 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 c hemicals 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 tw o groups is preserved. T his is in contrast to models that predict ranks only ( e.g. , the difference between the n th 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 t o 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 ] . O f five models reviewed, including EPA tools, only two were found to meet all four criteria for HT exposure models

. The two models found meeting th e requirements were t he 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 high

throughput mode and making quantitative e xposure predictions [ 5 ] . Based upon the compound

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

. The USEtox model makes predictions for partitioning into 11 well

- mixed c ompartments

– 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 wat er, 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 approximate ly reflect North America, USEtox assumes a continent of size 2.47*10 7 km 2 and a population of 9.98x10 8 , corresponding to a population density of ~40 people / km 2 . To predict individual exposure

, the population intake fraction (kg/kg) was divided by 9.98x10 8 for the continental population, converted to mg, and divided by 70 kg for bodyweight [ 6 ]

.New High

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The RAIDAR model simulates chemical partitioning into air, water, soil, se diment 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, an d 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 x10 8 to match USEtox's population density and scaled by 9.01x10 6 /10 5 to 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 (pesticid e 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 mo dels. F or pesticides

, application was assumed to be equally into soil and air (50% soil, 5 0% 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 lan d, water, or public treatment works are effectively releases into water [ 46

, 47 ] . Since the vast majority of the chemicals of interest here ar e 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 ingestio n) 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 (US Etox 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 populat ion 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 wi th relatively low hydrophobicity (log K OW < 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. T riangles plotted in Figure 10 show compounds that would be considered to be "likely bioaccumulators" (log K OW > 4.5 and a bioaccumulation factor (BAF) > 1000) [ 49 ]

. The m ost likely bioaccumulators predicted , as shown in Figure 10

, have high intake fractions [ 6 ]

.New High

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Table 1 : Screening Level Exposure Models (Including EPA

- Expo

- Box Models)

Model Full Name Parameterized from Minimal Data Sets Broad Applicability

Across Chemicals Automated (Batch Mode) Operation Quantitative

Predictions (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 NoNew High

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Figure 10 : Comparison of USEtox and RAIDAR predict ed 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 K OW < 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

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Figure 11 : Far field models predict the distribut ion of chemicals into environmental media, from which human exposure can be inf erred 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 r ow correspond s 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 w ith maximum distance used to compare clusters). Figure from Wambaugh et al. ( [ 6 ] ).New High

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

Available D ata

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 sampl e data ( from the blood and urine of survey participants ) reflects only a snapshot drawn from a lifetime of exposure to environmental chemical s, and there are presumably many possible exposure scenarios (with respect to e.g. , route, magnitude, and duration) that would be consistent with that snapshot. T hese 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 . I n the case of NHANES

, this includes ch ildren under the age of 6

, and indeterminate occupational exposures. However, a s additional monitoring data sets become available

, we can perform new model evaluations and calibrations with that data to reflect new scenarios and chemicals/chemistries.

E x posures 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 exposur e model predictions, and allow calibration and an empirical determination of predicti ve 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 fir st generation SEEM analysis focused on the total U.S. population, in the second generation analysis d emographic subsets with sufficient statistical power were constructed by considering one factor ( e.g. , gender or age). Typically

, we assumed that two facto r 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 t he 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 wid e region of predicted intake fractions.New High

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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

- 3 for both models, while this occurs for ~12% of t he 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

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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 i dentify 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 k nown otherwise, and, wherever SMILES descriptors were available for parent and metabolite, confirmed by manual inspection [ 6 ] . The proportions of different product molec ules 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 compound s (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. I n 2010

, NHANES began reporting urine flow data along with chemical mo nitoring 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 NHAN ES 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 th at fell below the limit of detecti on . I t was assumed that the population distribution of compounds in urine was lognormal.New High

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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 estima tion of exposure. Figure from Wambaugh et al. ( [ 6 ] ).New High

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### 2d. S ystematic Evaluation of Forward and Reverse Prediction across Many Chemicals

T he 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) f orward prediction of exposures, which involves model curation and parameterization; (2) inference of exposures from monitoring data; (3) sys tematic 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 Ba yesian 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 distrib utions. 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

, 5 6 ]

. Second

, to estimate the relative predictive power of the forward models ( i.e. , weights) [ 6 ]

.

L inear regression is used to evaluate the predictive power of mult iple 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 mod el and to estimate the uncertaint y. Multivariate regression on the set of available high

- throughput models provides regression coefficients for each of the models and t he 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 cal ibrated predictor provide s 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 bio monitoring 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 dat a 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

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The Bayesian implementation of the SEEM framework for ExpoCast analysis proceeds in four stages:

Analysis Stage 1: Steady

- state R TK for e stimating 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 s tep 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 metab olites ( i.e. , w hen 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 metaboli zed 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 method ology 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.

T he relationships hypothesized in the data are de scribed 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 compu tations 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 distrib ution 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 analysi s , 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 D iri chlet

distributed α i set to 1

.

S amples from the poste rior distribution are generated via Markov Chain Monte Carlo (MCMC) [ 57 ]

. In pri nciple 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

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Markov Chain Monte Carlo for l og geometric mean population exp osures to parent compounds

w as performed via JAGS v3.1.0 [ 58 ] . For the second generation SEEM analysis, s eparate 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 p roduction v olumes

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

- censo red, 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

Th e 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 indicat or for near

- v ersus 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 e stimates 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 paren tal 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, identif ied as Σ. Although it is an estimate, we treat Σ as a known quantity. Then, the following likelihood is maximized :New High

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where n is the number of parent chemicals (80 in this analysis), ly is the vector of log

transformed 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 USEtox

predicted 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 m odel 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 mo del selected in stage 3 , the RTK model from step 1, and the production volume sampling from step 2 . T he 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 infer red ranges of exposures that are consistent with biomarkers measured in ur ine samples and reported by NHANES. The posterior distributions established using NHAN ES 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 regr ession coefficients, and to propagate that uncertainty into our predictions.New High

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### 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 b ased 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 r eflecting 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, s ummary data reported by the CDC on chemicals in urin e 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 estima tes, 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, t hose chemicals with indoor/consumer use yield significantly greater NHANES

- inferred exposures. Neither USEtox nor RAIDAR total p redicted
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 estimat es were computed as quantiles of the resulting posterior predictive distributions. The ability of this model to predict potential exposure is evaluated in Figure 13

, w here 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 p redictor 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

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Table 2 : Calibration Model Selection by AIC.

Model Number Description Mean AIC Normalize

d AIC R

squared Mean 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

- 237.05New High

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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 + lVNew High

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The relative un certainty 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 d imethylphosphate 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. F or 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 verti cal confidence interval.

Gener ally, the chemical properties ( Figure 4 ), chemical specific model predictions ( Figure 10 ), and production volume data ( Figure 15

, Figure 16 ) for the 18 50 chemicals without NHANES data appear to be within the range o f the 8 2 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 thi s 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 chemical s 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 the se traditional metrics of production and far field environmental release.New High

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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 li ne 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 fa r 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 statist ically significant (p = 0.017). Figure from Wambaugh et al. ( [ 6 ] ).New High

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Figure 14 : Predicted human exposure s (mg/kg/day) for 1936 chemicals using the model calibrated in the first generation SEEM analysis with e xposures 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 ] ).New High

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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 ] ).New High

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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 ] ).New High

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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 half

lives 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 expos ure 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 e nvironmental media and derivation of food from the environment [ 44

, 45 ] . Mini mally 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 chem icals 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 o n 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 ex posure estimates using minimal information and estimate uncertainty using monitoring data. I n 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 predictio ns are below this level

.New High

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### 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 det ection 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 pro perties 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). Caveat s 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 chem ical 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 bes t factors were consistent across all demographics.New High

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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 wi th 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 vol ume” 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 subse t. For this analysis, Hamiltonian Monte Carlo was performed via Stan ( http://mc

- stan.org ). As is shown in Figure 18 , the correlatio ns obtained are much improved over the previous model ( Figure 13 ). The calibrated model has an R 2 of 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 i n Table 3 , for the entire 7785 chemical library under study. The highest overall upper 95% limit for the general populations was for d iethyl 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 e xposure for m ethyl p araben, 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 s ome 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 pop ulation.New High

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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 1 500 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 poten tial 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.New High

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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 d escriptors ( 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). Bayesia n 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 (R 2 ~0.5). Compare with Figure 13 to see the improvement of the new, heuristic model.New High

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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 th e 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, whi ch were used to determine the linear regression models Figure 20 . Demographic

specific 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.New High

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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 signif icant 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 fo r 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 co ncentrations 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 current ly being studied in the NHANES and by the U.S. EPA . I n 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 th ose 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 diff erence 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 o ut 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 a cross demographic groups.

The distribution of values for the regression coefficient in Figure 20 indicates the trend corresponding to each heuristic. For all demogr aphic 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 estimat ed 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 . M ost 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. , b isphenol a , m ethyl p araben) are known to be inert ingredients in other classes of products that do not conform to the strict ingredient repor ting standards of pesticides.New High

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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). Se parate linear regressions were made of the inferred exposures on the five optimal descriptors for different demographic groups monitored by the CDC NHANES studies

. T he 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.New High

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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 Fem ales, 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). T he distribution of intercepts ( i.e ., the contribution from sources not explained by the top five factors), d oes appear to vary between demographics.New High

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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 gener ation model (R 2 ~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 chemi cal

- 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 wil l 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 k nowledge 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 chil dren 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.New High

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Figure 22 : Amount of v ariance (R 2 ) 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 in ferred from the NHANES urine data.New High

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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 home

care 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 th ese 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 quan tities 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 7785New High

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### 2g. Limitations of Approach

The EPA has identified o ver 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. , se e 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 reporte d in Mitchell et al. [ 5 ] , most of these screening tools cannot be
automa ted 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, runni ng 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 e valuation of potential human exposure.

In contrast , Wambaugh et al. ( [ 6 ] ) demonstrated a method for generating rapid

, exposure estimates using minimal information and e stimates 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 HT E 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 acros s 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 empiric al e stimate of our uncertainty. A ny 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 environmentalNew High

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media ( e.g. , air, water, soi l) 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

E xposure inf

erence from the NHANES data was also conducted under the assumption of steady

state conditions [ 50 ] . T his 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 s easonal , 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 t hese assumptions are a key contribut or to the overall uncertainty estimated in Wambaugh et al. ( [ 6 ] ) . The benefit of these assumptions is that the evaluation and calibration of expos ure 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. A t this time, the steady

- state assumption appears to be the best available for HT E 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 C hemical

- Product C ategorization ( CPCat ) database [ 68 ] , characterization of populati on 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 high

throughput 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

- through put 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 che micals with little associated data should be evaluated first.New High

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### 2h. Conclusion s

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 onl y 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. F or 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 ch emicals 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 . A dditional 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 c hemicals.

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

ma de 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, derma l 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 unce rtainty 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 prope rties [ 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 ]

.

E xpanded monitoring data are needed to better characterize ac tual 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 areNew High

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becoming available that can simultaneously screen for thousands of xenobiotic chemicals as well as endogenous markers of biological response and exposure in plasma [ 72 ]

.

F or 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 base d on exposure.New High

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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

, 7 3

- 78 ] . HTS methods exist for identif ying 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 b ridge 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 enviro nmental chemicals [ 8

, 78 ]

. HTTK methods can estimate the dose (mg/kg BW/day) needed t o produce steady

- state plasma concentrations (C ss , 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 . T h is 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 relativ ely 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 concen tration is at steady

- state with respect to the environment [ 8 ] . Dyna mic simulation ( e.g. , more realistic, sporadic dosing) of physiologically

based toxicokinetic (PBTK) models allow s the prediction of chemical concentration in specific tissues at different times . PBTK models are more complicated and are typically considere d higher tier models [ 77

, 81

, 88 ] . However, it is possible to rapidly parameteri ze generic PBTK models . T hese 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 pr otective) 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 a nd 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 ri sk to humans has been complexityNew High

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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, w e 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, pa rticularly 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 po or 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 fo r 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.New High

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### 3b. HTTK for Environmental Chemicals

Toxicokinetic Models Used

We used t wo general TK models

. The first, a “three

- compartment” model ( Figure 23 ) for determining plasma concent rations did not determin e 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 filtr ation rate and f up ). We modeled h epatic 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 10 8 hepatocytes per gram liver and a liver density of 1.05 g/mL).

Figure 23 : The general, three compartment TK mod el, used by SimCYP for RTK [ 8 ]

. The second general T K 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 “well

stirred” approximation

Tissue to unbound fraction in plasma partition coefficients were predicted u sing Schmitt’sNew High

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method [ 89 ] fr om the in vitro f up , 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 partitioni ng 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 assumption s.

Determination of steady state

An analytic solutio n 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 (C ss ) 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) CL int , the intrinsic metabolic clearance of the parent compound by primary hepatocytes ( substrate depletion approach ) in well s of a multi

- compound plate [ 94 ] ; and (2) f up , the fraction of chemical unboundNew High

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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

con centration in the two linked wells gives the free fraction of chemical) [ 95 ] . For environmental chemicals, data on these two parameters w ere 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 w eight and structure were determined from the Distributed Structure

- Search able Toxicity Database (DSSTox) (http://www.epa.gov/dsstox) and hydrophobicity was predicted for all compounds using EPI Suite.

Monte Carlo simulation and parameter distributions

Fo r comparison with the results in Wetmore et al. [ 8 ] obtained with the SimCYP population simulator [ 84 ]

, Monte Carlo was perfo rmed 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 CL int w ere all varied in this manner. The f up 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 C ss , allowing estimation of the 5%, median, and 95% quantiles.

In vivo chemical data

Human in vivo C ss for a dose of 1 mg/kg BW/day were identified for 11 envi ronmental 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 C s s for the same dose. Rat in vivo data for pharmaceutical compounds were curated by the Netherlands Organisation for Applied Scientific Research.New High

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

The implementation of the vision outlined in Toxicity Testing in the 21 st 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 hum an 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 (C ss , in units of μM) equivalent to the activation concentrations observed in vitro [ 84

- 86 ] ). The ratio of lowest activity equivalent dose to the high est predicted exposure, the AER, provides a n 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 f up ) fails. With the se 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 wa s not reached in hundreds of years. The refore, 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 C ss derived from the three compartment model at steady

- state. Using a default value for absorption rate (k absorb = 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 C ss predictions for the cons tant infusion exposure that was used previously.New High

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Figure 25 : A hist ogram 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 C ss , we find that the C ss 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 C ss since stead

state is never reached. Since the constant infusion exposure predictions overestimate the more complicated simulated exposures, both of these discrepancies are health

- protective.New High

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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 C ss . 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 5 th , median, and upper 95 th percentile C ss for the same fixed dose. In Figure 27

, we use d 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 5 th and median C ss values are not impacted, but that the upper 95 t h ( 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 impa cts many chemicals.New High

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Also in the Wetmore et al. papers [ 8

, 87 ]

, a p

- value threshold of 0.10 was used for systemati c 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 t he 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 p

value for systematic clearance was between 0.5 and 0.1.

One of the drawbacks of he patocyte suspensions in clearance detection is that they are not sensitive in measuring clearance for lower turnover compounds. Many of the compounds in t he Wetmore et al. papers [ 8

, 87 ] for which no or not

- quite significant clearance was measured might be expected to y ield significant clearance when using plated hepatocytes over a longer time course [ 103 ] . F ollow

- up measurement of clearance rates using a plated system ( e.g ., Smith

, et. al [ 103 ] ) may be of value at higher tiers to e nsure that more accurate
, rather than conservative

, values are obtained.

We analyzed in vivo TK data on C ss 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 C ss . In Figure 28

, we compare our HTTK C ss predic tions from in vitro data (as in Figure 27 , but without MC simulation) with C ss values inferred from human in vivo studies. We observe poor (R 2 ~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).New High

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Figure 27 : Correlation between previously predicted C ss 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 C ss from in vitro data. We recreated the aspects of the SimCYP TK MC simulation software used to predict C ss 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.New High

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Figure 28 : Comparison of C ss predicted from in vitro HTTK with in vivo C ss values determined from the literature

. The dashed line indicates the identity (perfect predictor) line. Limited correlation was observed ( R 2 ~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

.New High

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Figure 29 : The recursive partitioning regression tree [ 104 ] for the discrepancy between the C ss predicted from in vitro data and the C ss measured in vivo [ 101 ]

.New High

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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 estim ate of the accuracy of HTTK

.New High

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Using the method of recursive partitioning [ 104 ] , a decision tree ( Figure 29 ) was constructed using physico

- chemical properties, i n 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 predomina ntly 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 (R 2 ~0.69) better than we can predict the C ss 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 on e chemical, so that for these chemicals we are confident that the C ss values predicted by HTTK is conservative.

In Figure 31 and Figure 32 , we compare in vivo time cour se 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 C urve or AUC). Neglecting route of administration, we can show correlation between in vivo data and HTPBTK predictions in Figure 31 (R 2 ~ 0.58). Howeve r, when oral doses are contrasted against intravenous and subcutaneous dose routes, we find greater correlation (R 2 ~ 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 f or all chemicals in Wetmore et al. [ 8 ] , the HTTK app roach appears to be health protective in this regard.

In Figure 32

, we show that peak plasma concentration (C max ) appears to be well describ ed by th e 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 C max are all for oral administration.

The in vivo data in Figure 31 and Figure 32 are dr awn from various treatments of 15 pharmaceutical chem icals 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.New High

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Figure 31 : C ompar ison 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 . T he predicted AUC for orally administered doses (po

– triangle) generally overestimates tissue dose with respect to intravenous (iv

– circle) or subcutaneous doses (sc

– sq uare) 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 or al doses (R 2 ~ 0.58).New High

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Figure 32 : C ompar ison of in vitro HTPBTK predictions for C max (maximum plasma concentration) with in vivo measurements from the literature

. A total of 15 pharmaceutical chemicals and six environmental chemi cals 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), intraven ous (iv

– circle), and subcutaneous (sc

– square) which was modeled as iv doses. The C max appears to be well described by the HTPBPK model (R 2 ~0.69)

.New High

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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 ]

. W e 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 o r RTK) [ 8

, 73

- 76

, 79

- 83 ]

.

A pproaches to address TK uncertainty and variability must be able to handle censored (limit of detection) measurements, as we d o 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 C ss 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 C ss .

For the metabolic cle arance assay , a stricter limit on determining whether there is systematic disappearance of the compound ( i.e. , setting metabolic elimination to zero for p

- v alue < 0.05 rather than 0.10) is more conservative since it decreases AER.

W e used simulation stud y 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 C ss . 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 o f 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 C ss 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 fu rniture 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 use d 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 95 th percentile) [ 91 ] . Further, any key determinants of variability that are not included in our simulation cannot be assessed for impa ct.New High

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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 d ata.

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, hepat ic, and other tissues, and poor absorption [ 73 ] . Until in vitro assays are developed for these pro cesses, 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 ge t an empirica l estimate of our uncertainty. A ny 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.New High

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### 3e. Conclusion s

For chemicals with limited data, the ratio of lowest putative bioactive dose to the highest possible exposure (AER) has be en proposed as a means for a n integrated approach to prioritizing and initial screening of chemicals [ 8

, 74

, 78 ] . Since the d e termination of bioactive dose relies on IVIVE, the methods used must be shown to be health protective (conservative) in the sense that the plasma concentrations resul ting 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 (C ss ) 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 C ss 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 C ss with in vivo measured C ss for some chemicals. We observe d poor (R 2 ~0.19), but usually conservative correlation. However, we can model the difference between in v ivo measurements and HTTK predictions ( i.e. , the residuals or errors), achieving a much greater ability to predict errors (R 2 ~0.69) than to make overall predictions. For those chemicals where the predicted residual is less than 0.1, the actual residuals a re less than 1 for all but one chemical, so that for these chemicals we are confident that the C ss 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, ra t 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 w ith 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 i s currently difficult to translate this data into quantitative bioavailability [ 102 ] . Thus, if in vivo data c ould 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 simulate d except when the protein binding assay (for f up ) fails. Drawing from the result of our analysis

, we organize the tiers ofNew High

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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 C ss from constant

infusion exposure If comparison with predicted
exposure indicates large AER then infusion C ss 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, 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 Transporter

specific 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 vi tro 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

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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 replica tes, 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 t o apply. 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 . 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

- stat e 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 su ch as half the limit of detection ( [ 8 ] ) may result in an artifactual underestimation of C ss 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 comp arison 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 neces sary. 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
restNew High

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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 C ss from constant infusion

exposure If comparison with predicted exposure indicates large AER then infusion C ss 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, 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 Transporter

specific 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 vi tro 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 restNew High

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## 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 n ew 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 ] )

. M odels must be : ( 1) capable of being parameterized from minimal data sets ; ( 2) broadl y 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 w ith 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 moni toring 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 . A ny approximations, omissions, or mistakes incorporated in the approach should increase the es timated uncertainty when evaluated systematically across chemicals.New High

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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, t here 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 toxic ity. 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. Altho ugh OSHA does not require MSDSs to be provided to consumers, many manufacturers and retailers are providing MSDS s as a public service. For example, Wal

- Mart has made the MSDS inventory of their products available online for more than 10 years. Another

sour ce is the Clinical Toxicology of Commercial Products (CTCP), which is a compendium of consumer product ingredients dating back three decades . The CTCP database include s 14,000 products and over 900 generic product formulations [ 110 ] . More recent efforts by the National Library of Medicine (NL M) is the Household Product Database (HPDB)

, which exists in a web

accessible 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 pro duct 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 fo r 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 focus ing 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, stream lining 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, t he chemical

- product categorization (CPCat) database, is described in the next section.New High

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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

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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 ingredient s 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 da tabase management system that can b e 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 qu ality that is commensurate with the needs of both the EDSP prior itization 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 o pening 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 bottle s 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 algor ithmically 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 assessmen t 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 second ary 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 “Alcoh ols, C16

- 18” resulted in a search of secondary

sources.New High

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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.screen

scraper.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 95,0 00 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 represen t 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 pr oduct 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 automat ed CAS

RN extraction). Mapping to existing chemicals in the ACToR database reduced the MSDS entries to a bout 9800.

Upon the completion of manual data curation and entry, we randomly selected 130 MSDSs to check the accuracy of the process. Entries were com pared 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 typic ally 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 re porting).New High

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Ingredient diversity : We observed wide variability in chemical ingredients across products, as evident in the network diagram presented in Figure 34 . In t he 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 pre sent 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 t o 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 ing redient is captured in over 70% of the unique chemical classes (14 of 20 classes). Parabens, p hthalates, and v olatile o rganic c ompounds 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 prod uct

- related activities. Bisphenol A, flame retardants ( p olybrominated and d iphenyl e thers) 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

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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 improveme nt, 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

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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. Figur e from Goldsmith et al. ( [ 67 ] ).New High

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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 comprehen sive 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 availab le 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 cla ss 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 clas ses: (1) g eneral product

- use: the chemical is used in a general product type ( e.g ., cleaning and washing); (2) s pecific product

- use: the chemical is used in a specific consumer product ( e.g ., oven cleaner); (3) t herapeutic

- use: the chemical is used as an i ngredient in a pharmaceutical with a biological target or to treat a specific disease ( e.g ., antibiotic); (4) f unctional

- use: the chemical has a functional role according to its properties ( e.g ., solvent); and (5) i ndustrial sector

- use: the chemical is use d 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

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Table 5 : Summary of data sources used to construct the CPCat database.

1 Class of category used for chemical categorization in the original data source.
2 Number of unique chemical categories in the original data source.
3 Cassettes are comprised of two or more CPCat terms separated by spaces that must be interpreted together.
4 Chemicals 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 4 36,062

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 442New High

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C PCat 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 d ifferent 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 com bined 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 indicat es 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, C PCat 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).

Num ber 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 cass ettes are perm uta tions 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 th eir 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 t o be found in sources of drinking water. Potential drinking water contaminants are only subject to testing after a determination of s ubstantial 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 ca n 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 assay s 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 n umber 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

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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 i ndicates combined cassettes.

CPC at Cassette CP C at Cassette CP C at 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 bev erage * 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_useNew High

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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 associat ion 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 spe cific 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 prioritize d 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 b ased 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

- to

product 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 applicati on in the EDSP.New High

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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 (Mg 3 H 2 (SiO 3 ) 4 ) 63 100

- 51

- 6 benzyl alcohol 61 57

- 11

- 4 stearic acid 60New High

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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 man y 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
popu lation 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 SH EDS 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

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Figure 36 : SHEDS

- HT input data streams and modules.New High

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Table 8 : SHEDS

- HT exposure pathways, scenarios, and routes with required input data.

Field Scenarios Routes Required Data Streams Near

field 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 non

dietary ingestion via hand

- to

mou th transfer) Consumer product chemical

composition Consumer product use patterns Chemical properties

Time

- activity data Various exposure factor distributions

Near

field 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
art icles 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 intakesNew High

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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 a pplication, 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 ing estion 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 an d 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 the n 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. Th e 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 Na tional 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 pathway

specific absorption fractions. Dermal absorption fraction distributions are chemical

- specific, asNew High

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they are scaled via predicted dermal permeability coeff icients 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 SHEDS

Multimedia 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 wat er consumed by individuals Calculated from NHANES

WWEIA 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 co nsumer 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, hand

to

- mouth and object

- to

mouth information, bathing and hand washing removals Distributions developed for SHEDS

- Multimedia [ 127 ] Appropriate daily

- level values for some parameters determined from fitting results from SHEDS

Multimedia runs

Residential Media Concentration Information: Source

- to

Concentration 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 SHEDS

Multimedia [ 127 ] Measured values (or other modeled values) for air and surface concentrations can be used if availableNew High

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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 ingr edients 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 peri odically, 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 Predicti ons for Chemicals in Consumer Products and Pesticides in Foods SHEDS

- HT exposure dose predictions for the U.S. general population for 2 111 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 span s 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 amon g 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 dose s were seen in females than in men, which is associated with lower mean body weights and higher prevalence of use o f 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

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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

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4aiiic. Evaluation with NHANES Inferences

Median predicted SHEDS exposure dose s are plotted against values for 39 chemicals estimated from NHANES biomarker data [ 6 ] in Figure 38 . The median SHEDS exposure dose s 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 ign ored). 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 post

application (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 numb ers 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 ar e performing a more detailed evaluation of these results within the SEEM framework [ 6 ] which will allow for iterative evaluation of the model, quantifica tion 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 chlorpyr ifos

- methyl is ignored).New High

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4ai v . 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 com putational 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 othe r 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, SHEDS

HT 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 upd ates to input data st r eams 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, requ ired 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 high

activity, high

- exposure chemicals. A total of 519 EDSP chemicals were included in the initial application of SHEDS

- HT describe d 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

m odel 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 co nsumer 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 ro bust mid

- tier assessments. Implementation of such longitudinal methods are currently planned.New High

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### 4b. Ecological Exposure

4bi. Overview

In order to prioritize thousands of chemicals on the basis of exposure, both human and ecological
exposure must be cons idered [ 1 ]

. To date the majority of ExpoCast research has focused on predicting human exposure. However, here we propose applying the SEEM framework to ecological exposu re 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 e cological and human exposure prioritizations of chemicals r egulated 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 envir onmental 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 e xposed biota using traditional external toxicity benchmarks ( e.g. , EC 50 , LC 50 , NOEL, etc. ), but also to predict expected biological concentrations (EBCs) in exposed biota. In turn, r esulting EBCs can then be used to assess dietary hazards to biota and huma ns 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 recipie nt. 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 HT E models include environmental fate and transport components, including prediction for ecological water concentrations. If a sufficient data set of ecological water monitoring co ncentrations 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 aqu atic 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

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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 conti nental 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, sedi ment 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 distributi on 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 b een 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, dependi ng on the fate and transport model used, were characterized inNew High

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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 eithe r release or emissions into the air
or water . S ince 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 7 5% 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 Repo rting/ Chemical Data Reporting ( IUR ) and used as a surrogate for the amount of chemical release d 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 u sing 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 we ll as the analytical chemistry limit of detection (LOD).New High

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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 ba sed on similarity calculated using Euclidean distance of the logarithm of the predicted kg in each media and complete linkage.New High

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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 se t 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 purpos es. Siting is generally not random

. T he 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 measuremen ts 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 t o 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
geogra phic 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.

W e can then use weighted regression to evaluate relationship between predictions from ecological HT E models and national

- level geometric means, and these regression weights will
pro vide 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

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e.g., posterior samples, asymptotic normality of MLE estimates.

In ad dition 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 stream s 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 p ost

- 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 . A ll other components are currently functional in ORD’s proof

- of

- concept prototype for EDSP.
To demonstrate the use of this software for addressing EDSP p rioritization 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_ED

PBT.xlsx ) Facilitates creation of EXAMS

Loading Generator Dietary & Drinking Water Assessment SHEDS

- HT KABAM BASSNew High

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preliminary exposure predictions for 74 chemicals from EDSP List 2 [ 139 ] with a log K ow > 3. Nine EXAMS river environments were developed and l oading rates to these rivers were estimated for 27 of the 74 selected chemical s using mean down

- the

- drain discharg es (DDD g/person

- day) by : (1) SHEDS

- HT, (2) POTW (publicly owned treatment works) removal efficiencies estimated by EPI S uite , 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 2.23 x 10

- 6 to 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 ( C wd mg/L), total water concentrations ( C wt mg/L), total sediment concentrations ( C st mg/kg dry wt), and whole

- body fish concentrations ( C f mg/kg wet wt) were then fitted to the log linear regression model :

where C is the concentration of interest, Q (m 3 /hr) is the river’s mean annual stream flow, L (kg/hr) is the river’s mean annual loading rate, K OW is the chemical ’s octanol

- water partition coefficient, HLC (atm  m 3 mol

- 1 ) is the chemicals ’s Henry’s Law Constant, and ( S w mg/L) is the chemical ’s aqueous solubility. These physicochemical properties were selected as predictor variables because these are the primary variabl es 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

o f 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 K ow dependencies. B4. Given t he 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 chemical s 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

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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 pathway s , 3. development of EXAMS modeling environments for populations/POTWs of co ncern 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 chemical s of concern.New High

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4bi. Limitations of Approach

As with human exposure, we do n ot 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 i nterval of th e 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 high

throughput exposure. However, high

- throughput exposure offers a means to determine whi ch 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 m onitoring 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 da ta 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 informatio n ( e.g. , K ow ) ; (3ii) chemical

- specific loadings ( e.g., kg/day releases to water) ; and (3iii) environmental a n d geographic descriptors for specific environments ( e.g. , distribution of rainfall, relative fraction of land to water).

The granularity of the mo dels, 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 b oth 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) d ata 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 wate r 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

- throughpu t 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 forwar d 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 modificationNew High

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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 i t has been validated for large lakes and rivers, but not for entire watersheds. As with human exposure, these models can be evalu ated 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

u ncertainty in chemical loadings , which are an overall multiplicative factor [ 140 ]

. A quatic 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 difference s among chemical properties. For the human exposure evaluation

, loadings had to be determined from the IUR U.S. national chemical production list . In reality, t he 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 interval s in different years.

W e can evaluate the sensitivity of predicted EECs to assumptions about loadings ( e.g., national production volumes vs. spatially

- specific releases) wherever both monitoring dat a and data on releases are available. Chemical use characteristics, such as down

- the

- drain rates and the nature of storage and transp ortation, may be useful for estimating aspects of loadings, analogously to how chemical use is hel pful 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 t o 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 stati stical power and allowing, among other things, estimation of more extreme quantiles than the median ( e.g., 95 th percentile and potentially even higher). It may be that the NWQMC data set is sufficiently large to allow region specific ( i.e. , spatially

varyi ng) 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 i nto 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 c once ntrations 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

n ot capture p eak concentration; thus exposure predictions calibrated to this data may beNew High

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underestimates. This also biases time

- averaged concentrations downward, on longer time scales (such as seasonal). Chemical

- induced effects might be expected to be assoc iated 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 heterogeneit y 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. Howe ver, 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 result s 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 med ium, 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 chemical s i n a search for exposures t hat are so promiscuous and sufficiently large that they potentially cause wide

- spread bioactivity.New High

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### 4c. New Data for HTTK

To date, in vitro PK data on ToxCast chemicals has been collected by researchers at the Hamner Institutes i n collaboration with the EPA [ 8

, 73

, 87 ] . As shown in Figure 43 , data on ~250 chemicals has been publishe d on human [ 8 ] , and an additional rat in vitro PK da ta 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 b eing 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 joi ned 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 concentr ation 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 reduc e 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 ]

.New High

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To better assess the performance of HTTK for environmental chemicals, two separate acti vities 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 studi es 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 in travenous 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 tes ting 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 vitr o ( 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.New High

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## 5. Future Applications

EDSP Integrated Activity and Exposure Based Prioritization and Screening

This whit epaper 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 bridg e between HTE and high

- throughput screening (HTS) bioactivity assays [ 7 ] . HTE and HTTK/RTK represent critical components for a n integrated activity and exposure based approach to prioritiz ation and initial screening of the EDSP chemical universe.

Recently, Judson et al. [ 74 ] d escribed 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 targ ets; (2) HTTK methods are then employed to determine the human dose needed for each che mical 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 predi ctions become targets for further investigation [ 8

, 74 ]

.

The EDSP integrated activity and exposure based approach to prioritization and initial screening

w ill in clude a number of datasets and considerations to represent critical points along the Adverse Outcome Pathways (AOP) for endocrine modalities. Inherent physico chemical properties will help identify those chemicals in the EDSP universe that are not a nticipated 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 univ erse 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 activi ty. Incorporating the output of these sources of information in a n 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.New High

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Figure 45 : Chemical p rioritization, 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 B W/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, pro cessing, 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 ev aluation (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 o f concern for futu reNew High

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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 analo g 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 HT S 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 predict ive 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 environme nt. 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 additio n, 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 gen erated HT S 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 exami ne 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 approach es 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
app roaches 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 prov ide 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 t o 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 deter mining 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 OP PT.New High

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## 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, Sastr y 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, Alexand er 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 (NHANES

SEEM

- 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 modelNew High

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