A Comparison of In Vitro Points of Departure with Human Blood Levels for Per- and Polyfluoroalkyl Substances (PFAS).

Per- and polyfluoroalkyl substances (PFAS) are widely used, and their fluorinated state contributes to unique uses and stability but also long half-lives in the environment and humans. PFAS have been shown to be toxic, leading to immunosuppression, cancer, and other adverse health outcomes. Only a small fraction of the PFAS in commerce have been evaluated for toxicity using in vivo tests, which leads to a need to prioritize which compounds to examine further. Here, we demonstrate a prioritization approach that combines human biomonitoring data (blood concentrations) with bioactivity data (concentrations at which bioactivity is observed in vitro) for 31 PFAS. The in vitro data are taken from a battery of cell-based assays, mostly run on human cells. The result is a Bioactive Concentration to Blood Concentration Ratio (BCBCR), similar to a margin of exposure (MoE). Chemicals with low BCBCR values could then be prioritized for further risk assessment. Using this method, two of the PFAS, PFOA (Perfluorooctanoic Acid) and PFOS (Perfluorooctane Sulfonic Acid), have BCBCR values < 1 for some populations. An additional 9 PFAS have BCBCR values < 100 for some populations. This study shows a promising approach to screening level risk assessments of compounds such as PFAS that are long-lived in humans and other species.

Suspect Screening Analysis of Pooled Human Serum Samples Using GC × GC/TOF-MS.

Humans interact with thousands of chemicals. This study aims to identify substances of emerging concern and in need of human health risk evaluations. Sixteen pooled human serum samples were constructed from 25 individual samples each from the National Institute of Environmental Health Sciences' Clinical Research Unit. Samples were analyzed using gas chromatography (GC) × GC/time-of-flight (TOF)-mass spectrometry (MS) in a suspect screening analysis, with follow-up confirmation analysis of 19 substances. A standard reference material blood sample was also analyzed through the confirmation process for comparison. The pools were stratified by sex (female and male) and by age (≤45 and >45). Publicly available information on potential exposure sources was aggregated to annotate presence in serum as either endogenous, food/nutrient, drug, commerce, or contaminant. Of the 544 unique substances tentatively identified by spectral matching, 472 were identified in females, while only 271 were identified in males. Surprisingly, 273 of the identified substances were found only in females. It is known that behavior and near-field environments can drive exposures, and this work demonstrates the existence of exposure sources uniquely relevant to females.

Characterizing Chemical Exposure Trends from NHANES Urinary Biomonitoring Data.

BACKGROUND: Xenobiotic metabolites are widely present in human urine and can indicate recent exposure to environmental chemicals. Proper inference of which chemicals contribute to these metabolites can inform human exposure and risk. Furthermore, longitudinal biomonitoring studies provide insight into how chemical exposures change over time. OBJECTIVES: We constructed an exposure landscape for as many human-exposure relevant chemicals over as large a time span as possible to characterize exposure trends across demographic groups and chemical types. METHODS: We analyzed urine data of nine 2-y cohorts (1999-2016) from the National Health and Nutrition Examination Survey (NHANES). Chemical daily intake rates (in milligrams per kilogram bodyweight per day) were inferred, using the R package bayesmarker, from metabolite concentrations in each cohort individually to identify exposure trends. Trends for metabolites and parents were clustered to find chemicals with similar exposure patterns. Exposure variation by age, gender, and body mass index were also assessed. RESULTS: Intake rates for 179 parent chemicals were inferred from 151 metabolites (96 measured in five or more cohorts). Seventeen metabolites and 44 parent chemicals exhibited fold-changes ≥10 between any two cohorts (deltamethrin, di-n-octyl phthalate, and di-isononyl phthalate had the greatest exposure increases). Di-2-ethylhexyl phthalate intake began decreasing in 2007, whereas both di-isobutyl and di-isononyl phthalate began increasing shortly before. Intake for four parabens was markedly higher in females, especially reproductive-age females, compared with males and children. Cadmium and arsenobetaine exhibited higher exposure for individuals >65 years of age and lower for individuals <20 years of age. DISCUSSION: With appropriate analysis, NHANES indicates trends in chemical exposures over the past two decades. Decreases in exposure are observable as the result of regulatory action, with some being accompanied by increases in replacement chemicals. Age- and gender-specific variations in exposure were observed for multiple chemicals. Continued estimation of demographic-specific exposures is needed to both monitor and identify potential vulnerable populations. https://doi.org/10.1289/EHP12188.

Screening for drinking water contaminants of concern using an automated exposure-focused workflow.

BACKGROUND: The number of chemicals present in the environment exceeds the capacity of government bodies to characterize risk. Therefore, data-informed and reproducible processes are needed for identifying chemicals for further assessment. The Minnesota Department of Health (MDH), under its Contaminants of Emerging Concern (CEC) initiative, uses a standardized process to screen potential drinking water contaminants based on toxicity and exposure potential. OBJECTIVE: Recently, MDH partnered with the U.S. Environmental Protection Agency (EPA) Office of Research and Development (ORD) to accelerate the screening process via development of an automated workflow accessing relevant exposure data, including exposure new approach methodologies (NAMs) from ORD's ExpoCast project. METHODS: The workflow incorporated information from 27 data sources related to persistence and fate, release potential, water occurrence, and exposure potential, making use of ORD tools for harmonization of chemical names and identifiers. The workflow also incorporated data and criteria specific to Minnesota and MDH's regulatory authority. The collected data were used to score chemicals using quantitative algorithms developed by MDH. The workflow was applied to 1867 case study chemicals, including 82 chemicals that were previously manually evaluated by MDH. RESULTS: Evaluation of the automated and manual results for these 82 chemicals indicated reasonable agreement between the scores although agreement depended on data availability; automated scores were lower than manual scores for chemicals with fewer available data. Case study chemicals with high exposure scores included disinfection by-products, pharmaceuticals, consumer product chemicals, per- and polyfluoroalkyl substances, pesticides, and metals. Scores were integrated with in vitro bioactivity data to assess the feasibility of using NAMs for further risk prioritization. SIGNIFICANCE: This workflow will allow MDH to accelerate exposure screening and expand the number of chemicals examined, freeing resources for in-depth assessments. The workflow will be useful in screening large libraries of chemicals for candidates for the CEC program.

Reproducibility of organ-level effects in repeat dose animal studies.

This work estimates benchmarks for new approach method (NAM) performance in predicting organ-level effects in repeat dose studies of adult animals based on variability in replicate animal studies. Treatment-related effect values from the Toxicity Reference database (v2.1) for weight, gross, or histopathological changes in the adrenal gland, liver, kidney, spleen, stomach, and thyroid were used. Rates of chemical concordance among organ-level findings in replicate studies, defined by repeated chemical only, chemical and species, or chemical and study type, were calculated. Concordance was 39 - 88%, depending on organ, and was highest within species. Variance in treatment-related effect values, including lowest effect level (LEL) values and benchmark dose (BMD) values when available, was calculated by organ. Multilinear regression modeling, using study descriptors of organ-level effect values as covariates, was used to estimate total variance, mean square error (MSE), and root residual mean square error (RMSE). MSE values, interpreted as estimates of unexplained variance, suggest study descriptors accounted for 52-69% of total variance in organ-level LELs. RMSE ranged from 0.41 - 0.68 log10-mg/kg/day. Differences between organ-level effects from chronic (CHR) and subchronic (SUB) dosing regimens were also quantified. Odds ratios indicated CHR organ effects were unlikely if the SUB study was negative. Mean differences of CHR - SUB organ-level LELs ranged from -0.38 to -0.19 log10 mg/kg/day; the magnitudes of these mean differences were less than RMSE for replicate studies. Finally, in vitro to in vivo extrapolation (IVIVE) was employed to compare bioactive concentrations from in vitro NAMs for kidney and liver to LELs. The observed mean difference between LELs and mean IVIVE dose predictions approached 0.5 log10-mg/kg/day, but differences by chemical ranged widely. Overall, variability in repeat dose organ-level effects suggests expectations for quantitative accuracy of NAM prediction of LELs should be at least ± 1 log10-mg/kg/day, with qualitative accuracy not exceeding 70%.

Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency.

Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.

Category-Based Toxicokinetic Evaluations of Data-Poor Per- and Polyfluoroalkyl Substances (PFAS) using Gas Chromatography Coupled with Mass Spectrometry.

Concern over per- and polyfluoroalkyl substances (PFAS) has increased as more is learned about their environmental presence, persistence, and bioaccumulative potential. The limited monitoring, toxicokinetic (TK), and toxicologic data available are inadequate to inform risk across this diverse domain. Here, 73 PFAS were selected for in vitro TK evaluation to expand knowledge across lesser-studied PFAS alcohols, amides, and acrylates. Targeted methods developed using gas chromatography-tandem mass spectrometry (GC-MS/MS) were used to measure human plasma protein binding and hepatocyte clearance. Forty-three PFAS were successfully evaluated in plasma, with fraction unbound (fup) values ranging from 0.004 to 1. With a median fup of 0.09 (i.e., 91% bound), these PFAS are highly bound but exhibit 10-fold lower binding than legacy perfluoroalkyl acids recently evaluated. Thirty PFAS evaluated in the hepatocyte clearance assay showed abiotic loss, with many exceeding 60% loss within 60 min. Metabolic clearance was noted for 11 of the 13 that were successfully evaluated, with rates up to 49.9 µL/(min × million cells). The chemical transformation simulator revealed potential (bio)transformation products to consider. This effort provides critical information to evaluate PFAS for which volatility, metabolism, and other routes of transformation are likely to modulate their environmental fates.

Plasma Protein Binding Evaluations of Per- and Polyfluoroalkyl Substances for Category-Based Toxicokinetic Assessment.

New approach methodologies (NAMs) that make use of in vitro screening and in silico approaches to inform chemical evaluations rely on in vitro toxicokinetic (TK) data to translate in vitro bioactive concentrations to exposure metrics reflective of administered dose. With 1364 per- and polyfluoroalkyl substances (PFAS) identified as of interest under Section 8 of the U.S. Toxic Substances Control Act (TSCA) and concern over the lack of knowledge regarding environmental persistence, human health, and ecological effects, the utility of NAMs to understand potential toxicities and toxicokinetics across these data-poor compounds is being evaluated. To address the TK data deficiency, 71 PFAS selected to span a wide range of functional groups and physico-chemical properties were evaluated for in vitro human plasma protein binding (PPB) by ultracentrifugation with liquid chromatography-mass spectrometry analysis. For the 67 PFAS successfully evaluated by ultracentrifugation, fraction unbound in plasma (fup) ranged from less than 0.0001 (pentadecafluorooctanoyl chloride) to 0.7302 (tetrafluorosuccinic acid), with over half of the PFAS showing PPB exceeding 99.5% (fup < 0.005). Category-based evaluations revealed that perfluoroalkanoyl chlorides and perfluorinated carboxylates (PFCAs) with 6-10 carbons were the highest bound, with similar median values for alkyl, ether, and polyether PFCAs. Interestingly, binding was lower for the PFCAs with a carbon chain length of ≥11. Lower binding also was noted for fluorotelomer carboxylic acids when compared to their carbon-equivalent perfluoroalkyl acids. Comparisons of the fup value derived using two PPB methods, ultracentrifugation or rapid equilibrium dialysis (RED), revealed RED failure for a subset of PFAS of high mass and/or predicted octanol-water partition coefficients exceeding 4 due to failure to achieve equilibrium. Bayesian modeling was used to provide uncertainty bounds around fup point estimates for incorporation into TK modeling. This PFAS PPB evaluation and grouping exercise across 67 structures greatly expand our current knowledge and will aid in PFAS NAM development.

Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments.

'Cell Painting' is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ∼100 µM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.

In vitro to in vivo extrapolation and high-content imaging for simultaneous characterization of chemically induced liver steatosis and markers of hepatotoxicity.

Chemically induced steatosis is characterized by lipid accumulation associated with mitochondrial dysfunction, oxidative stress and nucleus distortion. New approach methods integrating in vitro and in silico models are needed to identify chemicals that may induce these cellular events as potential risk factors for steatosis and associated hepatotoxicity. In this study we used high-content imaging for the simultaneous quantification of four cellular markers as sentinels for hepatotoxicity and steatosis in chemically exposed human liver cells in vitro. Furthermore, we evaluated the results with a computational model for the extrapolation of human oral equivalent doses (OED). First, we tested 16 reference chemicals with known capacities to induce cellular alterations in nuclear morphology, lipid accumulation, mitochondrial membrane potential and oxidative stress. Then, using physiologically based pharmacokinetic modeling and reverse dosimetry, OEDs were extrapolated from data of any stimulated individual sentinel response. The extrapolated OEDs were confirmed to be within biologically relevant exposure ranges for the reference chemicals. Next, we tested 14 chemicals found in food, selected from thousands of putative chemicals on the basis of structure-based prediction for nuclear receptor activation. Amongst these, orotic acid had an extrapolated OED overlapping with realistic exposure ranges. Thus, we were able to characterize known steatosis-inducing chemicals as well as data-scarce food-related chemicals, amongst which we confirmed orotic acid to induce hepatotoxicity. This strategy addresses needs of next generation risk assessment and can be used as a first chemical prioritization hazard screening step in a tiered approach to identify chemical risk factors for steatosis and hepatotoxicity-associated events.

A Data-Driven Approach to Estimating Occupational Inhalation Exposure Using Workplace Compliance Data.

A growing list of chemicals are approved for production and use in the United States and elsewhere, and new approaches are needed to rapidly assess the potential exposure and health hazard posed by these substances. Here, we present a high-throughput, data-driven approach that will aid in estimating occupational exposure using a database of over 1.5 million observations of chemical concentrations in U.S. workplace air samples. We fit a Bayesian hierarchical model that uses industry type and the physicochemical properties of a substance to predict the distribution of workplace air concentrations. This model substantially outperforms a null model when predicting whether a substance will be detected in an air sample, and if so at what concentration, with 75.9% classification accuracy and a root-mean-square error (RMSE) of 1.00 log10 mg m-3 when applied to a held-out test set of substances. This modeling framework can be used to predict air concentration distributions for new substances, which we demonstrate by making predictions for 5587 new substance-by-workplace-type pairs reported in the US EPA's Toxic Substances Control Act (TSCA) Chemical Data Reporting (CDR) industrial use database. It also allows for improved consideration of occupational exposure within the context of high-throughput, risk-based chemical prioritization efforts.

A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species.

Per- and polyfluoroalkyl substances (PFAS) are a diverse group of man-made chemicals that are commonly found in body tissues. The toxicokinetics of most PFAS are currently uncharacterized, but long half-lives (t½) have been observed in some cases. Knowledge of chemical-specific t½ is necessary for exposure reconstruction and extrapolation from toxicological studies. We used an ensemble machine learning method, random forest, to model the existing in vivo measured t½ across four species (human, monkey, rat, mouse) and eleven PFAS. Mechanistically motivated descriptors were examined, including two types of surrogates for renal transporters: (1) physiological descriptors, including kidney geometry, for renal transporter expression and (2) structural similarity of defluorinated PFAS to endogenous chemicals for transporter affinity. We developed a classification model for t½ (Bin 1: <12 h; Bin 2: <1 week; Bin 3: <2 months; Bin 4: >2 months). The model had an accuracy of 86.1% in contrast to 32.2% for a y-randomized null model. A total of 3890 compounds were within domain of the model, and t½ was predicted using the bin medians: 4.9 h, 2.2 days, 33 days, and 3.3 years. For human t½, 56% of PFAS were classified in Bin 4, 7% were classified in Bin 3, and 37% were classified in Bin 2. This model synthesizes the limited available data to allow tentative extrapolation and prioritization.

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis.

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency's ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some-but not all-cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.

A regression analysis using simple descriptors for multiple dermal datasets: Going from individual membranes to the full skin.

In silico methods to estimate and/or quantify skin absorption of chemicals as a function of chemistry are needed to realistically predict pharmacological, occupational, and environmental exposures. The Potts-Guy equation is a well-established approach, using multi-linear regression analysis describing skin permeability (Kp) in terms of the octanol/water partition coefficient (logP) and molecular weight (MW). In this work, we obtained regression equations for different human datasets relevant to environmental and cosmetic chemicals. Since the Potts-Guy equation was published in 1992, we explored recent datasets that include different skin layers, such as dermatomed (including dermis to a defined thickness) and full skin. Our work was consistent with others who have observed that fits to the Potts-Guy equation are stronger for experiments focused on the epidermis. Permeability estimates for dermatomed skin and full skin resulted in low regression coefficients when compared to epidermis datasets. An updated regression equation uses a combination of fitted permeability values obtained with a published 2D compartmental model previously evaluated. The resulting regression equation was: logKp = -2.55 + 0.65logP - 0.0085MW, R2 = 0.91 (applicability domain for all datasets: MW ranges from 18 to >584 g/mol and -4 to >5 for logP). This approach demonstrates the advantage of combining mechanistic with structural activity relationships in a single modeling approach. This combination approach results in an improved regression fit when compared to permeability estimates obtained using the Potts-Guy approach alone. The analysis presented in this work assumes a one-compartment skin absorption route; future modeling work will consider adding multiple compartments.

Characterizing surface water concentrations of hundreds of organic chemicals in United States for environmental risk prioritization.

BACKGROUND: Thousands of chemicals are observed in freshwater, typically at trace levels. Measurements are collected for different purposes, so sample characteristics vary. Due to inconsistent data availability for exposure and hazard, it is complex to prioritize which chemicals may pose risks. OBJECTIVE: We evaluated the influence of data curation and statistical practices aggregating surface water measurements of organic chemicals into exposure distributions intended for prioritizing based on nation-scale potential risk. METHODS: The Water Quality Portal includes millions of observations describing over 1700 chemicals in 93% of hydrologic subbasins across the United States. After filtering to maintain quality and applicability while including all possible samples, we compared concentrations across sample types. We evaluated statistical methods to estimate per-chemical distributions for chosen samples. Overlaps between resulting exposure ranges and distributions representing no-effect concentrations for multiple freshwater species were used to rank estimated chemical risks for further assessment. RESULTS: When we apply explicit data quality and statistical assumptions, we find that there are 186 organic chemicals for which we can make screening-level estimates of surface water chemical concentration. Of the original 1700 observed chemicals, this number decreased primarily due to a predominance of censored values (that is, observations indicating concentrations too low to be measured). We further identify 423 chemicals where all measurements were censored but, through consideration of detection limits, risk might still be prioritized based on the detection limits themselves. In the final set of 1.5 million samples, the median environmental concentration of one chemical (acetic acid) exceeded the 5th percentile of no-effect concentrations for the most delicate freshwater species (the highest priority risk condition identified here), and a further 29 chemicals were identified for possible further evaluation based on a small margin between occurrence and toxicity values. SIGNIFICANCE: This method shows the broad range of chemical concentrations seen for organic chemicals across the country and identifies methods of determining their central tendency, allowing for researchers to characterize higher-than-normal or lower-than-normal surface water conditions as well as providing an overall indication of the presence of organic chemicals in the United States. The highest chemical concentrations did not always indicate the highest-risk conditions. Even when accounting for the high level of uncertainty in these data due to differences in data collection and reporting across the set, some chemicals may still be categorized as higher environmental risk than others using this method, providing value to chemical safety decision makers and researchers by suggesting avenues for more focused investigation.

A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target.

In the real world, individuals are exposed to chemicals from sources that vary over space and time. However, traditional risk assessments based on in vivo animal studies typically use a chemical-by-chemical approach and apical disease endpoints. New approach methodologies (NAMs) in toxicology, such as in vitro high-throughput (HTS) assays generated in Tox21 and ToxCast, can more readily provide mechanistic chemical hazard information for chemicals with no existing data than in vivo methods. In this paper, we establish a workflow to assess the joint action of 41 modeled ambient chemical exposures in the air from the USA-wide National Air Toxics Assessment by integrating human exposures with hazard data from curated HTS (cHTS) assays to identify counties where exposure to the local chemical mixture may perturb a common biological target. We exemplify this proof-of-concept using CYP1A1 mRNA up-regulation. We first estimate internal exposure and then convert the inhaled concentration to a steady state plasma concentration using physiologically based toxicokinetic modeling parameterized with county-specific information on ages and body weights. We then use the estimated blood plasma concentration and the concentration-response curve from the in vitro cHTS assay to determine the chemical-specific effects of the mixture components. Three mixture modeling methods were used to estimate the joint effect from exposure to the chemical mixture on the activity levels, which were geospatially mapped. Finally, a Monte Carlo uncertainty analysis was performed to quantify the influence of each parameter on the combined effects. This workflow demonstrates how NAMs can be used to predict early-stage biological perturbations that can lead to adverse health outcomes that result from exposure to chemical mixtures. As a result, this work will advance mixture risk assessment and other early events in the effects of chemicals.

Simulating toxicokinetic variability to identify susceptible and highly exposed populations.

BACKGROUND: Toxicokinetic (TK) data needed for chemical risk assessment are not available for most chemicals. To support a greater number of chemicals, the U.S. Environmental Protection Agency (EPA) created the open-source R package "httk" (High Throughput ToxicoKinetics). The "httk" package provides functions and data tables for simulation and statistical analysis of chemical TK, including a population variability simulator that uses biometrics data from the National Health and Nutrition Examination Survey (NHANES). OBJECTIVE: Here we modernize the "HTTK-Pop" population variability simulator based on the currently available data and literature. We provide explanations of the algorithms used by "httk" for variability simulation and uncertainty propagation. METHODS: We updated and revised the population variability simulator in the "httk" package with the most recent NHANES biometrics (up to the 2017-18 NHANES cohort). Model equations describing glomerular filtration rate (GFR) were revised to more accurately represent physiology and population variability. The model output from the updated "httk" package was compared with the current version. RESULTS: The revised population variability simulator in the "httk" package now provides refined, more relevant, and better justified estimations. SIGNIFICANCE: Fulfilling the U.S. EPA's mission to provide open-source data and models for evaluations and applications by the broader scientific community, and continuously improving the accuracy of the "httk" package based on the currently available data and literature.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment.

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

The chemical landscape of high-throughput new approach methodologies for exposure.

The rapid characterization of risk to humans and ecosystems from exogenous chemicals requires information on both hazard and exposure. The U.S. Environmental Protection Agency's ToxCast program and the interagency Tox21 initiative have screened thousands of chemicals in various high-throughput (HT) assay systems for in vitro bioactivity. EPA's ExpoCast program is developing complementary HT methods for characterizing the human and ecological exposures necessary to interpret HT hazard data in a real-world risk context. These new approach methodologies (NAMs) for exposure include computational and analytical tools for characterizing multiple components of the complex pathways chemicals take from their source to human and ecological receptors. Here, we analyze the landscape of exposure NAMs developed in ExpoCast in the context of various chemical lists of scientific and regulatory interest, including the ToxCast and Tox21 libraries and the Toxic Substances Control Act (TSCA) inventory. We examine the landscape of traditional and exposure NAM data covering chemical use, emission, environmental fate, toxicokinetics, and ultimately external and internal exposure. We consider new chemical descriptors, machine learning models that draw inferences from existing data, high-throughput exposure models, statistical frameworks that integrate multiple model predictions, and non-targeted analytical screening methods that generate new HT monitoring information. We demonstrate that exposure NAMs drastically improve the coverage of the chemical landscape compared to traditional approaches and recommend a set of research activities to further expand the development of HT exposure data for application to risk characterization. Continuing to develop exposure NAMs to fill priority data gaps identified here will improve the availability and defensibility of risk-based metrics for use in chemical prioritization and screening. IMPACT: This analysis describes the current state of exposure assessment-based new approach methodologies across varied chemical landscapes and provides recommendations for filling key data gaps.

Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods.

Current computational technologies hold promise for prioritizing the testing of the thousands of chemicals in commerce. Here, a case study is presented demonstrating comparative risk-prioritization approaches based on the ratio of surrogate hazard and exposure data, called margins of exposure (MoEs). Exposures were estimated using a U.S. EPA's ExpoCast predictive model (SEEM3) results and estimates of bioactivity were predicted using: 1) Oral equivalent doses (OEDs) derived from U.S. EPA's ToxCast high-throughput screening program, together with in vitro to in vivo extrapolation and 2) thresholds of toxicological concern (TTCs) determined using a structure-based decision-tree using the Toxtree open source software. To ground-truth these computational approaches, we compared the MoEs based on predicted noncancer TTC and OED values to those derived using the traditional method of deriving points of departure from no-observed adverse effect levels (NOAELs) from in vivo oral exposures in rodents. TTC-based MoEs were lower than NOAEL-based MoEs for 520 out of 522 (99.6%) compounds in this smaller overlapping dataset, but were relatively well correlated with the same (r 2 = 0.59). TTC-based MoEs were also lower than OED-based MoEs for 590 (83.2%) of the 709 evaluated chemicals, indicating that TTCs may serve as a conservative surrogate in the absence of chemical-specific experimental data. The TTC-based MoE prioritization process was then applied to over 45,000 curated environmental chemical structures as a proof-of-concept for high-throughput prioritization using TTC-based MoEs. This study demonstrates the utility of exploiting existing computational methods at the pre-assessment phase of a tiered risk-based approach to quickly, and conservatively, prioritize thousands of untested chemicals for further study.