Evaluating scientific confidence in the concordance of in vitro and in vivo protective points of departure

To fulfil the promise of reducing reliance on mammalian in vivo laboratory animal studies, new approach methods (NAMs) need to provide a confident basis for regulatory decision-making. However, previous attempts to develop in vitro NAMs-based points of departure (PODs) have yielded mixed results, with PODs from U.S. EPA's ToxCast, for instance, appearing more conservative (protective) but poorly correlated with traditional in vivo studies. Here, we aimed to address this discordance by reducing the heterogeneity of in vivo PODs, accounting for species differences, and enhancing the biological relevance of in vitro PODs. However, we only found improved in vitro-to-in vivo concordance when combining the use of Bayesian model averaging-based benchmark dose modeling for in vivo PODs, allometric scaling for interspecies adjustments, and human-relevant in vitro assays with multiple induced pluripotent stem cell-derived models. Moreover, the available sample size was only 15 chemicals, and the resulting level of concordance was only fair, with correlation coefficients <0.5 and prediction intervals spanning several orders of magnitude. Overall, while this study suggests several ways to enhance concordance and thereby increase scientific confidence in vitro NAMs-based PODs, it also highlights challenges in their predictive accuracy and precision for use in regulatory decision making.

Hazard and risk characterization of 56 structurally diverse PFAS using a targeted battery of broad coverage assays using six human cell types

Per- and poly-fluoroalkyl substances (PFAS) are extensively used in commerce leading to their prevalence in the environment. Due to their chemical stability, PFAS are considered to be persistent and bioaccumulative; they are frequently detected in both the environment and humans. Because of this, PFAS as a class (composed of hundreds to thousands of chemicals) are contaminants of very high concern. Little information is available for the vast majority of PFAS, and regulatory agencies lack safety data to determine whether exposure limits or restrictions are needed. Cell-based assays are a pragmatic approach to inform decision-makers on potential health hazards; therefore, we hypothesized that a targeted battery of human in vitro assays can be used to determine whether there are structure-bioactivity relationships for PFAS, and to characterize potential risks by comparing bioactivity (points of departure) to exposure estimates. We tested 56 PFAS from 8 structure-based subclasses in concentration response (0.1-100 μM) using six human cell types selected from target organs with suggested adverse effects of PFAS - human induced pluripotent stem cell (iPSC)-derived hepatocytes, neurons, and cardiomyocytes, primary human hepatocytes, endothelial and HepG2 cells. While many compounds were without effect; certain PFAS demonstrated cell-specific activity highlighting the necessity of using a compendium of in vitro models to identify potential hazards. No class-specific groupings were evident except for some chain length- and structure-related trends. In addition, margins of exposure (MOE) were derived using empirical and predicted exposure data. Conservative MOE calculations showed that most tested PFAS had a MOE in the 1-100 range; ∼20% of PFAS had MOE<1, providing tiered priorities for further studies. Overall, we show that a compendium of human cell-based models can be used to derive bioactivity estimates for a range of PFAS, enabling comparisons with human biomonitoring data. Furthermore, we emphasize that establishing structure-bioactivity relationships may be challenging for the tested PFAS.

Assessing proarrhythmic potential of environmental chemicals using a high throughput in vitro-in silico model with human induced pluripotent stem cell-derived cardiomyocytes

QT prolongation and the potentially fatal arrhythmia Torsades de Pointes are common causes for withdrawing or restricting drugs; however, little is known about similar liabilities of environmental chemicals. Current in vitro-in silico models for testing proarrhythmic liabilities, using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM), provide an opportunity to address this data gap. These methods are still low- to medium-throughput and not suitable for testing the tens of thousands of chemicals in commerce. We hypothesized that combining high-throughput population- based in vitro testing in hiPSC-CMs with a fully in silico data analysis workflow can offer sensitive and specific predictions of proarrhythmic potential. We calibrated the model with a published hiPSC-CM dataset of drugs known to be positive or negative for proarrhythmia and tested its performance using internal cross-validation and external validation. Additionally, we used computational down-sampling to examine three study designs for hiPSC-CM data: one replicate of one donor, five replicates of one donor, and one replicate of a population of five donors. We found that the population of five donors had the best performance for predicting proarrhythmic potential. The resulting model was then applied to predict the proarrhythmic potential of environmental chemicals, additionally characterizing risk through margin of exposure (MOE) calculations. Out of over 900 environmental chemicals tested, over 150 were predicted to have proarrhythmic potential, but only seven chemicals had a MOE < 1. We conclude that a high-throughput in vitro-in silico approach using population-based hiPSC-CM testing provides a reasonable strategy to screen environmental chemicals for proarrhythmic potential.

Advancing probabilistic risk assessment by integrating human biomonitoring, new approach methods, and Bayesian modeling: A case study with the mycotoxin deoxynivalenol

Deoxynivalenol (DON) is a mycotoxin frequently observed in cereals and cereal-based foods, with reported toxicological effects including reduced body weight, immunotoxicity and reproductive defects. The European Food Safety Authority used traditional risk assessment approaches to derive a deterministic Tolerable Daily Intake (TDI) of 1 μg/kg-day, however data from human biomarkers studies indicate widespread and variable exposure worldwide, necessitating more sophisticated and advanced methods to quantify population risk. The World Health Organization/International Programme on Chemical Safety (WHO/IPCS) has previously used DON as a case example in replacing the TDI with a probabilistic toxicity value, using default uncertainty and variability distributions to derive the Human Dose corresponding to an effect size M in the Ith percentile of the population (HDMI) for M = 5 % decrease in body weight and I = 1 %. In this study, we extend this case study by incorporating (1) Bayesian modeling approaches, (2) using both in vivo data and in vitro population new approach methods to replace default distributions for interspecies toxicokinetic (TK) differences and intraspecies TK and toxicodynamic (TD) variability, and (3) integrating biomonitoring data and probabilistic dose-response functions to characterize population risk distributions. We first derive an HDMI of 5.5 [1.4-24] μg/kg-day, also using TK modeling to converted the HDMI to Biomonitoring Equivalents, BEMI for comparison with biomonitoring data, with a blood BEMI of 0.53 [0.17-1.6] μg/L and a urinary excretion BEMI of 3.9 [1.0-16] μg/kg-day. We then illustrate how this integrative approach can advance quantitative risk characterization using two human biomonitoring datasets, estimating both the fraction of population with an effect size M ≥ 5 % as well as the distribution of effect sizes. Overall, we demonstrate that integration of Bayesian modeling, human biomonitoring data, and in vitro population-based TD data within the WHO/IPCS probabilistic framework yields more accurate, precise, and comprehensive risk characterization.

Application of Ion Mobility Spectrometry-Mass Spectrometry for Compositional Characterization and Fingerprinting of a Library of Diverse Crude Oil Samples

Exposure characterization of crude oils, especially in time-sensitive circumstances such as spills and disasters, is a well-known analytical chemistry challenge. Gas chromatography-mass spectrometry is commonly used for "fingerprinting" and origin tracing in oil spills; however, this method is both time-consuming and lacks the resolving power to separate co-eluting compounds. Recent advances in methodologies to analyze petroleum substances using high-resolution analytical techniques have demonstrated both improved resolving power and higher throughput. One such method, ion mobility spectrometry-mass spectrometry (IMS-MS), is especially promising because it is both rapid and high-throughput, with the ability to discern among highly homologous hydrocarbon molecules. Previous applications of IMS-MS to crude oil analyses included a limited number of samples and did not provide detailed characterization of chemical constituents. We analyzed a diverse library of 195 crude oil samples using IMS-MS and applied a computational workflow to assign molecular formulas to individual features. The oils were from 12 groups based on geographical and geological origins: non-US (1 group), US onshore (3), and US Gulf of Mexico offshore (8). We hypothesized that information acquired through IMS-MS data would provide a more confident grouping and yield additional fingerprint information. Chemical composition data from IMS-MS was used for unsupervised hierarchical clustering, as well as machine learning-based supervised analysis to predict geographic and source rock categories for each sample; the latter also yielded several novel prospective biomarkers for fingerprinting of crude oils. We found that IMS-MS data have complementary advantages for fingerprinting and characterization of diverse crude oils and that proposed polycyclic aromatic hydrocarbon biomarkers can be used for rapid exposure characterization. Environ Toxicol Chem 2023;42:2336-2349. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Air Pollutant Patterns and Human Health Risk following the East Palestine, Ohio, Train Derailment

On February 3, 2023, a train carrying numerous hazardous chemicals derailed in East Palestine, OH, spurring temporary evacuation of residents and a controlled burn of some of the hazardous cargo. Residents reported health symptoms, including headaches and respiratory, skin, and eye irritation. Initial data from U.S. Environmental Protection Agency (EPA) stationary air monitors indicated levels of potential concern for air toxics based on hazard quotient calculations. To provide complementary data, we conducted mobile air quality sampling on February 20 and 21 using proton transfer reaction-mass spectrometry. Measurements were taken at 1 s intervals along routes designed to sample both close to and farther from the derailment. Mobile air monitoring indicated that average concentrations of benzene, toluene, xylenes, and vinyl chloride were below minimal risk levels for intermediate and chronic exposures, similar to EPA stationary monitoring data. Levels of acrolein were high relative to those of other volatile organic compounds, with spatial analyses showing levels in East Palestine up to 6 times higher than the local rural background. Nontargeted analyses identified levels of additional unique compounds above background levels, some displaying spatiotemporal patterns similar to that of acrolein and others exhibiting distinct hot spots. These initial findings warrant follow-up mobile air quality monitoring to characterize longitudinal exposure and risk levels.

Unseen Risk: Mapping Contamination Hazards to Enhance Risk Perception in Galena Park, Texas

As extreme weather events have become more frequently observed in recent decades, concerns about exposure to potential flood risk have increased, especially in underserved and socially vulnerable communities. Galena Park, Texas, is a socially vulnerable community that also confronts escalated physical vulnerabilities due to existing flood risks from Buffalo Bayou and the Houston Ship Channel as well as proximity to industrial facilities that emit chemical pollution. To better understand the underlying risks that Galena Park is facing, this research assesses and visualizes the existing contamination hazards associated with the chemical facilities within Galena Park. Through this process, we (1) compute the environmental, health, and physical hazards associated with industrial facilities, (2) spatially geocode the points of contamination sources and flood exposure, and (3) increase awareness of existing risk by visualizing and distributing related information using an ArcGIS Dashboard. The results indicate that there are 169 points of location from 127 industrial facilities, and 24 points were inducing potential chemicals. In total, 126 chemicals have potential physical, health, and environmental hazards. On average, each facility has 2.4 chemicals that could cause potential hazards with a range of zero to 57 chemicals. When examining the specific physical, health, and environmental risks associated with the chemicals, on average each facility has 14.6 types of risks associated with it. This includes, on average, 9.8 types of health hazards, 1.53 physical hazards, and 2.3 environmental hazards per facility. When analyzing the spatial relationship between the chemical exposure and the current flood risk using the Dashboard, it is noticeable that most of the industrial facilities are located in the south of Galena Park, near Buffalo Bayou, where a variety of industrial facilities are clustered. Through this study, we spatially mapped the existing risks in Galena Park that are not readily available to the community and risks that are not currently tangible or visible. The utility of ArcGIS Dashboards affords the opportunity to translate massive databases into digestible knowledge that can be shared and utilized within the community. This study also takes another step toward building community resilience by providing knowledge that can be used to prepare for and respond to disasters. Visualizing unseen risks and promoting awareness can enhance risk perception when supported by scientific knowledge. Further investigation is necessary to enhance preparedness behaviors, identify proper evacuation techniques and routes, and build community networks to comprehensively promote resilience to multi-hazard circumstances.

Imidacloprid exposure is detectable in over one third of wild bird samples from diverse Texas ecoregions

Avian decline is occurring globally with neonicotinoid insecticides posed as a potentially contributing factor. Birds can be exposed to neonicotinoids through coated seeds, soil, water, and insects, and experimentally exposed birds show varied adverse effects including mortality and disruption of immune, reproductive, and migration physiology. However, few studies have characterized exposure in wild bird communities over time. We hypothesized that neonicotinoid exposure would vary temporally and based on avian ecological traits. Birds were banded and blood sampled at eight non-agricultural sites across four Texas counties. Plasma from 55 species across 17 avian families was analyzed for the presence of 7 neonicotinoids using high performance liquid chromatography-tandem mass spectrometry. Imidacloprid was detected in 36 % of samples (n = 294); this included quantifiable concentrations (12 %; 10.8-36,131 pg/mL) and concentrations that were below the limit of quantification (25 %). Additionally, two birds were exposed to imidacloprid, acetamiprid (18,971.3 and 6844 pg/mL) and thiacloprid (7022.2 and 17,367 pg/mL), whereas no bird tested positive for clothianidin, dinotefuran, nitenpyram, or thiamethoxam, likely reflecting higher limits of detection for all compounds compared to imidacloprid. Birds sampled in spring and fall had higher incidences of exposure than those sampled in summer or winter. Subadult birds had higher incidences of exposure than adult birds. Among the species for which we tested more than five samples, American robin (Turdus migratorius) and red-winged blackbird (Agelaius phoeniceus) had significantly higher incidences of exposure. We found no relationships between exposure and foraging guild or avian family, suggesting birds with diverse life histories and taxonomies are at risk. Of seven birds resampled over time, six showed neonicotinoid exposure at least once with three showing exposures at multiple time points, indicating continued exposure. This study provides exposure data to inform ecological risk assessment of neonicotinoids and avian conservation efforts.

Community-facing toxicokineticmodels to estimate PFAS serum levels based on life history and drinking water exposures

BACKGROUND

Per- and polyfluoroalkyl substances (PFAS) are drinking water contaminants. Tools to assess the potential body burden associated with drinking PFAS-contaminated water may be helpful for public health assessment of exposed communities.

METHODS

We implemented a suite of one-compartment toxicokinetic models using extensively calibrated toxicokinetic parameters (half-life and volume of distribution). We implemented the models both in the R programming language for research purposes, and as a web estimator for the general public (built in typescript.js). These models simulate exposure to PFAS water concentrations for individuals with varying characteristics such as age, sex, weight, and breastfeeding history. The models account for variability and uncertainty in parameter inputs to produce Monte Carlo-based estimates of serum concentration. For children, the models additionally account for gestational exposure, lactational exposure, and potential exposure through formula feeding. For adults who have borne children, the models account for clearance through birth and breastfeeding. We ran simulations of individuals with known PFAS water and serum concentrations to evaluate the model. We then compared the predicted serum PFAS concentrations to measured data.

RESULTS

The models accurately estimate individual-level serum levels for each PFAS for most adults within ½ order of magnitude. We found that the models somewhat overestimated serum concentrations for children in the tested locations, and that these overestimates are generally within an order of magnitude.

DISCUSSION

This paper presents scientifically robust models that allow users to estimate serum PFAS concentrations based on known PFAS water concentrations and physiologic information. However, accuracy in historical water concentration inputs, exposure from non-drinking water sources, and life-history characteristics of individuals present a complex problem for individual estimation. Additional refinements to the model suite to improve the prediction of individual results may consist of including duration of exposure and additional life-history characteristics.

A tiered testing strategy based on in vitro phenotypic and transcriptomic data for selecting representative petroleum UVCBs for toxicity evaluation in vivo

Hazard evaluation of substances of "unknown or variable composition, complex reaction products and biological materials" (UVCBs) remains a major challenge in regulatory science because their chemical composition is difficult to ascertain. Petroleum substances are representative UVCBs and human cell-based data have been previously used to substantiate their groupings for regulatory submissions. We hypothesized that a combination of phenotypic and transcriptomic data could be integrated to make decisions as to selection of group-representative worst-case petroleum UVCBs for subsequent toxicity evaluation in vivo. We used data obtained from 141 substances from 16 manufacturing categories previously tested in 6 human cell types (induced pluripotent stem cell [iPSC]-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, and MCF7 and A375 cell lines). Benchmark doses for gene-substance combinations were calculated, and both transcriptomic and phenotype-derived points of departure (PODs) were obtained. Correlation analysis and machine learning were used to assess associations between phenotypic and transcriptional PODs and to determine the most informative cell types and assays, thus representing a cost-effective integrated testing strategy. We found that 2 cell types-iPSC-derived-hepatocytes and -cardiomyocytes-contributed the most informative and protective PODs and may be used to inform selection of representative petroleum UVCBs for further toxicity evaluation in vivo. Overall, although the use of new approach methodologies to prioritize UVCBs has not been widely adopted, our study proposes a tiered testing strategy based on iPSC-derived hepatocytes and cardiomyocytes to inform selection of representative worst-case petroleum UVCBs from each manufacturing category for further toxicity evaluation in vivo.

Refining risk estimates for lead in drinking water based on the impact of genetics and diet on blood lead levels using the Collaborative Cross mouse population

Blood lead (Pb) level (BLL) is a commonly used biomarker to evaluate associations with health effects. However, interventions to reduce the adverse effects of Pb require relating BLL to external exposure. Moreover, risk mitigation actions need to ensure protection of more susceptible individuals with a greater tendency to accumulate Pb. Because little data is available to quantify inter-individual variability in biokinetics of Pb, we investigated the influence of genetics and diet on BLL in the genetically diverse Collaborative Cross (CC) mouse population. Adult female mice from 49 CC strains received either a standard mouse chow or a chow mimicking the American diet while being provided water ad libitum with 1000 ppm Pb for 4 weeks. In both arms of the study, inter-strain variability was observed; however, in American diet-fed animals, the BLL was greater and more variable. Importantly, the degree of variation in BLL among strains on the American diet was greater (2.3) than the default variability estimate (1.6) used in setting the regulatory standards. Genetic analysis identified suggestive diet-associated haplotypes that were associated with variation in BLL, largely contributed by the PWK/PhJ strain. This study quantified the variation in BLL that is due to genetic background, diet, and their interactions, and observed that it may be greater than that assumed for current regulatory standards for Pb in drinking water. Moreover, this work highlights the need of characterizing inter-individual variation in BLL to ensure adequate public health interventions aimed at reducing human health risks from Pb.

Development of physiologically-based gut absorption model for probabilistic prediction of environmental chemical bioavailability

Absorption in the gastrointestinal tract is a key factor determining the bioavailability of chemicals after oral exposure but is frequently assumed to have a conservative value of 100% for environmental chemicals, particularly in the context of high-throughput toxicokinetics for in vitro-to-in vivo extrapolation (IVIVE). For pharmaceutical compounds, the physiologically based advanced compartmental absorption and transit (ACAT) model has been used extensively to predict gut absorption but has not generally been applied to environmental chemicals. Here we develop a probabilistic environmental compart­mental absorption and transit (PECAT) model, adapting the ACAT model to environmental chemicals. We calibrated the model parameters to human in vivo, ex vivo, and in vitro datasets of drug permeability and fractional absorption by con­sidering two key factors: (1) differences between permeability in Caco-2 cells and in vivo permeability in the jejunum, and (2) differences in in vivo permeability across different gut segments. Incorporating these factors probabilistically, we found that given Caco-2 permeability measurements, predictions of the PECAT model are consistent with the (limited) available gut absorption data for environmental chemicals. However, the substantial chemical-to-chemical variability observed in the cal­ibration data often led to wide probabilistic confidence bounds in the predicted fraction absorbed and resulting steady state blood concentration. Thus, while the PECAT model provides a statistically rigorous, physiologically based approach for incor­porating in vitro data on gut absorption into toxicokinetic modeling and IVIVE, it also highlights the need for more accurate in vitro models and data for measuring gut segment-specific in vivo permeability of environmental chemicals.

Beyond the cancer slope factor: Broad application of Bayesian and probabilistic approaches for cancer dose-response assessment

Traditional cancer slope factors derived from linear low-dose extrapolation give little consideration to uncertainties in dose-response model choice, interspecies extrapolation, and human variability. As noted previously by the National Academies, probabilistic methods can address these limitations, but have only been demonstrated in a few case studies. Here, we applied probabilistic approaches for Bayesian Model Averaging (BMA), interspecies extrapolation, and human variability distributions to 255 animal cancer bioassay datasets previously used by governmental agencies. We then derived predictions for both population cancer incidence and individual cancer risk. For model uncertainty, we found that lower confidence limits from BMA and from U.S. Environmental Protection Agency (EPA)'s Benchmark Dose Software (BMDS) correlated highly, with 86% differing by <10-fold. Incorporating other uncertainties and human variability, the lower confidence limits of the probabilistic risk-specific dose (RSD) at 10-6 population incidence were typically 3- to 30-fold lower than traditional slope factors. However, in a small (<7%) number of cases of highly non-linear experimental dose-response, the probabilistic RSDs were >10-fold less stringent. Probabilistic RSDs were also protective of individual risks of 10-4 in >99% of the population. We conclude that implementing Bayesian and probabilistic methods provides a more scientifically rigorous basis for cancer dose-response assessment and thereby improves overall cancer risk characterization.

Probabilistic Points of Departure and Reference Doses for Characterizing Human Noncancer and Developmental/Reproductive Effects for 10,145 Chemicals

BACKGROUND

Regulatory toxicity values used to assess and manage chemical risks rely on the determination of the point of departure (POD) for a critical effect, which results from a comprehensive and systematic assessment of available toxicity studies. However, regulatory assessments are only available for a small fraction of chemicals.

OBJECTIVES

Using in vivo experimental animal data from the U.S. Environmental Protection Agency's Toxicity Value Database, we developed a semiautomated approach to determine surrogate oral route PODs, and corresponding toxicity values where regulatory assessments are unavailable.

METHODS

We developed a curated data set restricted to effect levels, exposure routes, study designs, and species relevant for deriving toxicity values. Effect levels were adjusted to chronic human equivalent benchmark doses (BMDh). We hypothesized that a quantile of the BMDh distribution could serve as a surrogate POD and determined the appropriate quantile by calibration to regulatory PODs. Finally, we characterized uncertainties around the surrogate PODs from intra- and interstudy variability and derived probabilistic toxicity values using a standardized workflow.

RESULTS

The BMDh distribution for each chemical was adequately fit by a lognormal distribution, and the 25th percentile best predicted the available regulatory PODs [R2≥0.78, residual standard error (RSE)≤0.53 log10 units]. We derived surrogate PODs for 10,145 chemicals from the curated data set, differentiating between general noncancer and reproductive/developmental effects, with typical uncertainties (at 95% confidence) of a factor of 10 and 12, respectively. From these PODs, probabilistic reference doses (1% incidence at 95% confidence), as well as human population effect doses (10% incidence), were derived.

DISCUSSION

In providing surrogate PODs calibrated to regulatory values and deriving corresponding toxicity values, we have substantially expanded the coverage of chemicals from 744 to 8,023 for general noncancer effects, and from 41 to 6,697 for reproductive/developmental effects. These results can be used across various risk assessment and risk management contexts, from hazardous site and life cycle impact assessments to chemical prioritization and substitution. https://doi.org/10.1289/EHP11524.

Cancer risk associated with soil distribution of polycyclic aromatic hydrocarbons within three environmental justice neighborhoods in Houston, Texas

Residents and advocacy groups began voicing concerns over the environmental quality located in the neighborhoods of Kashmere Gardens, Fifth Ward, and Denver Harbor in Houston, TX, following the confirmation of a cancer cluster in 2019 and another in 2021. These neighborhoods are in close proximity to a railyard and former wood treatment plant known to have utilized coal tar creosote and contain polycyclic aromatic hydrocarbons (PAHs). This research took core soil samples in September and October 2020 from 46 sites to assess for the presence and concentration of the U.S. Environmental Protection Agency's (USEPA) 7 Carcinogenic PAHs. Results showed the cumulative concentration of these PAHs in each sample was variable with a range of 13,767 ng/g to 328 ng/g and a mean of 2,517.2 ng/g ± 3122. A regional soil screening evaluation revealed that 40 of the 46 soil samples were in excess of the USEPAs most conservative screening levels of 1.0 × 10-6 increased cancer risk, but none exceeding levels considered actionable for remediation. This study is a fundamental first step for quantifying the environmental pollutants in this minority-majority community. Findings revealed a low risk of cancer risk based on current PAH concentrations alone but cannot assess contributions from other contaminants or from past, possibly higher, levels of contamination. Further research is needed to identify the potential casual pathways of the observed cancer cluster and to explore possible remediation needs.

Characterizing vulnerabilities to climate change across the United States

Climate change will cause a range of related risks, including increases in infectious and chronic disease, intensified social and economic stresses, and more frequent extreme weather events. Vulnerable groups will be disproportionately affected due to greater exposure to climate risks and lower ability to prepare, adapt, and recover from their effects. Better understanding of the intersection of vulnerability and climate change risks is required to identify the most important drivers of future climate risks and effectively build resilience and deploy targeted adaptation efforts. Incorporating community stakeholder input, we identified and integrated available public health, social, economic, environmental, and climate data in the United States (U.S.), comprising 184 indicators, to develop a Climate Vulnerability Index (CVI) composed of four baseline vulnerabilities (health, social/economic, infrastructure, and environment) and three climate change risks (health, social/economic, extreme events). We find that the vulnerability to and risks from climate change are highly heterogeneous across the U.S. at the census tract scale, and geospatially cluster into complementary areas with similar climate risks but differing baseline vulnerabilities. Our results therefore demonstrate that not only are climate change risks both broadly and variably distributed across the U.S., but also that existing disparities are often further exacerbated by climate change. The CVI thus lays a data-driven, scientific foundation for future research on the intersection of climate change risks with health and other inequalities, while also identifying health impacts of climate change as the greatest research gap. Moreover, given U.S. government initiatives surrounding climate and equity, the CVI can be instrumental in empowering communities and policymakers to better prioritize resources and target interventions, providing a template for addressing local-scale climate and environmental justice globally.

Dosing Methods to Enable Cell-Based In Vitro Testing of Complex Substances: A Case Study with a PAH Mixture

Cell-based testing of multi-constituent substances and mixtures for their potential adverse health effects is difficult due to their complex composition and physical-chemical characteristics. Various extraction methods are typically used to enable studies in vitro; however, a limited number of solvents are biocompatible with in vitro studies and the extracts may not fully represent the original test article's composition. While the methods for dosing with "difficult-to-test" substances in aquatic toxicity studies are well defined and widely used, they are largely unsuited for small-volume (100 microliters or less) in vitro studies with mammalian cells. Therefore, we aimed to evaluate suitability of various scaled-down dosing methods for high-throughput in vitro testing by using a mixture of polycyclic aromatic hydrocarbons (PAH). Specifically, we compared passive dosing via silicone micro-O-rings, cell culture media-accommodated fraction, and traditional solvent (dimethyl sulfoxide) extraction procedures. Gas chromatography-tandem mass spectrometry (GC-MS/MS) was used to evaluate kinetics of PAH absorption to micro-O-rings, as well as recovery of PAH and the extent of protein binding in cell culture media with and without cells for each dosing method. Bioavailability of the mixture from different dosing methods was also evaluated by characterizing in vitro cytotoxicity of the PAH mixture using EA.hy926 and HepG2 human cell lines. Of the tested dosing methods, media accommodated fraction (MAF) was determined to be the most appropriate method for cell-based studies of PAH-containing complex substances and mixtures. This conclusion is based on the observation that the highest fraction of the starting materials can be delivered using media accommodated fraction approach into cell culture media and thus enable concentration-response in vitro testing.

Bayesian Estimation of Human Population Toxicokinetics of PFOA, PFOS, PFHxS, and PFNA from Studies of Contaminated Drinking Water

BACKGROUND

Setting health-protective standards for poly- and perfluoroalkyl substances (PFAS) exposure requires estimates of their population toxicokinetics, but existing studies have reported widely varying PFAS half-lives (T_½) and volumes of distribution (V_d).

OBJECTIVES

We combined data from multiple studies to develop harmonized estimates of T_½ and V_d, along with their interindividual variability, for four PFAS commonly found in drinking water: perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), perfluorononanoic acid (PFNA), and perfluorohexane sulfonate (PFHxS).

METHODS

We identified published data on PFAS concentrations in human serum with corresponding drinking water measurements, separated into training and testing data sets. We fit training data sets to a one-compartment model incorporating interindividual variability, time-dependent drinking water concentrations, and background exposures. Use of a hierarchical Bayesian approach allowed us to incorporate informative priors at the population level, as well as at the study level. We compared posterior predictions to testing data sets to evaluate model performance.

RESULTS

Posterior median (95% CI) estimates of T_½ (in years) for the population geometric mean were 3.14 (2.69, 3.73) for PFOA, 3.36 (2.52, 4.42) for PFOS, 2.35 (1.65, 3.16) for PFNA, and 8.30 (5.38, 13.5) for PFHxS, all of which were within the range of previously published values. The extensive individual-level data for PFOA allowed accurate estimation of population variability, with a population geometric standard deviation of 1.57 (95% CI: 1.42, 1.73); data from other PFAS were also consistent with this degree of population variability. V_d estimates ranged from 0.19 to 0.43L/kg across the four PFAS, which tended to be slightly higher than previously published estimates.

DISCUSSION

These results have direct application in both risk assessment (quantitative interspecies extrapolation and uncertainty factors for interindividual variability) and risk communication (interpretation of monitoring data). In addition, this study provides a rigorous methodology for further refinement with additional data, as well as application to other PFAS. https://doi.org/10.1289/EHP10103.

Calibrating COVID-19 community transmission risk levels to reflect infection prevalence

Many organizations, including the US Centers for Disease Control and Prevention, have developed risk indexes to help determine community transmission levels for the ongoing COVID-19 pandemic. These risk indexes are largely based on newly reported cases and percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests, which are well-established as biased estimates of COVID-19 transmission. However, transmission risk indexes should accurately and precisely communicate community risks to decision-makers and the public. Therefore, transmission risk indexes would ideally quantify actual, and not just reported, levels of disease prevalence or incidence. Here, we develop a robust data-driven framework for determining and communicating community transmission risk levels using reported cases and test positivity. We use this framework to evaluate the previous CDC community risk level metrics that were proposed as guidelines for determining COVID-19 transmission risk at community level in the US. Using two recently developed data-driven models for COVID-19 transmission in the US to compute community-level prevalence, we show that there is substantial overlap of prevalence between the different community risk levels from the previous CDC guidelines. Using our proposed framework, we redefined the risk levels and their threshold values. We show that these threshold values would have substantially reduced the overlaps of underlying community prevalence between counties/states in different community risk levels between 3/19/2020-9/9/2021. Our study demonstrates how the previous CDC community risk level indexes could have been calibrated to infection prevalence to improve their power to accurately determine levels of COVID-19 transmission in local communities across the US. This method can be used to inform the design of future COVID-19 transmission risk indexes.

Reanalysis of Trichloroethylene and Tetrachloroethylene Metabolism to Glutathione Conjugates Using Human, Rat, and Mouse Liver in Vitro Models to Improve Precision in Risk Characterization

BACKGROUND

Both trichloroethylene (TCE) and tetrachloroethylene (PCE) are high-priority chemicals subject to numerous human health risk evaluations by a range of agencies. Metabolism of TCE and PCE determines their ultimate toxicity; important uncertainties exist in quantitative characterization of metabolism to genotoxic moieties through glutathione (GSH) conjugation and species differences therein.

OBJECTIVES

This study aimed to address these uncertainties using novel in vitro liver models, interspecies comparison, and a sensitive assay for quantification of GSH conjugates of TCE and PCE, S-(1,2-dichlorovinyl)glutathione (DCVG) and S-(1,2,2-trichlorovinyl) glutathione (TCVG), respectively.

METHODS

Liver in vitro models used herein were suspension, 2-D culture, and micropatterned coculture (MPCC) with primary human, rat, and mouse hepatocytes, as well as human induced pluripotent stem cell (iPSC)-derived hepatocytes (iHep).

RESULTS

We found that, although efficiency of metabolism varied among models, consistent with known differences in their metabolic capacity, formation rates of DCVG and TCVG generally followed the patterns human ≥ rat ≥ mouse , and primary hepatocytes > iHep . Data derived from MPCC were most consistent with estimates from physiologically based pharmacokinetic models calibrated to in vivo data.

DISCUSSION

For TCE, the new data provided additional empirical support for inclusion of GSH conjugation-mediated kidney effects as critical for the derivation of noncancer toxicity values. For PCE, the data reduced previous uncertainties regarding the extent of TCVG formation in humans; this information was used to update several candidate kidney-specific noncancer toxicity values. Overall, MPCC-derived data provided physiologically relevant estimates of GSH-mediated metabolism of TCE and PCE to reduce uncertainties in interspecies extrapolation that constrained previous risk evaluations, thereby increasing the precision of risk characterizations of these high-priority toxicants. https://doi.org/10.1289/EHP12006.

Modeling the transplacental transfer of small molecules using machine learning: a case study on per- and polyfluorinated substances (PFAS)

BACKGROUND

Despite their large numbers and widespread use, very little is known about the extent to which per- and polyfluoroalkyl substances (PFAS) can cross the placenta and expose the developing fetus.

OBJECTIVE

The aim of our study is to develop a computational approach that can be used to evaluate the of extend to which small molecules, and in particular PFAS, can cross to cross the placenta and partition to cord blood.

METHODS

We collected experimental values of the concentration ratio between cord and maternal blood (R_CM) for 260 chemical compounds and calculated their physicochemical descriptors using the cheminformatics package Mordred. We used the compiled database to, train and test an artificial neural network (ANN). And then applied the best performing model to predict R_CM for a large dataset of PFAS chemicals (n = 7982). We, finally, examined the calculated physicochemical descriptors of the chemicals to identify which properties correlated significantly with R_CM.

RESULTS

We determined that 7855 compounds were within the applicability domain and 127 compounds are outside the applicability domain of our model. Our predictions of R_CM for PFAS suggested that 3623 compounds had a log R_CM > 0 indicating preferable partitioning to cord blood. Some examples of these compounds were bisphenol AF, 2,2-bis(4-aminophenyl)hexafluoropropane, and nonafluoro-tert-butyl 3-methylbutyrate.

SIGNIFICANCE

These observations have important public health implications as many PFAS have been shown to interfere with fetal development. In addition, as these compounds are highly persistent and many of them can readily cross the placenta, they are expected to remain in the population for a long time as they are being passed from parent to offspring.

IMPACT

Understanding the behavior of chemicals in the human body during pregnancy is critical in preventing harmful exposures during critical periods of development. Many chemicals can cross the placenta and expose the fetus, however, the mechanism by which this transport occurs is not well understood. In our study, we developed a machine learning model that describes the transplacental transfer of chemicals as a function of their physicochemical properties. The model was then used to make predictions for a set of 7982 per- and polyfluorinated alkyl substances that are listed on EPA's CompTox Chemicals Dashboard. The model can be applied to make predictions for other chemical categories of interest, such as plasticizers and pesticides. Accurate predictions of R_CM can help scientists and regulators to prioritize chemicals that have the potential to cause harm by exposing the fetus.

Evaluation of Metabolism of a Defined Pesticide Mixture through Multiple In Vitro Liver Models

The evaluation of exposure to multiple contaminants in a mixture presents a number of challenges. For example, the characterization of chemical metabolism in a mixture setting remains a research area with critical knowledge gaps. Studies of chemical metabolism typically utilize suspension cultures of primary human hepatocytes; however, this model is not suitable for studies of more extended exposures and donor-to-donor variability in a metabolic capacity is unavoidable. To address this issue, we utilized several in vitro models based on human-induced pluripotent stem cell (iPSC)-derived hepatocytes (iHep) to characterize the metabolism of an equimolar (1 or 5 µM) mixture of 20 pesticides. We used iHep suspensions and 2D sandwich cultures, and a microphysiological system OrganoPlate^® 2-lane 96 (Mimetas^TM) that also included endothelial cells and THP-1 cell-derived macrophages. When cell culture media were evaluated using gas and liquid chromatography coupled to tandem mass spectrometry methods, we found that the parent molecule concentrations diminished, consistent with metabolic activity. This effect was most pronounced in iHep suspensions with a 1 µM mixture, and was lowest in OrganoPlate^® 2-lane 96 for both mixtures. Additionally, we used ion mobility spectrometry-mass spectrometry (IMS-MS) to screen for metabolite formation in these cultures. These analyses revealed the presence of five primary metabolites that allowed for a more comprehensive evaluation of chemical metabolism in vitro. These findings suggest that iHep-based suspension assays maintain higher metabolic activity compared to 2D sandwich and OrganoPlate^® 2-lane 96 model. Moreover, this study illustrates that IMS-MS can characterize in vitro metabolite formation following exposure to mixtures of environmental contaminants.

Cumulative Risk Meets Inter-Individual Variability: Probabilistic Concentration Addition of Complex Mixture Exposures in a Population-Based Human In Vitro Model

Although humans are continuously exposed to complex chemical mixtures in the environment, it has been extremely challenging to investigate the resulting cumulative risks and impacts. Recent studies proposed the use of “new approach methods,” in particular in vitro assays, for hazard and dose−response evaluation of mixtures. We previously found, using five human cell-based assays, that concentration addition (CA), the usual default approach to calculate cumulative risk, is mostly accurate to within an order of magnitude. Here, we extend these findings to further investigate how cell-based data can be used to quantify inter-individual variability in CA. Utilizing data from testing 42 Superfund priority chemicals separately and in 8 defined mixtures in a human cell-based population-wide in vitro model, we applied CA to predict effective concentrations for cytotoxicity for each individual, for “typical” (median) and “sensitive” (first percentile) members of the population, and for the median-to-sensitive individual ratio (defined as the toxicodynamic variability factor, TDVF). We quantified the accuracy of CA with the Loewe Additivity Index (LAI). We found that LAI varies more between different mixtures than between different individuals, and that predictions of the population median are generally more accurate than predictions for the “sensitive” individual or the TDVF. Moreover, LAI values were generally <1, indicating that the mixtures were more potent than predicted by CA. Together with our previous studies, we posit that new approach methods data from human cell-based in vitro assays, including multiple phenotypes in diverse cell types and studies in a population-wide model, can fill critical data gaps in cumulative risk assessment, but more sophisticated models of in vitro mixture additivity and bioavailability may be needed. In the meantime, because simple CA models may underestimate potency by an order of magnitude or more, either whole-mixture testing in vitro or, alternatively, more stringent benchmarks of cumulative risk indices (e.g., lower hazard index) may be needed to ensure public health protection.

Development of an Evidence-Based Risk Assessment Framework

Assessment of potential human health risks associated with environmental and other agents requires careful evaluation of all available and relevant evidence for the agent of interest, including both data-rich and data-poor agents. With the advent of new approach methodologies in toxicological risk assessment, guidance on integrating evidence from mul-tiple evidence streams is needed to ensure that all available data is given due consideration in both qualitative and quantitative risk assessment. The present report summarizes the discussions among academic, government, and private sector participants from North America and Europe in an international workshop convened to explore the development of an evidence-based risk assessment framework, taking into account all available evidence in an appropriate manner in order to arrive at the best possible characterization of potential human health risks and associated uncertainty. Although consensus among workshop participants was not a specific goal, there was general agreement on the key consider-ations involved in evidence-based risk assessment incorporating 21st century science into human health risk assessment. These considerations have been embodied into an overarching prototype framework for evidence integration that will be explored in more depth in a follow-up meeting.

A Population-Based Human In Vitro Approach to Quantify Inter-Individual Variability in Responses to Chemical Mixtures

Human cell-based population-wide in vitro models have been proposed as a strategy to derive chemical-specific estimates of inter-individual variability; however, the utility of this approach has not yet been tested for cumulative exposures in mixtures. This study aimed to test defined mixtures and their individual components and determine whether adverse effects of the mixtures were likely to be more variable in a population than those of the individual chemicals. The in vitro model comprised 146 human lymphoblastoid cell lines from four diverse subpopulations of European and African descent. Cells were exposed, in concentration−response, to 42 chemicals from diverse classes of environmental pollutants; in addition, eight defined mixtures were prepared from these chemicals using several exposure- or hazard-based scenarios. Points of departure for cytotoxicity were derived using Bayesian concentration−response modeling and population variability was quantified in the form of a toxicodynamic variability factor (TDVF). We found that 28 chemicals and all mixtures exhibited concentration−response cytotoxicity, enabling calculation of the TDVF. The median TDVF across test substances, for both individual chemicals or defined mixtures, ranged from a default assumption (101/2) of toxicodynamic variability in human population to >10. The data also provide a proof of principle for single-variant genome-wide association mapping for toxicity of the chemicals and mixtures, although replication would be necessary due to statistical power limitations with the current sample size. This study demonstrates the feasibility of using a set of human lymphoblastoid cell lines as an in vitro model to quantify the extent of inter-individual variability in hazardous properties of both individual chemicals and mixtures. The data show that population variability of the mixtures is unlikely to exceed that of the most variable component, and that similarity in genome-wide associations among components may be used to accrue additional evidence for grouping of constituents in a mixture for cumulative assessments.

Characterization of Compositional Variability in Petroleum Substances

In the process of registration of substances of Unknown or Variable Composition, Complex Reaction Products or Biological Materials (UVCBs), information sufficient to enable substance identification must be provided. Substance identification for UVCBs formed through petroleum refining is particularly challenging due to their chemical complexity, as well as variability in refining process conditions and composition of the feedstocks. This study aimed to characterize compositional variability of petroleum UVCBs both within and across product categories. We utilized ion mobility spectrometry (IMS)-MS as a technique to evaluate detailed chemical composition of independent production cycle-derived samples of 6 petroleum products from 3 manufacturing categories (heavy aromatic, hydrotreated light paraffinic, and hydrotreated heavy paraffinic). Atmospheric pressure photoionization and drift tube IMS-MS were used to identify structurally related compounds and quantified between- and within-product variability. In addition, we determined both individual molecules and hydrocarbon blocks that were most variable in samples from different production cycles. We found that detailed chemical compositional data on petroleum UVCBs obtained from IMS-MS can provide the information necessary for hazard and risk characterization in terms of quantifying the variability of the products in a manufacturing category, as well as in subsequent production cycles of the same product.

Biosensor applications in contaminated estuaries: Implications for disaster research response

BACKGROUND

Given the time and monetary costs associated with traditional analytical chemistry, there remains a need to rapidly characterize environmental samples for priority analysis, especially within disaster research response (DR2). As PAHs are both ubiquitous and occur as complex mixtures at many National Priority List sites, these compounds are of interest for post-disaster exposures.

OBJECTIVE

This study tests the field application of the KinExA Inline Biosensor in Galveston Bay and the Houston Ship Channel (GB/HSC) and in the Elizabeth River, characterizing the PAH profiles of these region's soils and sediments. To our knowledge, this is the first application of the biosensor to include soils.

METHODS

The biosensor enables calculation of total free PAHs in porewater (C free), which is confirmed through gas chromatography-mass spectrometry (GC-MS) analysis. To determine potential risk of the collected soils the United States Environmental Protection (USEPA) Agency's Regional Screening Level (RSL) Calculator is used along with the USEPA Region 4 Ecological Screening Values (R4-ESV) and Refined Screening Values (R4-RSV).

RESULTS

Based on GC-MS results, all samples had PAH-related hazard indices below 1, indicating low noncarcinogenic risks, but some samples exceeded screening levels for PAH-associated cancer risks. Combining biosensor-based C free with Total Organic Carbon yields predictions highly correlated (r > 0.5) both with total PAH concentrations as well as with hazard indices and cancer risks. Additionally, several individual parent PAH concentrations in both the GB/HSC and Elizabeth River sediments exceeded the R4- ESV and R4-RSV values, indicating a need for follow-up sediment studies.

CONCLUSIONS

The resulting data support the utility of the biosensor for future DR2 efforts to characterize PAH contamination, enabling preliminary PAH exposure risk screening to aid in prioritization of environmental sample analysis.

Potential Human Health Hazard of Post-Hurricane Harvey Sediments in Galveston Bay and Houston Ship Channel: A Case Study of Using In Vitro Bioactivity Data to Inform Risk Management Decisions

Natural and anthropogenic disasters may be associated with redistribution of chemical contaminants in the environment; however, current methods for assessing hazards and risks of complex mixtures are not suitable for disaster response. This study investigated the suitability of in vitro toxicity testing methods as a rapid means of identifying areas of potential human health concern. We used sediment samples (n = 46) from Galveston Bay and the Houston Ship Channel (GB/HSC) areas after hurricane Harvey, a disaster event that led to broad redistribution of chemically-contaminated sediments, including deposition of the sediment on shore due to flooding. Samples were extracted with cyclohexane and dimethyl sulfoxide and screened in a compendium of human primary or induced pluripotent stem cell (iPSC)-derived cell lines from different tissues (hepatocytes, neuronal, cardiomyocytes, and endothelial) to test for concentration-dependent effects on various functional and cytotoxicity phenotypes (n = 34). Bioactivity data were used to map areas of potential concern and the results compared to the data on concentrations of polycyclic aromatic hydrocarbons (PAHs) in the same samples. We found that setting remediation goals based on reducing bioactivity is protective of both "known" risks associated with PAHs and "unknown" risks associated with bioactivity, but the converse was not true for remediation based on PAH risks alone. Overall, we found that in vitro bioactivity can be used as a comprehensive indicator of potential hazards and is an example of a new approach method (NAM) to inform risk management decisions on site cleanup.

A new approach method for characterizing inter-species toxicodynamic variability

Inter-species differences in toxicodynamics are often a critical source of uncertainty in safety evaluations and typically dealt with using default adjustment factors. In vitro studies that use cells from different species demonstrated some success for estimating the relationships between life span and/or body weight and sensitivity to cytotoxicity; however, no apparent investigation evaluated the utility of these models for risk assessment. It was hypothesized that an in vitro model using dermal fibroblasts derived from diverse species and individuals might be utilized to inform the extent of inter-species and inter-individual variability in toxicodynamics. To test this hypothesis and characterize both inter-species and inter-individual variability in cytotoxicity, concentration-response cytotoxicity screening of 40 chemicals in primary dermal fibroblasts from 68 individuals of 54 diverse species was conducted. Chemicals examined included drugs, environmental pollutants, and food/flavor/fragrance agents; most of these were previously assessed either in vivo or in vitro for inter-species or inter-individual variation. Species included humans, the typical preclinical species and representatives from other orders of mammals and birds. Data demonstrated that both inter-species and inter-individual components of variability contribute to the observed differences in sensitivity to cell death. Further, it was found that the magnitude of the observed inter-species and inter-individual differences was chemical-dependent. This study contributes to the paradigm shift in risk assessment from reliance on in vivo toxicity testing to higher-throughput in vitro or alternative approaches, extending the strategy to replace use of default adjustment factors with experimental characterization of toxicodynamic inter-individual variability and to also address toxicodynamic inter-species variability.

Intra- and Inter-Species Variability in Urinary N7-(1-Hydroxy-3-buten-2-yl)guanine Adducts Following Inhalation Exposure to 1,3-Butadiene

1,3-Butadiene is a known carcinogen primarily targeting lymphoid tissues, lung, and liver. Cytochrome P450 activates butadiene to epoxides which form covalent DNA adducts that are thought to be a key mechanistic event in cancer. Previous studies suggested that inter-species, -tissue, and -individual susceptibility to adverse health effects of butadiene exposure may be due to differences in metabolism and other mechanisms. In this study, we aimed to examine the extent of inter-individual and inter-species variability in the urinary N7-(1-hydroxy-3-buten-2-yl)guanine (EB-GII) DNA adduct, a well-known biomarker of exposure to butadiene. For a population variability study in mice, we used the collaborative cross model. Female and male mice from five strains were exposed to filtered air or butadiene (590 ppm, 6 h/day, 5 days/week for 2 weeks) by inhalation. Urine samples were collected, and the metabolic activation of butadiene by DNA-reactive species was quantified as urinary EB-GII adducts. We quantified the degree of EB-GII variation across mouse strains and sexes; then, we compared this variation with the data from rats (exposed to 62.5 or 200 ppm butadiene) and humans (0.004-2.2 ppm butadiene). We show that sex and strain are significant contributors to the variability in urinary EB-GII levels in mice. In addition, we find that the degree of variability in urinary EB-GII in collaborative cross mice, when expressed as an uncertainty factor for the inter-individual variability (UF_H), is relatively modest (≤threefold) possibly due to metabolic saturation. By contrast, the variability in urinary EB-GII (adjusted for exposure) observed in humans, while larger than the default value of 10-fold, is largely consistent with UF_H estimates for other chemicals based on human data for non-cancer endpoints. Overall, these data demonstrate that urinary EB-GII levels, particularly from human studies, may be useful for quantitative characterization of human variability in cancer risks to butadiene.

Characterizing baseline legacy chemical contamination in urban estuaries for disaster-research through systematic evidence mapping: A case study

Natural disasters such as floods and hurricanes impact urbanized estuarine environments. Some impacts pose potential environmental and public health risks because of legacy or emerging chemical contamination. However, characterizing the baseline spatial and temporal distribution of environmental chemical contamination before disasters remains a challenge. To address this gap, we propose using systematic evidence mapping (SEM) in order to comprehensively integrate available data from diverse sources. We demonstrate this approach is useful for tracking and clarifying legacy chemical contamination reporting in an urban estuary system. We conducted a systematic search of peer-reviewed articles, government monitoring data, and grey literature. Inclusion/exclusion criteria are used as defined by a Condition, Context, Population (CoCoPop) statement for literature from 1990 to 2019. Most of the peer-reviewed articles reported dioxins/furans or mercury within the Houston Ship Channel (HSC); there was limited reporting of other organics and metals. In contrast, monitoring data from two agencies included 89-280 individual chemicals on a near-annual basis. Regionally, peer-reviewed articles tended to record metals in Lower Galveston Bay (GB) but organics in the HSC, while the agency databases spanned a wider spatial range in GB/HSC. This SEM has shown that chemical data from peer-reviewed and grey literature articles are sparse and inconsistent. Even with inclusion of government monitoring data, full spatial and temporal distributions of baseline levels of legacy chemicals are difficult to determine. There is thus a need to expand the chemical, spatial, and temporal coverage of sampling and environmental data reporting in GB/HSC.

Prediction of hepatic drug clearance with a human microfluidic four-cell liver acinus microphysiology system

Predicting human hepatic clearance remains a fundamental challenge in both pharmaceutical drug development and toxicological assessments of environmental chemicals, with concerns about both accuracy and precision of in vitro-derived estimates. Suggested sources of these issues have included differences in experimental protocols, differences in cell sourcing, and use of a single cell type, liver parenchymal cells (hepatocytes). Here we investigate the ability of human microfluidic four-cell liver acinus microphysiology system (LAMPS) to make predictions as to hepatic clearance for seven representative compounds: Caffeine, Pioglitazone, Rosiglitazone, Terfenadine, Tolcapone, Troglitazone, and Trovafloxacin. The model, whose reproducibility was recently confirmed in an inter-lab comparison, was constructed using primary human hepatocytes or human induced pluripotent stem cell (iPSC)-derived hepatocytes and 3 human cell lines for the endothelial, Kupffer and stellate cells. We calculated hepatic clearance estimates derived from experiments using LAMPS or traditional 2D cultures and compared the outcomes with both in vivo human clinical study-derived and in vitro human hepatocyte suspension culture-derived values reported in the literature. We found that, compared to in vivo clinically-derived values, the LAMPS model with iPSC-derived hepatocytes had higher precision as compared to primary cells in suspension or 2D culture, but, consistent with previous studies in other microphysiological systems, tended to underestimate in vivo clearance. Overall, these results suggest that use of LAMPS and iPSC-derived hepatocytes together with an empirical scaling factor warrants additional study with a larger set of compounds, as it has the potential to provide more accurate and precise estimates of hepatic clearance.

Cardiotoxicity Hazard and Risk Characterization of ToxCast Chemicals Using Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes from Multiple Donors

Heart disease remains a significant human health burden worldwide with a significant fraction of morbidity attributable to environmental exposures. However, the extent to which the thousands of chemicals in commerce and the environment may contribute to heart disease morbidity is largely unknown, because in contrast to pharmaceuticals, environmental chemicals are seldom tested for potential cardiotoxicity. Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes have become an informative in vitro model for cardiotoxicity testing of drugs with the availability of cells from multiple individuals allowing in vitro testing of population variability. In this study, we hypothesized that a panel of iPSC-derived cardiomyocytes from healthy human donors can be used to screen for the potential cardiotoxicity hazard and risk of environmental chemicals. We conducted concentration-response testing of 1029 chemicals (drugs, pesticides, flame retardants, polycyclic aromatic hydrocarbons (PAHs), plasticizers, industrial chemicals, food/flavor/fragrance agents, etc.) in iPSC-derived cardiomyocytes from 5 donors. We used kinetic calcium flux and high-content imaging to derive quantitative measures as inputs into Bayesian population concentration-response modeling of the effects of each chemical. We found that many environmental chemicals pose a hazard to human cardiomyocytes in vitro with more than half of all chemicals eliciting positive or negative chronotropic or arrhythmogenic effects. However, most of the tested environmental chemicals for which human exposure and high-throughput toxicokinetics data were available had wide margins of exposure and, thus, do not appear to pose a significant human health risk in a general population. Still, relatively narrow margins of exposure (<100) were estimated for some perfuoroalkyl substances and phthalates, raising concerns that cumulative exposures may pose a cardiotoxicity risk. Collectively, this study demonstrated the value of using a population-based human in vitro model for rapid, high-throughput hazard and risk characterization of chemicals for which little to no cardiotoxicity data are available from guideline studies in animals.

Environmental impacts of Hurricane Florence flooding in eastern North Carolina: temporal analysis of contaminant distribution and potential human health risks

BACKGROUND

Hurricane Florence made landfall in North Carolina in September 2018 causing extensive flooding. Several potential point sources of hazardous substances and Superfund sites sustained water damage and contaminants may have been released into the environment.

OBJECTIVE

This study conducted temporal analysis of contaminant distribution and potential human health risks from Hurricane Florence-associated flooding.

METHODS

Soil samples were collected from 12 sites across four counties in North Carolina in September 2018, January and May 2019. Chemical analyses were performed for organics by gas chromatography-mass spectrometry. Metals were analyzed using inductively coupled plasma mass spectrometry. Hazard index and cancer risk were calculated using EPA Regional Screening Level Soil Screening Levels for residential soils.

RESULTS

PAH and metals detected downstream from the coal ash storage pond that leaked were detected and were indicative of a pyrogenic source of contamination. PAH at these sites were of human health concern because cancer risk values exceeded 1 × 10^-6 threshold. Other contaminants measured across sampling sites, or corresponding hazard index and cancer risk, did not exhibit spatial or temporal differences or were of concern.

SIGNIFICANCE

This work shows the importance of rapid exposure assessment following natural disasters. It also establishes baseline levels of contaminants for future comparisons.

Using test positivity and reported case rates to estimate state-level COVID-19 prevalence and seroprevalence in the United States

Accurate estimates of infection prevalence and seroprevalence are essential for evaluating and informing public health responses and vaccination coverage needed to address the ongoing spread of COVID-19 in each United States (U.S.) state. However, reliable, timely data based on representative population sampling are unavailable, and reported case and test positivity rates are highly biased. A simple data-driven Bayesian semi-empirical modeling framework was developed and used to evaluate state-level prevalence and seroprevalence of COVID-19 using daily reported cases and test positivity ratios. The model was calibrated to and validated using published state-wide seroprevalence data, and further compared against two independent data-driven mathematical models. The prevalence of undiagnosed COVID-19 infections is found to be well-approximated by a geometrically weighted average of the positivity rate and the reported case rate. Our model accurately fits state-level seroprevalence data from across the U.S. Prevalence estimates of our semi-empirical model compare favorably to those from two data-driven epidemiological models. As of December 31, 2020, we estimate nation-wide a prevalence of 1.4% [Credible Interval (CrI): 1.0%-1.9%] and a seroprevalence of 13.2% [CrI: 12.3%-14.2%], with state-level prevalence ranging from 0.2% [CrI: 0.1%-0.3%] in Hawaii to 2.8% [CrI: 1.8%-4.1%] in Tennessee, and seroprevalence from 1.5% [CrI: 1.2%-2.0%] in Vermont to 23% [CrI: 20%-28%] in New York. Cumulatively, reported cases correspond to only one third of actual infections. The use of this simple and easy-to-communicate approach to estimating COVID-19 prevalence and seroprevalence will improve the ability to make public health decisions that effectively respond to the ongoing COVID-19 pandemic.

Spatial and temporal distribution of surface water contaminants in the Houston Ship Channel after the Intercontinental Terminal Company Fire

BACKGROUND

The fire at the Intercontinental Terminals Company (ITC, Deer Park, La Porte, TX, USA) from March 17 to 20, 2019 resulted in substantial releases of chemical contaminants to the environment, including the surface waters of the Houston Ship Channel.

OBJECTIVE

To characterize spatial and temporal trends, as well as potential human health risks, from these releases.

METHODS

Out of 433 substances with available data, seven were selected for analysis: benzene, toluene, ethylbenzene, xylenes, oil and grease, suspended solids, and total petroleum hydrocarbons. Spatial and temporal concentration trends were characterized, and hazard quotients and cancer risks were calculated to estimate the potential for human health impacts from these contaminants.

RESULTS

Temporal analysis showed presence of these chemical contaminants in water immediately after the event; their concentrations dissipated substantially within 4 weeks. The spatial distribution of contaminants indicated the highest concentrations in the waterways within about 1 km of the ITC. The greatest potential human health risks stemmed from presence of benzene.

SIGNIFICANCE

A short-term but substantial spike in the concentrations of a number of hazardous contaminants occurred near the incident, with concentrations returning to apparent baseline levels within 1 month likely due to a combination of volatization, dilution and degradation.

Key Characteristics of Cardiovascular Toxicants

BACKGROUND

The concept of chemical agents having properties that confer potential hazard called key characteristics (KCs) was first developed to identify carcinogenic hazards. Identification of KCs of cardiovascular (CV) toxicants could facilitate the systematic assessment of CV hazards and understanding of assay and data gaps associated with current approaches.

OBJECTIVES

We sought to develop a consensus-based synthesis of scientific evidence on the KCs of chemical and nonchemical agents known to cause CV toxicity along with methods to measure them.

METHODS

An expert working group was convened to discuss mechanisms associated with CV toxicity.

RESULTS

The group identified 12 KCs of CV toxicants, defined as exogenous agents that adversely interfere with function of the CV system. The KCs were organized into those primarily affecting cardiac tissue (numbers 1-4 below), the vascular system (5-7), or both (8-12), as follows: 1) impairs regulation of cardiac excitability, 2) impairs cardiac contractility and relaxation, 3) induces cardiomyocyte injury and death, 4) induces proliferation of valve stroma, 5) impacts endothelial and vascular function, 6) alters hemostasis, 7) causes dyslipidemia, 8) impairs mitochondrial function, 9) modifies autonomic nervous system activity, 10) induces oxidative stress, 11) causes inflammation, and 12) alters hormone signaling.

DISCUSSION

These 12 KCs can be used to help identify pharmaceuticals and environmental pollutants as CV toxicants, as well as to better understand the mechanistic underpinnings of their toxicity. For example, evidence exists that fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] air pollution, arsenic, anthracycline drugs, and other exogenous chemicals possess one or more of the described KCs. In conclusion, the KCs could be used to identify potential CV toxicants and to define a set of test methods to evaluate CV toxicity in a more comprehensive and standardized manner than current approaches. https://doi.org/10.1289/EHP9321.

Risk Characterization of Environmental Samples Using In Vitro Bioactivity and Polycyclic Aromatic Hydrocarbon Concentrations Data

Methods to assess environmental exposure to hazardous chemicals have primarily focused on quantification of individual chemicals, although chemicals often occur in mixtures, presenting challenges to the traditional risk characterization framework. Sampling sites in a defined geographic region provide an opportunity to characterize chemical contaminants, with spatial interpolation as a tool to provide estimates for non-sampled sites. At the same time, the use of in vitro bioactivity measurements has been shown to be informative for rapid risk-based decisions. In this study, we measured in vitro bioactivity in 39 surface soil samples collected immediately after flooding associated with Hurricane Harvey in Texas in a residential area known to be inundated with polycyclic aromatic hydrocarbon (PAH) contaminants. Bioactivity data were from a number of functional and toxicity assays in 5 human cell types, such as induced pluripotent stem cell-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, as well as human umbilical vein endothelial cells. Data on concentrations of PAH in these samples were also available and the combination of data sources offered a unique opportunity to assess the joint spatial variation of PAH components and bioactivity. We found significant evidence of spatial correlation of a subset of PAH contaminants and of cell-based phenotypes. In addition, we show that the cell-based bioactivity data can be used to predict environmental concentrations for several PAH contaminants, as well as overall PAH summaries and cancer risk. This study's impact lies in its demonstration that cell-based profiling can be used for rapid hazard screening of environmental samples by anchoring the bioassays to concentrations of PAH. This work sets the stage for identification of the areas of concern and direct quantitative risk characterization based on bioactivity data, thereby providing an important supplement to traditional individual chemical analyses by shedding light on constituents that may be missed from targeted chemical monitoring.

Quantitative Characterization of Population-Wide Tissue- and Metabolite-Specific Variability in Perchloroethylene Toxicokinetics in Male Mice

Quantification of interindividual variability is a continuing challenge in risk assessment, particularly for compounds with complex metabolism and multi-organ toxicity. Toxicokinetic variability for perchloroethylene (perc) was previously characterized across 3 mouse strains and in 1 mouse strain with various degrees of liver steatosis. To further characterize the role of genetic variability in toxicokinetics of perc, we applied Bayesian population physiologically based pharmacokinetic (PBPK) modeling to the data on perc and metabolites in blood/plasma and tissues of male mice from 45 inbred strains from the Collaborative Cross (CC) mouse population. After identifying the most influential PBPK parameters based on global sensitivity analysis, we fit the model with a hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation. We found that the data from 3 commonly used strains were not representative of the full range of variability in perc and metabolite blood/plasma and tissue concentrations across the CC population. Using interstrain variability as a surrogate for human interindividual variability, we calculated dose-dependent, chemical-, and tissue-specific toxicokinetic variability factors (TKVFs) as candidate science-based replacements for the default uncertainty factor for human toxicokinetic variability of 100.5. We found that toxicokinetic variability factors for glutathione conjugation metabolites of perc showed the greatest variability, often exceeding the default, whereas those for oxidative metabolites and perc itself were generally less than the default. Overall, we demonstrate how a combination of a population-based mouse model such as the CC with Bayesian population PBPK modeling can reduce uncertainty in human toxicokinetic variability and increase accuracy and precision in quantitative risk assessment.

A Bayesian Method for Population-wide Cardiotoxicity Hazard and Risk Characterization Using an In Vitro Human Model

Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes are an established model for testing potential chemical hazards. Interindividual variability in toxicodynamic sensitivity has also been demonstrated in vitro; however, quantitative characterization of the population-wide variability has not been fully explored. We sought to develop a method to address this gap by combining a population-based iPSC-derived cardiomyocyte model with Bayesian concentration-response modeling. A total of 136 compounds, including 54 pharmaceuticals and 82 environmental chemicals, were tested in iPSC-derived cardiomyocytes from 43 nondiseased humans. Hierarchical Bayesian population concentration-response modeling was conducted for 5 phenotypes reflecting cardiomyocyte function or viability. Toxicodynamic variability was quantified through the derivation of chemical- and phenotype-specific variability factors. Toxicokinetic modeling was used for probabilistic in vitro-to-in vivo extrapolation to derive population-wide margins of safety for pharmaceuticals and margins of exposure for environmental chemicals. Pharmaceuticals were found to be active across all phenotypes. Over half of tested environmental chemicals showed activity in at least one phenotype, most commonly positive chronotropy. Toxicodynamic variability factor estimates for the functional phenotypes were greater than those for cell viability, usually exceeding the generally assumed default of approximately 3. Population variability-based margins of safety for pharmaceuticals were correctly predicted to be relatively narrow, including some below 10; however, margins of exposure for environmental chemicals, based on population exposure estimates, generally exceeded 1000, suggesting they pose little risk at current general population exposures even to sensitive subpopulations. Overall, this study demonstrates how a high-throughput, human population-based, in vitro-in silico model can be used to characterize toxicodynamic population variability in cardiotoxic risk.

Polycyclic Aromatic Hydrocarbons in Houston Parks After Hurricane Harvey

Unprecedented inland precipitation and catastrophic flooding associated with Hurricane Harvey potentially redistributed contaminants from industrial sites and transportation infrastructure to recreational areas that make up networks of green infrastructure, creeks, and waterways used for flood control throughout the Greater Houston Area. Sediment samples were collected in parks located near the Buffalo Bayou watershed 1 week after Hurricane Harvey made landfall and again 7 weeks later. Total concentrations of the U.S. Environmental Protection Agency's (EPA's) 16 priority polycyclic aromatic hydrocarbons (PAHs) were measured in each sample at both time points. Diagnostic ratios were calculated to improve understanding of potential sources of PAHs after flooding. Diagnostic ratios suggest vehicular traffic to be a potential source for PAHs in parks. Although the concentrations of PAHs in all samples were below EPA actionable levels, given that no background values were available for comparison, it is difficult to quantify the impact flooding from Hurricane Harvey had on PAH concentrations in Houston parks. However, given the high frequency of flooding in Houston, and the concentration of industrial facilities and transportation infrastructure adjacent to recreation areas, these data demonstrate that PAHs were still present after unprecedented flooding. This study may also serve as a baseline for future efforts to understand the environmental health impacts of disasters.

Data Processing Workflow to Identify Structurally Related Compounds in Petroleum Substances Using Ion Mobility Spectrometry-Mass Spectrometry

Ion mobility spectrometry coupled with mass spectrometry (IMS-MS) is a post-ionization separation technique that can be used for rapid multidimensional analyses of complex samples. IMS-MS offers untargeted analysis, including ion-specific conformational data derived as collisional cross section (CCS) values. Here, we combine nitrogen gas drift tube CCS (^DTCCS_N2) and Kendrick mass defect (KMD) analyses based on CH2 and H functional units to enable compositional analyses of petroleum substances. First, polycyclic aromatic compound standards were analyzed by IMS-MS to demonstrate how CCS assists the identification of isomeric species in homologous series. Next, we used case studies of a gasoline standard previously characterized for paraffin, isoparaffin, aromatic, naphthene, and olefinic (PIANO) compounds, and a crude oil sample to demonstrate the application of the KMD analyses and CCS filtering. Finally, we propose a workflow that enables confident molecular formula assignment to the IMS-MS-derived features in petroleum samples. Collectively, this work demonstrates how rapid untargeted IMS-MS analysis and the proposed data processing workflow can be used to provide confident compositional characterization of hydrocarbon-containing substances.

Quantitative in Vitro-to-in Vivo Extrapolation for Mixtures: A Case Study of Superfund Priority List Pesticides

In vitro cell-based toxicity testing methods generate large amounts of data informative for risk-based evaluations. To allow extrapolation of the quantitative outputs from cell-based tests to the equivalent exposure levels in humans, reverse toxicokinetic (RTK) modeling is used to conduct in vitro-to-in vivo extrapolation (IVIVE) from in vitro effective concentrations to in vivo oral dose equivalents. IVIVE modeling approaches for individual chemicals are well-established; however, the potential implications of chemical-to-chemical interactions in mixture settings on IVIVE remains largely unexplored. We hypothesized that chemical co-exposures could modulate both protein binding efficiency and hepatocyte clearance of the chemicals in a mixture, which would in turn affect the quantitative IVIVE toxicokinetic parameters. To test this hypothesis, we used 20 pesticides from the Agency for Toxic Substances and Disease Registry (ATSDR) Substance Priority List, both individually and as equimolar mixtures, and investigated the concentration-dependent effects of chemical interactions on in vitro toxicokinetic parameters. Plasma protein binding efficiency was determined by using ultracentrifugation, and hepatocyte clearance was estimated in suspensions of cryopreserved primary human hepatocytes. We found that for single chemicals, the protein binding efficiencies were similar at different test concentrations. In a mixture, however, both protein binding efficiency and hepatocyte clearance were affected. When IVIVE was conducted using mixture-derived toxicokinetic data, more conservative estimates of Activity-to-Exposure Ratios (AERs) were produced as compared to using data from single chemical experiments. Because humans are exposed to mixtures of chemicals, this study is significant as it demonstrates the importance of incorporating mixture-derived parameters into IVIVE for in vitro bioactivity data in order to accurately prioritize risks and facilitate science-based decision-making.

In Vitro Bioavailability of the Hydrocarbon Fractions of Dimethyl Sulfoxide Extracts of Petroleum Substances

Determining the in vitro bioavailable concentration is a critical, yet unmet need to refine in vitro-to-in vivo extrapolation for unknown or variable composition, complex reaction product or biological material (UVCB) substances. UVCBs such as petroleum substances are commonly subjected to dimethyl sulfoxide (DMSO) extraction in order to retrieve the bioactive polycyclic aromatic compound (PAC) portion for in vitro testing. In addition to DMSO extraction, protein binding in cell culture media and dilution can all influence in vitro bioavailable concentrations of aliphatic and aromatic compounds in petroleum substances. However, these in vitro factors have not been fully characterized. In this study, we aimed to fill in these data gaps by characterizing the effects of these processes using both a defined mixture of analytical standards containing aliphatic and aromatic hydrocarbons, as well as 4 refined petroleum products as prototypical examples of UVCBs. Each substance was extracted with DMSO, and the protein binding in cell culture media was measured by using solid-phase microextraction. Semiquantitative analysis for aliphatic and aromatic compounds was achieved via gas chromatography-mass spectrometry. Our results showed that DMSO selectively extracted PACs from test substances, and that chemical profiles of PACs across molecular classes remained consistent after extraction. With respect to protein binding, chemical profiles were retained at a lower dilution (higher concentration), but a greater dilution factor (ie, lower concentration) resulted in higher protein binding in cell medium, which in turn altered the ultimate chemical profile of bioavailable PACs. Overall, this case study demonstrates that extraction procedures, protein binding in cell culture media, and dilution factors prior to in vitro testing can all contribute to determining the final bioavailable concentrations of bioactive constituents of UVCBs in vitro. Thus, in vitro-to-in vivo extrapolation for UVCBs may require greater attention to the concentration-dependent and compound-specific differences in recovery and bioavailability.

Exposure and Toxicity Characterization of Chemical Emissions and Chemicals in Products: Global Recommendations and Implementation in USEtox

PURPOSE

Reducing chemical pressure on human and environmental health is an integral part of the global sustainability agenda. Guidelines for deriving globally applicable, life cycle based indicators are required to consistently quantify toxicity impacts from chemical emissions as well as from chemicals in consumer products. In response, we elaborate the methodological framework and present recommendations for advancing near-field/far-field exposure and toxicity characterization, and for implementing these recommendations in the scientific consensus model USEtox.

METHODS

An expert taskforce was convened by the Life Cycle Initiative hosted by UN Environment to expand existing guidance for evaluating human toxicity impacts from exposure to chemical substances. This taskforce evaluated advances since the original release of USEtox. Based on these advances, the taskforce identified two major aspects that required refinement, namely integrating near-field and far-field exposure and improving human dose-response modeling. Dedicated efforts have led to a set of recommendations to address these aspects in an update of USEtox, while ensuring consistency with the boundary conditions for characterizing life cycle toxicity impacts and being aligned with recommendations from agencies that regulate chemical exposure. The proposed framework was finally tested in an illustrative rice production and consumption case study.

RESULTS AND DISCUSSION

On the exposure side, a matrix system is proposed and recommended to integrate far-field exposure from environmental emissions with near-field exposure from chemicals in various consumer product types. Consumer exposure is addressed via submodels for each product type to account for product characteristics and exposure settings. Case study results illustrate that product-use related exposure dominates overall life cycle exposure. On the effect side, a probabilistic dose-response approach combined with a decision tree for identifying reliable points of departure is proposed for non-cancer effects, following recent guidance from the World Health Organization. This approach allows for explicitly considering both uncertainty and human variability in effect factors. Factors reflecting disease severity are proposed to distinguish cancer from non-cancer effects, and within the latter discriminate reproductive/developmental and other non-cancer effects. All proposed aspects have been consistently implemented into the original USEtox framework.

CONCLUSIONS

The recommended methodological advancements address several key limitations in earlier approaches. Next steps are to test the new characterization framework in additional case studies and to close remaining research gaps. Our framework is applicable for evaluating chemical emissions and product-related exposure in life cycle assessment, chemical alternatives assessment and chemical substitution, consumer exposure and risk screening, and high-throughput chemical prioritization.