The exposure data landscape for manufactured chemicals

Identifying uncertainty in physical-chemical property estimation with IFSQSAR

This study describes the development and evaluation of six new models for predicting physical-chemical (PC) properties that are highly relevant for chemical hazard, exposure, and risk estimation: solubility (in water SW and octanol SO), vapor pressure (VP), and the octanol-water (KOW), octanol-air (KOA), and air-water (KAW) partition ratios. The models are implemented in the Iterative Fragment Selection Quantitative Structure-Activity Relationship (IFSQSAR) python package, Version 1.1.0. These models are implemented as Poly-Parameter Linear Free Energy Relationship (PPLFER) equations which combine experimentally calibrated system parameters and solute descriptors predicted with QSPRs. Two other ancillary models have been developed and implemented, a QSPR for Molar Volume (MV) and a classifier for the physical state of chemicals at room temperature. The IFSQSAR methods for characterizing applicability domain (AD) and calculating uncertainty estimates expressed as 95% prediction intervals (PI) for predicted properties are described and tested on 9,000 measured partition ratios and 4,000 VP and SW values. The measured data are external to IFSQSAR training and validation datasets and are used to assess the predictivity of the models for "novel chemicals" in an unbiased manner. The 95% PI intervals calculated from validation datasets for partition ratios needed to be scaled by a factor of 1.25 to capture 95% of the external data. Predictions for VP and SW are more uncertain, primarily due to the challenges in differentiating their physical state (i.e., liquids or solids) at room temperature. The prediction accuracy of the models for log KOW, log KAW and log KOA of novel, data-poor chemicals is estimated to be in the range of 0.7 to 1.4 root mean squared error of prediction (RMSEP), with RMSEP in the range 1.7-1.8 for log VP and log SW. Scientific contributionNew partitioning models integrate empirical PPLFER equations and QSARs, allowing for seamless integration of experimental data and model predictions. This work tests the real predictivity of the models for novel chemicals which are not in the model training or external validation datasets.

Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment

The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.

Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances

Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration "surrogates," which can yield diminished predictive performance relative to targeted analysis. To evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from "global" chemical surrogates. As expected, the targeted approaches performed best, with major benefits realized from matched calibration curves and internal standard correction. In comparison to the benchmark targeted approach, the most generalizable qNTA approach (using "global" surrogates) showed a decrease in accuracy by a factor of ~4, an increase in uncertainty by a factor of ~1000, and a decrease in reliability by ~5%, on average. Using "expert-selected" surrogates (n = 3) instead of "global" surrogates (n = 25) for qNTA yielded improvements in predictive accuracy (by ~1.5×) and uncertainty (by ~70×) but at the cost of further-reduced reliability (by ~5%). Overall, our results illustrate the utility of qNTA approaches for a subclass of emerging contaminants and present a framework on which to develop new approaches for more complex use cases.

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.

Towards improved screening of toxins for Parkinson's risk

Parkinson's disease (PD) is a chronic, progressive and disabling neurodegenerative disorder. The prevalence of PD has risen considerably over the past decades. A growing body of evidence suggest that exposure to environmental toxins, including pesticides, solvents and heavy metals (collectively called toxins), is at least in part responsible for this rapid growth. It is worrying that the current screening procedures being applied internationally to test for possible neurotoxicity of specific compounds offer inadequate insights into the risk of developing PD in humans. Improved screening procedures are therefore urgently needed. Our review first substantiates current evidence on the relation between exposure to environmental toxins and the risk of developing PD. We subsequently propose to replace the current standard toxin screening by a well-controlled multi-tier toxin screening involving the following steps: in silico studies (tier 1) followed by in vitro tests (tier 2), aiming to prioritize agents with human relevant routes of exposure. More in depth studies can be undertaken in tier 3, with whole-organism (in)vertebrate models. Tier 4 has a dedicated focus on cell loss in the substantia nigra and on the presumed mechanisms of neurotoxicity in rodent models, which are required to confirm or refute the possible neurotoxicity of any individual compound. This improved screening procedure should not only evaluate new pesticides that seek access to the market, but also critically assess all pesticides that are being used today, acknowledging that none of these has ever been proven to be safe from a perspective of PD. Importantly, the improved screening procedures should not just assess the neurotoxic risk of isolated compounds, but should also specifically look at the cumulative risk conveyed by exposure to commonly used combinations of pesticides (cocktails). The worldwide implementation of such an improved screening procedure, would be an essential step for policy makers and governments to recognize PD-related environmental risk factors.

Length of employment in workplaces handling hazardous chemicals and risk of cancer among Japanese men

OBJECTIVES

In Japan, the risk of developing cancer among workers employed in workplaces where chemical substances are handled is unclear. This study aimed to assess the association between cancer risk and employment in workplaces handling hazardous chemicals.

METHODS

The Inpatient Clinico-Occupational Survey of the Rosai Hospital Group data of 120 278 male patients with incident cancer and 217 605 hospital controls matched for 5-year age group, hospital (34 hospitals) and year of admission (2005-2019) were analysed. Cancer risk in relation to lifetime employment in workplaces using regulated chemicals was assessed while controlling for age, region and year of diagnosis, smoking, alcohol consumption and occupation. Further analysis stratified by smoking history was performed to examine interaction effects.

RESULTS

In the longest group of employment in tertiles, ORs were increased for all cancers (OR=1.13; 95% CI: 1.07 to 1.19) and lung (OR=1.82; 95% CI: 1.56 to 2.13), oesophageal (OR=1.73; 95% CI: 1.18 to 2.55), pancreatic (OR=2.03; 95% CI: 1.40 to 2.94) and bladder (OR=1.40; 95% CI: 1.12 to 1.74) cancers. Employment of 1+ years was associated with risk for lung cancer; 11+ years for pancreatic and bladder cancers; and 21+ years for all cancers and oesophageal cancer. These positive relationships were particularly obvious among patients with a history of smoking; however, no significant interaction between smoking and length of employment was observed.

CONCLUSIONS

There is a high risk of cancer among workers, especially smokers, employed in workplaces handling regulated chemicals in Japan. Thus, future measures for chemical management in workplaces are needed to prevent avoidable cancers.

AirPen: A Wearable Monitor for Characterizing Exposures to Particulate Matter and Volatile Organic Compounds

Exposure to air pollution is a leading risk factor for disease and premature death, but technologies for assessing personal exposure to particulate and gaseous air pollutants, including the timing and location of such exposures, are limited. We developed a small, quiet, wearable monitor, called the AirPen, to quantify personal exposures to fine particulate matter (PM_2.5) and volatile organic compounds (VOCs). The AirPen combines physical sample collection (PM onto a filter and VOCs onto a sorbent tube) with a suite of low-cost sensors (for PM, VOCs, temperature, pressure, humidity, light intensity, location, and motion). We validated the AirPen against conventional personal sampling equipment in the laboratory and then conducted a field study to measure at-work and away-from-work exposures to PM_2.5 and VOCs among employees at an agricultural facility in Colorado, USA. The resultant sampling and sensor data indicated that personal exposures to benzene, toluene, ethylbenzene, and xylenes were dominated by a specific workplace location. These results illustrate how the AirPen can be used to advance our understanding of personal exposure to air pollution as a function of time, location, source, and activity, even in the absence of detailed activity diary data.

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.

How Many Chemicals in Commerce Have Been Analyzed in Environmental Media? A 50 Year Bibliometric Analysis

Over the past 50 years, there has been a tremendous expansion in the measurement of chemical contaminants in environmental media. But how many chemicals have actually been determined, and do they represent a significant fraction of substances in commerce or of chemicals of concern? To address these questions, we conducted a bibliometric survey to identify what individual chemicals have been determined in environmental media and their trends over the past 50 years. The CAplus database of CAS, a Division of the American Chemical Society, was searched for indexing roles "analytical study" and "pollutant" yielding a final list of 19,776 CAS Registry Numbers (CASRNs). That list was then used to link the CASRNs to biological studies, yielding a data set of 9.251 × 10⁶ total counts of the CASRNs over a 55 year period. About 14,150 CASRNs were substances on various priority lists or their close analogs and transformation products. The top 100 most reported CASRNs accounted for 34% of the data set, confirming previous studies showing a significant bias toward repeated measurements of the same substances due to regulatory needs and the challenges of determining new, previously unmeasured, compounds. Substances listed in the industrial chemical inventories of Europe, China, and the United States accounted for only about 5% of measured substances. However, pharmaceuticals and current use pesticides were widely measured accounting for 50-60% of total CASRN counts for the period 2000-2015.

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.

Human risk associated with exposure to mixtures of antiandrogenic chemicals evaluated using in vitro hazard and human biomonitoring data

BACKGROUND

Scientific evidence for underestimated toxicity from unintentional exposure to chemical mixtures is mounting. Yet, harmonized approaches on how to assess the actual risk of mixtures is lacking. As part of the European Joint programme 'Human Biomonitoring for Europe' we explored a novel methodology for mixture risk assessment of chemicals affecting male reproductive function.

METHODOLOGY

We explored a methodology for chemical mixture risk assessment based on human in vitro data combined with human exposure data, thereby circumventing the drawbacks of using hazard data from rodents and estimated exposure intake levels. Human androgen receptor (hAR) antagonism was selected as the most important molecular initiating event linked to adverse outcomes on male reproductive health.

RESULTS

Our work identified 231 chemicals able to interfere with hAR activity. Among these were 61 finally identified as having both reliable hAR antagonist and human biomonitoring data. Calculation of risk quotients indicated that PCBs (118, 138, 157), phthalates (BBP, DBP, DIBP), benzophenone-3, PFOS, methylparaben, triclosan, some pesticides (i.e cypermethrin, β-endosulfan, methylparathion, p,p-DDE), and a PAH metabolite (1-hydroxypyrene) contributed to the mixture effect. The major chemical mixture drivers were PCB 118, BBP, PFOS, DBP, and the UV filter benzophenone-3, together contributing with 75% of the total mixture effect that was primarily driven by high exposure values.

CONCLUSIONS

This viable way forward for mixture risk assessment of chemicals has the advantages of (1) being a more comprehensive mixture risk assessment also covering data-poor chemicals, and (2) including human data only. However, the approach is subjected to uncertainties in terms of in vitro to in vivo extrapolation, it is not ready for decision making, and needs further development. Still, the results indicate a concern for adverse effects on reproductive function in highly exposed boys, especially when considering additional exposure to data-poor chemicals and chemicals acting by other mechanisms of action.

Critical review on phytoremediation of polyfluoroalkyl substances from environmental matrices: Need for global concern

Poly- and perfluoroalkyl substances (PFAS) are a class of emerging organic contaminants that are impervious to standard physicochemical treatments. The widespread use of PFAS poses serious environmental issues. PFAS pollution of soils and water has become a significant issue due to the harmful effects of these chemicals both on the environment and public health. Owing to their complex chemical structures and interaction with soil and water, PFAS are difficult to remove from the environment. Traditional soil remediation procedures have not been successful in reducing or removing them from the environment. Therefore, this review focuses on new phytoremediation techniques for PFAS contamination of soils and water. The bioaccumulation and dispersion of PFAS inside plant compartments has shown great potential for phytoremediation, which is a promising and unique technology that is realistic, cost-effective, and may be employed as a wide scale in situ remediation strategy.

Charting the landscape of the environmental exposome

The exposome depicts the total exposures in the lifetime of an organism. Human exposome comprises exposures from environmental and humanistic sources. Biological, chemical, and physical environmental exposures pose potential health threats, especially to susceptible populations. Although still in its nascent stage, we are beginning to recognize the vast and dynamic nature of the exposome. In this review, we systematically summarize the biological and chemical environmental exposomes in three broad environmental matrices-air, soil, and water; each contains several distinct subcategories, along with a brief introduction to the physical exposome. Disease-related environmental exposures are highlighted, and humans are also a major source of disease-related biological exposures. We further discuss the interactions between biological, chemical, and physical exposomes. Finally, we propose a list of outstanding challenges under the exposome research framework that need to be addressed to move the field forward. Taken together, we present a detailed landscape of environmental exposome to prime researchers to join this exciting new field.

Disparities in chemical exposures among pregnant women and neonates by socioeconomic and demographic characteristics: A nontargeted approach

BACKGROUND

Exposure to environmental chemicals during pregnancy adversely affects maternal and infant health, and identifying socio-demographic differences in exposures can inform contributions to health inequities.

METHODS

We recruited 294 demographically diverse pregnant participants in San Francisco from the Mission Bay/Moffit Long (MB/ML) hospitals, which serve a primarily higher income population, and Zuckerberg San Francisco General Hospital (ZSFGH), which serves a lower income population. We collected maternal and cord sera, which we screened for 2420 unique formulas and their isomers using high-resolution mass spectrometry using LC-QTOF/MS. We assessed differences in chemical abundances across socioeconomic and demographic groups using linear regression adjusting for false discovery rate.

RESULTS

Our participants were racially diverse (31% Latinx, 16% Asian/Pacific Islander, 5% Black, 5% other or multi-race, and 43% white). A substantial portion experienced financial strain (28%) and food insecurity (20%) during pregnancy. We observed significant abundance differences in maternal (9 chemicals) and cord sera (39 chemicals) between participants who delivered at the MB/ML hospitals versus ZSFGH. Of the 39 chemical features differentially detected in cord blood, 18 were present in pesticides, one per- or poly-fluoroalkyl substance (PFAS), 21 in plasticizers, 24 in cosmetics, and 17 in pharmaceuticals; 4 chemical features had unknown sources. A chemical feature annotated as 2,4-dichlorophenol had higher abundances among Latinx compared to white participants, those delivering at ZSFGH compared to MB/ML, those with food insecurity, and those with financial strain. Post-hoc QTOF analyses indicated the chemical feature was either 2,4-dichlorophenol or 2,5-dichlorophenol, both of which have potential endocrine-disrupting effects.

CONCLUSIONS

Chemical exposures differed between delivery hospitals, likely due to underlying social conditions faced by populations served. Differential exposures to 2,4-dichlorophenol or 2,5-dichlorophenol may contribute to disparities in adverse outcomes.

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.

Evaluation of a rapid, generic human gestational dose model

Chemical risk assessment considers potentially susceptible populations including pregnant women and developing fetuses. Humans encounter thousands of chemicals in their environments, few of which have been fully characterized. Toxicokinetic (TK) information is needed to relate chemical exposure to potentially bioactive tissue concentrations. Observational data describing human gestational exposures are unavailable for most chemicals, but physiologically based TK (PBTK) models estimate such exposures. Development of chemical-specific PBTK models requires considerable time and resources. As an alternative, generic PBTK approaches describe a standardized physiology and characterize chemicals with a set of standard physical and TK descriptors - primarily plasma protein binding and hepatic clearance. Here we report and evaluate a generic PBTK model of a human mother and developing fetus. We used a published set of formulas describing the major anatomical and physiological changes that occur during pregnancy to augment the High-Throughput Toxicokinetics (httk) software package. We simulated the ratio of concentrations in maternal and fetal plasma and compared to literature in vivo measurements. We evaluated the model with literature in vivo time-course measurements of maternal plasma concentrations in pregnant and non-pregnant women. Finally, we prioritized chemicals measured in maternal serum based on predicted fetal brain concentrations. This new model can be used for TK simulations of 859 chemicals with existing human-specific in vitro TK data as well as any new chemicals for which such data become available. This gestational model may allow for in vitro to in vivo extrapolation of point of departure doses relevant to reproductive and developmental toxicity.

Exposure Prioritization (Ex Priori): A Screening-Level High-Throughput Chemical Prioritization Tool

To estimate potential chemical risk, tools are needed to prioritize potential exposures for chemicals with minimal data. Consumer product exposures are a key pathway, and variability in consumer use patterns is an important factor. We designed Ex Priori, a flexible dashboard-type screening-level exposure model, to rapidly visualize exposure rankings from consumer product use. Ex Priori is Excel-based. Currently, it is parameterized for seven routes of exposure for 1108 chemicals present in 228 consumer product types. It includes toxicokinetics considerations to estimate body burden. It includes a simple framework for rapid modeling of broad changes in consumer use patterns by product category. Ex Priori rapidly models changes in consumer user patterns during the COVID-19 pandemic and instantly shows resulting changes in chemical exposure rankings by body burden. Sensitivity analysis indicates that the model is sensitive to the air emissions rate of chemicals from products. Ex Priori's simple dashboard facilitates dynamic exploration of the effects of varying consumer product use patterns on prioritization of chemicals based on potential exposures. Ex Priori can be a useful modeling and visualization tool to both novice and experienced exposure modelers and complement more computationally intensive population-based exposure models.

Using liver models generated from human-induced pluripotent stem cells (iPSCs) for evaluating chemical-induced modifications and disease across liver developmental stages

The liver is a pivotal organ regulating critical developmental stages of fetal metabolism and detoxification. Though numerous studies have evaluated links between prenatal/perinatal exposures and adverse health outcomes in the developing fetus, the central role of liver to health disruptions resulting from these exposures remains understudied, especially concerning early development and later-in-life health outcomes. While numerous in vitro methods for evaluating liver toxicity have been established, the use of iPSC-derived hepatocytes appears to be particularly well suited to contribute to this critical research gap due to their potential to model a diverse range of disease phenotypes and different stages of liver development. The following key aspects are reviewed: (1) an introduction to developmental liver toxicity; (2) an introduction to embryonic and induced pluripotent stem cell models; (3) methods and challenges for deriving liver cells from stem cells; and (4) applications for iPSC-derived hepatocytes to evaluate liver developmental stages and their associated responses to insults. We conclude that iPSC-derived hepatocytes have great potential for informing liver toxicity and underlying disease mechanisms via the generation of patient-specific iPSCs; implementing large-scale drug and chemical screening; evaluating general biological responses as a potential surrogate target cell; and evaluating inter-individual disease susceptibility and response variability.

Advances in computational methods along the exposure to toxicological response paradigm

Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.

Predicting emerging chemical content in consumer products using machine learning

Chemical ingredients in consumer products are continually changing. To understand our exposure to chemicals and their consequent risk, we need to know their concentrations in products, or chemical weight fractions. Unfortunately, manufacturers rarely report comprehensive weight fraction data on product labels. The goal of this study was to evaluate the utility of machine learning strategies for predicting weight fractions when chemical constituent data are limited. A "data-poor" framework was developed and tested using a small dataset on consumer products containing engineered nanomaterials to represent emerging substances. A second, more traditional framework was applied to a "data-rich" product dataset comprised of bulk-scale organic chemicals for comparison purposes. Feature variables included chemical properties, functional use categories (e.g., antimicrobial), product categories (e.g., makeup), product matrix categories, and whether weight fractions were manufacturer-reported or experimentally obtained. Classification into three weight fraction bins was done using a random forest or nonlinear support vector classifier. An ablation study revealed that functional use data improved predictive performance when included alongside chemical property data, suggesting the utility of functional use categories in evaluating the safety and sustainability of emerging chemicals. Models could roughly stratify material-product observations into order of magnitude weight fractions with moderate success; the best of these achieved an average balanced accuracy of 73% on the nanomaterials product data. Framework comparisons also revealed a positive trend in sample size versus average balanced accuracy, suggesting great promise for machine learning approaches with continued investment in chemical data collection.

Uncertainty estimation strategies for quantitative non-targeted analysis

Non-targeted analysis (NTA) methods are widely used for chemical discovery but seldom employed for quantitation due to a lack of robust methods to estimate chemical concentrations with confidence limits. Herein, we present and evaluate new statistical methods for quantitative NTA (qNTA) using high-resolution mass spectrometry (HRMS) data from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT). Experimental intensities of ENTACT analytes were observed at multiple concentrations using a semi-automated NTA workflow. Chemical concentrations and corresponding confidence limits were first estimated using traditional calibration curves. Two qNTA estimation methods were then implemented using experimental response factor (RF) data (where RF = intensity/concentration). The bounded response factor method used a non-parametric bootstrap procedure to estimate select quantiles of training set RF distributions. Quantile estimates then were applied to test set HRMS intensities to inversely estimate concentrations with confidence limits. The ionization efficiency estimation method restricted the distribution of likely RFs for each analyte using ionization efficiency predictions. Given the intended future use for chemical risk characterization, predicted upper confidence limits (protective values) were compared to known chemical concentrations. Using traditional calibration curves, 95% of upper confidence limits were within ~tenfold of the true concentrations. The error increased to ~60-fold (ESI+) and ~120-fold (ESI-) for the ionization efficiency estimation method and to ~150-fold (ESI+) and ~130-fold (ESI-) for the bounded response factor method. This work demonstrates successful implementation of confidence limit estimation strategies to support qNTA studies and marks a crucial step towards translating NTA data in a risk-based context.

ER/AR Multi-Conformational Docking Server: A Tool for Discovering and Studying Estrogen and Androgen Receptor Modulators

The prediction of the estrogen receptor (ER) and androgen receptor (AR) activity of a compound is quite important to avoid the environmental exposures of endocrine-disrupting chemicals. The Estrogen and Androgen Receptor Database (EARDB, http://eardb.schanglab.org.cn/) provides a unique collection of reported ERα, ERβ, or AR protein structures and known small molecule modulators. With the user-uploaded query molecules, molecular docking based on multi-conformations of a single target will be performed. Moreover, the 2D similarity search against known modulators is also provided. Molecules predicted with a low binding energy or high similarity to known ERα, ERβ, or AR modulators may be potential endocrine-disrupting chemicals or new modulators. The server provides a tool to predict the endocrine activity for compounds of interests, benefiting for the ER and AR drug design and endocrine-disrupting chemical identification.

Quantitative non-targeted analysis: Bridging the gap between contaminant discovery and risk characterization

Chemical risk assessments follow a long-standing paradigm that integrates hazard, dose-response, and exposure information to facilitate quantitative risk characterization. Targeted analytical measurement data directly support risk assessment activities, as well as downstream risk management and compliance monitoring efforts. Yet, targeted methods have struggled to keep pace with the demands for data regarding the vast, and growing, number of known chemicals. Many contemporary monitoring studies therefore utilize non-targeted analysis (NTA) methods to screen for known chemicals with limited risk information. Qualitative NTA data has enabled identification of previously unknown compounds and characterization of data-poor compounds in support of hazard identification and exposure assessment efforts. In spite of this, NTA data have seen limited use in risk-based decision making due to uncertainties surrounding their quantitative interpretation. Significant efforts have been made in recent years to bridge this quantitative gap. Based on these advancements, quantitative NTA data, when coupled with other high-throughput data streams and predictive models, are poised to directly support 21st-century risk-based decisions. This article highlights components of the chemical risk assessment process that are influenced by NTA data, surveys the existing literature for approaches to derive quantitative estimates of chemicals from NTA measurements, and presents a conceptual framework for incorporating NTA data into contemporary risk assessment frameworks.

Incorporating human exposure information in a weight of evidence approach to inform design of repeated dose animal studies

Human health risks from chronic exposures to environmental chemicals are typically estimated from potential human exposure estimates and dose-response data obtained from repeated-dose animal toxicity studies. Various criteria are available for selecting the top (highest) dose used in these animal studies. For example, toxicokinetic (TK) and toxicological data provided by shorter-term or dose range finding studies can be evaluated in a weight of evidence approach to provide insight into the dose range that would provide dose-response data that are relevant to human exposures. However, there are concerns that a top dose resulting from the consideration of TK data may be too low compared to other criteria, such as the limit dose or the maximum tolerated dose. In this paper, we address several concerns related to human exposures by discussing 1) the resources and methods available to predict human exposure levels and the associated uncertainty and variability, and 2) the margin between predicted human exposure levels and the dose levels used in repeated-dose animal studies. A series of case studies, ranging from data-rich to data-poor chemicals, are presented to demonstrate that expected human exposures to environmental chemicals are typically orders of magnitude lower than no-observed-adverse-effect levels/lowest-observed-adverse-effect levels (NOAELs/LOAELs) when available (used as conservative surrogates for top doses). The results of these case studies support that a top dose based, in part, on TK data is typically orders of magnitude higher than expected human exposure levels.

Application of an in Vitro Assay to Identify Chemicals That Increase Estradiol and Progesterone Synthesis and Are Potential Breast Cancer Risk Factors

BACKGROUND

Established breast cancer risk factors, such as hormone replacement therapy and reproductive history, are thought to act by increasing estrogen and progesterone (P4) activity.

OBJECTIVE

We aimed to use in vitro screening data to identify chemicals that increase the synthesis of estradiol (E2) or P4 and evaluate potential risks.

METHOD

Using data from a high-throughput (HT) in vitro steroidogenesis assay developed for the U.S. Environmental Protection Agency (EPA) ToxCast program, we identified chemicals that increased estradiol (E2-up) or progesterone (P4-up) in human H295R adrenocortical carcinoma cells. We prioritized chemicals by their activity. We compiled in vivo studies and assessments about carcinogenicity and reproductive/developmental (repro/dev) toxicity. We identified exposure sources and predicted intakes from the U.S. EPA's ExpoCast.

RESULTS

We found 296 chemicals increased E2 (182) or P4 (185), with 71 chemicals increasing both. In vivo data often showed effects consistent with this mechanism. Of the E2- and P4-up chemicals, about 30% were likely repro/dev toxicants or carcinogens, whereas only 5-13% were classified as unlikely. However, most of the chemicals had insufficient in vivo data to evaluate their effects. Of 45 chemicals associated with mammary gland effects, and also tested in the H294R assay, 29 increased E2 or P4, including the well-known mammary carcinogen 7,12-dimethylbenz(a)anthracene. E2- and P4-up chemicals include pesticides, consumer product ingredients, food additives, and drinking water contaminants.

DISCUSSION

The U.S. EPA's in vitro screening data identified several hundred chemicals that should be considered as potential risk factors for breast cancer because they increased E2 or P4 synthesis. In vitro data is a helpful addition to current toxicity assessments, which are not sensitive to mammary gland effects. Relevant effects on the mammary gland are often not noticed or are dismissed, including for 2,4-dichlorophenol and cyfluthrin. Fifty-three active E2-up and 59 active P4-up chemicals that are in consumer products, food, pesticides, or drugs have not been evaluated for carcinogenic potential and are priorities for study and exposure reduction. https://doi.org/10.1289/EHP8608.

Utility of In Vitro Bioactivity as a Lower Bound Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization

Use of high-throughput, in vitro bioactivity data in setting a point-of-departure (POD) has the potential to accelerate the pace of human health safety evaluation by informing screening-level assessments. The primary objective of this work was to compare PODs based on high-throughput predictions of bioactivity, exposure predictions, and traditional hazard information for 448 chemicals. PODs derived from new approach methodologies (NAMs) were obtained for this comparison using the 50th (PODNAM, 50) and the 95th (PODNAM, 95) percentile credible interval estimates for the steady-state plasma concentration used in in vitro to in vivo extrapolation of administered equivalent doses. Of the 448 substances, 89% had a PODNAM, 95 that was less than the traditional POD (PODtraditional) value. For the 48 substances for which PODtraditional < PODNAM, 95, the PODNAM and PODtraditional were typically within a factor of 10 of each other, and there was an enrichment of chemical structural features associated with organophosphate and carbamate insecticides. When PODtraditional < PODNAM, 95, it did not appear to result from an enrichment of PODtraditional based on a particular study type (eg, developmental, reproductive, and chronic studies). Bioactivity:exposure ratios, useful for identification of substances with potential priority, demonstrated that high-throughput exposure predictions were greater than the PODNAM, 95 for 11 substances. When compared with threshold of toxicological concern (TTC) values, the PODNAM, 95 was greater than the corresponding TTC value 90% of the time. This work demonstrates the feasibility, and continuing challenges, of using in vitro bioactivity as a protective estimate of POD in screening-level assessments via a case study.

Particulate Matter Exposure across Latino Ethnicities

The Hispanic/Latino health paradox is the well-known health advantage seen across the Hispanic/Latino racial category in the US. However, this racial category collapses several distinct ethnic groups with varying spatial distributions. Certain populations, such as Dominicans and Cubans, are concentrated in specific areas, compared to more dispersed groups such as Mexicans. Historical peculiarities have brought these populations into contact with specific types of environmental exposures. This paper takes a first step towards unraveling these diverse exposure profiles by estimating how exposure to particulate matter varies across demographic groups and narrows down which types of industries and chemicals are contributing the most to air toxins. Exposure to particulate matter is estimated for 72,271 census tracts in the continental US to evaluate how these exposures correlate with the proportion of the population classified within the four largest groups that make up the Hispanic population in the US: Mexican, Puerto Rican, Cuban, and Dominican. Using linear mixed models, with the state nested within US Environmental Protection Agency regulatory region, and controls for population density, we find that the Dominican population is significantly less exposed to PM_2.5 and PM₁₀ compared to non-Hispanic Whites. Moreover, those tracts with a higher proportion of Cuban residents are significantly less exposed to PM_2.5. However, those tracts with a higher proportion of foreign-born, Mexicans, and Puerto Ricans had significantly higher levels of exposure to all sizes of particulate matter. We discuss the need to consider the chemical components of these particles to better understand the risk of exposure to air pollution.

Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors

The intrinsic metabolic clearance rate (Cl_int) and the fraction of the chemical unbound in plasma (f_up) serve as important parameters for high-throughput toxicokinetic (TK) models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under the U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on bioactivity/exposure ratios (BERs), in which a BER < 1 indicates that exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6484 chemicals), we found that the proportion of chemicals with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848, 90.4%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

An Overview of Per- and Polyfluoroalkyl Substances (PFAS) in the Environment: Source, Fate, Risk and Regulations

The current article reviews the state of art of the perfluoroalkyl and polyfluoroalkyl substances (PFASs) compounds and provides an overview of PFASs occurrence in the environment, wildlife, and humans. This study reviews the issues concerning PFASs exposure and potential risks generated with a focus on PFAS occurrence and transformation in various media, discusses their physicochemical characterization and treatment technologies, before discussing the potential human exposure routes. The various toxicological impacts to human health are also discussed. The article pays particular attention to the complexity and challenging issue of regulating PFAS compounds due to the arising uncertainty and lack of epidemiological evidence encountered. The variation in PFAS regulatory values across the globe can be easily addressed due to the influence of multiple scientific, technical, and social factors. The varied toxicology and the insufficient definition of PFAS exposure rate are among the main factors contributing to this discrepancy. The lack of proven standard approaches for examining PFAS in surface water, groundwater, wastewater, or solids adds more technical complexity. Although it is agreed that PFASs pose potential health risks in various media, the link between the extent of PFAS exposure and the significance of PFAS risk remain among the evolving research areas. There is a growing need to address the correlation between the frequency and the likelihood of human exposure to PFAS and the possible health risks encountered. Although USEPA (United States Environmental Protection Agency) recommends the 70 ng/L lifetime health advisory in drinking water for both perfluorooctane sulfonate (PFO) perfluorooctanoic acid (PFOA), which is similar to the Australian regulations, the German Ministry of Health proposed a health-based guidance of maximum of 300 ng/L for the combination of PFOA and PFOS. Moreover, there are significant discrepancies among the US states where the water guideline levels for the different states ranged from 13 to 1000 ng L−1 for PFOA and/or PFOS. The current review highlighted the significance of the future research required to fill in the knowledge gap in PFAS toxicology and to better understand this through real field data and long-term monitoring programs.

Combined effects of occupational exposure to hazardous operations and lifestyle-related factors on cancer incidence

We aimed to examine whether the number of types of hazardous operations at work experienced through a lifetime is associated with cancer incidence, and additionally examined the combined effects with lifestyle‐related factors. Using a nationwide, multicenter, hospital inpatient dataset (2005‐2015), we conducted a matched case‐control study with 1 149 296 study subjects. We classified the participants into those with none, 1, or 2 or more types of hazardous operation experience, based on information of special medical examinations taken, mandatory in Japan for workers engaged in hazardous operations. Using those with no experience as the reference group, we estimated the odds ratios for cancer incidence (all sites, lung, stomach, colon and rectum, liver, pancreas, bile duct, and bladder) by conditional logistic regression with multiple imputations. We also examined the effects of the combination with hazardous operations and lifestyle‐related factors. We observed increased risks for cancer of all sites, and lung, pancreas, and bladder cancer associated with the experience of hazardous operations. Multivariable‐adjusted ORs (95% CIs) of cancer incidence of all sites were 1 (reference), 1.16 (1.12, 1.21), and 1.17 (1.08, 1.27) for none, 1, and 2 or more types of hazardous operation experience, respectively (P for trend <.001). Potential combined associations of hazardous operations with smoking were observed for lung, pancreas, and bladder cancer, and with diabetes for pancreas cancer. Engaging in hazardous operations at work and in combination with lifestyle‐related factors may increase the risk of cancer. We highlight the potential for those engaged in hazardous work to avoid preventable cancers.

Analysis of consumer behavior for the estimation of the exposure to chemicals in personal care products

In this study, the main objective was to implement an integrative modelling framework in order to support the prioritization and screening of chemicals present in personal care products (PCPs) regarding their potential to expose users across multiple possible pathways. Here, we implemented an exposure-based framework based on product intake fractions (PiFs) calculated using a two-compartment model reproducing the skin uptake and the competing volatilization of chemicals applied on skin during PCP use. The implemented framework enabled to simultaneously and comprehensively accommodate coupled chemical specific parameters (i.e. physical and chemical properties of the candidate chemicals), exposure information specific for product-chemical combinations, and survey data informing on consumer behavior. A case-study, based on the usage pattern data of 22 PCPs investigated among Swiss individuals (Garcia-Hidalgo et al., 2017a) and 113 candidate chemicals chosen for their suspected presence in the PCP categories of interest was defined to evaluate the applicability of the framework. Nonnegative matrix factorization (NMF) and hierarchical clustering were subsequently applied to identify chemicals with the highest exposure potential and to highlight most relevant mixtures of chemicals on the basis of the specific usage patterns of the considered survey individuals.

Application of a combined aggregate exposure pathway and adverse outcome pathway (AEP-AOP) approach to inform a cumulative risk assessment: A case study with phthalates

Advancements in measurement and modeling capabilities are providing unprecedented access to estimates of chemical exposure and bioactivity. With this influx of new data, there is a need for frameworks that help organize and disseminate information on chemical hazard and exposure in a manner that is accessible and transparent. A case study approach was used to demonstrate integration of the Adverse Outcome Pathway (AOP) and Aggregate Exposure Pathway (AEP) frameworks to support cumulative risk assessment of co-exposure to two phthalate esters that are ubiquitous in the environment and that are associated with disruption of male sexual development in the rat: di(2-ethylhexyl) phthalate (DEHP) and di-n-butyl phthalate (DnBP). A putative AOP was developed to guide selection of an in vitro assay for derivation of bioactivity values for DEHP and DnBP and their metabolites. AEPs for DEHP and DnBP were used to extract key exposure data as inputs for a physiologically based pharmacokinetic (PBPK) model to predict internal metabolite concentrations. These metabolite concentrations were then combined using in vitro-based relative potency factors for comparison with an internal dose metric, resulting in an estimated margin of safety of ~13,000. This case study provides an adaptable workflow for integrating exposure and toxicity data by coupling AEP and AOP frameworks and using in vitro and in silico methodologies for cumulative risk assessment.

Integrating Exposure Knowledge and Serum Suspect Screening as a New Approach to Biomonitoring: An Application in Firefighters and Office Workers

Firefighters (FF) are exposed to recognized and probable carcinogens, yet there are few studies of chemical exposures and associated health concerns in women FFs, such as breast cancer. Biomonitoring often requires a priori selection of compounds to be measured, and so, it may not detect relevant, lesser known, exposures. The Women FFs Biomonitoring Collaborative (WFBC) created a biological sample archive and conducted a general suspect screen (GSS) to address this data gap. Using liquid chromatography-quadrupole time-of-flight tandem mass spectrometry, we sought to identify candidate chemicals of interest in serum samples from 83 women FFs and 79 women office workers (OW) in San Francisco. We identified chemical peaks by matching accurate mass from serum samples against a custom chemical database of 722 slightly polar phenolic and acidic compounds, including many of relevance to firefighting or breast cancer etiology. We then selected tentatively identified chemicals for confirmation based on the following criteria: (1) detection frequency or peak area differences between OW and FF; (2) evidence of mammary carcinogenicity, estrogenicity, or genotoxicity; and (3) not currently measured in large biomonitoring studies. We detected 620 chemicals that matched 300 molecular formulas in the WFBC database, including phthalate metabolites, phosphate flame-retardant metabolites, phenols, pesticides, nitro and nitroso compounds, and per- and polyfluoroalkyl substances. Of the 20 suspect chemicals selected for validation, 8 were confirmed-including two alkylphenols, ethyl paraben, BPF, PFOSAA, benzophenone-3, benzyl p-hydroxybenzoate, and triphenyl phosphate-by running a matrix spike of the reference standards and using m/z, retention time, and the confirmation of at least two fragment ions as criteria for matching. GSS provides a powerful high-throughput approach to identify and prioritize novel chemicals for biomonitoring and health studies.

Toward a Global Understanding of Chemical Pollution: A First Comprehensive Analysis of National and Regional Chemical Inventories

Chemicals, while bringing benefits to society, may be released during their lifecycles and possibly cause harm to humans and ecosystems. Chemical pollution has been mentioned as one of the planetary boundaries within which humanity can safely operate, but is not comprehensively understood. Here, 22 chemical inventories from 19 countries and regions are analyzed to achieve a first comprehensive overview of chemicals on the market as an essential first step toward a global understanding of chemical pollution. Over 350 000 chemicals and mixtures of chemicals have been registered for production and use, up to three times as many as previously estimated and with substantial differences across countries/regions. A noteworthy finding is that the identities of many chemicals remain publicly unknown because they are claimed as confidential (over 50 000) or ambiguously described (up to 70 000). Coordinated efforts by all stakeholders including scientists from different disciplines are urgently needed, with (new) areas of interest and opportunities highlighted here.

Review of the environmental prenatal exposome and its relationship to maternal and fetal health

Environmental chemicals comprise a major portion of the human exposome, with some shown to impact the health of susceptible populations, including pregnant women and developing fetuses. The placenta and cord blood serve as important biological windows into the maternal and fetal environments. In this article we review how environmental chemicals (defined here to include man-made chemicals [e.g., flame retardants, pesticides/herbicides, per- and polyfluoroalkyl substances], toxins, metals, and other xenobiotic compounds) contribute to the prenatal exposome and highlight future directions to advance this research field. Our findings from a survey of recent literature indicate the need to better understand the breadth of environmental chemicals that reach the placenta and cord blood, as well as the linkages between prenatal exposures, mechanisms of toxicity, and subsequent health outcomes. Research efforts tailored towards addressing these needs will provide a more comprehensive understanding of how environmental chemicals impact maternal and fetal health.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼ 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

Learning from the past and considering the future of chemicals in the environment

Knowledge of the hazards and associated risks from chemicals discharged to the environment has grown considerably over the past 40 years. This improving awareness stems from advances in our ability to measure chemicals at low environmental concentrations, recognition of a range of effects on organisms, and a worldwide growth in expertise. Environmental scientists and companies have learned from the experiences of the past; in theory, the next generation of chemicals will cause less acute toxicity and be less environmentally persistent and bioaccumulative. However, researchers still struggle to establish whether the nonlethal effects associated with some modern chemicals and substances will have serious consequences for wildlife. Obtaining the resources to address issues associated with chemicals in the environment remains a challenge.

Prediction of Acute Oral Systemic Toxicity Using a Multifingerprint Similarity Approach

The implementation of nonanimal approaches is of particular importance to regulatory agencies for the prediction of potential hazards associated with acute exposures to chemicals. This work was carried out in the framework of an international modeling initiative organized by the Acute Toxicity Workgroup (ATWG) of the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) with the participation of 32 international groups across government, industry, and academia. Our contribution was to develop a multifingerprints similarity approach for predicting five relevant toxicology endpoints related to the acute oral systemic toxicity that are: the median lethal dose (LD50) point prediction, the "nontoxic" (LD50 > 2000 mg/kg) and "very toxic" (LD50<50 mg/kg) binary classification, and the multiclass categorization of chemicals based on the United States Environmental Protection Agency and Globally Harmonized System of Classification and Labeling of Chemicals schemes. Provided by the ICCVAM's ATWG, the training set used to develop the models consisted of 8944 chemicals having high-quality rat acute oral lethality data. The proposed approach integrates the results coming from a similarity search based on 19 different fingerprint definitions to return a consensus prediction value. Moreover, the herein described algorithm is tailored to properly tackling the so-called toxicity cliffs alerting that a large gap in LD50 values exists despite a high structural similarity for a given molecular pair. An external validation set made available by ICCVAM and consisting in 2896 chemicals was employed to further evaluate the selected models. This work returned high-accuracy predictions based on the evaluations conducted by ICCVAM's ATWG.

Advances on a Decision Analytic Approach to Exposure-Based Chemical Prioritization

The volume and variety of manufactured chemicals is increasing, although little is known about the risks associated with the frequency and extent of human exposure to most chemicals. The EPA and the recent signing of the Lautenberg Act have both signaled the need for high-throughput methods to characterize and screen chemicals based on exposure potential, such that more comprehensive toxicity research can be informed. Prior work of Mitchell et al. using multicriteria decision analysis tools to prioritize chemicals for further research is enhanced here, resulting in a high-level chemical prioritization tool for risk-based screening. Reliable exposure information is a key gap in currently available engineering analytics to support predictive environmental and health risk assessments. An elicitation with 32 experts informed relative prioritization of risks from chemical properties and human use factors, and the values for each chemical associated with each metric were approximated with data from EPA's CP_CAT database. Three different versions of the model were evaluated using distinct weight profiles, resulting in three different ranked chemical prioritizations with only a small degree of variation across weight profiles. Future work will aim to include greater input from human factors experts and better define qualitative metrics.

Inventory and evaluation of publicly available sources of information on hazards and risks of industrial chemicals

Regulatory authorities from developing countries have expressed a need for guidance in locating environmental, health and safety (EHS) information on industrial chemicals. In response, possible sources were identified via a search of the Internet using relevant terms and by soliciting suggestions from more than 200 knowledgeable stakeholders. This initially identified greater than 100 databases, 41 of which were chosen for further profiling and analysis based on their size and comprehensiveness. They were divided for analysis into three distinct groups: (1) data portals that provide information seekers with an efficient simultaneous search of multiple, third-party owned and maintained databases; (2) primary EHS information sources; and (3) databases that provide only EHS-type regulatory decisions but not raw data. Descriptive evaluations of each database were performed, including: (1) scope; (2) ease of access and use; (3) breadth and depth of EHS information available; (4) quality of the underlying information; and (5) procedures to keep the information current. We conclude that, although there exists EHS information to support screening level hazard and risk assessment for the majority of the highest production volume chemicals, information gaps for lower production volume chemicals persist, and Confidential Business Information claims for some chemicals can limit the information available to the general public. A lack of information on uses and exposures to chemicals, particularly in developing countries is especially challenging. Nevertheless, there are reasons (e.g. advances in regulations, marketplace pressures, and computational toxicology science) to be optimistic that going forward information gaps can be closed at an accelerated rate.

Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization

High(er) throughput toxicokinetics (HTTK) encompasses in vitro measures of key determinants of chemical toxicokinetics and reverse dosimetry approaches for in vitro-in vivo extrapolation (IVIVE). With HTTK, the bioactivity identified by any in vitro assay can be converted to human equivalent doses and compared with chemical intake estimates. Biological variability in HTTK has been previously considered, but the relative impact of measurement uncertainty has not. Bayesian methods were developed to provide chemical-specific uncertainty estimates for 2 in vitro toxicokinetic parameters: unbound fraction in plasma (fup) and intrinsic hepatic clearance (Clint). New experimental measurements of fup and Clint are reported for 418 and 467 chemicals, respectively. These data raise the HTTK chemical coverage of the ToxCast Phase I and II libraries to 57%. Although the standard protocol for Clint was followed, a revised protocol for fup measured unbound chemical at 10%, 30%, and 100% of physiologic plasma protein concentrations, allowing estimation of protein binding affinity. This protocol reduced the occurrence of chemicals with fup too low to measure from 44% to 9.1%. Uncertainty in fup was also reduced, with the median coefficient of variation dropping from 0.4 to 0.1. Monte Carlo simulation was used to propagate both measurement uncertainty and biological variability into IVIVE. The uncertainty propagation techniques used here also allow incorporation of other sources of uncertainty such as in silico predictors of HTTK parameters. These methods have the potential to inform risk-based prioritization based on the relationship between in vitro bioactivities and exposures.

EPA's DSSTox database: History of development of a curated chemistry resource supporting computational toxicology research

The US Environmental Protection Agency's (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database, launched publicly in 2004, currently exceeds 875 K substances spanning hundreds of lists of interest to EPA and environmental researchers. From its inception, DSSTox has focused curation efforts on resolving chemical identifier errors and conflicts in the public domain towards the goal of assigning accurate chemical structures to data and lists of importance to the environmental research and regulatory community. Accurate structure-data associations, in turn, are necessary inputs to structure-based predictive models supporting hazard and risk assessments. In 2014, the legacy, manually curated DSSTox_V1 content was migrated to a MySQL data model, with modern cheminformatics tools supporting both manual and automated curation processes to increase efficiencies. This was followed by sequential auto-loads of filtered portions of three public datasets: EPA's Substance Registry Services (SRS), the National Library of Medicine's ChemID, and PubChem. This process was constrained by a key requirement of uniquely mapped identifiers (i.e., CAS RN, name and structure) for each substance, rejecting content where any two identifiers were conflicted either within or across datasets. This rejected content highlighted the degree of conflicting, inaccurate substance-structure ID mappings in the public domain, ranging from 12% (within EPA SRS) to 49% (across ChemID and PubChem). Substances successfully added to DSSTox from each auto-load were assigned to one of five qc_levels, conveying curator confidence in each dataset. This process enabled a significant expansion of DSSTox content to provide better coverage of the chemical landscape of interest to environmental scientists, while retaining focus on the accuracy of substance-structure-data associations. Currently, DSSTox serves as the core foundation of EPA's CompTox Chemicals Dashboard [https://comptox.epa.gov/dashboard], which provides public access to DSSTox content in support of a broad range of modeling and research activities within EPA and, increasingly, across the field of computational toxicology.

Progress in data interoperability to support computational toxicology and chemical safety evaluation

New approach methodologies (NAMs) in chemical safety evaluation are being explored to address the current public health implications of human environmental exposures to chemicals with limited or no data for assessment. For over a decade since a push toward "Toxicity Testing in the 21st Century," the field has focused on massive data generation efforts to inform computational approaches for preliminary hazard identification, adverse outcome pathways that link molecular initiating events and key events to apical outcomes, and high-throughput approaches to risk-based ratios of bioactivity and exposure to inform relative priority and safety assessment. Projects like the interagency Tox21 program and the US EPA ToxCast program have generated dose-response information on thousands of chemicals, identified and aggregated information from legacy systems, and created tools for access and analysis. The resulting information has been used to develop computational models as viable options for regulatory applications. This progress has introduced challenges in data management that are new, but not unique, to toxicology. Some of the key questions require critical thinking and solutions to promote semantic interoperability, including: (1) identification of bioactivity information from NAMs that might be related to a biological process; (2) identification of legacy hazard information that might be related to a key event or apical outcomes of interest; and, (3) integration of these NAM and traditional data for computational modeling and prediction of complex apical outcomes such as carcinogenesis. This work reviews a number of toxicology-related efforts specifically related to bioactivity and toxicological data interoperability based on the goals established by Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles. These efforts are essential to enable better integration of NAM and traditional toxicology information to support data-driven toxicology applications.

Developing novel in vitro methods for the risk assessment of developmental and placental toxicants in the environment

During pregnancy, the placenta is critical for the regulation of maternal homeostasis and fetal growth and development. Exposures to environmental chemicals during pregnancy can be detrimental to the health of the placenta and therefore adversely impact maternal and fetal health. Though research on placental-derived developmental toxicity is expanding, testing is limited by the resources required for traditional test methods based on whole animal experimentation. Alternative strategies utilizing in vitro methods are well suited to contribute to more efficient screening of chemical toxicity and identification of biological mechanisms underlying toxicity outcomes. This review aims to summarize methods that can be used to evaluate toxicity resulting from exposures during the prenatal period, with a focus on newer in vitro methods centered on placental toxicity. The following key aspects are reviewed: (i) traditional test methods based on animal developmental toxicity testing, (ii) in vitro methods using monocultures and explant models, as well as more recently developed methods, including co-cultures, placenta-on-a-chip, and 3-dimensional (3D) cell models, (iii) endpoints that are commonly measured using in vitro designs, and (iv) the translation of in vitro methods into chemical evaluations and risk assessment applications. We conclude that findings from in vitro placental models can contribute to the screening of potentially hazardous chemicals, elucidation of chemical mechanism of action, incorporation into adverse outcome pathways, estimation of doses eliciting toxicity, derivation of extrapolation factors, and characterization of overall risk of adverse outcomes, representing key components of chemical regulation in the 21^st century.