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Exposure to environmental chemicals can impair neurodevelopment1–4. Oligodendrocytes that wrap around axons to boost neurotransmission may be particularly vulnerable to chemical toxicity as they develop throughout fetal development and into adulthood5,6. However, few environmental chemicals have been assessed for potential risks to oligodendrocyte development. Here, we utilized a high-throughput developmental screen and human cortical brain organoids, which revealed environmental chemicals in two classes that disrupt oligodendrocyte development through distinct mechanisms. Quaternary compounds, ubiquitous in disinfecting agents, hair conditioners, and fabric softeners, were potently and selectively cytotoxic to developing oligodendrocytes through activation of the integrated stress response. Organophosphate flame retardants, commonly found in household items such as furniture and electronics, were non-cytotoxic but prematurely arrested oligodendrocyte maturation. Chemicals from each class impaired human oligodendrocyte development in a 3D organoid model of prenatal cortical development. In analysis of epidemiological data from the CDC’s National Health and Nutrition Examination Survey, adverse neurodevelopmental outcomes were associated with childhood exposure to the top organophosphate flame retardant identified by our oligodendrocyte toxicity platform. Collectively, our work identifies toxicological vulnerabilities specific to oligodendrocyte development and highlights common household chemicals with high exposure risk to children that warrant deeper scrutiny for their impact on human health.

We hypothesized that typical tissue and clinical chemistry (ClinChem) end points measured in rat toxicity studies exhibit chemical-independent biological thresholds beyond which cancer occurs. Using the rat in vivo TG-GATES study, 75 chemicals were examined across chemical-dose-time comparisons that could be linked to liver tumor outcomes. Thresholds for liver weight to body weight (LW/BW) and 21 serum ClinChem end points were defined as the maximum and minimum values for those exposures that did not lead to liver tumors in rats. Upper thresholds were identified for LW/BW (117%), aspartate aminotransferase (195%), alanine aminotransferase (141%), alkaline phosphatase (152%), and total bilirubin (115%), and lower thresholds were identified for phospholipids (82%), relative albumin (93%), total cholesterol (82%), and total protein (94%). Thresholds derived from the TG-GATES data set were consistent across other acute and subchronic rat studies. A training set of ClinChem and LW/BW thresholds derived from a 38 chemical training set from TG-GATES was predictive of liver tumor outcomes for a test set of 37 independent TG-GATES chemicals (91%). The thresholds were most predictive when applied to 7d treatments (98%). These findings provide support that biological thresholds for common end points in rodent studies can be used to predict chemical tumorigenic potential.

In vivo developmental neurotoxicity (DNT) testing is resource intensive and lacks information on cellular processes affected by chemicals. To address this, DNT new approach methodologies (NAMs) are being evaluated, including: the microelectrode array neuronal network formation assay; and high-content imaging to evaluate proliferation, apoptosis, neurite outgrowth, and synaptogenesis. This work addresses 3 hypotheses: (1) a broad screening battery provides a sensitive marker of DNT bioactivity; (2) selective bioactivity (occurring at noncytotoxic concentrations) may indicate functional processes disrupted; and, (3) a subset of endpoints may optimally classify chemicals with in vivo evidence for DNT. The dataset was comprised of 92 chemicals screened in all 57 assay endpoints sourced from publicly available data, including a set of DNT NAM evaluation chemicals with putative positives (53) and negatives (13). The DNT NAM battery provides a sensitive marker of DNT bioactivity, particularly in cytotoxicity and network connectivity parameters. Hierarchical clustering suggested potency (including cytotoxicity) was important for classifying positive chemicals with high sensitivity (93%) but failed to distinguish patterns of disrupted functional processes. In contrast, clustering of selective values revealed informative patterns of differential activity but demonstrated lower sensitivity (74%). The false negatives were associated with several limitations, such as the maximal concentration tested or gaps in the biology captured by the current battery. This work demonstrates that this multi-dimensional assay suite provides a sensitive biomarker for DNT bioactivity, with selective activity providing possible insight into specific functional processes affected by chemical exposure and a basis for further research.

Although evidence linking environmental chemicals to breast cancer is growing, mixtures-based exposure evaluations are lacking.This study aimed to identify environmental chemicals in use inventories that co-occur and share properties with chemicals that have association with breast cancer, highlighting exposure combinations that may alter disease risk.The occurrence of chemicals within chemical use categories was characterized using the Chemical and Products Database. Co-exposure patterns were evaluated for chemicals that have an association with breast cancer (BC), no known association (NBC), and understudied chemicals (UC) identified through query of the Silent Spring Institute's Mammary Carcinogens Review Database and the U.S. Environmental Protection Agency's Toxicity Reference Database. UCs were ranked based on structure and physicochemical similarities and co-occurrence patterns with BCs within environmentally relevant exposure sources.A total of 6793 chemicals had data available for exposure source occurrence analyses. 50 top-ranking UCs spanning five clusters of co-occurring chemicals were prioritized, based on shared properties with co-occuring BCs, including chemicals used in food production and consumer/personal care products, as well as potential endocrine system modulators.Results highlight important co-exposure conditions that are likely prevalent within our everyday environments that warrant further evaluation for possible breast cancer risk.Most environmental studies on breast cancer have focused on evaluating relationships between individual, well-known chemicals and breast cancer risk. This study set out to expand this research field by identifying understudied chemicals and mixtures that may occur in everyday environments due to their patterns of commercial use. Analyses focused on those that co-occur alongside chemicals associated with breast cancer, based upon in silico chemical database querying and analysis. Particularly in instances when understudied chemicals share physicochemical properties and structural features with carcinogens, these chemical mixtures represent conditions that should be studied in future clinical, epidemiological, and toxicological studies.

DL-glufosinate ammonium (DL-GLF) is a registered herbicide for which a guideline Developmental Neurotoxicity (DNT) study has been conducted. Offspring effects included altered brain morphometrics, decreased body weight, and increased motor activity. Guideline DNT studies are not available for its enriched isomers L-GLF acid and L-GLF ammonium; conducting one would be time consuming, resource-intensive, and possibly redundant given the existing DL-GLF DNT. To support deciding whether to request a guideline DNT study for the L-GLF isomers, DL-GLF and the L-GLF isomers were screened using in vitro assays for network formation and neurite outgrowth. DL-GLF and L-GLF isomers were without effects in both assays. DL-GLF and L-GLF (1-100 μM) isomers increased mean firing rate of mature networks to 120-140% of baseline. In vitro toxicokinetic assessments were used to derive administered equivalent doses (AEDs) for the in vitro testing concentrations. The AED for L-GLF was ∼3X higher than the NOAEL from the DL-GLF DNT indicating that the available guideline study would be protective of potential DNT due to L-GLF exposure. Based in part on the results of these in vitro studies, EPA is not requiring L-GLF isomer guideline DNT studies, thereby providing a case study for a useful application of DNT screening assays.

Summary Many applications of chemical screening are performed in concentration or dose–response mode, and it is necessary to extract appropriate parameters, including whether the chemical/assay pair is active and if so, what are concentrations where activity is seen. Typically, multiple mathematical models or curve shapes are tested against the data to assess the best fit. There are several commercial programs used for this purpose as well as open-source libraries. A widely used system for managing high-throughput screening (HTS) concentration–response data is tcpl (ToxCast Pipeline). The current implementation of tcpl has the concentration–response modeling code tightly integrated with the data management and databasing aspects of HTS data processing. Tcplfit2 is a stand-alone version of the curve-fitting and hitcalling core of tcpl that has been extended to include a large number of standard curve classes and to use benchmark dose modeling. This package will be useful for HTS concentration–response data such as high-throughput whole genome transcriptomics. Availability and implementation tcplfit2 is written in R and is available from CRAN.

Several primary sources of publicly available, quantitative dose-response data from traditional toxicology study designs relevant to predictive toxicology applications are now available, including the redeveloped U.S. Environmental Protection Agency’s Toxicity Reference Database (ToxRefDB v2.0), the Health Assessment Workspace Collaborative (HAWC), and the National Toxicology Program’s Chemical Program’s Chemical Effects in Biological Systems (CEBS). These resources provide effect level information but modeling these data to a curve may be more informative for predictive toxicology applications. Benchmark Dose Software (BMDS) has been recognized broadly and used for regulatory applications at multiple agencies. However, the current BMDS software was not amenable to modeling large datasets. Herein we describe development and use of a Python package that implements a wrapper around BMDS, a software that requires manual input in the dose-response modeling process (i.e., best-fitting model-selection, reporting, and dose-dropping). In the Python BMDS, users can select the BMDS version, customize model recommendation logic, and export summaries of the resultant BMDS output. Further, using the Python interface, a web-based application programming interface (API) has been developed for easy integration into other software systems, pipelines, or databases. Software utility was demonstrated via modeling nearly 28,000 datasets in ToxRefDB v2.0, re-creation of an existing, published large-scale analysis, and demonstration of usage in software such as CEBS and HAWC. Python BMDS enables rapid-batch processing of dose-response datasets using a modeling software with broad acceptance in the toxicology community, thereby providing an important tool for leveraging the publicly available quantitative toxicology data in a reproducible manner.

Acceptance of new approach methodologies (NAMs) for use in characterizing chemical hazard and risk requires informed expectations regarding the minimum precision and maximum accuracy of their results. Uncertainty in NAMs derived from variability in the traditional reference data used to train or validate performance of the NAM, and uncertainty in the modeling procedures themselves, limits NAM performance. Herein, we review current approaches to characterizing uncertainty in NAMs. We discuss variability in in vivo data used as a reference for NAM development and validation; the quantitative uncertainty in concentration–response modeling for high-throughput in vitro bioactivity screening; the uncertainties associated with in vitro to in vivo extrapolation using toxicokinetic information; and the quantitative uncertainty in the experimental inputs and modeled outputs from quantitative structure activity relationship models for prediction of point of departure doses. Communication of the amount of uncertainty, both in the input and output for NAMs, often involves a confidence or prediction interval around a given potency estimate, derived from an understanding of the variability in the data modeled. Tuning expectations of NAM performance to an understanding of the reproducibility and variability, both of traditional approaches and NAM approaches, provides a path for the adoption of NAMs as alternatives in screening chemicals for risk.

Thousands of chemicals to which humans are potentially exposed have not been evaluated for potential developmental neurotoxicity (DNT), driving efforts to develop a battery of in vitro screening approaches for DNT hazard. Here, 136 unique chemicals were evaluated for potential DNT hazard using a network formation assay (NFA) in cortical cells grown on microelectrode arrays. The effects of chemical exposure from 2 h postplating through 12 days in vitro (DIV) on network formation were evaluated at DIV 5, 7, 9, and 12, with cell viability assessed at DIV 12. Only 82 chemicals altered at least 1 network development parameter. Assay results were reproducible; 10 chemicals tested as biological replicates yielded qualitative results that were 100% concordant, with consistent potency values. Toxicological tipping points were determined for 58 chemicals and were similar to or lower than the lowest 50% effect concentrations (EC50) for all parameters. When EC50 and tipping point values from the NFA were compared to the range of potencies observed in ToxCast assays, the NFA EC50 values were less than the lower quartile for ToxCast assay potencies for a subset of chemicals, many of which are acutely neurotoxic in vivo. For 13 chemicals with available in vivo DNT data, estimated administered equivalent doses based on NFA results were similar to or lower than administered doses in vivo. Collectively, these results indicate that the NFA is sensitive to chemicals acting on nervous system function and will be a valuable contribution to an in vitro DNT screening battery.

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.

To date, approximately 200 chemicals have been tested in US Environmental Protection Agency (EPA) or Organization for Economic Co-operation and Development (OECD) developmental neurotoxicity (DNT) guideline studies, leaving thousands of chemicals without traditional animal information on DNT hazard potential. To address this data gap, a battery of in vitro DNT new approach methodologies (NAMs) has been proposed. Evaluation of the performance of this battery will increase the confidence in its use to determine DNT chemical hazards. One approach to evaluate DNT NAM performance is to use a set of chemicals to evaluate sensitivity and specificity. Since a list of chemicals with potential evidence of in vivo DNT has been established, this study aims to develop a curated list of "negative" chemicals for inclusion in a "DNT NAM evaluation set". A workflow, including a literature search followed by an expert-driven literature review, was used to systematically screen 39 chemicals for lack of DNT effect. Expert panel members evaluated the scientific robustness of relevant studies to inform chemical categorizations. Following review, the panel discussed each chemical and made categorical determinations of "Favorable", "Not Favorable", or "Indeterminate" reflecting acceptance, lack of suitability, or uncertainty given specific limitations and considerations, respectively. The panel determined that 10, 22, and 7 chemicals met the criteria for "Favorable", "Not Favorable", and "Indeterminate", for use as negatives in a DNT NAM evaluation set. Ultimately, this approach not only supports DNT NAM performance evaluation but also highlights challenges in identifying large numbers of negative DNT chemicals.

New approach methodologies (NAMs) for chemical hazard assessment are often evaluated via comparison to animal studies; however, variability in animal study data limits NAM accuracy. The US EPA Toxicity Reference Database (ToxRefDB) enables consideration of variability in effect levels, including the lowest effect level (LEL) for a treatment-related effect and the lowest observable adverse effect level (LOAEL) defined by expert review, from subacute, subchronic, chronic, multi-generation reproductive, and developmental toxicity studies. The objectives of this work were to quantify the variance within systemic LEL and LOAEL values, defined as potency values for effects in adult or parental animals only, and to estimate the upper limit of NAM prediction accuracy. Multiple linear regression (MLR) and augmented cell means (ACM) models were used to quantify the total variance, and the fraction of variance in systemic LEL and LOAEL values explained by available study descriptors (e.g., administration route, study type). The MLR approach considered each study descriptor as an independent contributor to variance, whereas the ACM approach combined categorical descriptors into cells to define replicates. Using these approaches, total variance in systemic LEL and LOAEL values (in log(10)-mg/kg/day units) ranged from 0.74 to 0.92. Unexplained variance in LEL and LOAEL values, approximated by the residual mean square error (MSE), ranged from 0.20–0.39. Considering subchronic, chronic, or developmental study designs separately resulted in similar values. Based on the relationship between MSE and R-squared for goodness-of-fit, the maximal R-squared may approach 55 to 73% for a NAM-based predictive model of systemic toxicity using these data as reference. The root mean square error (RMSE) ranged from 0.47 to 0.63 log(10)-mg/kg/day, depending on dataset and regression approach, suggesting that a two-sided minimum prediction interval for systemic effect levels may have a width of 58 to 284-fold. These findings suggest quantitative considerations for building scientific confidence in NAM-based systemic toxicity predictions.

Phenotypic profiling assays are untargeted screening assays that measure a large number (hundreds to thousands) of cellular features in response to a stimulus and often yield diverse and unanticipated profiles of phenotypic effects, leading to challenges in distinguishing active from inactive treatments. Here, we compare a variety of different strategies for hit identification in imaging-based phenotypic profiling assays using a previously published Cell Painting data set. Hit identification strategies based on multiconcentration analysis involve curve fitting at several levels of data aggregation (e.g., individual feature level, aggregation of similarly derived features into categories, and global modeling of all features) and on computed metrics (e.g., Euclidean and Mahalanobis distance metrics and eigenfeatures). Hit identification strategies based on single-concentration analysis included measurement of signal strength (e.g., total effect magnitude) and correlation of profiles among biological replicates. Modeling parameters for each approach were optimized to retain the ability to detect a reference chemical with subtle phenotypic effects while limiting the false-positive rate to 10%. The percentage of test chemicals identified as hits was highest for feature-level and category-based approaches, followed by global fitting, whereas signal strength and profile correlation approaches detected the fewest number of active hits at the fixed false-positive rate. Approaches involving fitting of distance metrics had the lowest likelihood for identifying high-potency false-positive hits that may be associated with assay noise. Most of the methods achieved a 100% hit rate for the reference chemical and high concordance for 82% of test chemicals, indicating that hit calls are robust across different analysis approaches.

Mitochondrial toxicity drives several adverse health outcomes. Current high-throughput screening assays for chemically induced mitochondrial toxicity typically measure changes to mitochondrial structure and may not detect known mitochondrial toxicants. We adapted a respirometric screening assay (RSA) measuring mitochondrial function to screen ToxCast chemicals in HepG2 cells using a tiered testing strategy. Of 1042 chemicals initially screened at a singlemaximal concentration, 243 actives were identified and rescreened at 7 concentrations. Concentration-response data for 3 respiration phases confirmed activity and indicated a mechanism for 193 mitochondrial toxicants: 149 electron transport chain inhibitors (ETCi), 15 uncouplers and 29 adenosine triphosphate synthase inhibitors. Subsequently, an electron flow assay was used to identify the target complex for 84 of the 149 ETCi. Sixty reference chemicals were used to compare the RSA to existing ToxCast and Tox21 mitochondrial toxicity assays. The RSA was most predictive (accuracy = 90%) of mitochondrial toxicity. The Tox21 mitochondrial membrane potential assay was also highly predictive (accuracy = 87%) of bioactivity but underestimated the potency of well-known ETCi and provided no mechanistic information. The tiered RSA approach accurately identifies and characterizes mitochondrial toxicants acting through diverse mechanisms and at a throughput sufficient to screen large chemical inventories. The electron flow assay provides additional confirmation and detailed mechanistic understanding for ETCi, the most common type of mitochondrial toxicants among ToxCast chemicals. The mitochondrial toxicity screening approach described herein may inform hazard assessment and the in vitro bioactive concentrations used to derive relevant doses for screening level chemical assessment using new approach methodologies.

Thyroperoxidase (TPO) is an enzyme essential for thyroid hormone (TH) synthesis and a target site for a number of xenobiotics that disrupt TH homeostasis. An in vitro high-throughput screening assay for TPO inhibition, the Amplex UltraRed-TPO (AUR-TPO), has been used to screen the ToxCast chemical libraries for this action. Output from this assay would be most useful if it could be readily translated into an in vivo response, namely a reduction of TH in serum. To this end, the relationship between TPO inhibition in vitro and serum TH decreases was examined in rats exposed to 2 classic TPO inhibitors, propylthiouracil (PTU) and methimazole (MMI). Serum and gland PTU, MMI, and TH levels were quantified using tandem liquid chromatography mass spectrometry. Thyroperoxidase activity was determined in thyroid gland microsomes treated with PTU or MMI in vitro and ex vivo from thyroid gland microsomes prepared from exposed animals. A quantitative model was constructed by contrasting in vitro and ex vivo AUR-TPO results and the in vivo time-course and dose-response analysis. In vitro:ex vivo correlations of AUR-TPO outputs indicated that less than 30% inhibition of TPO in vitro was sufficient to reduce serum T4 by 20%, a degree of regulatory significance. Although further testing of model estimates using other TPO inhibitors is essential for verification of these initial findings, the results of this study provide a means to translate in vitro screening assay results into predictions of in vivo serum T4 changes to inform risk assessment.

Advances in technology within biomedical sciences have led to an inundation of data across many fields, raising new challenges in how best to integrate and analyze these resources. For example, rapid chemical screening programs like the US Environmental Protection Agency’s ToxCast and the collaborative effort, Tox21, have produced massive amounts of information on putative chemical mechanisms where assay targets are identified as genes; however, systematically linking these hypothesized mechanisms with in vivo toxicity endpoints like disease outcomes remains problematic. Herein we present a novel use of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene associations with biological concepts as represented by Medical Subject Headings (MeSH terms) in PubMed. Resources that tag genes to articles were integrated, then cross-species orthologs were identified using UniRef50 clusters. MeSH term frequency was normalized to reflect the MeSH tree structure, and then the resulting GeneID-MeSH associations were ranked using NPMI. The resulting network, called Entity MeSH Co-occurrence Network (EMCON), is a scalable resource for the identification and ranking of genes for a given topic of interest. The utility of EMCON was evaluated with the use case of breast carcinogenesis. Topics relevant to breast carcinogenesis were used to query EMCON and retrieve genes important to each topic. A breast cancer gene set was compiled through expert literature review (ELR) to assess performance of the search results. We found that the results from EMCON ranked the breast cancer genes from ELR higher than randomly selected genes with a recall of 0.98. Precision of the top five genes for selected topics was calculated as 0.87. This work demonstrates that EMCON can be used to link in vitro results to possible biological outcomes, thus aiding in generation of testable hypotheses for furthering understanding of biological function and the contribution of chemical exposures to disease.

Concern that chemicals in the environment can disrupt the endocrine systems of humans and ecological species (fish, frogs, birds) has driven the development of bioassays to test for endocrine activity. In the last decade, there has been increased focus on in vitro and high-throughput screening (HTS) assays and in silico models that allow the rapid evaluation of hundreds to thousands of compounds. We review efforts at the US EPA and the National Toxicology Program to develop assays and models to predict estrogen, androgen and steroidogenesis activity. These approaches include in vitro HTS assays, computational models that link the results of multiple assays and reduce the impact of assay artifacts and noise, and structure-based QSAR models. These approaches have been shown to be robust enough to move from research to application in regulatory risk assessment.

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.

Synthesis of 11 steroid hormones in human adrenocortical carcinoma cells (H295R) was measured in a high-throughput steroidogenesis assay (HT-H295R) for 656 chemicals in concentration-response as part of the US Environmental Protection Agency's ToxCast program. This work extends previous analysis of the HT-H295R dataset and model by examining the utility of a novel prioritization metric based on the Mahalanobis distance that reduced these 11-dimensional data to 1-dimension via calculation of a mean Mahalanobis distance (mMd) at each chemical concentration screened for all hormone measures available. Herein, we evaluated the robustness of mMd values, and demonstrate that covariance and variance of the hormones measured appear independent of the chemicals screened and are inherent to the assay; the Type I error rate of the mMd method is less than 1%; and, absolute fold changes (up or down) of 1.5 to 2-fold have sufficient power for statistical significance. As a case study, we examined hormone responses for aromatase inhibitors in the HT-H295R assay and found high concordance with other ToxCast assays for known aromatase inhibitors. Finally, we used mMd and other ToxCast cytotoxicity data to demonstrate prioritization of the most selective and active chemicals as candidates for further in vitro or in silico screening.

The present study adapted an existing high content imaging-based high-throughput phenotypic profiling (HTPP) assay known as “Cell Painting” for bioactivity screening of environmental chemicals. This assay uses a combination of fluorescent probes to label a variety of organelles and measures a large number of phenotypic features at the single cell level in order to detect chemical-induced changes in cell morphology. First, a small set of candidate phenotypic reference chemicals (n = 14) known to produce changes in the cellular morphology of U-2 OS cells were identified and screened at multiple time points in concentration-response format. Many of these chemicals produced distinct cellular phenotypes that were qualitatively similar to those previously described in the literature. A novel workflow for phenotypic feature extraction, concentration-response modeling and determination of in vitro thresholds for chemical bioactivity was developed. Subsequently, a set of 462 chemicals from the ToxCast library were screened in concentration-response mode. Bioactivity thresholds were calculated and converted to administered equivalent doses (AEDs) using reverse dosimetry. AEDs were then compared to effect values from mammalian toxicity studies. In many instances (68%), the HTPP-derived AEDs were either more conservative than or comparable to the in vivo effect values. Overall, we conclude that the HTPP assay can be used as an efficient, cost-effective and reproducible screening method for characterizing the biological activity and potency of environmental chemicals for potential use in in vitro-based safety assessments.

Thyroid hormone receptors (TRs) are critical endocrine receptors that regulate a multitude of processes in adult and developing organisms, and thyroid hormone disruption is of high concern for neurodevelopmental and reproductive toxicities in particular. To date, only a small number of chemical classes have been identified as possible TR modulators, and the receptors appear highly selective with respect to the ligand structural diversity. Thus, the question of whether TRs are an important screening target for protection of human and wildlife health remains.Our goal was to evaluate the hypothesis that there is limited structural diversity among environmentally relevant chemicals capable of modulating TR activity via the collaborative interagency Tox21 project.We screened the Tox21 chemical library (8,305 unique structures) in a quantitative high-throughput, cell-based reporter gene assay for TR agonist or antagonist activity. Active compounds were further characterized using additional orthogonal assays, including mammalian one-hybrid assays, coactivator recruitment assays, and a high-throughput, fluorescent imaging, nuclear receptor translocation assay.Known agonist reference chemicals were readily identified in the TR transactivation assay, but only a single novel, direct agonist was found, the pharmaceutical betamipron. Indirect activation of TR through activation of its heterodimer partner, the retinoid-X-receptor (RXR), was also readily detected by confirmation in an RXR agonist assay. Identifying antagonists with high confidence was a challenge with the presence of significant confounding cytotoxicity and other, non-TR-specific mechanisms common to the transactivation assays. Only three pharmaceuticals-mefenamic acid, diclazuril, and risarestat-were confirmed as antagonists.The results support limited structural diversity for direct ligand effects on TR and imply that other potential target sites in the thyroid hormone axis should be a greater priority for bioactivity screening for thyroid axis disruptors. https://doi.org/10.1289/EHP5314.

Background: Extensive clinical and experimental research documents the potential for chemical disruption of thyroid hormone (TH) signaling through multiple molecular targets. Perturbation of TH signaling can lead to abnormal brain development, cognitive impairments, and other adverse outcomes in humans and wildlife. To increase chemical safety screening efficiency and reduce vertebrate animal testing, in vitro assays that identify chemical interactions with molecular targets of the thyroid system have been developed and implemented. Objectives: We present an adverse outcome pathway (AOP) network to link data derived from in vitro assays that measure chemical interactions with thyroid molecular targets to downstream events and adverse outcomes traditionally derived from in vivo testing. We examine the role of new in vitro technologies, in the context of the AOP network, in facilitating consideration of several important regulatory and biological challenges in characterizing chemicals that exert effects through a thyroid mechanism. Discussion: There is a substantial body of knowledge describing chemical effects on molecular and physiological regulation of TH signaling and associated adverse outcomes. Until recently, few alternative nonanimal assays were available to interrogate chemical effects on TH signaling. With the development of these new tools, screening large libraries of chemicals for interactions with molecular targets of the thyroid is now possible. Measuring early chemical interactions with targets in the thyroid pathway provides a means of linking adverse outcomes, which may be influenced by many biological processes, to a thyroid mechanism. However, the use of in vitro assays beyond chemical screening is complicated by continuing limits in our knowledge of TH signaling in important life stages and tissues, such as during fetal brain development. Nonetheless, the thyroid AOP network provides an ideal tool for defining causal linkages of a chemical exerting thyroid-dependent effects and identifying research needs to quantify these effects in support of regulatory decision making. https://doi.org/10.1289/EHP5297

This AOP describes one adverse outcome that may result from the inhibition of thyroperoxidase (TPO) during mammalian development. Chemical inhibition of TPO results in decreased thyroid hormone (TH) synthesis, and subsequent reduction in circulating concentrations of THs. THs are essential for normal human brain development, both prenatally and postnatally, modulating genes critical for a normal neuroanatomical development, with subsequent effects on neurophysiology, and finally neurological function. Therefore, chemicals that interfere with TH synthesis have the potential to cause TH insufficiency that may result in adverse neurodevelopmental effects in offspring. Herein, we discuss the implications of developmental TPO inhibition for hippocampal anatomy, function, and ultimately neural function. The hippocampus is known to be critically involved in cognitive, emotional, and memory function. The adverse consequences of TH insufficiency depend both on severity and developmental timing, indicating that exposure to TPO inhibitors may produce different effects at different developmental windows of exposure.

The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA’s Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

The Toxicity Reference Database (ToxRefDB) structures information from over 5,000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling results for nearly 28,000 datasets from 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.

The US Environmental Protection Agency Endocrine Disruptor Screening Program (EDSP) is a tiered screening approach to determine the potential for a chemical to interact with estrogen, androgen, or thyroid hormone systems and/or perturb steroidogenesis. Use of high-throughput screening (HTS) to predict hazard and exposure is shifting the EDSP approach to (1) prioritization of chemicals for further screening; and (2) targeted use of EDSP Tier 1 assays to inform specific data needs. In this work, toxicology data for three triazole fungicides (triadimefon, propiconazole, and myclobutanil) were evaluated, including HTS results, EDSP Tier 1 screening (and other scientifically relevant information), and EPA guideline mammalian toxicology study data. The endocrine-related bioactivity predictions from HTS and information that satisfied the EDSP Tier 1 requirements were qualitatively concordant. Current limitations in the available HTS battery for thyroid and steroidogenesis pathways were mitigated by inclusion of guideline toxicology studies in this analysis. Similar margins (3–5 orders of magnitude) were observed between HTS-predicted human bioactivity and exposure values and between in vivo mammalian bioactivity and EPA chronic human exposure estimates for these products’ registered uses. Combined HTS hazard and human exposure predictions suggest low priority for higher-tiered endocrine testing of these triazoles. Comparison with the mammalian toxicology database indicated that this HTS-based prioritization would have been protective for any potential in vivo effects that form the basis of current risk assessment for these chemicals. This example demonstrates an effective, human health protective roadmap for EDSP evaluation of pesticide active ingredients via prioritization using HTS and guideline toxicology information.

Screening certain environmental chemicals for their ability to interact with endocrine targets, including the androgen receptor (AR), is an important global concern. We previously developed a model using a battery of eleven in vitro AR assays to predict in vivo AR activity. Here we describe a revised mathematical modeling approach that also incorporates data from newly available assays and demonstrate that subsets of assays can provide close to the same level of predictivity. These subset models are evaluated against the full model using 1820 chemicals, as well as in vitro and in vivo reference chemicals from the literature. Agonist batteries of as few as six assays and antagonist batteries of as few as five assays can yield balanced accuracies of 95% or better relative to the full model. Balanced accuracy for predicting reference chemicals is 100%. An approach is outlined for researchers to develop their own subset batteries to accurately detect AR activity using assays that map to the pathway of key molecular and cellular events involved in chemical-mediated AR activation and transcriptional activity. This work indicates in vitro bioactivity and in silico predictions that map to the AR pathway could be used in an integrated approach to testing and assessment for identifying chemicals that interact directly with the mammalian AR.

Human health risk assessment for environmental chemical exposure is limited by a vast majority of chemicals with little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative structure activity relationship (QSAR) models based on chemical structure information, can predict hazard in the absence of experimental data. Risk assessment requires identification of a quantitative point-of-departure (POD) value, the point on the dose-response curve that marks the beginning of a low-dose extrapolation. This study presents two sets of QSAR models to predict POD values (POD(QSAR)) for repeat dose toxicity. For training and validation, a publicly available in vivo toxicity dataset for 3592 chemicals was compiled using the U.S. Environmental Protection Agency’s Toxicity Value database (ToxValDB). The first set of QSAR models predict point-estimates of POD values (POD(QSAR)) using structural and physicochemical descriptors for repeat dose study types and species combinations. A random forest QSAR model using study type and species as descriptors showed the best performance, with an external test set root mean square error (RMSE) of 0.71 log(10)-mg/kg/day and coefficient of determination (R(2)) of 0.53. The second set of QSAR models predict the 95% confidence intervals for POD(QSAR) using a constructed POD distribution with a mean equal to the median POD value and a standard deviation of 0.5 log(10)-mg/kg/day, based on previously published typical study-to-study variability that may lead to uncertainty in model predictions. Bootstrap resampling of the pre-generated POD distribution was used to derive point-estimates and 95% confidence intervals for each POD prediction. Enrichment analysis to evaluate the accuracy of POD(QSAR) showed that 80% of the 5% most potent chemicals were found in the top 20% of the most potent chemical predictions, suggesting that the repeat dose POD QSAR models presented here may help inform screening level human health risk assessments in the absence of other data.

New approach methodologies (NAMs) for chemical hazard assessment are often evaluated via comparison to animal studies; however, variability in animal study data limits NAM accuracy. The US EPA Toxicity Reference Database (ToxRefDB) enables consideration of variability in effect levels, including the lowest effect level (LEL) for a treatment-related effect and the lowest observable adverse effect level (LOAEL) defined by expert review, from subacute, subchronic, chronic, multi-generation reproductive, and developmental toxicity studies. The objectives of this work were to quantify the variance within systemic LEL and LOAEL values, defined as potency values for effects in adult or parental animals only, and to estimate the upper limit of NAM prediction accuracy. Multiple linear regression (MLR) and augmented cell means (ACM) models were used to quantify the total variance, and the fraction of variance in systemic LEL and LOAEL values explained by available study descriptors (e.g., administration route, study type). The MLR approach considered each study descriptor as an independent contributor to variance, whereas the ACM approach combined categorical descriptors into cells to define replicates. Using these approaches, total variance in systemic LEL and LOAEL values (in log10-mg/kg/day units) ranged from 0.74 to 0.92. Unexplained variance in LEL and LOAEL values, approximated by the residual mean square error (MSE), ranged from 0.20-0.39. Considering subchronic, chronic, or developmental study designs separately resulted in similar values. Based on the relationship between MSE and R-squared for goodness-of-fit, the maximal R-squared may approach 55 to 73% for a NAM-based predictive model of systemic toxicity using these data as reference. The root mean square error (RMSE) ranged from 0.47 to 0.63 log10-mg/kg/day, depending on dataset and regression approach, suggesting that a two-sided minimum prediction interval for systemic effect levels may have a width of 58 to 284-fold. These findings suggest quantitative considerations for building scientific confidence in NAM-based systemic toxicity predictions.

Toxicokinetics (TK) is required information for chemical risk assessment. Unfortunately, TK data are not available for most chemicals in commerce and the environment. New approach methodologies (NA...

The U.S. Environmental Protection Agency Endocrine Disruptor Screening Program and the Organization for Economic Co-operation and Development (OECD) have used the human adrenocarcinoma (H295R) cell-based assay to predict chemical perturbation of androgen and estrogen production. Recently, a high-throughput H295R (HT-H295R) assay was developed as part of the ToxCast program that includes measurement of 11 hormones, including progestagens, corticosteroids, androgens, and estrogens. To date, 2012 chemicals have been screened at 1 concentration; of these, 656 chemicals have been screened in concentration-response. The objectives of this work were to: (1) develop an integrated analysis of chemical-mediated effects on steroidogenesis in the HT-H295R assay and (2) evaluate whether the HT-H295R assay predicts estrogen and androgen production specifically via comparison with the OECD-validated H295R assay. To support application of HT-H295R assay data to weight-of-evidence and prioritization tasks, a single numeric value based on Mahalanobis distances was computed for 654 chemicals to indicate the magnitude of effects on the synthesis of 11 hormones. The maximum mean Mahalanobis distance (maxmMd) values were high for strong modulators (prochloraz, mifepristone) and lower for moderate modulators (atrazine, molinate). Twenty-five of 28 reference chemicals used for OECD validation were screened in the HT-H295R assay, and produced qualitatively similar results, with accuracies of 0.90/0.75 and 0.81/0.91 for increased/decreased testosterone and estradiol production, respectively. The HT-H295R assay provides robust information regarding estrogen and androgen production, as well as additional hormones. The maxmMd from this integrated analysis may provide a data-driven approach to prioritizing lists of chemicals for putative effects on steroidogenesis.

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.

Rapid high throughput in vitro screening (HTS) assays are now available for characterizing dose–responses in assays that have been selected for their sensitivity in detecting estrogen-related endpoints. For example, EPA’s ToxCast™ program recently released endocrine assay results for more than 1800 substances and the interagency Tox21 consortium is in the process of releasing data for approximately 10,000 chemicals. But such activity measurements alone fall short for the purposes of priority setting or screening because the relevant exposure context is not considered. Here, we extend the method of exposure:activity profiling by calculating the exposure:activity ratios (EARs) using human exposure estimates and AC50 values for a range of chemicals tested in a suite of seven estrogenic assays in ToxCast™ and Tox21. To provide additional context, relative estrogenic exposure:activity quotients (REEAQ) were derived by comparing chemical-specific EARs to the EAR of the ubiquitous dietary phytoestrogen, genistein (GEN). Although the activity of a substance in HTS-endocrine assays is not a measure of health hazard or risk, understanding how such a dose compares to human exposures provides a valuable additional metric that can be used in decision-making; substances with small EARs and REEAQs would indicate low priority for further endocrine screening or testing.