IntroductionAnimal studies have historically informed toxicological testing and safety assessments. However, assessment of the variability in both quantitative and qualitative results has been limited. Biological variability, experimental differences, interpretation of categorical endpoints, and data availability and curation approaches all contribute to the quantified variability.MethodsA literature review was conducted to identify publications describing variability analyses for in vivo toxicology studies. Variability analyses were evaluated and summarized for a variety of toxicological endpoints: ocular irritation, dermal sensitization and irritation, acute oral and inhalation lethality, subchronic and chronic toxicity, carcinogenicity, neurotoxicity including DNT, endocrine, and genotoxicity.ResultsThis review summarizes published investigations of variability within mammalian toxicological studies that have been largely conducted in accordance with health effects test guidelines. The results of this review suggest that replicability of in vivo toxicological guideline studies varies widely by study type, endpoint complexity, and classification approach.DiscussionWhile any test system will have inherent variability, understanding its sources and impact on study interpretation will help ensure that appropriate confidence is applied when using the test method. Furthermore, such information aids in establishing relevant metrics to serve as baselines for informing performance characterization of new approach methodologies (NAMs). Future evaluation of NAMs should be contextualized using estimates of uncertainty and variance of the traditional study data to demonstrate “better” performance compared to traditional testing approaches. Robust understanding of guideline study performance is important for risk assessments, where it is important to find species-relevant NAMs that can perform at least as well as existing bioassays.

The aim of in vitro - in vivo extrapolation (IVIVE) of dose is to predict potential whole animal effects from perturbations observed in vitro. Reliance on nominal (administered) concentration (assumed dose) does not account for chemical distribution to cellular and noncellular elements of an in vitro assay system. Chemical distribution can reduce the free concentration available to cause effects and can result in an inaccurate estimate of the intracellular concentration causing any observed perturbations. There are mathematical, chemical property-based partitioning models for predicting cellular concentrations when not measured experimentally. This work evaluated two of these in vitro disposition models, Armitage et al., 2021 and Kramer et al., 2010, using a total of 153 experimental intracellular concentration measurements of 43 chemicals, along with parameters describing measurement conditions, from 12 peer reviewed studies in addition to data generated for this study. Intracellular concentrations were more accurately predicted by both the Armitage model (root mean squared log10 error (RMSLE) = 1.12) and the Kramer model (RMSLE = 1.30) than by the nominal concentration (RMSLE = 1.45). Although limited by the amount of available measurement data, these results indicate that mathematical modeling of in vitro distribution can improve the accuracy of IVIVE for toxicology.

The US Environmental Protection Agency has evaluated thousands of environmental chemicals within the ToxCast and Toxicology in the 21st Century (Tox21) programs using high-throughput screening (HTS) assays for molecular targets across the hypothalamic-pituitary-thyroid axis. The thyroid stimulating hormone receptor (TSHR) is a critical regulator of thyroid development and function and essential for thyroid hormone synthesis. Hundreds of chemicals have been identified as potential modulators of the TSHR in a Tox21 HTS assay, but the mechanistic and biological relevance to humans is uncertain. The objectives of this study were to select a subset of active chemicals from the Tox21 TSHR assay, screen for agonist or antagonist activity in human primary thyrocyte assays to evaluate mechanistic effects on the native TSHR, and then extend screening to assess functional effects on thyroid hormone synthesis in human thyroid microtissues. A total of 72 (agonist mode) and 64 (antagonist mode) chemicals were selected for screening. A conventional two-dimensional screening assay was implemented as a primary screening strategy to evaluate thyroglobulin protein production as a biomarker for TSHR-dependent bioactivity in primary thyrocytes. Active chemicals were triaged for secondary screening in three-dimensional thyroid microtissue assays to evaluate the functional relevance to thyroid hormone synthesis. Final results revealed 2 agonist and 13 antagonist chemicals that demonstrated concordant activity across the 2 assay formats. The results support a strategic tiered testing paradigm whereby chemicals flagged for hazard potential from targeted HTS assays are evaluated in assays with enhanced biological relevance to the target tissue of interest to inform hazard characterization for putative thyroid-disrupting chemicals in humans.

New approach methodologies (NAMs) are an increasing priority in the field of toxicology to fill data gaps and reduce time and resources in chemical safety assessment. We describe an NAMs workflow that integrates an in vitro high-throughput bioassay with an in silico computational model. In defining this workflow, we propose, as a crucial step of in silico development, the identification of explicit "purpose contexts": a priori definitions of the scope and intent of an in silico solution, which provide natural targets for the mechanistic interpretation, validation, and output design of the model. By inspecting data from an in vitro assay measuring the displacement of fluorescent probe 8-anilino-1-naphthalenesulfonic acid (ANSA) from the serum transport protein transthyretin (TTR) as a proxy for potential disruption of thyroxine (T4) binding, in collaboration with the experimenters, we developed three relevant purpose contexts for this in silico modeling effort: (1) examination and confirmation of the in vitro assay principle via orthogonal information, (2) immediate integration with the in vitro experimental cycle to reduce costs and enhance hit rates, and (3) ultimate replacement of the use of single-concentration screening as a prioritization strategy for bioactivity testing of bulk chemical libraries. From these purpose contexts, we derived the foundations of a robust and transparent quantitative structure-activity relationship (QSAR) model that is constructively fit for purpose, characterized by first-principles mechanistic analysis, strict data quality evaluation, contextually rigorous performance testing and, finally, delivery of a quantitative recommendation schedule to simultaneously improve in vitro hit rates and in silico model learning potential.

The larval zebrafish (Danio rerio) locomotor response light/ dark (LMR L/D) assay may inform developmental neurotoxicity (DNT) hazard, but a lack of standardized data analysis techniques has limited cross-laboratory data comparisons and integration. The present work proposes a concentration-response modeling workflow for evaluating chemicals tested in the LMR L/D assay, comprising 13 endpoints measuring different types of behavioral response (habituation, average speed, startle response, and range of activity). Data were previously collected for 61 chemicals tested in the LMR L/D assay, including 47 chemicals with evidence of in vivo DNT and 14 putative negatives. Response values were normalized using a Box-Cox transformation to address challenges such as inter-fish variability and non-normally distributed data. We found that the commonly studied endpoints in the LMR L/D assay (four in this study) were the most active overall; however, we identified ten chemicals that were only active in the less commonly studied endpoints (nine in this study), suggesting the potential added value of these endpoints for detecting behavioral changes in developing zebrafish. Importantly, we found that bidirectional curve-fitting and biphasic concentration-response models were informative for modeling zebrafish behavior, particularly for effects occurring at lower concentrations. This standardized concentration-response modeling workflow for the LMR L/D assay successfully detected chemical-induced changes in zebrafish behavior and contributes to a global effort to increase data transferability, reproducibility, and integration across LMR L/D labs and other DNT assay technologies.

The EPA New Chemicals Collaborative Research Program (NCCRP) seeks to maximize the efficiency and robustness of the new chemical review process under the Toxic Substances Control Act (TSCA) using new approach methods (NAMs) that represent the best available science. Consideration of the possible health hazards to pregnant women, and their developing offspring, for new chemical submissions is challenged by an insufficient number of acceptable screening modalities to quickly identify potential hazards to humans. The DevTox Germ Layer Reporter (GLR) assay platform evaluates chemical effects on early germ layer development and has been demonstrated to be a viable option for rapid chemical screening. The objective of this study was to screen a structurally diverse set of 171 representative chemicals selected from the TSCA non-confidential active inventory, and 54 in vitro assay reference chemicals, for potential developmental toxicity in the DevTox GLR-Endo assay. Assay performance metrics, as well as predictivity across a set of 16 reference developmental toxicants, were consistent with prior reporting and within acceptable parameters. Of the 38 reference chemicals not previously evaluated in the assay, 25 were identified as active, with 13 demonstrating selectivity for the SOX17 assay endpoint. For the test chemical set, 60 of the 165 chemicals analyzed were active, with 29 exhibiting a degree of selective activity. The results provide coverage of a critical toxicological domain for a set of chemicals relevant to the development of NAM-based methods for the NCCRP and may inform the development of tools in a NAMs-based hazard assessment strategy.

The US Environmental Protection Agency (US EPA) and other regulatory agencies routinely assess whether certain chemical exposures might result in harmful health effects. Traditional human health assessments rely upon expert judgment of dose-effect linkages observed in animal toxicology or human studies. Because both collection of toxicology data and synthesis of information might take multiple years to complete, there are relatively few available assessments for decision-making. Identifying methods that yield significant time and resource efficiencies to the process will have scalable public health benefits. To address the need, US EPA developed the database-calibrated assessment process (DCAP) to generate oral, non-cancer human health toxicity values that builds on previously published approaches and guidance. The approach uses the US EPA Toxicity Values Database (ToxValDB) that contains dose-response summary values (DRSVs) from in vivo toxicity studies. The DRSVs are converted to an oral, chronic, human equivalent dose using a series of standard conversion factors. A point-of-departure (POD) is then calculated across a distribution of studies for a given chemical using a calibration percentile that is benchmarked to critical effect PODs from published human health assessments. Traditional and process-specific uncertainties are incorporated to derive a calibrated toxicity value (CTV), defined as an estimate of a daily oral dose to the human population that is likely to be without appreciable risk of adverse non-cancer health effects over a lifetime. This review presents the rationale and methods for the approach, resulting in reporting of 1001 CTVs for chemicals that currently lack a human health assessment.

Background: Humans are primary drivers of environmental-contaminant exposures worldwide, including in drinking-water (DW). In the United States, point-of-use DW (POU-DW) is supplied via private tapwater (TW), public-supply TW, and bottled water (BW). Differences in management, monitoring, and messaging and lack of directly-intercomparable exposure data influence the actual and perceived quality and safety of different DW supplies and directly impact consumer decision-making. Objectives: The purpose of this paper is to provide a meta-analysis (quantitative synthesis) of POU-DW contaminant-mixture exposures and corresponding potential human-health effects of private-TW, public-TW, and BW by aggregating exposure results and harmonizing apical-health-benchmark-weighted and bioactivity-weighted effects predictions across previous studies by this research group. Discussion: Simultaneous exposures to multiple inorganic and organic contaminants of known or suspected human-health concern are common across all three DW supplies, with substantial variability observed in each and no systematic difference in predicted cumulative risk between supplies. Differences in contaminant or contaminant-class exposures, with important implications for DW-quality improvements, were observed and attributed to corresponding differences in regulation and compliance monitoring. Conclusion: The results indicate that human-health risks from contaminant exposures are common to and comparable in all three DW-supplies, including BW. Importantly, this study's target analytical coverage, which exceeds that currently feasible for water purveyors or homeowners, nevertheless is a substantial underestimation of the breadth of contaminant mixtures in the environment and potentially present in DW. Thus, the results emphasize the need for improved understanding of the adverse human-health implications of long-term exposures to low-level inorganic-/organic-contaminant mixtures across all three distribution pipelines and do not support commercial messaging of BW as a systematically safer alternative to public-TW. Regardless of the supply, increased public engagement in source-water protection and drinking-water treatment is necessary to reduce risks associated with long-term DW-contaminant exposures, especially in vulnerable populations, and to reduce environmental waste and plastics contamination.

The use of new approach methods (NAMs), including high-throughput, in vitro bioactivity data, in setting a point-of-departure (POD) will accelerate the pace of human health hazard assessments. Combining hazard and exposure predictions into a bioactivity:exposure ratio (BER) for use in risk-based prioritization and utilizing NAM-based bioactivity flags to indicate potential hazards of interest for further prediction or mechanism-based screening together comprise a prospective approach for management of substances with limited traditional toxicity testing data. In this work, we demonstrate a NAM-based assessment case study conducted via the Accelerating the Pace of Chemical Risk Assessment initiative, a consortium of international research and regulatory scientists. The primary objective was to develop a reusable and adaptable approach for addressing chemicals with limited traditional toxicity data using a NAM-based POD, BER, and bioactivity-based flags for indication of putative endocrine, developmental, neurological, and immunosuppressive effects via data generation and interpretation for 200 substances. Multiple data streams, including in silico and in vitro NAMs, were used. High-throughput transcriptomics and phenotypic profiling data, as well as targeted biochemical and cell-based assays, were combined with generic high-throughput toxicokinetic models parameterized with chemical-specific data to estimate dose for comparison to exposure predictions. This case study further enables regulatory scientists from different international purviews to utilize efficient approaches for prospective chemical management, addressing hazard and risk-based data needs, while reducing the need for animal studies. This work demonstrates the feasibility of using a battery of toxicodynamic and toxicokinetic NAMs to provide a NAM-based POD for screening-level assessment.

Toxicokinetic (TK) modeling provides critical information linking chemical exposures to tissue concentrations, predicting persistence in the body and determining the route(s) of elimination. Unfortunately, TK data are not available for most chemicals in commerce and the environment. To better understand and address these important information gaps, researchers and regulatory scientists from the international consortium of Accelerating the Pace of Chemical Risk Assessment herein present a flexible framework for characterizing the suitability of TK new approach methods (NAMs) to address chemical risk questions. High throughput toxicokinetics (HTTK) combines chemical-specific in vitro measures of TK with reproducible transparent and open-source TK models. HTTK supports the interpretation of data from in vitro bioactivity NAMs in a public health risk context and enhances the interpretation of biomonitoring data. A tiered framework has been developed focusing on two key aspects: (1) the regulatory decision context and (2) chemical properties and data. Differing levels of certainty are needed for relative risk prioritization, prospective risk assessment, and for protecting susceptible populations. Here HTTK is described with respect to measurement and modeling applications, relevant decision contexts, applicable chemistry, value of information, and certainty of predictions. In some cases, quantitative structure-property relationship (QSPR) models exist as alternatives to measurement and are discussed when they are appropriate. A series of examples applying the decision trees in specific public health scenarios are provided to illustrate that writing short responses, prompted by the decision trees and supported by the discussion and references collected here, may provide defensible written justification for or against the use of HTTK. The framework is intended to serve as a guide to chemical regulators and risk assessors who are interested to know when and where HTTK might be used for public health safety or risk decision making and when further expert guidance is needed.

New approach methods (NAMs) have been prioritized to reduce the use of animals for chemical safety assessment while continuing to protect human health and the environment. A key challenge of generating toxicity data is the implementation of a standardized analysis approach for transparent and reproducible benchmark concentration (BMC) estimation and uncertainty quantification for assay developers, regulators, and other stakeholders. In this study, we compared the bioactivity results of 321 chemical samples from four established BMC analysis pipelines used for evaluation of developmental neurotoxicity (DNT) NAMs data: the ToxCast pipeline (tcpl), CRStats, DNT DIVER (Curvep and Hill pipelines). We found an overall activity hit call concordance of 77.2% and highly correlated BMC estimations (r = 0.92 ± 0.02 SD), demonstrating generally good agreement across pipelines. Discordance appeared to be explained predominantly by noise within the data and borderline activity (activity occuring near the benchmark response level). Evaluation of the BMC confidence intervals indicated that pipeline selection may impact the estimation of the BMC lower bound. Consideration of biphasic models appeared important for capturing biologically-relevant changes in activity in the DNT battery. Lastly, different approaches to compute 'selective' bioactivity (activity below the threshold of cytotoxicity) were compared, identifying the CRstats classification model as more stringent for classifying selective activity. Overall, these findings indicated greater confidence in NAMs bioactivity results and emphasize the importance of understanding strengths and uncertainties of concentration-response modeling pipelines for informing biological interpretation and application decision making.

A new R package, ctxR, is presented. ctxR is an open-source R Client package used to interact with the Computational Toxicology and Exposure Application Programming Interfaces (CTX APIs) in R. The CTX APIs were developed by the US EPA’s Center for Computational Toxicology and Exposure, and provide users transparent, reproducible access in a programmatic manner to the data surfaced on the CompTox Chemicals Dashboard (CCD) and other data sources. The utility of this package is demonstrated through a case study that compares traditional Points of Departure (PODs) and PODs based on New Approach Methods (NAMs), referred to as PODNAM, relative to estimates of external exposure as an indicator of level of risk. This analysis combines a wide variety of data and showcases the power of programmatic data access that ctxR can provide to the scientific community.

The Toxicity Values Database, ToxValDB, was developed by the U.S. EPA Center for Computational Toxicology and Exposure as a resource to curate, store, standardize, and make accessible a wide range of human health-relevant toxicity information. The database originated in response to the need for harmonized and computationally accessible toxicology data. The scope and design of the database have evolved over time since its first release in 2016. Herein, the newly redesigned structure and development of ToxValDB v9.6.1 is described. The database is a compilation of three classes of summary-level values for chemical substances: in vivo toxicity study results (e.g., lowest-and no-observed adverse effect level), derived toxicity values (e.g., maximum acceptable oral dose), and media exposure guidelines (e.g., maximum contaminant level for drinking water). The current version of the database (9.6.1) contains 242,149 records covering 41,769 unique chemicals from 36 sources (55 source tables). With all records in a consistent structure normalized to a standardized vocabulary, the chemical and data landscape of ToxValDB v9.6.1 can be evaluated. To illustrate chemical coverage, the available data were mapped to chemical lists of regulatory importance. Further, the distribution of oral administered doses within in vivo toxicity studies was assessed by annotated chemical class. The harmonized in vivo data within ToxValDB have many applications including use in chemical screening and prioritization for human health assessment, modeling predictions, and benchmarking for New Approach Methods (NAMs), as well as to address a diverse range of novel research questions.

BACKGROUND:With thousands of chemicals in commerce and the environment, rapid identification of potential hazards is a critical need. Combining broad molecular profiling with targeted in vitro assays, such as high-throughput transcriptomics (HTTr) and receptor screening assays, could improve identification of chemicals that perturb key molecular targets associated with adverse outcomes. OBJECTIVES:We aimed to link transcriptomic readouts to individual molecular targets and integrate transcriptomic predictions with orthogonal receptor-level assays in a proof-of-concept framework for chemical hazard prioritization. METHODS:Transcriptomic profiles generated via TempO-Seq in U-2 OS and HepaRG cell lines were used to develop signatures composed of genes uniquely responsive to reference chemicals for distinct molecular targets. These signatures were applied to 75 reference and 1,126 nonreference chemicals screened via HTTr in both cell lines. Selective bioactivity toward each signature was determined by comparing potency estimates against the bulk of transcriptomic bioactivity for each chemical. Chemicals predicted by transcriptomics were confirmed for target bioactivity and selectivity using available orthogonal assay data from the US Environmental Protection Agency ToxCast program. A subset of 37 selectively acting chemicals from HTTr that did not have sufficient orthogonal data were prospectively tested using one of five receptor-level assays. RESULTS:Of the 1,126 nonreference chemicals screened, 201 demonstrated selective bioactivity in at least one transcriptomic signature and 57 were confirmed as selective nuclear receptor agonists. Chemicals bioactive for each signature were significantly associated with orthogonal assay bioactivity, and signature-based points-of-departure were equally or more sensitive than biological pathway altering concentrations in 95.4% of signature-prioritized chemicals. Prospective profiling found that 18 of 37 (49%) chemicals without prior orthogonal assay data were bioactive against the predicted receptor. DISCUSSION:Our work demonstrates that integrating transcriptomics with targeted orthogonal assays in a tiered framework can support Next Generation Risk Assessment by informing putative molecular targets and prioritizing chemicals for further testing. https://doi.org/10.1289/EHP16024.

Although rodent toxicity testing plays an important role in evaluating human hazards of environmental and industrial chemicals, evaluating the concordance of the rodent testing results with human effects is challenging because these chemicals cannot be tested in humans. In this study, we evaluate the quantitative and qualitative concordance of lowest observed adverse effect levels (LOAELs) and adverse endpoints between in vivo and in vitro models of human health and human clinical trials of pharmaceuticals. Rodent human equivalent dose-adjusted LOAEL (LOAELHED) values and human LOAEL values for the sensitive effect in each species were moderately correlated in a protective context. When matched rodent and human effects were evaluated, the quantitative correlation in dose did not improve, and the qualitative balanced accuracy in effects was low, suggesting limited predictivity. Absolute differences in rodent LOAELHED and human LOAEL values were nearly 1 log10 unit with rodent LOAELHED values consistently higher; however, rodent LOAELHED values were less than the human LOAEL values for >95% of drugs when divided by typical composite uncertainty factors. In comparison, in vitro bioactivity administered equivalent dose (AED) values showed a similar moderate correlation and absolute differences with human LOAEL values, but in vitro bioactivity AED values were consistently lower. When in vitro bioactivity AED values were compared with rodent LOAELHED values, the correlation was lower and differences larger relative to human LOAEL comparison. Overall, the study expands previous efforts evaluating the concordance of rodent toxicological testing results with human responses and presents objective expectations for alternative toxicity testing approaches.

Per- and polyfluoroalkyl substances (PFAS) comprise a large class of human-made chemicals that are in widespread use and present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterized for their hazard profiles, the vast majority of PFAS have not been extensively studied. A comprehensive evaluation to characterize the hazard profiles of the thousands of available PFAS would require extensive resources in terms of cost, number of animals and time. An alternative and more efficient approach is to develop a structural chemical categorization approach to prioritize which PFAS or categories of PFAS should be subject to additional study. To that end, the U.S. Environmental Protection Agency (EPA), in collaboration with the National Institute of Environmental Health Sciences (NIEHS) Division of Translational Toxicology (DTT), initiated a research project in 2018 to screen approximately 150 PFAS through a battery of alternative model organisms, in vitro cell and biochemical assays, and in vitro toxico kinetic (TK) assays in order to inform chemical category and read-across approaches. The aim of this review summarizes the experimental testing undertaken, how data were processed, what insights were derived from a category perspective and how these might potentially inform subsequent tiered testing.

The Toxic Substances Control Act (TSCA) requires the US EPA to evaluate the hazard and exposure of new and existing chemicals. New chemical notifications are typically data-poor and EPA has historically relied upon approaches including chemical categories to fill data gaps. As part of a multi-year Research Program, opportunities are being explored to leverage New Approach Methods (NAMs) in hazard and exposure assessments. Data from a battery of in vitro NAMs will be generated to form a case study for an adaptable approach to inform new chemical assessments. Herein, a cheminformatics workflow was developed to identify a set of similar to 300 representative candidate chemicals for in vitro screening from the TSCA non-confidential active inventory. The freely available web application ClassyFire was used to categorize all discrete organic structures from the TSCA inventory into one of 68 primary structural categories. Large primary categories were subcategorized into smaller categories using hierarchical agglomerative clustering, ultimately yielding 180 structural terminal categories. The inventory was filtered to substances that lacked previous ToxCast bioactivity screening, were associated with physicochemical property predictions indicating non-volatile solids or liquids, and had a higher chance of procurement. Amenability predictions for liquid chromatography-mass spectrometry were also generated to provide an indication of which chemicals lent themselves to aqueous-based screening and analytical verification in solvated samples. Structures associated with transformation in solvent, potentially explosive or highly reactive, were excluded. Potential candidate substances were selected on the basis of being structurally representative of the terminal category and meeting other screenability conditions. A final set of 318 candidate chemicals were proposed to undergo analytical quality control and screening in a range of broad and targeted biological technologies for human health-relevant end points. Finally, in silico tools were applied to explore predicted hazard profiles of these candidate substances relative to the full inventory.

Exposure to environmental chemicals can impair neurodevelopment, and oligodendrocytes may be particularly vulnerable, as their development extends from gestation into adulthood. However, few environmental chemicals have been assessed for potential risks to oligodendrocytes. Here, using a high-throughput developmental screen in cultured cells, we identified environmental chemicals in two classes that disrupt oligodendrocyte development through distinct mechanisms. Quaternary compounds, ubiquitous in disinfecting agents and personal care products, were potently and selectively cytotoxic to developing oligodendrocytes, whereas organophosphate flame retardants, commonly found in household items such as furniture and electronics, prematurely arrested oligodendrocyte maturation. Chemicals from each class impaired oligodendrocyte development postnatally in mice and in a human 3D organoid model of prenatal cortical development. Analysis of epidemiological data showed that adverse neurodevelopmental outcomes were associated with childhood exposure to the top organophosphate flame retardant identified by our screen. This work identifies toxicological vulnerabilities for oligodendrocyte development and highlights the need for deeper scrutiny of these compounds’ impacts on human health. Oligodendrocytes are vulnerable to chemical toxicity during development. However, few environmental chemicals have been identified as potential hazards. Here, the authors discover chemicals in common household products as harmful to oligodendrocyte development.

The presence of numerous chemical contaminants from industrial, agricultural, and pharmaceutical sources in water supplies poses a potential risk to human and ecological health. Current chemical analyses suffer from limitations, including chemical coverage and high cost, and broad-coverage in vitro assays such as transcriptomics may further improve water quality monitoring by assessing a large range of possible effects. Here, we used high-throughput transcriptomics to assess the activity induced by field-derived water extracts in MCF7 breast carcinoma cells. Wastewater and surface water extracts induced the largest changes in expression among cell proliferation-related genes and neurological, estrogenic, and antibiotic pathways, whereas drinking and reclaimed water extracts that underwent advanced treatment showed substantially reduced bioactivity on both gene and pathway levels. Importantly, reclaimed water extracts induced fewer changes in gene expression than laboratory blanks, which reinforces previous conclusions based on targeted assays and improves confidence in bioassay-based monitoring of water quality.

New approach methodologies (NAMs) aim to accelerate the pace of chemical risk assessment while simultaneously reducing cost and dependency on animal studies. High Throughput Transcriptomics (HTTr) is an emerging NAM in the field of chemical hazard evaluation for establishing in vitro points-of-departure and providing mechanistic insight. In the current study, 1201 test chemicals were screened for bioactivity at eight concentrations using a 24-h exposure duration in the human- derived U-2 OS osteosarcoma cell line with HTTr. Assay reproducibility was assessed using three reference chemicals that were screened on every assay plate. The resulting transcriptomics data were analyzed by aggregating signal from genes into signature scores using gene set enrichment analysis, followed by concentration-response modeling of signatures scores. Signature scores were used to predict putative mechanisms of action, and to identify biological pathway altering concentrations (BPACs). BPACs were consistent across replicates for each reference chemical, with replicate BPAC standard deviations as low as 5.6 × 10-3 μM, demonstrating the internal reproducibility of HTTr-derived potency estimates. BPACs of test chemicals showed modest agreement (R2 = 0.55) with existing phenotype altering concentrations from high throughput phenotypic profiling using Cell Painting of the same chemicals in the same cell line. Altogether, this HTTr based chemical screen contributes to an accumulating pool of publicly available transcriptomic data relevant for chemical hazard evaluation and reinforces the utility of cell based molecular profiling methods in estimating chemical potency and predicting mechanism of action across a diverse set of chemicals.