ToxCast is used as a cost-effective approach for efficiently prioritizing the toxicity testing of thousands of chemicals. It uses data from state-of-the-art high throughput screening (HTS) bioassay and builds computational models to forecast potential chemical toxicity in humans. ToxRefDB stores data related to ToxCast.

ToxCast high-throughput assay information including assay annotation user guide, assay target information, study design information and quality statistics on the assays. This version is Mac compatible.

Bioactivity data for p,p'-DDD and analogues from ToxCast assays conducted in liver cells were sourced from the EPA’s CompTox Chemistry Dashboard. The links also provide access to the ToxCast assay information and annotation data user guide. This dataset is associated with the following publication: Lizarraga, L., J. Dean, J. Kaiser, S. Wesselkamper, J. Lambert, and J. Zhao. A Case Study on the Application of An Expert-driven Read-Across Approach in Support of Quantitative Risk Assessment of p,p’-Dichlorodiphenyldichloroethane. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 103: 301-313, (2019).

A data set of 500 chemicals evaluated for their ability to induce cleft palate in animal prenatal developmental studies was compiled from Toxicity Reference Database and the biomedical literature, which included 63 cleft palate active and 437 inactive chemicals. To characterize the potential molecular targets for chemical‐induced cleft palate, we mined the ToxCast high‐throughput screening database for patterns and linkages in bioactivity profiles and chemical structural descriptors. The following datasets can be obtained via the links and files in the Data section: Phase II ToxCast assay data results (Judson et al., 2010); The Gene Score data set derived from ToxCast; ToxRefDB version 1 (Knudsen et al., 2009; Martin, Judson, et al., 2009); The ToxPrint chemotypes (Yang et al., 2015). This dataset is associated with the following publication: Baker, N., N. Sipes, J. Franzosa, D. Belair, B. Abbott, R. Judson, and T. Knudsen. Characterizing cleft palate toxicants using ToxCast data, chemical structure, and the biomedical literature. Birth Defects Research. John Wiley & Sons, Inc., Hoboken, NJ, USA, 1-21, (2019).

ToxCast bioactivity data and model predictions for the estrogen receptor (ER) and androgen receptor (AR) pathways were obtained from the inks provided. This dataset is associated with the following publication: Lizarraga, L., J. Dean, J. Kaiser, S. Wesselkamper, J. Lambert, and J. Zhao. A Case Study on the Application of An Expert-driven Read-Across Approach in Support of Quantitative Risk Assessment of p,p’-Dichlorodiphenyldichloroethane. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 103: 301-313, (2019).

ToxCast assay information for chemicals detected in the Lac du Flambeau Chain of lakes, Wisconsin, USA, August 2020-May 2021. (XLSX)

ToxCast assays used in computation of exposure-activity ratios from chemical analysis of surface water samples for pharmaceuticals in the Lac du Flambeau Chain of Lakes, Wisconsin, USA, August 2020-May 2021. (XLSX)

In line with the “healthy aging” principle, we aim to assess the exposure map and health risks of environmental chemicals in the elderly. Blood samples from 918 elderly individuals in Wuhan, China, were analyzed using the combined gas/liquid-mass spectrometry technology to detect levels of 118 environmental chemicals. Cluster analysis identified exposure profiles, while risk indexes and bioanalytical equivalence percentages were calculated using EPA’s ToxCast database. The detection rates for 87 compounds exceeded 70%. DEHP, DiBP, naphthalene, phenanthrene, DnBP, pyrene, anthracene, permethrin, fluoranthene, and PFOS showed the highest concentrations. Fat-soluble pollutants varied across lifestyles. In cluster 2, which was characterized by higher concentrations of fat-soluble substances, the proportion of smokers or drinkers was higher than that of nonsmokers or nondrinkers. Pesticides emerged as the most active environmental chemicals in peroxisome proliferator-activated receptor gamma antagonist, thyroid hormone receptor (TR) antagonist, TR agonist, and androgen receptor (AR) agonist activity assays. Additionally, PAEs and polycyclic aromatic hydrocarbons played significant roles as active contaminants for the corresponding targets of AR antagonists and estrogen receptor alpha. We proposed a list of priority pollutants linked to endocrine-disrupting toxic effects in the elderly, which may provide the groundwork for further research into environmental etiology.

Thousands of chemicals are directly added to or come in contact with food, many of which have undergone little to no toxicological evaluation. The landscape of the food-relevant chemical universe was evaluated using cheminformatics, and subsequently the bioactivity of food-relevant chemicals across the publicly available ToxCast highthroughput screening program was assessed. In total, 8659 food-relevant chemicals were compiled including direct food additives, food contact substances, and pesticides. Of these food-relevant chemicals, 4719 had curated structure definition files amenable to defining chemical fingerprints, which were used to cluster chemicals using a selforganizing map approach. Pesticides, and direct food additives clustered apart from one another with food contact substances generally in between, supporting that these categories not only reflect different uses but also distinct chemistries. Subsequently, 1530 food-relevant chemicals were identified in ToxCast comprising 616 direct food additives, 371 food contact substances, and 543 pesticides. Bioactivity across ToxCast was filtered for cytotoxicity to identify selective chemical effects. Initiating analyses from strictly chemical-based methodology or bioactivity/cytotoxicity-driven evaluation presents unbiased approaches for prioritizing chemicals. Although bioactivity in vitro is not necessarily predictive of adverse effects in vivo, these data provide insight into chemical properties and cellular targets through which foodrelevant chemicals elicit bioactivity.

This dataset is associated with the following publication: Karmaus , A., D. Filer , M. Martin , and K. Houck. (FOOD AND CHEMICAL TOXICOLOGY) Evaluation of food-relevant chemicals in the ToxCast high-throughput screening program. FOOD AND CHEMICAL TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 92: 188-196, (2016).

Previous work identified a ‘cytotoxic burst’ (CTB) phenomenon wherein large numbers of the ToxCast assays begin to respond at or near test chemical concentrations that elicit cytotoxicity, and a statistical approach to defining the bounds of the CTB was developed. To focus AOP development on the molecular targets corresponding to ToxCast assays indicating pathway-specific effects, we conducted a meta-analysis to identify which assays most frequently respond at concentrations below the CTB. A preliminary list of potentially important, target-specific assays was determined by ranking assays by the fraction of chemical hits below the CTB compared to the number of chemicals tested. Additional priority assays were identified using a diagnostic-odds-ratio approach which gives greater ranking to assays with high specificity but low responsivity. Combined, the two prioritization methods identified several novel targets (e.g., peripheral benzodiazepine and progesterone receptors) to prioritize for AOP development, and affirmed the importance of a number of existing AOPs aligned with ToxCast targets (e.g., thyroperoxidase, estrogen receptor, aromatase).

This dataset is associated with the following publication: Fay, K., J. Swintek, D. Villeneuve, S. Edwards, M. Nelms, B. Blackwell, and G. Ankley. Differentiating pathway-specific from non-specific effects in high-throughput toxicity data: A foundation for prioritizing adverse outcome pathway development. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 163(2): 500-515, (2018).

The tcpl package provides a set of tools for processing and modeling high-throughput and high-content chemical screening data.

This dataset is associated with the following publication: Filer, D.L., P. Kothiya, R.W. Setzer, R.S. Judson, and M.T. Martin. (BIOINFORMATICS) tcpl: The ToxCast Pipeline for High-Throughput Screening Data. BIOINFORMATICS. Oxford University Press, Cary, NC, USA, 1-3, (2016).

description: Background: High-content imaging (HCI) allows simultaneous measurement of multiple cellular phenotypic changes and is an important tool for evaluating the biological activity of chemicals. Objectives: Our goal was to analyze dynamic cellular changes using HCI to identify the tipping point at which the cells did not show recovery towards a normal phenotypic state. Methods: HCI was used to evaluate the effects of 967 chemicals (in concentrations ranging from 0.4 to 200 M) on HepG2 cells over a 72-hr exposure period. The HCI end points included p53, c-Jun, histone H2A.x, -tubulin, histone H3, alpha tubulin, mitochondrial membrane potential, mitochondrial mass, cell cycle arrest, nuclear size, and cell number. A computational model was developed to interpret HCI responses as cell-state trajectories. Results: Analysis of cell-state trajectories showed that 336 chemicals produced tipping points and that HepG2 cells were resilient to the effects of 334 chemicals up to the highest concentration (200 M) and duration (72 hr) tested. Tipping points were identified as concentration-dependent transitions in system recovery, and the corresponding critical concentrations were generally between 5 and 15 times (25th and 75th percentiles, respectively) lower than the concentration that produced any significant effect on HepG2 cells. The remaining 297 chemicals require more data before they can be placed in either of these categories. Conclusions: These findings show the utility of HCI data for reconstructing cell state trajectories and provide insight into the adaptation and resilience of in vitro cellular systems based on tipping points. Cellular tipping points could be used to define a point of departure for risk-based prioritization of environmental chemicals. This dataset is associated with the following publication: Shah , I., W. Setzer , J. Jack, K. Houck , R. Judson , T. Knudsen , J. Liu, M. Martin , D. Reif, A.M. Richard , R.S. Thomas , K. Crofton , D.J. Dix , and R.J. Kavlock. (Envir. Health Perspect.) Using ToxCast data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-33, (2015).; abstract: Background: High-content imaging (HCI) allows simultaneous measurement of multiple cellular phenotypic changes and is an important tool for evaluating the biological activity of chemicals. Objectives: Our goal was to analyze dynamic cellular changes using HCI to identify the tipping point at which the cells did not show recovery towards a normal phenotypic state. Methods: HCI was used to evaluate the effects of 967 chemicals (in concentrations ranging from 0.4 to 200 M) on HepG2 cells over a 72-hr exposure period. The HCI end points included p53, c-Jun, histone H2A.x, -tubulin, histone H3, alpha tubulin, mitochondrial membrane potential, mitochondrial mass, cell cycle arrest, nuclear size, and cell number. A computational model was developed to interpret HCI responses as cell-state trajectories. Results: Analysis of cell-state trajectories showed that 336 chemicals produced tipping points and that HepG2 cells were resilient to the effects of 334 chemicals up to the highest concentration (200 M) and duration (72 hr) tested. Tipping points were identified as concentration-dependent transitions in system recovery, and the corresponding critical concentrations were generally between 5 and 15 times (25th and 75th percentiles, respectively) lower than the concentration that produced any significant effect on HepG2 cells. The remaining 297 chemicals require more data before they can be placed in either of these categories. Conclusions: These findings show the utility of HCI data for reconstructing cell state trajectories and provide insight into the adaptation and resilience of in vitro cellular systems based on tipping points. Cellular tipping points could be used to define a point of departure for risk-based prioritization of environmental chemicals. This dataset is associated with the following publication: Shah , I., W. Setzer , J. Jack, K. Houck , R. Judson , T. Knudsen , J. Liu, M. Martin , D. Reif, A.M. Richard , R.S. Thomas , K. Crofton , D.J. Dix , and R.J. Kavlock. (Envir. Health Perspect.) Using ToxCast data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-33, (2015).

This document describes the protocol for US EPA ToxCast data analysis. The EBTC Tox21 working group and US EPA ToxCast scientists Drs. Richard Judson, Keith Houck and Nicole Kleistreuer were consulted for comment and approval prior to publication of this protocol. Any disputes were arbitrated by the Working Group stream leaders KT and HD. The protocol was reviewed by the EBTC Board of Trustees and signed off by EBTC's Executive Director, Dr Katya Tsaioun. After sign-off, this protocol was published in the Zonodo.org repository, prior to completing of the analysis and data analysis. {"references": ["http://www.hindawi.com/journals/bmri/2016/9737920/", "http://linkinghub.elsevier.com/retrieve/pii/S1359644616300411", "https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btw680"]}

On the basis of two pilot high-content screens of ToxCast Phase I chemicals, we previously demonstrated that exposure of zebrafish embryos to abamectin and butafenacil abolished spontaneous activity and induced severe anemia, respectively. Therefore, the objective of this study was (1) to determine whether high-throughput in vitro screening data from the ToxCast program would have prioritized abamectin and butafenacil for further testing and (2) to determine whether a single three-day zebrafish embryo assay is a strong predictor of Toxicological Priority Index (ToxPi) scores derived from ToxCast data. Using publically available ToxCast assay end point data and target information, we calculated assay hit rates, developed hazard classifications, and relied on the ToxPi Graphical User Interface to generate ToxPi charts and scores within a biological process-driven configuration. Overall, our findings suggest that embryonic zebrafish may be valuable for prioritizing ToxCast testing as well as addressing toxicity pathways that may not be represented by the ToxCast assay battery.

The U.S. Environmental Protection Agency (EPA) launched the ToxCast program in 2007 with the goal of evaluating high-throughput in vitro assays to prioritize chemicals that need toxicity testing. Their goal was to develop predictive bioactivity signatures for toxic compounds using a set of in vitro assays and/or in silico properties. In 2009, Pfizer joined the ToxCast initiative by contributing 52 compounds with preclinical and clinical data for profiling across the multiple assay platforms available. Here, we describe the initial analysis of the Pfizer subset of compounds within the ToxCast chemical (n = 1814) and in vitro assay (n = 486) space. An analysis of the hit rate of Pfizer compounds in the ToxCast assay panel allowed us to focus our mining of assays potentially most relevant to the attrition of our compounds. We compared the bioactivity profile of Pfizer compounds to other compounds in the ToxCast chemical space to gain insights into common toxicity pathways. Additionally, we explored the similarity in the chemical and biological spaces between drug-like compounds and environmental chemicals in ToxCast and compared the in vivo profiles of a subset of failed pharmaceuticals having high similarity in both spaces. We found differences in the chemical and biological spaces of pharmaceuticals compared to environmental chemicals, which may question the applicability of bioactivity signatures developed exclusively based on the latter to drug-like compounds if used without prior validation with the ToxCast Phase-II chemicals. Finally, our analysis has allowed us to identify novel interactions for our compounds in particular with multiple nuclear receptors that were previously not known. This insight may help us to identify potential liabilities with future novel compounds.

This file contains the data used to generate hit calls from neural activity recordings on microelectrode array (MEA) plates treated with ToxCast compounds at a single concentration.

This dataset is associated with the following publication: Strickland, J., M. Martin, A. Richard, K. Houck, and T. Shafer. Screening the ToxCast phase II libraries for alterations in network function using cortical neurons grown on multi-well microelectrode array (mwMEA) plates. Archives of Toxicology. Springer, New York, NY, USA, 92(1): 487-500, (2018).

There is a growing need in the field of exposure science for monitoring methods that rapidly screen environmental media for suspect contaminants. Measurement and analysis platforms, based on high resolution mass spectrometry (HRMS), now exist to meet this need. Here we describe results of a study that links HRMS data with exposure predictions from the U.S. EPA's ExpoCast™ program and in vitro bioassay data from the U.S. interagency Tox21 consortium. Vacuum dust samples were collected from 56 households across the U.S. as part of the American Healthy Homes Survey (AHHS). Sample extracts were analyzed using liquid chromatography time-of-flight mass spectrometry (LC–TOF/MS) with electrospray ionization. On average, approximately 2000 molecular features were identified per sample (based on accurate mass) in negative ion mode, and 3000 in positive ion mode. Exact mass, isotope distribution, and isotope spacing were used to match molecular features with a unique listing of chemical formulas extracted from EPA's Distributed Structure-Searchable Toxicity (DSSTox) database. A total of 978 DSSTox formulas were consistent with the dust LC–TOF/molecular feature data (match score ≥ 90); these formulas mapped to 3228 possible chemicals in the database. Correct assignment of a unique chemical to a given formula required additional validation steps. Each suspect chemical was prioritized for follow-up confirmation using abundance and detection frequency results, along with exposure and bioactivity estimates from ExpoCast and Tox21, respectively. Chemicals with elevated exposure and/or toxicity potential were further examined using a mixture of 100 chemical standards. A total of 33 chemicals were confirmed present in the dust samples by formula and retention time match; nearly half of these do not appear to have been associated with house dust in the published literature. Chemical matches found in at least 10 of the 56 dust samples include Piperine, N,N-Diethyl-m-toluamide (DEET), Triclocarban, Diethyl phthalate (DEP), Propylparaben, Methylparaben, Tris(1,3-dichloro-2-propyl)phosphate (TDCPP), and Nicotine. This study demonstrates a novel suspect screening methodology to prioritize chemicals of interest for subsequent targeted analysis. The methods described here rely on strategic integration of available public resources and should be considered in future non-targeted and suspect screening assessments of environmental and biological media.

This dataset is associated with the following publication: Rager, J.E., M. Strynar , S. Liang, R.L. McMahen, A. Richard , C.M. Grukle, J. Wambaugh , K. Isaacs , R. Judson , A. Williams , and J. Sobus. Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring. ENVIRONMENT INTERNATIONAL. Elsevier Science Ltd, New York, NY, USA, 88: 269-280, (2016).

Data files for "Eccles KM, Karmaus AL, Kleinstreuer NC, Parham F, Rider CV, Wambaugh JF, Messier KP. A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target. Sci Total Environ. 2023 Jan 10;855:158905. doi: 10.1016/j.scitotenv.2022.158905. Epub 2022 Sep 21. PMID: 36152849"

The US EPA Toxicity Forecasting (ToxCast) project has involved the generation of large amount of high throughput in vitro data (Over 4000 chemicals tested in between 100 and 700 assays). This data is generated in a consistent manner, and includes a wide variety of chemicals including industrial and consumer products, food additives, pesticides, and drugs. These chemicals were not chosen because they were expected to be active, resulting in a database containing a balance of positive and negative data points. As such this data is useful for computational model construction. This in vitro data has been used at the EPA and elsewhere in modelling approaches, including computational modelling for specific target binding as biological descriptors for toxicity prediction, and pharmacokinetic modelling of human dose responses. All analyses in the generation of the burst flag hit-call matrix and extraction of chemicals for the targets in this study (AR and GR) were performed using R v3.1.2. This dataset is associated with the following publication: Allen, T.E., M.D. Nelms, S.W. Edwards, J.M. Goodman, S. Gutsell, and P.J. Russell. In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 54(12): 7461-7470, (2020).

Supplemental datafiles for journal article 'High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells'.