ToxCast is an initiative by the U.S. Environmental Protection Agency (EPA) aimed at predicting the potential toxicity of various chemical compounds. It involves high-throughput screening assays that evaluate thousands of chemicals across multiple biological endpoints. These endpoints cover a wide range of effects, including cell cycle disruptions, interactions with steroid receptors, and cytotoxicity.

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.

Background: Chemical toxicity testing is being transformed by advances in biology and computer modeling, concerns over animal use and the thousands of environmental chemicals lacking toxicity data. EPA's ToxCast program aims to address these concerns by screening and prioritizing chemicals for potential human toxicity using in vitro assays and in silico approaches. Objectives: This project aims to evaluate the use of in vitro assays for understanding the types of molecular and pathway perturbations caused by environmental chemicals and to build initial prioritization models of in vivo toxicity. Methods: We tested 309 mostly pesticide active chemicals in 467 assays across 9 technologies, including high-throughput cell-free assays and cell-based assays in multiple human primary cells and cell lines, plus rat primary hepatocytes. Both individual and composite scores for effects on genes and pathways were analyzed. Results: Chemicals display a broad spectrum of activity at the molecular and pathway levels. Many expected interactions are seen, including endocrine and xenobiotic metabolism enzyme activity. Chemicals range in promiscuity across pathways, from no activity to affecting dozens of pathways. We find a statistically significant inverse association between the number of pathways perturbed by a chemical at low in vitro concentrations and the lowest in vivo dose at which a chemical causes toxicity. We also find associations between a small set in vitro assays and rodent liver lesion formation. Conclusions: This approach promises to provide meaningful data on the thousands of untested environmental chemicals, and to guide targeted testing of environmental contaminants.

Data for a single chemical endpoint pair for thousands of chemicals and 821 assay endpoints for 20 variables such as the activity or hit call/activity concentrations/whether the chemical was tested in a specific assay/etc.

This documentation for the ToxCast assay endpoints is in a format outlined by the OECD Guidance Document 211 (GD211) for describing non-guideline in vitro test methods and their interpretation. The intent of GD 211 is to harmonize non-guideline, in vitro method descriptions to allow assessment of the relevance of the test method for biological responses of interest and the quality of the data produced. These reports are a work in progress and will be iteratively updated as more information becomes available.Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25

The tcpl package was developed to process high-throughput and high-content screening data generated by the ToxCast program. ToxCast is screening thousands of chemicals with hundreds of assays coming from numerous and diverse biochemical and cell-based technology platforms. The diverse data, received in heterogeneous formats from numerous vendors, are transformed to a standard computable format and loaded into the tcpl database by vendor-specific R scripts. Once data is loaded into the database, ToxCast utilizes the generalized processing functions provided in this package to process, normalize, model, qualify, flag, inspect, and visualize the data. While developed primarily for ToxCast, we have attempted to make the tcpl package generally applicable to chemical-screening community.

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.

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.

Abbreviations and data for manuscript Figures in the main text and supplemental. This dataset is associated with the following publication: Stoker, T., J. Want, A. Murr, J. Bailey, and A.R. Buckalew. High-Throughput Screening of ToxCast PFAS Chemical Library for Potential Inhibitors of the Human Sodium Iodide Symporter. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 36(3): 380-389, (2023).

Title: ToxCast Cytotoxicity-Associated Burst Paragraph: Chemical toxicity can arise from disruption of specific biomolecular functions or through more generalized cell stress and cytotoxicity-mediated processes. Evaluating the ToxCast high-throughput screening library, many chemicals show activation of large numbers of assays over a narrow range of concentrations in which cell stress and cytotoxicity are also seen. We term this phenomenon the cytotoxicity-associated “burst”. Whereas some of the assay activity in this concentration range may represent chemical effects on the intended target of the assay, some of it is not. In such situations, activity represents a false positive response that can be ascribed to assay interference processes. This phenomenon raises the need to establish a concentration threshold at which each chemical begins to drive activity across multiple and diverse cell stress and cytotoxicity assays by initiating this cytotoxicity-associated burst of activity. Judson R, et al . Analysis of the Effects of Cell Stress and Cytotoxicity on In Vitro Assay Activity Across a Diverse Chemical and Assay Space. Toxicol Sci. 2016 Oct;153(2):409. doi: 10.1093/toxsci/kfw148. Epub 2016 Sep 7. Erratum for: Toxicol Sci. 2016 Aug;152(2):323-39. PMID: 27605417; PMCID: PMC7297301.

These data are based on ToxCast invitroDBv3.5

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).

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).

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).

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).

These are the raw data files for TOXSCI manuscript 19-0578 entitled, “Respirometric Screening and Characterization of Mitochondrial Toxicants Within the ToxCast Phase I and II Chemical Libraries”: Description from readme.txt file: 1) sc_seahorse.lvl0.merged.data.csv- contains all mapped raw OCR data from tier 1 single-concentration RSA screening of 1,042 Toxcast Phase I and II chemicals 2) mc_seahorse.lvl0.merged.data.csv- contains all mapped raw OCR data from tier 2 multi-concentration RSA screening of 249 actives from tier 1 3) EFA.lvl0.merged.data.csv- contains all mapped raw OCR data from tier 3 EFA screening of 149 putative electron transport chain inhibitors 4) mc5_mc6_ncct_mito_nov2019.csv- level 5 and 6 outputs from ToxCast pipeline (tcpl) analysis 5) RawMC3_ToxCast_by_aeid.csv- level 3 tcpl outputs for all mitochondrial ToxCast assays 6) RawMC5_ToxCast_by_aeid.csv- level 5 tcpl outputs for all mitochondrial ToxCast assays 7) ref.set.chems.csv- sixty reference chemicals used to compared assay performance 8) study_code.R- R script used to analyze data and generate figures and tables. This dataset is associated with the following publication: Hallinger, D., H. Lindsay, K. Friedman, D. Suarez, and S. Simmons. Respirometric Screening and Characterization of Mitochondrial Toxicants Within the ToxCast Phase I and II Chemical Libraries. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 176(1): 175-192, (2020).

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).

MoleculeNet ToxCast

ToxCast dataset [1], part of MoleculeNet [2] benchmark. It is intended to be used through scikit-fingerprints library. The task is to predict 617 toxicity targets from a large library of compounds based on in vitro high-throughput screening. All tasks are binary. Note that targets have missing values. Algorithms should be evaluated only on present labels. For training data, you may want to impute them, e.g. with zeros.

Characteristic Description

Tasks… See the full description on the dataset page: https://huggingface.co/datasets/scikit-fingerprints/MoleculeNet_ToxCast.

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 1,042 chemicals initially screened at a single maximal concentration, 243 actives were identified and re-screened at seven concentrations. Concentration-response data for three respiration phases confirmed activity and indicated a mechanism for 193 mitochondrial toxicants: 149 electron transport chain inhibitors (ETCi), 15 uncouplers and 29 ATP synthase inhibitors. Subsequently, an electron flow assay (EFA) 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 EFA 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.

This excel spreadsheet contains the resultant data for over from inhibition assays with human Deiodinase 1 screened against the ToxCast Phase 1 chemical library and a few additional chemicals. Over 1800 chemicals were tested in total. It contains the list of chemicals tested and the median, minimum, and maximum inhibition for each chemical screened at 200 µM. Chemicals that gave greater than 50% inhibition were screened in concentration response mode, and the median, min, max inhibition at each concentration for those chemicals are included. Propylthiouracil was used in each plate as a positive control and the concentration-response data for those curves are also included. This dataset is associated with the following publication: Hornung, M., J. Korte, J. Olker, J. Denny, C. Knutsen, P. Hartig, M. Cardon, and S. Degitz. Screening the ToxCast Phase 1 chemical library for inhibition of deiodinase type 1 activity. TOXICOLOGICAL SCIENCES. Society of Toxicology, 162(2): 570-581, (2018).

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).