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).

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.

In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4382 in vivo studies for 1201 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. In order to address the complexity problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using Random Forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 20% of the variance reducing the root mean squared error (RMSE) from 0.96 log10 mg/kg/day to 0.85 log10 mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 38% of the variance with an RMSE of 0.76 log10 mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log10 mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.1 to 0.8 log10 mg/kg/day. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and cytotoxicity, demonstrating the importance of accounting for kinetics and non-specific bioactivity in predicting systemic effect levels. Herein, we have generated an externally predictive model of systemic effect levels for use as a safety assessment tool and have generated forward predictions for thousands of chemicals. This dataset is associated with the following publication: Truong, L., G. Ouedraogo, L. Pham, J. Clouzeau, S. Loisel-Joubert, D. Blanchet, H. Noçairi, W. Setzer, R. Judson, C. Grulke, K. Mansouri, and M. Martin. (Archives of Toxicology) Predicting In Vivo Effect Levels for Repeat Dose Systemic Toxicity using Chemical, Biological, Kinetic and Study Covariates. Archives of Toxicology. Springer, New York, NY, USA, 92(2): 587-600, (2018).

In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4382 in vivo studies for 1201 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. In order to address the complexity problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using Random Forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 20% of the variance reducing the root mean squared error (RMSE) from 0.96 log10 mg/kg/day to 0.85 log10 mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 38% of the variance with an RMSE of 0.76 log10 mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log10 mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.1 to 0.8 log10 mg/kg/day. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and cytotoxicity, demonstrating the importance of accounting for kinetics and non-specific bioactivity in predicting systemic effect levels. Herein, we have generated an externally predictive model of systemic effect levels for use as a safety assessment tool and have generated forward predictions for thousands of chemicals. This dataset is associated with the following publication: Truong, L., G. Ouedraogo, L. Pham, J. Clouzeau, S. Loisel-Joubert, D. Blanchet, H. Noçairi, W. Setzer, R. Judson, C. Grulke, K. Mansouri, and M. Martin. (Archives of Toxicology) Predicting In Vivo Effect Levels for Repeat Dose Systemic Toxicity using Chemical, Biological, Kinetic and Study Covariates. Archives of Toxicology. Springer, New York, NY, USA, 92(2): 587-600, (2018).

Supplemental data for "Schaupp CM, Maloney EM, Mattingly K, Olker JH, Villeneuve DL. Comparison of in silico, in vitro, and in vivo toxicity benchmarks suggests a role for ToxCast data in ecological hazard assessment. Toxicol Sci. 2023 Jul 25:kfad072. doi: 10.1093/toxsci/kfad072. Epub ahead of print. PMID: 37490521.". This dataset is associated with the following publication: Schaupp, C., E. Maloney, K. Mattingly, J. Olker, and D. Villeneuve. Comparison of in silico, in vitro, and in vivo toxicity benchmarks suggests a role for ToxCast data in ecological hazard assessment.. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 195(2): 145-154, (2023).

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).

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).

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.

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).

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.