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).

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.

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.

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 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 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 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 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 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.

ToxRefDB comprises information from over fifty years of in vivo toxicity data. The database includes information for over 1000 chemicals, and is being used as a primary source of data for evaluating efforts of the ToxCast program [4,5], as well as for numerous predictive and retrospective analyses. This dataset is associated with the following publication: Watford, S., L. Pham, J. Wignall, R. Shin, M.T. Martin, and K. Friedman. ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses. REPRODUCTIVE TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 89: 145-158, (2019).