The Tox21 Programs federal partners include the Environmental Protection Agency (EPA), the Food and Drug Administration (FDA) and NIH, with leadership from NCATS and the National Toxicology Program (NTP) at the National Institute of Environmental Health Sciences (NIEHS). These agencies work together to advance in vitro toxicological testing. The Tox21 Program can be separated into three NCATS teams: Systems Toxicology, Genomic Toxicology, and Computational Toxicology. The Tox21 Computational Toxicology team has enhanced a variety of tools that are routinely used by Tox21 partners to access each others data. The team performed data analysis of more than 19 assays that were identified, developed, optimized, and/or screened by the Tox21 systems toxicology team and gene expression data generated by the Tox21 Genomic Toxicology team. These activities include normalization and correction, fitting of concentration-response curves to generate potency and efficacy measures, classification of curves based on a set of criteria that included significance of fit (measured by p-values), completeness of fit, and efficacy, evaluation of assay performance by data reproducibility, data driven selection of compounds for follow up studies, and identification of genes and pathways involved in cell responses to chemical exposure. The Tox21 Computational Toxicology team has also updated the PubChem deposition tool to facilitate the deposition of the Tox21 10K triplicate screen data into PubChem. The 10K data from all assays screened up to FY15 have been made public in PubChem totaling 120 assay entries (AIDs) and nearly 65 million data points. The Tox21 Computational Toxicology team has also completed the Tox21 Data Challenge 2014, a crowdsourcing competition that developed computational models based on the 10K data from 12 pathway assays, including assays from both the nuclear receptor signaling panel and the stress response pathway panel. The Challenge was launched on July 16, 2014 and closed for scoring on November 14, 2014. The winners were announced on January 26, 2015. 125 participants registered for the Challenge representing 18 different countries. 378 model submissions from 40 teams were received for final evaluation. The winning models all achieved >80% accuracy, with several models exceeding 90% accuracy. Nine articles from the Challenge participants, including the articles describing all the winning models, have been published (one more under review) in a special issue in Frontiers in Environmental Science dedicated to the Tox21 Challenge. The winning models are being made publicly available to the scientific community to aid in compound prioritization. The Tox21 data repository and browser has been updated to include a list of all Tox21 publications and a public data browser. The Tox21 public data browser provides the public with visualization of Tox21 qHTS data including concentration-response curves, curve fitting results and different activity metrics along with chemical structure and analytical QC results. Data are searchable by assay and/or chemical. Results from multiple assays and/or chemicals can be overlaid for ease of comparison. All data as well as assay descriptions and detailed screening protocols (SLPs) are available for download. The Tox21 Computational Toxicology team has continued to work with the Tox21 chemical working group to generate PDF files for the Tox21 10K library chemical QC results, including the 4-month compound stability test results. These results have been released in the public Tox21 Browser (https://tripod.nih.gov/tox21/) and the links to the PDFs have been provided for Tox21 compounds deposited in PubChem. Furthermore, the Web based browser for the NCATS BioPlanet database has been updated. The BioPlanet collects a list of all human pathways that allow the Tox21 Program to design assays to measure their chemical responses. The BioPlanet currently annotates nearly 1,700 unique human pathways by source, biological function and process, disease and toxicity relevance, and availability of probing assays. Manual curation of pathway annotations is continuing including pathway tagging and gene-gene connection validation. This tool will be made available to the Tox21 partners and the public in due course. In addition, the Tox21 Computational Toxicology team has been working with the Tox21 Genomic Toxicology team to develop methods for concentration response gene expression data analysis including strategies for point-of-departure (PoD) determination. A software platform has been developed that provides easy visualization and analysis of concentration response gene expression data.

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2
Fiscal Year
2016
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Translational Science
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Delavan, Brian; Roberts, Ruth; Huang, Ruili et al. (2018) Computational drug repositioning for rare diseases in the era of precision medicine. Drug Discov Today 23:382-394
Li, Shuaizhang; Hsu, Chia-Wen; Sakamuru, Srilatha et al. (2018) Identification of Angiogenesis Inhibitors Using a Co-culture Cell Model in a High-Content and High-Throughput Screening Platform. SLAS Technol 23:217-225
Lynch, Caitlin; Zhao, Jinghua; Huang, Ruili et al. (2018) Identification of Estrogen-Related Receptor ? Agonists in the Tox21 Compound Library. Endocrinology 159:744-753
Xia, Menghang; Huang, Ruili; Shi, Qiang et al. (2018) Comprehensive Analyses and Prioritization of Tox21 10K Chemicals Affecting Mitochondrial Function by in-Depth Mechanistic Studies. Environ Health Perspect 126:077010
Han, Yan; Zhao, Jinghua; Huang, Ruili et al. (2018) Omics-Based Platform for Studying Chemical Toxicity Using Stem Cells. J Proteome Res 17:579-589
Huang, Ruili; Xia, Menghang; Sakamuru, Srilatha et al. (2018) Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles. Sci Rep 8:3783
Li, Shuaizhang; Huang, Ruili; Solomon, Samuel et al. (2017) Identification of acetylcholinesterase inhibitors using homogenous cell-based assays in quantitative high-throughput screening platforms. Biotechnol J 12:
Torimoto-Katori, Nao; Huang, Ruili; Kato, Harutoshi et al. (2017) In Silico Prediction of hPXR Activators Using Structure-Based Pharmacophore Modeling. J Pharm Sci 106:1752-1759
Wu, Leihong; Liu, Zhichao; Auerbach, Scott et al. (2017) Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury. J Chem Inf Model 57:1000-1006
Lynch, Caitlin; Sakamuru, Srilatha; Huang, Ruili et al. (2017) Identifying environmental chemicals as agonists of the androgen receptor by using a quantitative high-throughput screening platform. Toxicology 385:48-58

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