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Principal Investigator: Ruili Huang
Organization: NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES
Fiscal Year: 2023
Award: $637,571
Funding agency: National Center for Advancing Translational Sciences
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 nine assays that were identified, developed, optimized, and/or screened by the Tox21 systems toxicology team and gene expression and high throughput neurotoxicity assay 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. In addition, the Tox21 Computational Toxicology team has updated the web-based, automated structure-activity relationship (SAR) analysis tool for the systems toxicology team to conduct SAR analysis on all 10K library screens. The Tox21 Computational Toxicology team has also updated the Tox21 Assay Tracking System that stores the assay annotations and detailed experimental conditions and screening protocols for all the Tox21 assays. The 10K data from all assays screened up to FY22 have been made public in PubChem totaling 229 assay entries (AIDs) and nearly 102 million data points.
Public Health Impact Statement
During the course of a lifetime, most people are exposed to many different environmental chemicals. These substances can be found in food, water, household cleaning products and elsewhere. In some cases, these chemicals can be toxic, and in others, researchers lack sufficient data about safety. Medicines also contain chemicals, and in fact, more than 30 percent of promising pharmaceuticals have failed in human clinical trials because they are found to be toxic, despite promising pre-clinical studies in animal and other models. The Tox21 Program works to create alternative methods for assessing chemical toxicity that are less expensive and time-consuming than traditional approaches will improve how scientists evaluate environmental chemicals and develop new medicines to benefit public health.
The Tox21 public data browser has been updated with the latest assay results from the 10K library screens totaling 78 assays. This 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. In FY23, the Tox21 Computational Toxicology team has been working with other NCATS teams to update the NCATS Pharmaceutical Collection (NPC), which is part of the Tox21 10K compound library, with drugs approved in recent years to include an up-to-date list of all approved drugs. In addition, the teams have been working on constructing a collection of clinically tested compounds to complement the NPC in FY23. The Tox21 Computational Toxicology team is also leading one of the Tox21 cross-partner projects Expansion of Pathway Coverage by Tox21 High-Throughput Screening Assays for Better Prediction of Adverse Drug Effects, which utilizes the NCATS BioPlanet (https://tripod.nih.gov/bioplanet/) as a tool to define the biological space and identify assays for screening. This project has generated eleven assay datasets up to FY23 and these data are being applied to build computational models to predict liver and cardiotoxicity. The initial modeling results have been published in Toxicology and Applied Pharmacology in FY23. In addition, the Tox21 Computational Toxicology team has continued to work with the Tox21 Genomic Toxicology team to refine methods for concentration response gene expression data analysis including strategies for point-of-departure (PoD) determination. A web version of the software platform for visualization and analysis of concentration response gene expression data has been developed and will be made public in due course.
In FY23, the Tox21 Computational Toxicology team has been collaborating with the Drug Metabolism and Pharmacokinetics (DMPK) Core of DPI to develop predictive models for inhibitors and substrates of the human cytochrome P450 (CYP) 3A7, which is an important xenobiotic metabolizing enzyme in human embryonic, fetal, and newborn liver, resulting in one publication in Journal of Chemical Information and Modeling in FY23. The Tox21 Computational Toxicology team collaborated with the Systems Toxicology team on the development of computational models to identify selective inhibitors of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) based on qHTS assay data, and the characterization of pesticides for their bioactivity and potential targets of toxicity using their Tox21 assay activity profiles. These collaborative efforts resulted in two papers published in Journal of Chemical Information and Modeling and Toxicology and Applied Pharmacology, respectively, in FY23. The Tox21 Computational Toxicology team continued to collaborate with the TDB biology team in FY23 to develop computational models for the identification of novel antiviral compounds, including anti-SARS-CoV-2 compounds, using chemical structure and viral sequence information. A manuscript summarizing the results is currently under preparation.
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