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Ruili Huang
NIH National Center for Advancing Translational Sciences (NCATS)

Dr. Ruili Huang is the informatics group leader on the toxicity profiling team at the NIH National Center for Advancing Translational Sciences (NCATS). She also serves as a co-chair of the Tox21 chemical library working group. Dr. Huang and her group contribute to quantitative high-throughput screening (qHTS) data processing and interpretation and development and implementation of software tools and algorithms that facilitate NCATS’ data pipeline. As a computational toxicology team, Dr. Huang’s group evaluates qHTS assay performance for prioritization, analyzes compound in vitro toxicity profiling data to generate hypotheses on compound mechanisms of toxicity, and develops computational models for better prediction of in vivo toxicity. Additionally, her group integrates biological pathway information and qHTS assay data to support interpretation of results. Dr. Huang received her Ph.D. in chemistry from Iowa State University, trained as a computational biologist at the National Cancer Institute, and joined NCATS in 2006.

OpenTox Summer School 2022 

Use and Interpretation of Tox21 data

The U.S. Tox21 program has developed in vitro assays to test large collections of environmental chemicals in a quantitative high-throughput screening (qHTS) format, using triplicate 15-dose titrations to generate over 100 million data points to date. Counter screens are also employed to minimize interferences from non-target specific assay artifacts, such as compound autofluorescence and cytotoxicity. These datasets can aid in the identification of previously uncharacterized toxicants as well as the development of computational models for toxicity prediction. In this session, technical aspects and caveats associated with the Tox21 qHTS assays will be discussed. In addition, a data processing method applied to deal with the biological and technological artifacts will be described. This process includes steps that evaluate the qHTS data for technical quality in terms of signal reproducibility, and integrate signals from repeated assay runs, primary readouts, and counter screens to produce a final call on on-target compound activity. A demonstration of example analysis results and applications to computational modeling will be given.

OpenTox Virtual Conference 2022

Application of Tox21 data in toxicity prediction

Toxicology in the 21st Century (Tox21) is a U.S. federal collaborative program that develops high-throughput in vitro assays to efficiently evaluate a chemical's potential to cause adverse health effects. To date, the Tox21 program has screened a library of approximately 10,000 (10K) structurally diverse environmental chemicals and drugs against a battery of more than 70 in vitro assays, generating over 100 million data points that have been made publicly available. In this presentation, machine learning efforts utilizing the Tox21 in vitro assay data to develop predictive models for in vivo and in vitro toxicity endpoints will be reviewed. These models can be applied as efficient virtual screens for potentially toxic compounds, and identification of mechanisms of chemical induced toxicity. Combining the computational models with existing Tox21 assay data will enable more time- and cost-efficient prioritization of chemicals for in-depth toxicological evaluations.