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.
Session 3. Toxicity Profiling, Screening and Prediction
Traditional toxicity testing methods are often reliant on animal models, which are time consuming, costly, and often not extrapolatable to humans. Recent advances in high-throughput screening (HTS), in silico models, and machine learning techniques provide efficient alternatives to toxicity testing that can supplement traditional methods. HTS allows researchers to test large numbers of compounds rapidly, providing valuable data on their toxicological profiles. In parallel, predictive modeling leverages existing datasets to forecast the toxicity of untested compounds, reducing reliance on time-consuming and ethically challenging methods. This session will discuss recent progress in high-throughput profiling of environmental toxicants using in vitro systems and the integration of diverse data types for toxicity prediction.
