Weihsueh Chiu, Ph.D.
Texas A&M University
NIEHS grantees and colleagues developed a computational tool that uses the properties of a chemical to predict its toxicity. They determined that the tool can predict a toxicity value with an error of less than a factor of 10, making it useful for quickly assessing relative risks of chemicals for which traditional toxicity data or human health assessments are unavailable.
Using a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies, the researchers developed quantitative structure-activity relationship (QSAR) models. The QSAR models use information about known relationships between chemical structures and biological activity to predict the activities of chemicals that do not have existing toxicity values.
The authors compared their QSAR model predictions to those based on high-throughput screening (HTS) assays. HTS assays rapidly measure the potential toxicity of large numbers of chemicals by exposing cells to chemicals and assessing cellular changes. The researchers found that the QSAR model predictions were more accurate and more precise than those based on HTS assays.
According to the authors, this tool can fill a critical gap in the assessment of chemicals without sufficient toxicity data. The researchers have made the tool publicly available on the Conditional Toxicity Value Predictor website, where users can calculate predicted toxicity values for chemicals or retrieve existing toxicity values used to build the QSAR models.
Citation: Wignall JA, Muratov E, Sedykh A, Guyton KZ, Tropsha A, Rusyn I, Chiu WA. 2018. Conditional Toxicity Value (CTV) Predictor: an in silico approach for generating quantitative risk estimates for chemicals. Environ Health Perspect 126(5):057008.