Structure-based QSAR Models to Predict Repeat Dose Toxicity Points of Departure.

ABSTRACT: Human health risk assessment for environmental chemical exposure is limited by a vast majority of chemicals with little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative structure activity relationship (QSAR) models based on chemical structure information, can predict hazard in the absence of experimental data. Risk assessment requires identification of a quantitative point-of-departure (POD) value, the point on the dose-response curve that marks the beginning of a low-dose extrapolation. This study presents two sets of QSAR models to predict POD values (PODQSAR) for repeat dose toxicity. For training and validation, a publicly available in vivo toxicity dataset for 3592 chemicals was compiled using the U.S. Environmental Protection Agency's Toxici

SUBMITTER: Pradeep P 

PROVIDER: S-EPMC8128699 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature