Quantitative Structure–Activity Relationship (QSAR) Models in Early Hazard Prediction
Quantitative structure–activity relationship (QSAR) modeling represents a computational method used to predict the biological activity or toxicity of chemical compounds based on their molecular structure. In regulatory toxicology, QSAR tools are increasingly employed for early hazard identification, prioritization of test compounds, and support for read-across approaches when experimental data are limited. These models provide a valuable first layer of assessment, especially in the preclinical stages of drug development.
QSAR models are built using large databases of chemicals with known toxicological profiles. Molecular descriptors—such as hydrophobicity, electronic properties, hydrogen bonding capacity, and molecular weight—are used to correlate chemical structure with observed toxicity endpoints. Depending on the model, the predicted outcomes may include genotoxicity, mutagenicity, skin sensitization, carcinogenicity, or specific organ toxicity.
Regulatory agencies such as the FDA and ECHA accept QSAR predictions under defined conditions, particularly when validated models are used and the chemical under evaluation falls within the applicability domain. For new drug entities, QSAR models can flag potential structural alerts or off-target toxicities before laboratory testing, allowing developers to modify structures, prioritize lead candidates, or anticipate regulatory concerns.
Despite their utility, QSAR predictions must be interpreted cautiously and ideally supported by experimental validation. False positives and negatives can occur, particularly when the compound falls outside the structural space of the model’s training set. However, when used in conjunction with in vitro assays and expert judgment, QSAR tools accelerate decision-making, reduce reliance on animal testing, and contribute to a more efficient drug development process.