(Q)SAR Assessment
QSAR (Quantitative Structure-Activity Relationship) is an ideal tool for high-throughput screening of large numbers of chemicals without using laboratory resources, animal testing, or excessive costs. The ICH-M7 guideline is the first international guideline addressing QSAR use for evaluating human health effects. Reliable datasets are essential for developing and improving QSAR models. For example, the ongoing Ames/QSAR international collaborative study benefits QSAR vendors, users, and regulators by integrating QSAR results with experimental assays. (NIHS ICEM2017 reference)
Scientific literature studies, data migration from analogue compounds, and preclinical/clinical trials are all used to determine pharmacological and toxicological profiles. Computational chemistry methods provide alternatives to animal testing, predicting endpoints such as physicochemical, pharmacokinetic, toxicological, ecotoxicity, and environmental effects.
Computational toxicology reduces animal experiments, saves time and resources, and simplifies the application of QSAR models to new structures. QSAR models predict substance properties based on molecular structures.
OECD recommends that QSAR models have a clear algorithm, defined applicability domain, and demonstrate robustness, predictability, and fit. Expert “rule-based” QSAR systems assess scientific data to identify toxicophores, generating alert-based predictions. Statistical systems rely on empirical data to assign probabilities using structural and chemical characteristics.
At Maven, we combine statistical and expert-based QSAR models to perform consistent toxicological assessments and environmental evaluations. Our toxicology team has broad experience analyzing pharmaceutical impurities, cosmetics, and biocides, with support from our consulting partner Led Scope for statistical QSAR assessments.
