QSAR (Quantitative Structure-Activity Relationship) is an ideal tool as high throughput screening for huge number of chemicals without costs, labours, and animals. The ICH-M7 guideline is the first international guideline addressing the use of QSARs for evaluating human health effect. Many highly reliable data sets are essential to allow the development and improvement of QSAR models. The Ames/QSAR international collaborative study is successfully ongoing. Its outcome gives a lot of benefits to QSAR vendors, QSAR users, and regulatory. The integrated approach with QSAR results increases the sensitivity and specificity of the Ames assay for predicting rodent carcinogens. It can support to judge the Ames results with molecular mechanism.
(https://www.nihs.go.jp/dgm/ICEM2017Honma.pdf)
A full scientific literature study, data migration from analogue compounds, or preclinical and clinical trials are all options for determining the pharmacological and toxicological profile of chemical structures. Nonetheless, the need to generate data using methods other than animal experiments is widely recognised, leading to the growth of computational chemistry as a field. Different endpoints, such as physicochemical properties, pharmacokinetic and toxicological endpoints, or ecotoxicity and environmental aspects, can be predicted using computational predictions from reliable (Q)SARs techniques.
Computational toxicology is well-known, and its usage is actively encouraged because it reduces animal experiments while also saving time and resources. Furthermore, computational methods make it simple and quick to apply QSAR models to the investigation of new structures.
Quantitative relation structure activity techniques (QSAR) are computational models that anticipate a substance’s attributes based on its molecular structure. The construction of an unambiguous algorithm, a defined applicability domain, and an acceptable fit, robustness, and predictability of the QSAR system are desirable criteria that must be met by the models utilised, according to the OECD.
Expert “rule-based” systems developed by QSAR are based on a thorough examination and assessment of the plausible mechanisms and scientific facts available. The scientific review allows toxicities linked with specific structural groupings, known as toxicophores, to be identified. An alert-based expert system is derived from this analysis, which leads to the prediction of certain qualities in the compound under consideration. QSAR outcomes will be as excellent as the available dataset, given that this alarm and prediction system is based on existing scientific data.
Statistical systems are based on empirical data, allowing for the assignment of a probability value based on the possibility of any individual parameter occurring. Internal structural group comparison, physiochemical characteristics, adjacent groups, and chemical environment are all used in these programmes.
In Maven we commonly use both statistical and expert based QSAR models to perform consistenttoxicological assessments as well as environmental evaluations. Our toxicology team have a broad experience on QSAR analysis of pharmaceutical impurities, cosmetics or biocides, with the support of our consultant partner Led scope for statistical QSAR assessments.
