Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Methods Mol Biol. 2022;2425:201-215. doi: 10.1007/978-1-0716-1960-5_9.
Screening compounds for potential carcinogenicity is of major importance for prevention of environmentally induced cancers. A large sequence of predictive models, ranging from short-term biological assays (e.g., mutagenicity tests) to theoretical models, has been attempted in this field. Theoretical approaches such as (Q)SAR are highly desirable for identifying carcinogens, since they actively promote the replacement, reduction, and refinement of animal tests. This chapter reports and describes some of the most noted (Q)SAR models based on human expert knowledge and statistical approaches, aiming at predicting the carcinogenicity of chemicals. Additionally, the performance of the selected models has been evaluated, and the results are interpreted in details by applying these predictive models to some pharmaceutical molecules.
筛选具有潜在致癌性的化合物对于预防环境诱导的癌症至关重要。在该领域，人们尝试了一系列从短期生物测定（例如，致突变试验）到理论模型的预测模型。（Q）SAR 等理论方法非常适合识别致癌物，因为它们积极促进动物试验的替代、减少和优化。本章报告并描述了一些基于人类专业知识和统计方法的最著名的（Q）SAR 模型，旨在预测化学品的致癌性。此外，还评估了所选模型的性能，并通过将这些预测模型应用于一些药物分子来详细解释结果。
