a IRCCS -Istituto di Ricerche Farmacologiche Mario Negri , Milano , Italy.
e Chemical Food Safety Group, Nestlé Research Center , Lausanne , Switzerland.
SAR QSAR Environ Res. 2018 Aug;29(8):591-611. doi: 10.1080/1062936X.2018.1497702. Epub 2018 Jul 27.
Results from the Ames test are the first outcome considered to assess the possible mutagenicity of substances. Many QSAR models and structural alerts are available to predict this endpoint. From a regulatory point of view, the recommendation from international authorities is to consider the predictions of more than one model and to combine results in order to develop conclusions about the mutagenicity risk posed by chemicals. However, the results of those models are often conflicting, and the existing inconsistency in the predictions requires intelligent strategies to integrate them. In our study, we evaluated different strategies for combining results of models for Ames mutagenicity, starting from a set of 10 diverse individual models, each built on a dataset of around 6000 compounds. The novelty of our study is that we collected a much larger set of about 18,000 compounds and used the new data to build a family of integrated models. These integrations used probabilistic approaches, decision theory, machine learning, and voting strategies in the integration scheme. Results are discussed considering balanced or conservative perspectives, regarding the possible uses for different purposes, including screening of large collection of substances for prioritization.
结果从艾姆斯试验被认为是第一个结果来评估物质的可能诱变。许多定量构效关系模型和结构警示是可用于预测这个终点。从监管的角度来看，国际权威机构的建议是考虑超过一个模型的预测，并结合结果，以制定有关化学物质的致突变风险的结论。然而，这些模型的结果往往是相互矛盾的，现有的预测不一致需要智能策略来整合它们。在我们的研究中，我们评估了不同的策略来结合艾姆斯致突变性模型的结果，从一组 10 个不同的个体模型开始，每个模型都建立在大约 6000 个化合物的数据集上。我们的研究的新颖之处在于，我们收集了一个更大的数据集，大约 18000 个化合物，并使用新的数据来构建一组集成模型。这些集成使用了概率方法、决策理论、机器学习和投票策略在集成方案。结果是讨论考虑平衡或保守的观点，关于不同目的的可能用途，包括筛选大量物质的优先级。
