<i>In silico</i> exploratory study using structure–activity relationship models and metabolic information for prediction of mutagenicity based on the Ames test and rodent micronucleus assay

The mutagenic potential of chemicals is a cause of growing concern, due to the possible impact on human health. In this paper we have developed a knowledge-based approach, combining information from structure–activity relationship (SAR) and metabolic triggers generated from the metabolic fate of chemicals in biological systems for prediction of mutagenicity <i>in vitro</i> based on the Ames test and <i>in vivo</i> based on the rodent micronucleus assay. In the first part of the work, a model was developed, which comprises newly generated SAR rules and a set of metabolic triggers. These SAR rules and metabolic triggers were further externally validated to predict mutagenicity <i>in vitro</i>, with metabolic triggers being used only to predict mutagenicity of chemicals, which were predicted unknown, by SARpy. Hence, this model has a higher accuracy than the SAR model, with an accuracy of 89% for the training set and 75% for the external validation set. Subsequently, the results of the second part of this work enlist a set of metabolic triggers for prediction of mutagenicity <i>in vivo</i>, based on the rodent micronucleus assay. Finally, the results of the third part enlist a list of metabolic triggers to find similarities and differences in the mutagenic response of chemicals <i>in vitro</i> and <i>in vivo</i>.

化学品的致突变潜能因其对人类健康可能造成的影响而日益受到学界关注。本文采用基于知识的方法，将构效关系（structure–activity relationship, SAR）信息与从生物体系中化学品代谢归宿推导得到的代谢触发因子相结合，分别基于艾姆斯试验（Ames test）构建体外（in vitro）致突变性预测模型，以及基于啮齿类微核试验（rodent micronucleus assay）构建体内（in vivo）致突变性预测模型。
本研究第一部分开发了一款集成模型，其包含新生成的SAR规则与一组代谢触发因子。随后对上述SAR规则及代谢触发因子开展外部验证，以用于体外致突变性预测；其中代谢触发因子仅用于预测SARpy无法给出明确判定的化学品致突变性。相较单一SAR模型，该集成模型的预测精度更优：训练集准确率达89%，外部验证集准确率达75%。
后续本研究第二部分的结果，基于啮齿类微核试验得到了一组用于体内致突变性预测的代谢触发因子。最后，第三部分的研究结果列出了一系列代谢触发因子，用于分析化学品在体外与体内的致突变响应之间的异同。