Gadaleta Domenico, Manganelli Serena, Manganaro Alberto, Porta Nicola, Benfenati Emilio
Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via Giuseppe La Masa 19, 20156 Milan, Italy.
Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via Giuseppe La Masa 19, 20156 Milan, Italy.
Toxicology. 2016 Aug 31;370:20-30. doi: 10.1016/j.tox.2016.09.008. Epub 2016 Sep 16.
Cancer is one of the main causes of death in Western countries, and a major issue for human health. Prolonged exposure to a number of chemicals was observed to be one of the primary causes of cancer in occupationally exposed persons. Thus, the development of tools for identifying hazardous chemicals and the increase of mechanistic understanding of their toxicity is a major goal for scientific research. We constructed a new knowledge-based expert system accounting the effect of different substituents for the prediction of mutagenicity (Ames test) of aromatic amines, a class of compounds of major concern because of their widespread application in industry. The herein presented model implements a series of user-defined structural rules extracted from a database of 616 primary aromatic amines, with their Ames test outcomes, aimed at identifying mutagenic and non-mutagenic chemicals. The chemical rationale behind such rules is discussed. Besides assessing the model's ability to correctly classify aromatic amines, its predictivity was further evaluated on a second database of 354 azo dyes, another class of chemicals of major concern, whose toxicity has been predicted on the basis of the toxicity of aromatic amines potentially generated from the metabolic reduction of the azo bond. Good performance in classification on both the amine (MCC, Matthews Correlation Coefficient=0.743) and the azo dye (MCC=0.584) datasets confirmed the predictive power of the model, and its suitability for use on a wide range of chemicals. Finally, the model was compared with a series of well-known mutagenicity predicting software. The good performance of our model compared with other mutagenicity models, especially in predicting azo dyes, confirmed the usefulness of this expert system as a reliable support to in vitro mutagenicity assays for screening and prioritization purposes. The model has been fully implemented as a KNIME workflow and is freely available for downstream users.
癌症是西方国家主要的死亡原因之一，也是人类健康的一个重大问题。长期接触多种化学物质被认为是职业暴露人群患癌的主要原因之一。因此，开发用于识别有害化学物质的工具以及增强对其毒性的机理理解是科学研究的一个主要目标。我们构建了一个基于知识的新专家系统，该系统考虑了不同取代基对芳香胺致突变性（艾姆斯试验）预测的影响，芳香胺是一类由于在工业中广泛应用而备受关注的化合物。本文提出的模型实施了一系列从包含616种伯芳香胺及其艾姆斯试验结果的数据库中提取的用户定义结构规则，旨在识别致突变和非致突变化学物质。讨论了这些规则背后的化学原理。除了评估该模型正确分类芳香胺的能力外，还在另一个包含354种偶氮染料的数据库上进一步评估了其预测能力，偶氮染料是另一类备受关注的化学物质，其毒性是根据偶氮键代谢还原可能产生的芳香胺的毒性来预测的。在胺类数据集（马修斯相关系数MCC = 0.743）和偶氮染料数据集（MCC = 0.584）上的良好分类性能证实了该模型的预测能力及其适用于广泛化学物质的特性。最后，将该模型与一系列知名的致突变性预测软件进行了比较。与其他致突变性模型相比，我们模型的良好性能，尤其是在预测偶氮染料方面，证实了这个专家系统作为体外致突变性试验用于筛选和优先级排序的可靠支持的有用性。该模型已完全实现为一个KNIME工作流程，可供下游用户免费使用。
