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采用免费软件的计算化学模型评估挥发性有机化合物致癌性和致突变性的预测能力。

Assessment of predictivity of volatile organic compounds carcinogenicity and mutagenicity by freeware in silico models.

机构信息

Post-Graduation Program on Science and Biotechnology, Fluminense Federal University, Niteroi, Brazil.

Faculty of Pharmacy, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.

出版信息

Regul Toxicol Pharmacol. 2017 Dec;91:1-8. doi: 10.1016/j.yrtph.2017.09.030. Epub 2017 Sep 29.

Abstract

The application of in silico methods is increasing on toxicological risk prediction for human and environmental health. This work aimed to evaluate the performance of three in silico freeware models (OSIRIS v.2.0, LAZAR, and Toxtree) on the prediction of carcinogenicity and mutagenicity of thirty-eight volatile organic compounds (VOC) related to chemical risk assessment for occupational exposure. Theoretical data were compared with assessments available in international databases. Confusion matrices and ROC curves were used to evaluate the sensitivity, specificity, and accuracy of each model. All three models (OSIRIS, LAZAR and Toxtree) were able to identify VOC with a potential carcinogenicity or mutagenicity risk for humans, however presenting differences concerning the specificity, sensitivity, and accuracy. The best predictive performances were found for OSIRIS and LAZAR for carcinogenicity and OSIRIS for mutagenicity, as these softwares presented a combination of negative predictive power and lower risk of false positives (high specificity) for those endpoints. The heterogeneity of results found with different softwares reinforce the importance of using a combination of in silico models to occupational toxicological risk assessment.

摘要

基于计算机的方法在人类和环境健康毒理学风险预测中的应用正在增加。本研究旨在评估三种基于计算机的免费软件模型(OSIRIS v.2.0、LAZAR 和 Toxtree)在预测与职业暴露化学风险评估相关的 38 种挥发性有机化合物(VOC)的致癌性和致突变性方面的性能。理论数据与国际数据库中的评估结果进行了比较。混淆矩阵和 ROC 曲线用于评估每个模型的敏感性、特异性和准确性。所有三种模型(OSIRIS、LAZAR 和 Toxtree)都能够识别出具有人类潜在致癌性或致突变性风险的 VOC,但在特异性、敏感性和准确性方面存在差异。对于致癌性,OSIRIS 和 LAZAR 的预测性能最佳,对于致突变性,OSIRIS 的预测性能最佳,因为这些软件在这些终点上表现出了阴性预测能力和较低的假阳性风险(高特异性)的组合。不同软件得到的结果存在异质性,这进一步强调了在职业毒理学风险评估中使用多种基于计算机的模型组合的重要性。

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