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Evaluating the mutagenic potential of aerosol organic compounds using informatics-based screening

机译：使用基于信息学的筛选评估气溶胶有机化合物的诱变潜力

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Whilst general policy objectives to reduce airborne particulate matter (PM) health effects are to reduce exposure to PM as a whole, emerging evidence suggests that more detailed metrics associating impacts with different aerosol components might be needed. Since it is impossible to conduct toxicological screening on all possible molecular species expected to occur in aerosol, in this study we perform a proof-of-concept evaluation on the information retrieved from in silico toxicological predictions, in which a subset (iN/i?=?104) of secondary organic aerosol (SOA) compounds were screened for their mutagenicity potential. An extensive database search showed that experimental data are available for 13?% of the compounds, while reliable predictions were obtained for 82?%. A multivariate statistical analysis of the compounds based on their physico-chemical, structural, and mechanistic properties showed that 80?% of the compounds predicted as mutagenic were grouped into six clusters, three of which (five-membered lactones from monoterpene oxidation, oxygenated multifunctional compounds from substituted benzene oxidation, and hydroperoxides from several precursors) represent new candidate groups of compounds for future toxicological screenings. These results demonstrate that coupling model-generated compositions to in silico toxicological screening might enable more comprehensive exploration of the mutagenic potential of specific SOA components.
机译：虽然减少空气中颗粒物（PM）对健康的影响的一般政策目标是减少整个PM的暴露量，但新兴证据表明，可能需要更详细的指标来将影响与不同的气溶胶成分相关联。由于不可能对预期会出现在气溶胶中的所有可能分子种类进行毒理学筛选，因此在本研究中，我们对从计算机毒理学预测中检索到的信息进行了概念验证评估，其中一部分（ N筛选次级有机气溶胶（SOA）化合物的诱变潜力。广泛的数据库搜索显示，有13％的化合物有实验​​数据，而有82％的化合物得到了可靠的预测。根据这些化合物的理化，结构和力学特性进行的多变量统计分析表明，预测为诱变化合物的80 %%分为六个簇，其中三个（单萜氧化的五元内酯，氧化的多功能来自取代苯氧化的化合物，以及来自几种前体的氢过氧化物）代表了用于未来毒理学筛选的新的候选化合物。这些结果表明，将模型生成的组合物与计算机毒理学筛选相结合可能可以更全面地探索特定SOA组件的诱变潜力。

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