Abstract

Cancer is a main concern for human health and there is a need of alternative methodologies to rapidly screen large quantitative of compounds that may represent a toxicological risk. Here a statistical analyses is performed on a benchmark database of experimental Ames data to identify chemical descriptors discriminating mutagens and non-mutagens. A total of 53 activating and deactivating modulators are identified, that flagged respectively a percentage of mutagen and non-mutagen up to 87%. Modulators are further combined to form synergistic cross-terms, accounting for the effect that combined properties may have on the final toxicity. Exclusion rules are defined as exception to the modulators. Synergistic cross-terms and exclusion rules improve the enrichment of mutagens/non-mutagens with respect of the original abundance in the dataset to values higher than 95%. The external predictivity of modulators and cross-terms reach balanced accuracy up to 0.775 that is analogous to other mutagenicity models from the literature, confirming the suitability of the rules to real-life screening of chemicals. Modulators are discussed for their mechanistic link to mutagenicity. This analysis confirms the key role of some properties (polarizability, shape, mass, presence of reactive functional groups or unsaturated planar systems) as driving elements for the initiation of the mutagenicity.

摘要

癌症是人类健康的主要问题，需要替代方法来快速筛选大量可能代表毒理学风险的化合物。这里对实验 Ames 数据的基准数据库进行统计分析，以识别区分诱变剂和非诱变剂的化学描述符。共鉴定了 53 种激活和失活调节剂，分别标记了高达 87% 的诱变剂和非诱变剂的百分比。调节剂进一步组合形成协同交叉项，说明组合特性可能对最终毒性产生的影响。排除规则被定义为调制器的例外。协同交叉项和排除规则将诱变剂/非诱变剂相对于数据集中原始丰度的富集提高到高于 95% 的值。调节剂和交叉项的外部预测达到平衡精度，最高可达 0.775，这与文献中的其他诱变性模型类似，证实了规则适用于现实生活中的化学品筛选。讨论了调节剂与致突变性的机制联系。该分析证实了一些特性（极化率、形状、质量、反应性官能团的存在或不饱和平面系统）作为引发致突变性的驱动因素的关键作用。775 类似于文献中的其他致突变性模型，证实了规则适用于现实生活中的化学品筛查。讨论了调节剂与致突变性的机制联系。该分析证实了一些特性（极化率、形状、质量、反应性官能团的存在或不饱和平面系统）作为引发致突变性的驱动因素的关键作用。775 类似于文献中的其他致突变性模型，证实了规则适用于现实生活中的化学品筛查。讨论了调节剂与致突变性的机制联系。该分析证实了一些特性（极化率、形状、质量、反应性官能团的存在或不饱和平面系统）作为引发致突变性的驱动因素的关键作用。