A descriptor-based analysis to highlight the mechanistic rationale of mutagenicity.

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.