A knowledge-based expert rule system for predicting mutagenicity (Ames test) of aromatic amines and azo compounds. (31st August 2016) - Gadaleta, Domenico; Manganelli, Serena; Manganaro, Alberto; Porta, Nicola; Benfenati, Emilio
Highlights: Exposure to environmental chemicals is a major cause of occupational cancer. Azo compounds and aromatic amines arise major environmental and health concerns. A rule-based expert system was derived predicting mutagenicity of aromatic amines. Toxicity of azo dyes was estimated based on their aromatic amine metabolic products. The expert system was implemented as a freely available KNIME workflow. Abstract: 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.