Integrating computational methods to predict mutagenicity of aromatic azo compounds. (2nd October 2017)

• Record Type: Journal Article
• Authors: Gadaleta, Domenico; Porta, Nicola; Vrontaki, Eleni; Manganelli, Serena; Manganaro, Alberto; Sello, Guido; Honma, Masamitsu; Benfenati, Emilio
• Is Part Of: Journal of environmental science and health. Volume 35:Number 4(2017)
• Page Start: 239
• Page End: 257
• Publication Date: 2017-10-02
• Subjects: Azo compounds -- mutagenicity -- (Q)SAR -- docking -- consensus model; Carcinogenesis -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmentally induced diseases -- Periodicals; Environmental Health -- periodicals; Carcinogens, Environmental -- periodicals; Hazardous Substances -- periodicals; 616.994071
• DOI: 10.1080/10590501.2017.1391521
• Languages: English
• ISSNs: 1059-0501

Abstract

ABSTRACT: Azo dyes have several industrial uses. However, these azo dyes and their degradation products showed mutagenicity, inducing damage in environmental and human systems. Computational methods are proposed as cheap and rapid alternatives to predict the toxicity of azo dyes. A benchmark dataset of Ames data for 354 azo dyes was employed to develop three classification strategies using knowledge-based methods and docking simulations. Results were compared and integrated with three models from the literature, developing a series of consensus strategies. The good results confirm the usefulness of in silico methods as a support for experimental methods to predict the mutagenicity of azo compounds.