Integrating computational methods to predict mutagenicity of aromatic azo compounds

Domenico Gadaleta, Nicola Porta, Eleni Vrontaki, Serena Manganelli, Alberto Manganaro, Guido Sello, Masamitsu Honma, Emilio Benfenati

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)239-257
Number of pages19
JournalJournal of Environmental Science and Health - Part C Environmental Carcinogenesis and Ecotoxicology Reviews
Volume35
Issue number4
DOIs
Publication statusPublished - Oct 2 2017

Keywords

  • (Q)SAR
  • Azo compounds
  • consensus model
  • docking
  • mutagenicity

ASJC Scopus subject areas

  • Health, Toxicology and Mutagenesis
  • Cancer Research

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