Topological structural alerts modulations of mammalian cell mutagenicity for halogenated derivatives

• Type: Article in International Scientific Journal
• Date: 2013

Authors

Perez Garrido, A (Author) Other; Giron Rodriguez, F (Author) Other; Helguera, AM (Author) Other; Borges, F (Author) FCUP; Combes, RD (Author) Other

Journal

Title: SAR and QSAR in Environmental Research

ISSN: 1062-936X

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 4 Citations

Scientific classification

FOS: Natural sciences > Chemical sciences

CORDIS: Physical sciences > Chemistry > Applied chemistry

Other information

Authenticus ID: P-008-HV7

Abstract (EN): Genotoxicity is a key toxicity endpoint for current regulatory requirements regarding new and existing chemicals. However, genotoxicity testing is time-consuming and costly, and involves the use of laboratory animals. This has motivated the development of computational approaches, designed to predict genotoxicity without the need to conduct laboratory tests. Currently, many existing computational methods, like quantitative structure-activity relationship (QSAR) models, provide limited information about the possible mechanisms involved in mutagenicity or predictions based on structural alerts (SAs) do not take statistical models into account. This paper describes an attempt to address this problem by using the TOPological Substructural MOlecular Design (TOPS-MODE) approach to develop and validate improved QSAR models for predicting the mutagenicity of a range of halogenated derivatives. Our most predictive model has an accuracy of 94.12%, exhibits excellent cross-validation and external set statistics. A reasonable interpretation of the model in term of SAs was achieved by means of bond contributions to activity. The results obtained led to the following conclusions: primary halogenated derivatives are more mutagenic than secondary ones; and substitution of chlorine by bromine increases mutagenicity while polyhalogenation decreases activity. The paper demonstrates the potential of the TOPS-MODE approach in developing QSAR models for identifying structural alerts for mutagenicity, combining high predictivity with relevant mechanistic interpretation. © 2013 © 2013 Taylor & Francis.

Language: English

Type (Professor's evaluation): Scientific