title: Quantitative structure activity relationship for the computational prediction of nitrocompounds carcinogenicity.
abstract: Several nitrocompounds have been screened for carcinogenicity in rodents, but this is a lengthy and expensive process, taking two years and typically costing 2.5 million dollars, and uses large numbers of animals. There is, therefore, much impetus to develop suitable alternative methods. One possible way of predicting carcinogenicity is to use quantitative structure-activity relationships (QSARs). QSARs have been widely utilized for toxicity testing, thereby contributing to a reduction in the need for experimental animals. This paper describes the results of applying a TOPological substructural molecular design (TOPS-MODE) approach for predicting the rodent carcinogenicity of nitrocompounds. The model described 79.10% of the experimental variance, with a standard deviation of 0.424. The predictive power of the model was validated by leave-one-out validation, with a determination coefficient of 0.666. In addition, this approach enabled the contribution of different fragments to carcinogenic potency to be assessed, thereby making the relationships between structure and carcinogenicity to be transparent. It was found that the carcinogenic activity of the chemicals analysed was increased by the presence of a primary amine group bonded to the aromatic ring, a manner that was proportional to the ring aromaticity. The nitro group bonded to an aromatic carbon atom is a more important determinant of carcinogenicity than the nitro group bonded to an aliphatic carbon. Finally, the TOPS-MODE approach was compared with four other predictive models, but none of these could explain more than 66% of the variance in the carcinogenic potency with the same number of variables.
authors.affiliation: Department of Chemistry, Faculty of Chemistry and Pharmacy, Central University of Las Villas, Santa Clara, Villa Clara 54830, Cuba.
authors.forename: Aliuska Helguera
authors.initials: AH
authors.lastname: Morales
authors.forename: Miguel Angel Cabrera
authors.initials: MA
authors.lastname: Pérez
authors.forename: Robert D
authors.initials: RD
authors.lastname: Combes
authors.forename: Maykel Pérez
authors.initials: MP
authors.lastname: González
mesh_terms: D000818:Animals; D015197:Carcinogenicity Tests / Q000706:statistics & numerical data; D002273:Carcinogens / Q000737:chemistry* / Q000633:toxicity; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005544:Forecasting / Q000379:methods; D051379:Mice; D008962:Models, Theoretical; D009574:Nitro Compounds / Q000737:chemistry* / Q000633:toxicity; D021281:Quantitative Structure-Activity Relationship; D051381:Rats
chemical_list: D002273:Carcinogens; D009574:Nitro Compounds