title: Predicting carcinogenicity and understanding the carcinogenic mechanism of N-nitroso compounds using a TOPS-MODE approach.
abstract: A linear discriminant analysis (LDA) coupled with an enhanced replacement method (ERM) was used as an alternative method to predict the carcinogenicity of N-nitroso compounds (NOCs) in rats. This presented LDA based on the topological substructural molecular descriptors (TOPS-MODE) approach was developed to predict the carcinogenic and noncarcinogenic activity on a data set of 111 NOCs with a good classification value of 90.1%. The predictive power of the LDA model was validated through an external validation set (37 compounds) with a prediction accuracy of 94.6% and a leave-one-out cross-validation procedure (LOOCV) with a good prediction of 86.5%. This methodology showed that the TOPS-MODE descriptors weighted, respectively, by bond dipole moment and Abraham solute descriptor dipolarity/polarizability affected the NOC carcinogenicity. The contributions of certain bonds and fragments to carcinogenicity were used to assess biotransformation and carcinogenic mechanisms. The positive contribution of the carbon-nitrogen single bond (between the N-nitroso group and α-carbon to the N-nitroso group) indicated that the α-hydroxylation reaction could occur at the α-carbon or otherwise not occur. Similarly, the contributions from the molecular fragment could be applied to indicate whether the fragments generated an alkylating agent. These results suggested that this approach could discriminate between carcinogenic and noncarcinogenic NOCs, thereby providing insight into the structural features and chemical factors related to NOC carcinogenicity.
authors.affiliation: Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.
authors.forename: Jintao
authors.initials: J
authors.lastname: Yuan
authors.forename: Yuepu
authors.initials: Y
authors.lastname: Pu
authors.forename: Lihong
authors.initials: L
authors.lastname: Yin
mesh_terms: D000465:Algorithms; D000818:Animals; D002273:Carcinogens / Q000737:chemistry* / Q000633:toxicity*; D016208:Databases, Factual; D016002:Discriminant Analysis; D009603:Nitroso Compounds / Q000737:chemistry* / Q000633:toxicity; D021281:Quantitative Structure-Activity Relationship; D051381:Rats
chemical_list: D002273:Carcinogens; D009603:Nitroso Compounds