Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China.
Chem Res Toxicol. 2012 Nov 19;25(11):2432-42. doi: 10.1021/tx3002912. Epub 2012 Oct 19.
This study aimed to determine the most significant molecular features associated with the liver specificity of the carcinogenicity of N-nitroso compounds (NOCs). Accordingly, quantitative structure-activity relationship (QSAR) analysis was performed to extract molecular information from NOCs using a topological substructural molecular descriptor (TOPS-MODE) approach. A linear discriminant analysis (LDA) model of a series of NOCs for rat liver was developed using TOPS-MODE descriptors to predict nonliver- and liver-carcinogenic NOCs. Two descriptors exclusively calculated from the molecular structures of the compounds were selected by a genetic algorithm. The descriptors were then weighted with bond distances as well as the Abraham solute descriptor partition between water and aqueous solvent systems to indicate the importance of their roles in liver specificity. The performances of the LDA model were rigorously validated by leave-one-out cross-validation and external validation, with the prediction accuracy reaching 88.3% and 80.0%, respectively. The contributions of the different molecular fragments to rat-liver specificity were computed. The results served as important information related to liver specificity and were analyzed from the chemical-molecular perspective. The resulting model can provide an efficient method to discriminate between as well as extrapolate nonliver- and liver-carcinogenic NOCs. The contribution of the entire nitrosamine molecule was determined as being responsible for the liver specificity of nitrosamine carcinogenicity. Although the QSAR showed limitations in complex hepatocarcinogenicity, the proposed method may considerably help elucidate the role of nitrosamines in liver specificity from the chemical-molecular perspective. The nature of these enzyme-substrate interactions is characterized. Insight into the chemical-structural and biological factors related to the liver-specific biological activity of NOCs is also provided.
本研究旨在确定与 N-亚硝化合物（NOCs）致癌性的肝脏特异性相关的最重要的分子特征。因此，使用拓扑子结构分子描述符（TOPS-MODE）方法，进行定量构效关系（QSAR）分析，从 NOCs 中提取分子信息。使用 TOPS-MODE 描述符为大鼠肝脏开发了一系列 NOC 的线性判别分析（LDA）模型，以预测非肝脏和肝脏致癌 NOCs。通过遗传算法选择了两个仅从化合物分子结构计算得出的描述符。然后，将这些描述符的权重与键距离以及水和水溶剂系统之间的 Abraham 溶质描述符分配相结合，以表明它们在肝脏特异性中的作用的重要性。通过留一法交叉验证和外部验证严格验证了 LDA 模型的性能，预测准确率分别达到 88.3%和 80.0%。计算了不同分子片段对大鼠肝脏特异性的贡献。这些结果提供了与肝脏特异性相关的重要信息，并从化学-分子角度进行了分析。所得到的模型可以提供一种有效的方法来区分非肝脏和肝脏致癌 NOCs，并进行外推。整个亚硝胺分子的贡献被确定为亚硝胺致癌性肝脏特异性的原因。尽管 QSAR 在复杂的肝癌发生中存在局限性，但该方法可能会从化学-分子角度极大地帮助阐明亚硝胺在肝脏特异性中的作用。这些酶-底物相互作用的性质被确定。还提供了与 NOCs 肝脏特异性生物活性相关的化学结构和生物学因素的深入了解。
