Helguera Aliuska Morales, Cordeiro M Natália D S, Pérez Miguel Angel Cabrera, Combes Robert D, González Maykel Pérez
REQUIMTE, Department of Chemistry, University of Porto, 4169-007 Porto, Portugal.
Toxicol Appl Pharmacol. 2008 Sep 1;231(2):197-207. doi: 10.1016/j.taap.2008.04.008. Epub 2008 Apr 22.
In this work, Quantitative Structure-Activity Relationship (QSAR) modelling was used as a tool for predicting the carcinogenic potency of a set of 39 nitroso-compounds, which have been bioassayed in male rats by using the oral route of administration. The optimum QSAR model provided evidence of good fit and performance of predicitivity from training set. It was able to account for about 84% of the variance in the experimental activity and exhibited high values of the determination coefficients of cross validations, leave one out and bootstrapping (q(2)(LOO)=78.53 and q(2)(Boot)=74.97). Such a model was based on spectral moments weighted with Gasteiger-Marsilli atomic charges, polarizability and hydrophobicity, as well as with Abraham indexes, specifically the summation solute hydrogen bond basicity and the combined dipolarity/polarizability. This is the first study to have explored the possibility of combining Abraham solute descriptors with spectral moments. A reasonable interpretation of these molecular descriptors from a toxicological point of view was achieved by means of taking into account bond contributions. The set of relationships so derived revealed the importance of the length of the alkyl chains for determining carcinogenic potential of the chemicals analysed, and were able to explain the difference between mono-substituted and di-substituted nitrosoureas as well as to discriminate between isomeric structures with hydroxyl-alkyl and alkyl substituents in different positions. Moreover, they allowed the recognition of structural alerts in classical structures of two potent nitrosamines, consistent with their biotransformation. These results indicate that this new approach has the potential for improving carcinogenicity predictions based on the identification of structural alerts.
在本研究中,定量构效关系(QSAR)建模被用作预测一组39种亚硝基化合物致癌潜力的工具,这些化合物已通过口服给药途径在雄性大鼠中进行了生物测定。最佳QSAR模型显示出与训练集良好的拟合度和预测性能。它能够解释约84%的实验活性方差,并在交叉验证、留一法和自抽样法中表现出较高的决定系数值(q(2)(LOO)=78.53和q(2)(Boot)=74.97)。该模型基于用加斯泰格-马尔西利原子电荷、极化率和疏水性加权的光谱矩,以及亚伯拉罕指数,特别是溶质氢键碱度总和和组合偶极矩/极化率。这是首次探索将亚伯拉罕溶质描述符与光谱矩相结合可能性的研究。通过考虑键的贡献,从毒理学角度对这些分子描述符进行了合理的解释。由此得出的一组关系揭示了烷基链长度对于确定所分析化学品致癌潜力的重要性,并能够解释单取代和二取代亚硝基脲之间的差异,以及区分不同位置带有羟烷基和烷基取代基的异构体结构。此外,它们还能识别两种强效亚硝胺经典结构中的结构警示,这与它们的生物转化一致。这些结果表明,这种新方法有潜力基于结构警示的识别来改进致癌性预测。