Yap C W, Cai C Z, Xue Y, Chen Y Z
Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543.
Toxicol Sci. 2004 May;79(1):170-7. doi: 10.1093/toxsci/kfh082. Epub 2004 Feb 19.
In an effort to facilitate drug discovery, computational methods for facilitating the prediction of various adverse drug reactions (ADRs) have been developed. So far, attention has not been sufficiently paid to the development of methods for the prediction of serious ADRs that occur less frequently. Some of these ADRs, such as torsade de pointes (TdP), are important issues in the approval of drugs for certain diseases. Thus there is a need to develop tools for facilitating the prediction of these ADRs. This work explores the use of a statistical learning method, support vector machine (SVM), for TdP prediction. TdP involves multiple mechanisms and SVM is a method suitable for such a problem. Our SVM classification system used a set of linear solvation energy relationship (LSER) descriptors and was optimized by leave-one-out cross validation procedure. Its prediction accuracy was evaluated by using an independent set of agents and by comparison with results obtained from other commonly used classification methods using the same dataset and optimization procedure. The accuracies for the SVM prediction of TdP-causing agents and non-TdP-causing agents are 97.4 and 84.6% respectively; one is substantially improved against and the other is comparable to the results obtained by other classification methods useful for multiple-mechanism prediction problems. This indicates the potential of SVM in facilitating the prediction of TdP-causing risk of small molecules and perhaps other ADRs that involve multiple mechanisms.
为了促进药物发现，已经开发了用于促进预测各种药物不良反应（ADR）的计算方法。到目前为止，对于预测发生频率较低的严重ADR的方法开发尚未给予足够的关注。其中一些ADR，如尖端扭转型室速（TdP），是某些疾病药物批准中的重要问题。因此，需要开发有助于预测这些ADR的工具。这项工作探索使用统计学习方法——支持向量机（SVM）来预测TdP。TdP涉及多种机制，而SVM是适用于此类问题的一种方法。我们的SVM分类系统使用了一组线性溶剂化能关系（LSER）描述符，并通过留一法交叉验证程序进行了优化。通过使用一组独立的药物以及与使用相同数据集和优化程序的其他常用分类方法所获得的结果进行比较，对其预测准确性进行了评估。SVM对导致TdP的药物和不导致TdP的药物的预测准确率分别为97.4%和84.6%；其中一个相对于其他用于多机制预测问题的分类方法所获得的结果有显著提高，另一个与之相当。这表明SVM在促进预测小分子导致TdP的风险以及可能其他涉及多种机制的ADR方面具有潜力。
