State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan 610041, PR China.
West China School of Pharmacy, Sichuan University, Sichuan 610041, PR China.
Comput Biol Med. 2014 Aug;51:122-7. doi: 10.1016/j.compbiomed.2014.05.005. Epub 2014 May 17.
Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery.
药物诱导性耳毒性是一种毒副作用，在药物发现中需要考虑这一重要问题。然而，目前用于评估药物诱导性耳毒性的实验方法通常既耗时又昂贵，表明它们不适合在药物发现的早期阶段对药物诱导性耳毒性进行大规模评估。因此，在这项研究中，我们使用最佳支持向量机（SVM）方法 GA-CG-SVM 建立了一种有效的药物诱导性耳毒性计算预测模型。基于包含具有不同药物诱导性耳毒性风险水平的药物的三个训练集，开发了三个 GA-CG-SVM 模型。为了进行比较，还在相同的训练集上使用了基于朴素贝叶斯（NB）和递归分区（RP）方法的模型。在所有预测模型中，GA-CG-SVM 模型 II 的表现最好，对两个独立测试集的预测准确率分别为 85.33%和 83.05%。总体而言，GA-CG-SVM 模型 II 的良好表现表明，它可用于药物发现早期阶段的药物诱导性耳毒性预测。
