|Other Titles||Quantitative structure-activity relationship model for prediction of cardiotoxicity of chemical components in traditional Chinese medicines|
北京大学公共卫生学院毒理学系, 北京 100191
国家中医药管理局中药配伍减毒重点研究室, 北京 100191
Quantitative structure-activity relationship
|Citation||北京大学学报（医学版）. 2017, 49(3), 551-556.|
|Abstract||目的:当前用于预测心脏毒性的定量构效关系(quantitative structure-activity relationship, QSAR)模型仅限于hERG通道抑制作用这一机制,应用范围较狭窄.本研究旨在构建包含各类心脏不良反应的QSAR模型,以应用于中药化学成分潜在心脏毒性的预测.方法: 从Toxicity Reference Database(ToxRefDB)和Side Effect Resource(SIDER)数据库中共收集1 109个具有心脏毒性的化合物和789个不具有心脏毒性的化合物作为构建QSAR模型的训练集,应用ADMET Predictor软件计算、筛选分子描述符,通过两种算法(支持向量机和人工神经网络)依次纳入不同数量分子描述符分别构建QSAR模型,通过10折交叉验证方法进行内部验证选择最优模型,然后通过查阅文献及数据库共收集19种具有心脏毒性和10种不具有心脏毒性的中药化学成分作为外部验证集,评价所建QSAR模型对于中药化学成分心脏毒性预测的适用性.结果: 经筛选后共有220种分子描述符参与建模,用支持向量机算法所建的最优模型为包含87种分子描述符的模型,其内部验证结果显示模型灵敏度为71%,特异度为70%,约登指数(Youden&#39;s index)和马修斯相关系数(Matthews correlation coefficient)均为0.41.用人工神经网络算法所建的最优模型为包含13个神经元及87种分子描述符的模型,其内部验证结果显示模型灵敏度为78%,特异度为77%,约登指数和马修斯相关系数均为0.54.通过29种中药化学成分验证显示,支持向量机模型外部验证结果灵敏度为95%,特异度为40%,整体预测的准确率达到76%;人工神经网络模型外部验证结果灵敏度为95%,特异度为60%,整体预测的准确率达到83%.结论:应用人工神经网络算法构建的模型预测能力要优于支持向量机算法构建的模型,通过已知毒性的中药化学成分验证表明,此QSAR模型有良好的灵敏度和预测准确率,可以用于中药化学成分心脏毒性的预测.|
Objective: Some quantitative structure-activity relationship (QSAR) models have been developed to predict cardiac toxicity of drugs, which have limited predictive power due to based on hERG channel inhibition.The objective of this study was try to develop a QSAR model based on all kinds of cardiac adverse effects, and to predict the potential cardiotoxicity of chemical components in traditional Chinese medicines (TCM).Methods: In this study, the compounds data of all kinds of cardiac adverse reactions were selected as the training set.The QSAR models were constructed based on 1 109 compounds with cardiotoxicity and 789 compounds without cardiotoxicity, which were available from the Toxicity Reference Database (ToxRefDB) and Side Effect Resource (SIDER) database.The ADMET Predictor software was applied to calculate and to screen the molecular descriptors, and to construct the QSAR models using support vector machine (SVM) and artificial neural networks (ANN) algorithm, respectively.The models were optimized using compound-based 10-fold cross validation.Then, the predictive performance for the potential cardiotoxicity of chemical components in TCM were assessed using external validation by 19 components in TCM with cardiotoxicity and 10 components in TCM without cardiotoxicity.Results: A total of 220 molecular descriptors were selected for modeling, and the best model using SVM algorithm contained 87 molecular descriptors.The internal validation results showed that the predictive sensitivity, specificity, the Youden&#39;s index (YI) and the Matthews correlation coefficient (MCC) were 71%, 70%, 0.41, and 0.41, respectively.The best model constructed using ANN algorithm contained 13 neurons and 87 molecular descriptors.The internal validation results showed that the predictive sensitivity, specificity, the YI and the MCC were 78%, 77%, 0.54, and 0.54, respectively.Both models were validated using external validation by the same set of 29 chemical components in TCM with or without cardiotoxicity, which were not included in the training set.The predictive performances of SVM or ANN model were as follows, respectively: sensitivity 95%, 95%;specificity 40%, 60%;and accuracy 76%, 83%.Conclusion: The predictive performance of the QSAR model using ANN algorithm was better than that of the model using SVM algorithm.The external validation study of 29 chemical components in TCM illustrated that the QSAR model was applicable for screening and predicting the potential cardiotoxicity of chemical components in TCM.
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