An In Silico Model for Predicting Drug-Induced Hepatotoxicity
An In Silico Model for Predicting Drug-Induced Hepatotoxicity
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DOI：
10.3390/ijms20081897
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发表时间：
2019-04-02
影响因子：
5.6
通讯作者：
Sun, Xiaobo
中科院分区：
文献类型：
--
作者：
He, Shuaibing;Ye, Tianyuan;Sun, Xiaobo
药物性肝损伤（drug induced liver injury，DILI）是药物临床试验失败的主要原因之一，严重阻碍了新药的开发。预先评估候选药物的DILI风险被认为是降低药物发现中的损耗率的有效策略。最近，在DILI的预测方面有持续的尝试。然而，成功预测DILI确实仍然是一个巨大的挑战。因此，迫切需要建立一个具有良好预测性能的定量构效关系（QSAR）模型。在这项工作中，我们报告了一个高质量的QSAR模型，预测外源性药物的DILI风险，结合使用八个有效的分类器和分子描述符马文提供。在模型开发中，通过全面的文献检索建立了由1254种DILI化合物组成的大规模和多样化的数据集。通过集成方法获得最佳模型，平均来自8个分类器的概率，准确度（ACC）为0.783，灵敏度（SE）为0.818，特异性（SP）为0.748，受试者工作特征曲线（AUC）下的面积为0.859。为了进一步验证，使用了三个外部测试集和一个大型阴性数据集。因此，内部和外部验证表明，我们的模型优于以前的研究显着。目前的研究所提供的数据也将是一个有价值的来源建模/数据挖掘在未来。
As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure-activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.