Pang Xiaocong, Zhang Baoyue, Mu Guangyan, Xia Jie, Xiang Qian, Zhao Xia, Liu Ailin, Du Guanhua, Cui Yimin
Department of Pharmacy, Peking University First Hospital Dahongluochang Street, Xicheng District Beijing 100034 China
Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College Xian Nong Tan Street Beijing 100050 China
RSC Adv. 2018 Oct 10;8(61):34783-34792. doi: 10.1039/c8ra06311g.
Cytochrome P450 3A4 (CYP3A4) is an important member of the CYP family and responsible for metabolizing a broad range of drugs. Potential drug-drug interactions (DDIs) caused by CYP3A4 inhibitors could lead to increasing risk of side-effects/toxicity or decreasing effectiveness. The evaluation of CYP3A4 inhibitory activity is time-consuming, labor-intensive, and costly, and it is necessary to establish virtual screening models for predicting CYP3A4 inhibitors. In this study, 4 classifier algorithms, including support vector machine (SVM), naive Bayesian (NB), recursive partitioning (RP), and K-nearest neighbor (KNN), were applied to discriminate CYP3A4 inhibitors from the non-inhibitors. Correlation analysis and stepwise linear regression methods were used for descriptor selection and optimization. The performance of classifiers was measured by 5-fold cross-validation, Y-scrambling and test set validation. Finally, the optimal NB model with Matthews correlation coefficients of 0.894 for the test set was developed to screen FDA-approved drugs and natural products database. As a result, 90 compounds from FDA-approved drug databases were predicted as inhibitors, and 46% of them were identified as known CYP3A4 inhibitors. 6 natural products were selected for further bioactivity assay and molecular docking. 2 of them with good docking score also exerted significant CYP3A4 inhibitory activities with IC values of 0.052 and 1.120 μM, respectively. This study proved the feasibility of a new method for predicting CYP3A4 inhibitory activity and preventing the occurrence of DDIs at early stage in drug development.
细胞色素P450 3A4（CYP3A4）是细胞色素P450家族的重要成员，负责代谢多种药物。由CYP3A4抑制剂引起的潜在药物相互作用（DDIs）可能会导致副作用/毒性风险增加或有效性降低。CYP3A4抑制活性的评估耗时、费力且成本高昂，因此有必要建立虚拟筛选模型来预测CYP3A4抑制剂。在本研究中，应用了4种分类算法，包括支持向量机（SVM）、朴素贝叶斯（NB）、递归划分（RP）和K近邻（KNN），以区分CYP3A4抑制剂和非抑制剂。采用相关性分析和逐步线性回归方法进行描述符选择和优化。通过5折交叉验证、Y随机化和测试集验证来衡量分类器的性能。最后，开发了测试集马修斯相关系数为0.894的最优NB模型，用于筛选FDA批准的药物和天然产物数据库。结果，FDA批准的药物数据库中有90种化合物被预测为抑制剂，其中46%被鉴定为已知的CYP3A4抑制剂。选择了6种天然产物进行进一步的生物活性测定和分子对接。其中2种对接分数良好的天然产物也表现出显著的CYP3A4抑制活性，IC值分别为0.052和1.120μM。本研究证明了一种预测CYP3A4抑制活性并在药物开发早期预防药物相互作用发生的新方法的可行性。
