Department of Clinical Pharmacology (P.C.N., J.O.M.) and Flinders Centre for Innovation in Cancer (P.C.N., R.A.M., J.O.M.), College of Medicine and Public Health, Flinders University, Adelaide, Australia
Department of Clinical Pharmacology (P.C.N., J.O.M.) and Flinders Centre for Innovation in Cancer (P.C.N., R.A.M., J.O.M.), College of Medicine and Public Health, Flinders University, Adelaide, Australia.
Drug Metab Dispos. 2019 Jun;47(6):616-631. doi: 10.1124/dmd.118.085167. Epub 2019 Mar 22.
Protein kinase inhibitors (KIs), which are mainly biotransformed by CYP3A4-catalyzed oxidation, represent a rapidly expanding class of drugs used primarily for the treatment of cancer. Ligand- and structure-based methods were applied here to investigate whether computational approaches may be used to predict the site(s) of metabolism (SOM) of KIs and to identify amino acids within the CYP3A4 active site involved in KI binding. A data set of the experimentally determined SOMs of 31 KIs known to undergo biotransformation by CYP3A4 was collated. The structure-based (molecular docking) approach employed three CYP3A4 X-ray crystal structures to account for structural plasticity of this enzyme. Docking pose and SOM predictivity were influenced by the X-ray crystal template used for docking and the scoring function used for ranking binding poses. The best prediction of SOM (77%) was achieved using the substrate (bromoergocryptine)-bound X-ray crystal template together with the potential of mean force score. Binding interactions of KIs with CYP3A4 active site residues were generally similar to those observed for other substrates of this enzyme. The ligand-based molecular superposition approach, using bromoergocryptine from the X-ray cocrystal structure as a template, poorly predicted (42%) the SOM of KIs, although predictivity improved to 71% when the docked conformation of sorafenib was used as the template. Among the web-based approaches examined, all web servers provided excellent predictivity, with one web server predicting the SOM of 87% of the data set molecules. Computational approaches may be used to predict the SOM of KIs, and presumably other classes of CYP3A4 substrates, but predictivity varies between methods.
蛋白激酶抑制剂（KIs）主要通过 CYP3A4 催化的氧化作用进行生物转化，是一类用于治疗癌症的快速扩展的药物。本文应用配体和结构方法，研究计算方法是否可用于预测 KIs 的代谢部位（SOM），并鉴定 CYP3A4 活性部位中与 KI 结合相关的氨基酸。本文整理了一组已知经 CYP3A4 生物转化的 31 种 KIs 的实验确定的 SOM 数据。基于结构的（分子对接）方法采用了三种 CYP3A4 X 射线晶体结构来解释该酶的结构可塑性。对接构象和 SOM 预测性受用于对接的 X 射线晶体模板和用于对结合构象进行排序的评分函数的影响。使用结合底物（溴麦角隐亭）的 X 射线晶体模板和平均力势评分，可实现 SOM 的最佳预测（77%）。KIs 与 CYP3A4 活性部位残基的结合相互作用通常与该酶其他底物的观察结果相似。使用 X 射线共晶结构中的溴麦角隐亭作为模板的基于配体的分子叠加方法，对 KIs 的 SOM 预测能力较差（42%），但当使用索拉非尼的对接构象作为模板时，预测能力提高到 71%。在所检查的基于网络的方法中，所有网络服务器都提供了出色的预测能力，其中一个网络服务器预测了数据集分子中 87%的 SOM。计算方法可用于预测 KIs 的 SOM，推测也可用于其他 CYP3A4 底物类别，但方法之间的预测能力存在差异。
