INSERM U1133, CNRS UMR 8251, Unit of functional and adaptive biology, Université de Paris, Paris 75013, France.
Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark.
Molecules. 2019 Jul 29;24(15):2747. doi: 10.3390/molecules24152747.
The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactions resulting from the inhibition of CES1. Based on an ensemble of 10 crystal structures complexed with different ligands and a set of 294 known CES1 ligands, we used docking (Autodock Vina) and machine learning methodologies (LDA, QDA and multilayer perceptron), considering the different energy terms from the scoring function to assess the best combination to enable the identification of CES1 inhibitors. The protocol was then applied on a library of 1114 FDA-approved drugs and eight drugs were selected for in vitro CES1 inhibition. An inhibition effect was observed for diltiazem (IC50 = 13.9 µM). Three others drugs (benztropine, iloprost and treprostinil), exhibited a weak CES1 inhibitory effects with IC50 values of 298.2 µM, 366.8 µM and 391.6 µM respectively. In conclusion, the binding site of CES1 is relatively flexible and can adapt its conformation to different types of ligands. Combining ensemble docking and machine learning approaches improves the prediction of CES1 inhibitors compared to a docking study using only one crystal structure.
人类羧酸酯酶 1（CES1）负责许多不同治疗药物的生物转化，可能通过药物相互作用导致不良反应和治疗失败的发生。本研究旨在解决 CES1 抑制引起的潜在药物相互作用问题。基于与不同配体结合的 10 个晶体结构的集合和 294 个已知 CES1 配体的集合，我们使用对接（Autodock Vina）和机器学习方法（LDA、QDA 和多层感知机），考虑来自评分函数的不同能量项，以评估最佳组合，从而能够识别 CES1 抑制剂。然后将该方案应用于 1114 种 FDA 批准药物的库中，并选择了 8 种药物进行体外 CES1 抑制研究。观察到地尔硫卓（IC50 = 13.9 µM）的抑制作用。另外三种药物（苯扎托品、伊洛前列素和曲前列素）的 CES1 抑制作用较弱，IC50 值分别为 298.2 µM、366.8 µM 和 391.6 µM。总之，CES1 的结合位点相对灵活，可以适应不同类型的配体。与仅使用一个晶体结构的对接研究相比，组合对接和机器学习方法可提高 CES1 抑制剂的预测能力。
