Jensen Ole, Brockmöller Jürgen, Dücker Christof
Institute of Clinical Pharmacology, University Medical Center Göttingen, D-37075 Göttingen, Germany.
J Med Chem. 2021 Mar 11;64(5):2762-2776. doi: 10.1021/acs.jmedchem.0c02047. Epub 2021 Feb 19.
OCT1 is the most highly expressed cation transporter in the liver and affects pharmacokinetics and pharmacodynamics. Newly marketed drugs have previously been screened as potential OCT1 substrates and verified by virtual docking. Here, we used machine learning with transport experiment data to predict OCT1 substrates based on classic molecular descriptors, pharmacophore features, and extended-connectivity fingerprints and confirmed them by uptake experiments. We virtually screened a database of more than 1000 substances. Nineteen predicted substances were chosen for testing. Sixteen of the 19 newly tested substances (85%) were confirmed as, mostly strong, substrates, including edrophonium, fenpiverinium, ritodrine, and ractopamine. Even without a crystal structure of OCT1, machine learning algorithms predict substrates accurately and may contribute not only to a more focused screening in drug development but also to a better molecular understanding of OCT1 in general.
有机阳离子转运体1（OCT1）是肝脏中表达最为丰富的阳离子转运蛋白，影响药物的药代动力学和药效学。此前，新上市药物已作为OCT1潜在底物进行筛选，并通过虚拟对接进行验证。在此，我们利用机器学习结合转运实验数据，基于经典分子描述符、药效团特征和扩展连接指纹预测OCT1底物，并通过摄取实验进行确认。我们对一个包含1000多种物质的数据库进行了虚拟筛选。选择了19种预测物质进行测试。19种新测试物质中有16种（85%）被确认为底物，其中大多数为强效底物，包括依酚氯铵、芬维铵、利托君和莱克多巴胺。即使没有OCT1的晶体结构，机器学习算法也能准确预测底物，这不仅有助于在药物研发中进行更有针对性的筛选，也有助于更全面地从分子层面理解OCT1。
