Nolan Tammy L, Geffert Laura M, Kolber Benedict J, Madura Jeffry D, Surratt Christopher K
Division of Pharmaceutical Sciences, Mylan School of Pharmacy, ‡Departments of Chemistry and Biochemistry, Center for Computational Sciences, and §Department of Biological Sciences, Duquesne University , 600 Forbes Avenue, Pittsburgh, Pennsylvania 15282, United States.
ACS Chem Neurosci. 2014 Sep 17;5(9):784-92. doi: 10.1021/cn500133b. Epub 2014 Jul 15.
Discovery of new inhibitors of the plasmalemmal monoamine transporters (MATs) continues to provide pharmacotherapeutic options for depression, addiction, attention deficit disorders, psychosis, narcolepsy, and Parkinson's disease. The windfall of high-resolution MAT structural information afforded by X-ray crystallography has enabled the construction of credible computational models. Elucidation of lead compounds, creation of compound structure-activity series, and pharmacologic testing are staggering expenses that could be reduced by using a MAT computational model for virtual screening (VS) of structural libraries containing millions of compounds. Here, VS of the PubChem small molecule structural database using the S1 (primary substrate) ligand pocket of a serotonin transporter homology model yielded 19 prominent "hit" compounds. In vitro pharmacology of these VS hits revealed four structurally unique MAT substrate uptake inhibitors with high nanomolar affinity at one or more of the three MATs. In vivo characterization of three of these hits revealed significant activity in a mouse model of acute depression at doses that did not elicit untoward locomotor effects. This constitutes the first report of MAT inhibitor discovery using exclusively the primary substrate pocket as a VS tool. Novel-scaffold MAT inhibitors offer hope of new medications that lack the many classic adverse effects of existing antidepressant drugs.
发现新的质膜单胺转运体(MATs)抑制剂持续为抑郁症、成瘾、注意力缺陷障碍、精神病、发作性睡病和帕金森病提供药物治疗选择。X射线晶体学提供的高分辨率MAT结构信息意外之财使得构建可信的计算模型成为可能。先导化合物的阐明、化合物结构-活性系列的创建以及药理学测试是巨大的开支,通过使用MAT计算模型对包含数百万种化合物的结构文库进行虚拟筛选(VS)可以减少这些开支。在这里,使用血清素转运体同源模型的S1(主要底物)配体口袋对PubChem小分子结构数据库进行虚拟筛选,得到了19种突出的“命中”化合物。这些虚拟筛选命中物的体外药理学研究揭示了四种结构独特的MAT底物摄取抑制剂,它们对三种MAT中的一种或多种具有高纳摩尔亲和力。对其中三种命中物的体内表征显示,在急性抑郁症小鼠模型中,在未引发不良运动效应的剂量下具有显著活性。这是首次仅使用主要底物口袋作为虚拟筛选工具发现MAT抑制剂的报告。新型骨架MAT抑制剂为缺乏现有抗抑郁药物许多经典不良反应的新药物带来了希望。