Noeske Tobias, Jirgensons Aigars, Starchenkovs Igors, Renner Steffen, Jaunzeme Ieva, Trifanova Dina, Hechenberger Mirko, Bauer Tanja, Kauss Valerjans, Parsons Christopher G, Schneider Gisbert, Weil Tanja
Merz Pharmaceuticals GmbH, Altenhöfer Allee 3, 60438 Frankfurt am Main, Germany.
ChemMedChem. 2007 Dec;2(12):1763-73. doi: 10.1002/cmdc.200700151.
A virtual screening study towards novel noncompetitive antagonists of the metabotropic glutamate receptor 1 (mGluR1) is described. Alignment-free topological pharmacophore descriptors (CATS) were used to encode the screening compounds. All virtual hits were characterized with respect to their allosteric antagonistic effect on mGluR1 in both functional and binding assays. Exceptionally high hit rates of up to 26 % were achieved, confirming the applicability of this virtual screening concept. Most of the compounds were found to be moderately active, however, one potent and subtype selective mGluR1 antagonist, 13 (IC(50): 0.362 microM, SEM +/-0.031; K(i): 0.753 microM, SEM +/-0.048), based on a coumarine scaffold was discovered. In a following activity optimization program a series of coumarine derivatives was synthesized. This led to the discovery of potent (60, IC(50): 0.058 microM, SEM +/-0.008; K(i): 0.293 microM, SEM +/-0.022) and subtype selective (rmGluR5 IC(50): 28.6 microM) mGluR1 antagonists. From our homology model of mGluR1 we derived a potential binding mode within the allosteric transmembrane region. Potential interacting patterns are proposed considering the difference of the binding pockets between rat and human receptors. The study demonstrates the applicability of ligand-based virtual screening for noncompetitive antagonists of a G-protein coupled receptor, resulting in novel, potent, and selective agents.
本文描述了一项针对代谢型谷氨酸受体1(mGluR1)新型非竞争性拮抗剂的虚拟筛选研究。使用无比对拓扑药效团描述符(CATS)对筛选化合物进行编码。所有虚拟命中化合物均通过功能和结合试验,对其对mGluR1的变构拮抗作用进行了表征。获得了高达26%的异常高命中率,证实了这种虚拟筛选概念的适用性。发现大多数化合物活性适中,然而,基于香豆素骨架发现了一种强效且亚型选择性的mGluR1拮抗剂13(IC(50):0.362 microM,标准误±0.031;K(i):0.753 microM,标准误±0.048)。在随后的活性优化计划中,合成了一系列香豆素衍生物。这导致发现了强效(60,IC(50):0.058 microM,标准误±0.008;K(i):0.293 microM,标准误±0.022)且亚型选择性(rmGluR5 IC(50):28.6 microM)的mGluR1拮抗剂。从我们的mGluR1同源模型中,我们推导了变构跨膜区域内的潜在结合模式。考虑到大鼠和人类受体结合口袋的差异,提出了潜在的相互作用模式。该研究证明了基于配体的虚拟筛选对于G蛋白偶联受体非竞争性拮抗剂的适用性,从而产生了新型、强效和选择性的药物。