Department für Pharmazie-Zentrum für Pharmaforschung, Ludwig-Maximilians-Universität München, Butenandtstr. 5-13, 81377 München, Germany.
ChemMedChem. 2012 Sep;7(9):1678-90. doi: 10.1002/cmdc.201200201. Epub 2012 Jun 11.
In the present study, the application of mass spectrometry (MS) binding assays as a tool for library screening is reported. For library generation, dynamic combinatorial chemistry (DCC) was used. These libraries can be screened by means of MS binding assays when appropriate measures are taken to render the libraries pseudostatic. That way, the efficiency of MS binding assays to determine ligand binding in compound screening with the ease of library generation by DCC is combined. The feasibility of this approach is shown for γ-aminobutyric acid (GABA) transporter 1 (GAT1) as a target, representing the most important subtype of the GABA transporters. For the screening, hydrazone libraries were employed that were generated in the presence of the target by reacting various sets of aldehydes with a hydrazine derivative that is delineated from piperidine-3-carboxylic acid (nipecotic acid), a common fragment of known GAT1 inhibitors. To ensure that the library generated is pseudostatic, a large excess of the nipecotic acid derivative is employed. As the library is generated in a buffer system suitable for binding and the target is already present, the mixtures can be directly analyzed by MS binding assays-the process of library generation and screening thus becoming simple to perform. The binding affinities of the hits identified by deconvolution were confirmed in conventional competitive MS binding assays performed with single compounds obtained by separate synthesis. In this way, two nipecotic acid derivatives exhibiting a biaryl moiety, 1-{2-[2'-(1,1'-biphenyl-2-ylmethylidene)hydrazine]ethyl}piperidine-3-carboxylic acid and 1-(2-{2'-[1-(2-thiophenylphenyl)methylidene]hydrazine}ethyl)piperidine-3-carboxylic acid, were found to be potent GAT1 ligands exhibiting pK(i) values of 6.186 ± 0.028 and 6.229 ± 0.039, respectively. This method enables screening of libraries, whether generated by conventional chemistry or DCC, and is applicable to all kinds of targets including membrane-bound targets such as G protein coupled receptors (GPCRs), ion channels and transporters. As such, this strategy displays high potential in the drug discovery process.
在本研究中，报告了将质谱 (MS) 结合测定法作为文库筛选工具的应用。为了生成文库，使用了动态组合化学 (DCC)。通过采取适当的措施使文库呈现伪静态，可以通过 MS 结合测定法对这些文库进行筛选。这样，就结合了 MS 结合测定法在化合物筛选中确定配体结合的效率和 DCC 生成文库的简便性。该方法的可行性已通过 γ-氨基丁酸 (GABA) 转运体 1 (GAT1) 作为靶标得到证明，GAT1 是 GABA 转运体最重要的亚型之一。为了进行筛选，使用了腙文库，该文库是通过将各种醛与从哌啶-3-羧酸 (哌啶酸) 衍生的腙反应在靶标存在下生成的，哌啶酸是已知 GAT1 抑制剂的常见片段。为了确保生成的文库是伪静态的，使用了大量的哌啶酸衍生物。由于文库是在适合结合的缓冲系统中生成的，并且靶标已经存在，因此可以直接通过 MS 结合测定法对混合物进行分析——文库生成和筛选的过程因此变得简单易行。通过单独合成获得的单化合物进行的常规竞争性 MS 结合测定法，对通过解卷积鉴定的命中物的结合亲和力进行了确认。通过这种方式，发现了两种具有联苯部分的哌啶酸衍生物，1-{2-[2'-(1,1'-联苯-2-基甲基)腙]乙基}哌啶-3-羧酸和 1-(2-{2'-[1-(2-噻吩基苯基)甲基]腙}乙基)哌啶-3-羧酸，它们是有效的 GAT1 配体，pK(i) 值分别为 6.186 ± 0.028 和 6.229 ± 0.039。该方法可用于筛选无论是通过常规化学还是 DCC 生成的文库，并且适用于包括 G 蛋白偶联受体 (GPCR)、离子通道和转运体等膜结合靶标在内的各种靶标。因此，该策略在药物发现过程中具有很高的潜力。
