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通过质谱结合分析筛选的聚焦假静态腙文库:优化对γ-氨基丁酸转运蛋白 1 的亲和力。

Focused pseudostatic hydrazone libraries screened by mass spectrometry binding assay: optimizing affinities toward γ-aminobutyric acid transporter 1.

机构信息

Center for Drug Research, Department of Pharmacy, Ludwig Maximilians University at Munich, Butenandtstrasse 9-13, D-81377 Munich, Germany.

出版信息

J Med Chem. 2013 Feb 14;56(3):1323-40. doi: 10.1021/jm301800j. Epub 2013 Feb 1.

DOI:10.1021/jm301800j
PMID:23336362
Abstract

Mass spectrometric (MS) binding assays, a powerful tool to determine affinities of single drug candidates toward chosen targets, were recently demonstrated to be suitable for the screening of compound libraries generated with reactions of dynamic combinatorial chemistry when rendering libraries pseudostatic. Screening of small hydrazone libraries targeting γ-aminobutyric acid transporter 1 (GAT1), the most abundant γ-aminobutyric acid (GABA) transporter in the central nervous system, revealed two nipecotic acid derived binders with submicromolar affinities. Starting from the biphenyl carrying hit as lead structure, the objective of the present study was to discover novel high affinity GAT1 binders by screening of biphenyl focused pseudostatic hydrazone libraries formed from hydrazine 10 and 36 biphenylcarbaldehydes 11c-al. Hydrazone 12z that carried a 2',4'-dichlorobiphenyl residue was found to be the most potent binder with low nanomolar affinity (pK(i) = 8.094 ± 0.098). When stable carba analogues of representative hydrazones were synthesized and evaluated, the best binder 13z was again displaying the 2',4'-dichlorobiphenyl moiety (pK(i) = 6.930 ± 0.021).

摘要

质谱(MS)结合分析是一种强大的工具,可用于确定单个药物候选物对选定靶标的亲和力,最近已被证明适用于筛选用动态组合化学反应生成的化合物库,当使库呈现假性静态时。筛选针对γ-氨基丁酸转运蛋白 1(GAT1)的小腙库,GAT1 是中枢神经系统中最丰富的γ-氨基丁酸(GABA)转运蛋白,发现了两种具有亚微摩尔亲和力的尼卡酸衍生结合物。从作为先导结构的带有联苯的命中物开始,本研究的目的是通过筛选由肼 10 和 36 个联苯甲醛 11c-al 形成的联苯聚焦假性静态腙库,发现新型高亲和力 GAT1 结合物。带有 2',4'-二氯联苯残基的腙 12z 被发现是最有效的结合物,具有低纳摩尔亲和力(pK(i) = 8.094 ± 0.098)。当稳定的代表性腙的碳类似物被合成并进行评估时,最好的结合物 13z 再次显示出 2',4'-二氯联苯部分(pK(i) = 6.930 ± 0.021)。

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