系统数据挖掘揭示协同性H3R/MCHR1配体。
Systematic Data Mining Reveals Synergistic H3R/MCHR1 Ligands.
作者信息
Schaller David, Hagenow Stefanie, Alpert Gina, Naß Alexandra, Schulz Robert, Bermudez Marcel, Stark Holger, Wolber Gerhard
机构信息
Pharmaceutical and Medicinal Chemistry, Freie Universität Berlin, Königin-Luise-Str. 2+4, 14195 Berlin, Germany.
Pharmaceutical and Medicinal Chemistry, Heinrich-Heine-Universität Düsseldorf, Universitätsstr. 1, 40225 Düsseldorf, Germany.
出版信息
ACS Med Chem Lett. 2017 May 4;8(6):648-653. doi: 10.1021/acsmedchemlett.7b00118. eCollection 2017 Jun 8.
In this study, we report a ligand-centric data mining approach that guided the identification of suitable target profiles for treating obesity. The newly developed method is based on identifying target pairs for synergistic positive effects and also encompasses the exclusion of compounds showing a detrimental effect on obesity treatment (off-targets). Ligands with known activity against obesity-relevant targets were compared using fingerprint representations. Similar compounds with activities to different targets were evaluated for the mechanism of action since activation or deactivation of drug targets determines the pharmacological effect. validation of the modeling results revealed that three known modulators of melanin-concentrating hormone receptor 1 (MCHR1) show a previously unknown submicromolar affinity to the histamine H3 receptor (HR). This synergistic activity may present a novel therapeutic option against obesity.
在本研究中，我们报告了一种以配体为中心的数据挖掘方法，该方法指导了用于治疗肥胖症的合适靶点谱的鉴定。新开发的方法基于识别具有协同积极作用的靶点对，并且还包括排除对肥胖症治疗显示有害作用的化合物（脱靶）。使用指纹表示法比较了对肥胖相关靶点具有已知活性的配体。对具有不同靶点活性的相似化合物的作用机制进行了评估，因为药物靶点的激活或失活决定了药理作用。对建模结果的验证表明，三种已知的促黑素细胞激素受体1（MCHR1）调节剂对组胺H3受体（HR）表现出先前未知的亚微摩尔亲和力。这种协同活性可能为对抗肥胖症提供一种新的治疗选择。
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