Systematic Data Mining Reveals Synergistic H3R/MCHR1 Ligands

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. In vitro 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 (H3R). This synergistic activity may present a novel therapeutic option against obesity.本研究报道了一种以配体为中心（ligand-centric）的数据挖掘方法，该方法可指导筛选适用于肥胖治疗的适宜靶点谱。该新开发的方法以识别具有协同正向作用的靶点对为核心，同时涵盖了对在肥胖治疗中呈现有害作用的化合物（脱靶靶点，off-targets）的排除流程。研究采用指纹表征（fingerprint representations）对已知具备抗肥胖相关靶点活性的配体开展比对分析；对于作用于不同靶点且活性相似的化合物，研究人员会对其作用机制进行评估，因为药物靶点的激活或失活直接决定了其药理学效应。建模结果的体外验证实验显示，三种已知的黑色素浓集激素受体1（melanin-concentrating hormone receptor 1, MCHR1）调节剂，对组胺H3受体（histamine H3 receptor, H3R）展现出此前未被报道的亚微摩尔级亲和力。这种协同活性有望为肥胖治疗提供一种全新的治疗选择。