Zheng Zhong, Huang Xi-Ping, Mangano Thomas J, Zou Rodger, Chen Xin, Zaidi Saheem A, Roth Bryan L, Stevens Raymond C, Katritch Vsevolod
Department of Biological Sciences and Department of Chemistry, Bridge Institute, University of Southern California , Los Angeles, California 90089, United States.
J Med Chem. 2017 Apr 13;60(7):3070-3081. doi: 10.1021/acs.jmedchem.7b00109. Epub 2017 Apr 3.
The ongoing epidemics of opioid overdose raises an urgent need for effective antiaddiction therapies and addiction-free painkillers. The κ-opioid receptor (KOR) has emerged as a promising target for both indications, raising demand for new chemotypes of KOR antagonists as well as G-protein-biased agonists. We employed the crystal structure of the KOR-JDTic complex and ligand-optimized structural templates to perform virtual screening of available compound libraries for new KOR ligands. The prospective virtual screening campaign yielded a high 32% hit rate, identifying novel fragment-like and lead-like chemotypes of KOR ligands. A round of optimization resulted in 11 new submicromolar KOR binders (best K = 90 nM). Functional assessment confirmed at least two compounds as potent KOR antagonists, while compound 81 was identified as a potent G biased agonist for KOR with minimal β-arrestin recruitment. These results support virtual screening as an effective tool for discovery of new lead chemotypes with therapeutically relevant functional profiles.
阿片类药物过量的持续流行迫切需要有效的抗成瘾疗法和无成瘾性的止痛药。κ-阿片受体(KOR)已成为这两种适应症的一个有前景的靶点,这增加了对新型KOR拮抗剂以及G蛋白偏向性激动剂化学类型的需求。我们利用KOR-JDTic复合物的晶体结构和配体优化的结构模板,对可用化合物库进行虚拟筛选以寻找新的KOR配体。前瞻性虚拟筛选活动产生了高达32%的命中率,确定了KOR配体的新型类片段和类先导化学类型。一轮优化产生了11种新的亚微摩尔级KOR结合剂(最佳K = 90 nM)。功能评估证实至少有两种化合物是有效的KOR拮抗剂,而化合物81被确定为一种有效的KOR G偏向性激动剂,对β-抑制蛋白的招募最少。这些结果支持虚拟筛选作为发现具有治疗相关功能特征的新先导化学类型的有效工具。