Biomedical Informatics Shared Resources , Rutgers Cancer Institute of New Jersey , Rutgers, The State University of New Jersey , 195 Little Albany Street , New Brunswick , New Jersey 08903 , United States.
Department of Medicinal Chemistry, Ernest Mario School of Pharmacy , Rutgers, The State University of New Jersey , 160 Frelinghuysen Road , Piscataway , New Jersey 08854 , United States.
J Chem Inf Model. 2019 Jan 28;59(1):486-497. doi: 10.1021/acs.jcim.8b00521. Epub 2018 Dec 14.
The Sigma 1 Receptor (S1R) has attracted intense interest as a pharmaceutical target for various therapeutic indications, including the treatment of neuropathic pain and the potentiation of opioid analgesia. Efforts by drug developers to rationally design S1R antagonists have been spurred recently by the 2016 publication of the high-resolution X-ray crystal structure of the ligand-bound human S1R. Until now, however, the absence in the published literature of a single, large-scale, and comprehensive quantitative structure-activity relationship (QSAR) model that encompasses a structurally diverse collection of S1R ligands has impaired rapid progress. To our best knowledge, the present study represents the first report of a statistically robust and highly predictive 3D-QSAR model (R = 0.92, Q = 0.62, R = 0.81) based on the X-ray crystal structure of human S1R and constructed from a pooled compilation of 180 S1R antagonists that encompass five structurally diverse chemical families investigated using identical experimental protocols. Best practices, as recommended by the Organization for Economic Cooperation and Development (OECD: http://www.oecd.org/ ), were adopted for pooling data from disparate sources and for QSAR model development and both internal and external model validation. The practical utility of the final 3D-QSAR model was tested by virtual screening of the DrugBank database of FDA approved drugs supplemented by eight reported S1R antagonists. Among the top-ranked 40 DrugBank hits, four approved drugs which were previously unknown as S1R antagonists were tested using in vitro radiolabeled human S1R binding assays. Of these, two drugs (diphenhydramine and phenyltoloxamine) exhibited potent S1R binding affinity with K = 58 nM and 160 nM, respectively. As diphenhydramine is approved as an antiallergic, and phenyltoloxamine as an analgesic and sedative, each of these compounds represents a viable starting point for a drug discovery campaign aimed at the development of novel S1R antagonists for a wide range of therapeutic indications.
Sigma1 受体（S1R）作为治疗各种适应症的药物靶点引起了极大的关注，包括治疗神经性疼痛和增强阿片类镇痛药的作用。药物开发商最近受到 2016 年发表的配体结合人 S1R 的高分辨率 X 射线晶体结构的启发，努力合理设计 S1R 拮抗剂。然而，到目前为止，在已发表的文献中缺乏一个单一的、大规模的、全面的定量构效关系（QSAR）模型，该模型涵盖了结构多样的 S1R 配体集合，这阻碍了快速进展。据我们所知，本研究是首次报道基于人 S1R 的 X 射线晶体结构构建的、基于统计学稳健且高度可预测的 3D-QSAR 模型（R = 0.92，Q = 0.62，R = 0.81）的报告，该模型由 180 种 S1R 拮抗剂组成，涵盖了使用相同实验方案研究的五个结构多样的化学家族。根据经济合作与发展组织（OECD：http://www.oecd.org/）的建议，采用最佳实践方法对来自不同来源的数据进行汇总，并对 QSAR 模型进行开发和内部及外部模型验证。通过虚拟筛选 DrugBank 数据库中的 FDA 批准药物并补充 8 种报道的 S1R 拮抗剂来测试最终的 3D-QSAR 模型的实际应用。在排名前 40 的 DrugBank 命中物中，有四种以前未知的作为 S1R 拮抗剂的批准药物通过体外放射性标记的人 S1R 结合测定法进行了测试。其中，两种药物（苯海拉明和苯甲托品）表现出与 K = 58 nM 和 160 nM 分别具有强 S1R 结合亲和力。由于苯海拉明被批准为抗变态反应药，而苯甲托品被批准为镇痛药和镇静药，因此这些化合物中的每一种都代表了一种可行的起点，可用于开发针对广泛治疗适应症的新型 S1R 拮抗剂的药物发现活动。
