1. Academic Validation
  2. Comprehensive 3D-QSAR Model Predicts Binding Affinity of Structurally Diverse Sigma 1 Receptor Ligands

Comprehensive 3D-QSAR Model Predicts Binding Affinity of Structurally Diverse Sigma 1 Receptor Ligands

  • J Chem Inf Model. 2019 Jan 28;59(1):486-497. doi: 10.1021/acs.jcim.8b00521.
Youyi Peng 1 Hiep Dong 2 William J Welsh 1 3
Affiliations

Affiliations

  • 1 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.
  • 2 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.
  • 3 Department of Pharmacology, Robert Wood Johnson Medical School , Rutgers, The State University of New Jersey , 661 Hoes Lane West , Piscataway , New Jersey 08854 , United States.
Abstract

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 (R2 = 0.92, Q2 = 0.62, Rpred2 = 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 Ki = 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.

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