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Analogs of Methyllycaconitine as Novel Noncompetitive Inhibitors of Nicotinic Receptors: Pharmacological Characterization, Computational Modeling, and Pharmacophore Development

Division of Pharmacology, College of Pharmacy (D.B.M., T.F.G.-C., S.B.M., R.A.E., D.L.B.) and Department of Neuroscience, College of Medicine (M.X.Z.), The Ohio State University, Columbus, Ohio; Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Maryland (C.C., P.W.S.); and Department of Chemistry and Biochemistry, Ohio University, Athens, Ohio (K.M.A., A.P.B., C.M.O., S.C.B.)

As a novel approach to drug discovery involving neuronal nicotinic acetylcholine receptors (nAChRs), our laboratory targeted nonagonist binding sites (i.e., noncompetitive binding sites, negative allosteric binding sites) located on nAChRs. Cultured bovine adrenal cells were used as neuronal models to investigate interactions of 67 analogs of methyllycaconitine (MLA) on native 34* nAChRs. The availability of large numbers of structurally related molecules presents a unique opportunity for the development of pharmacophore models for noncompetitive binding sites. Our MLA analogs inhibited nicotine-mediated functional activation of both native and recombinant 34* nAChRs with a wide range of IC₅₀ values (0.9–115 µM). These analogs had little or no inhibitory effects on agonist binding to native or recombinant nAChRs, supporting noncompetitive inhibitory activity. Based on these data, two highly predictive 3D quantitative structure-activity relationship (comparative molecular field analysis and comparative molecular similarity index analysis) models were generated. These computational models were successfully validated and provided insights into the molecular interactions of MLA analogs with nAChRs. In addition, a pharmacophore model was constructed to analyze and visualize the binding requirements to the analog binding site. The pharmacophore model was subsequently applied to search structurally diverse molecular databases to prospectively identify novel inhibitors. The rapid identification of eight molecules from database mining and our successful demonstration of in vitro inhibitory activity support the utility of these computational models as novel tools for the efficient retrieval of inhibitors. These results demonstrate the effectiveness of computational modeling and pharmacophore development, which may lead to the identification of new therapeutic drugs that target novel sites on nAChRs.

Address correspondence to: Dr. Dennis B. McKay, Division of Pharmacology, The Ohio State University, College of Pharmacy, 500 West 12th Avenue, Columbus, OH43210, E-mail: mckay.2{at}osu.edu