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First published on February 16, 2007; DOI: 10.1124/mol.106.033233


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Received for publication December 14, 2006.
Revised February 16, 2007.
Accepted for publication February 16, 2007.

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

Dennis B. McKay 1*, Cheng Chang 2, Tatitana F. Gonzalez 1, Susan B. McKay 1, Raed A. El-Hajj 1, Darrell L. Bryant 1, Michael X. Zhu 1, Peter W. Swaan 2, Kristjan M. Arason 3, Aravinda B. Pulipaka 3, Crina M. Orac 3, Stephen C. Bergmeier 3

1 The Ohio State University 2 University of Maryland 3 Ohio University

* Address correspondence to: E-mail: mckay.2{at}osu.edu

Abstract

As a novel approach to drug discovery involving neuronal nicotinic acetylcholine receptors (nAChRs), our laboratory is targeting non-agonist 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 {alpha}3{beta}4* 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 {alpha}3{beta}4* nAChRs with a wide range of IC50 values (0.9 to 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-QSAR (CoMFA and CoMSIA) models were generated. These computational models were successfully validated and provided insights into the molecular interactions of MLA analogs with nAChRs. Additionally, 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 in order to prospectively identify novel inhibitors. The rapid identification of 8 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.


Key words: Nicotinic cholinergic, Structure-activity relationships and modeling, Fluorescence techniques, Receptor binding studies, Structure/function/mechanism





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