Ligands of Neuronal Nicotinic Acetylcholine Receptor (nAChR): Inferences from the Hansch and 3-D Quantitative Structure-Activity Relationship (QSAR) Models
Authors: Nicolotti O.; Pellegrini-Calace M.; Altomare C.; Carotti A.; Carrieri A.; Sanz F.
Source: Current Medicinal Chemistry, Volume 9, Number 1, January 2002 , pp. 1-29(29)
Publisher: Bentham Science Publishers
Neuronal acetylcholine ion channel receptors (nAChRs), that exist in several subtypes resulting from a different organisation of various subunits around the central ion channel, are involved in a variety of functions and disorders of the central nervous system. There is evidence to implicate a deficit of nAChRs in the symptomatology of severe neurologic pathologies, such as Alzheimer's and Parkinson's diseases. Reliable three-dimensional structures of nAChRs are not available yet, and this hampers adopting structure-based approaches in designing new ligands. Also pharmacophore models are not reliable enough to be used in ligand-based approaches to drug design and little structure-activity work has been reported so far. This paper deals with structure-activity relationships of a wide series of nicotinic ligands. It provides results from a study of the quantitative structure activity relationships (QSARs) based on literature data of about 270 nicotinic agonists, belonging to various chemical classes. The QSAR study was carried out by using either a classical Hansch approach or a Comparative Molecular Field Analysis (CoMFA). Within each congeneric series, Hansch-type equations revealed detrimental steric effects as the factors mainly modulating the receptor affinity, whereas CoMFA allowed us to merge progressively models obtained for each class of congeners into a more general one that showed good crossvalidation statistics. The CoMFA coefficient isocontour maps illustrated, at the 3-D level, the most relevant interactions responsible for a high receptor affinity, whereas the robustness of the global three-dimensional QSAR / CoMFA (n = 206, q2 = 0.749, r2 = 0.847, s= 0.600) model was supported by the high value of the prediction statistics (r2pred = 0.961) and confirmed by the satisfactory predictions of the affinity data of an external set of 18 recently published ligands with chemical structures even quite diverse from those included in the training set.
Document Type: Review article
Publication date: 2002-01-01
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