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  • Title: Three-dimensional quantitative structure-selectivity relationships analysis guided rational design of a highly selective ligand for the cannabinoid receptor 2.
    Author: Brogi S, Corelli F, Di Marzo V, Ligresti A, Mugnaini C, Pasquini S, Tafi A.
    Journal: Eur J Med Chem; 2011 Feb; 46(2):547-55. PubMed ID: 21183257.
    Abstract:
    This paper describes a three-dimensional quantitative structure-selectivity relationships (3D-QSSR) study for selectivity of a series of ligands for cannabinoid CB1 and CB2 receptors. 3D-QSSR exploration was expected to provide design information for drugs with high selectivity toward the CB2 receptor. The proposed 3D computational model was performed by Phase and generated taking into account a number of structurally diverse compounds characterized by a wide range of selectivity index values. The model proved to be predictive, with r2 of 0.95 and Q2 of 0.63. In order to get prospective experimental validation, the selectivity of an external data set of 39 compounds reported in the literature was predicted. The correlation coefficient (r2=0.56) obtained on this unrelated test set provided evidence that the correlation shown by the model was not a chance result. Subsequently, we essayed the ability of our approach to help the design of new CB2-selective ligands. Accordingly, based on our interest in studying the cannabinergic properties of quinolones, the N-(adamantan-1-yl)-4-oxo-8-methyl-1-pentyl-1,4-dihydroquinoline-3-carboxamide (65) was considered as a potential synthetic target. The log(SI) value predicted by using our model was indicative of high CB2 selectivity for such a compound, thus spurring us to synthesize it and to evaluate its CB1 and CB2 receptor affinity. Compound 65 was found to be an extremely selective CB2 ligand as it displayed high CB2 affinity (Ki=4.9 nM), while being devoid of CB1 affinity (Ki>10,000 nM). The identification of a new selective CB2 receptor ligand lends support for the practicability of quantitative ligand-based selectivity models for cannabinoid receptors. These drug discovery tools might represent a valuable complementary approach to docking studies performed on homology models of the receptors.
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