Modeling the cannabinoid receptor: a three-dimensional quantitative structure-activity analysis.

The structure-activity relationship studies that have been reported for cannabinoids suggest that 1) the conformation of the C-ring at the C9 position, 2) the A-ring phenolic hydroxyl, and 3) the hydrophobic side chain are important determinants for the production of analgesia, as well as other cannabinoid effects. However, either these previous structure-activity studies described for cannabinoid compounds have not been quantitative in nature or the prediction of the activity of known and unknown compounds based on molecular structure has not been tested in a comprehensive manner. In this study we describe a three-dimensional molecular modeling program using comparative molecular field analysis to derive quantitative structure-activity relationships fitting pharmacological potencies and binding affinities of cannabinoids. The analysis has proven to accurately fit the pharmacological activity of cannabinoid analogs, with cross-validated r2 values of greater than 0.3 and final analysis r2 values of greater than 0.88. Additionally, this study has further characterized the steric and electrostatic properties that account for the variations in their potency. The results from this study indicate that steric repulsion behind the C-ring is associated with decreased predicted binding affinity and pharmacological potency. On the other hand, the steric bulk of a side chain that is extended up to seven carbons contributes to predictions of increased binding affinity and potency. The electrostatic fields of cannabinoid analogs also contribute to the predicted in vitro and in vivo potencies. If the biological activities we have investigated are assumed to be the result of interaction with a single binding site, this method indicates the structural and physicochemical properties necessary for binding to the receptor and producing an effect. By defining cannabinoid binding affinity and behavioral activity pharmacophores, this method can be used for designing cannabinoid agonists and it is capable of predicting the activity of unknowns, thereby serving to facilitate rational drug design.