Salvinorin A is a potent kappa opioid agonist isolated from the leaves of the Mexican Mint, Salvia divinorum. Recent studies have suggested this compound may be in part responsible for the hallucinogenic effects associated with the recreational use of the Salvia Divinorum extracts. The chemical structure of Salvinorin A, however, lacks key features historically associated with opiate activity, representing an entirely new class of opioid ligand. The work planned here, examines the molecular basis to Salvinorin A recognition and binding to the opioid receptors using a combination of chemical synthesis, molecular modeling, and ligand binding studies. Through this work, the potential use of Salvinorin A as a new opioid analgesic lead compound will be determined. This will be accomplished through the following key parts: 1) The determination of molecular basis to Salvinorin A binding and selectivity to the kappa opioid receptor. Using synthetic medicinal chemistry, a series of Salvinorin A analogs are synthesized and tested using ligand binding experiments. 2) The development of structure based models of Salvinorin A recognition that explain the structural activity relationship of synthetic analogs. Comparative molecular field analysis (CoMFA) techniques will be applied to identify potential sites of recognition within the opioid receptors and to propose synthetic modification of the Salvinorin A scaffold. 3) The identification of binding sites within the kappa opioid receptors that confer high affinity binding and selectivity of Salvinorin A. Ligand displacement studies will be performed using wild type and mutant opioid receptors to evaluate binding site models and provide insight to the design of Salvinorin A analogs. Initial targets for site directed mutagenesis will be derived from existing opiate structure-function studies and Salvinorin A receptor molecular models. Results of this work will direct further analog development of Salvinorin A (a next generation) and further define and refine our proposed computer binding and field models.
