Novel multidrug resistance reversal agents

Flavonolignan and flavone inhibitors of a Staphylococcus aureus multidrug resistance pump: structure-activity relationships

Although some progress has been reported on structure-activity relationships (SARs) for inhibitors of mammalian P-glycoprotein MDR efflux pumps, there is almost nothing in the literature regarding SARs for inhibitors of any bacterial efflux pump. Indeed, only a few of these have been described. Our discovery of a potent naturally occurring flavonolignan inhibitor of the NorA MDR pump of Staphylococcus aureus provided a structural foundation upon which SARs could be assessed via synthetic analogues. Several flavonolignans were prepared which proved to have greater potency than the natural isolate, 5'-methoxyhydnocarpin-D, while others showed decreased potency. Surprisingly, some simple alkylated flavones also were quite active MDR pump inhibitors. Variability of activity among the compounds tested was sufficient so that at least some SARs could be postulated and compared with those known for P-glycoprotein.

Classification of multidrug-resistance reversal agents using structure-based descriptors and linear discriminant analysis

Linear discriminant analysis is used to generate models to classify multidrug-resistance reversal agents based on activity. Models are generated and evaluated using multidrug-resistance reversal activity values for 609 compounds measured using adriamycin-resistant P388 murine leukemia cells. Structure-based descriptors numerically encode molecular features which are used in model formation. Two types of models are generated: one type to classify compounds as inactive, moderately active, and active (three-class problem) and one type to classify compounds as inactive or active without considering the moderately active class (two-class problem). Two activity distributions are considered, where the separation between inactive and active compounds is different. When the separation between inactive and active classes is small, a model based on nine topological descriptors is developed that produces a classification rate of 83.1% correct for an external prediction set. Larger separation between active and inactive classes raises the prediction set classification rate to 92.0% correct using a model with six topological descriptors. Models are further validated through Monte Carlo experiments in which models are generated after class labels have been scrambled. The classification rates achieved demonstrate that the models developed could serve as a screening mechanism to identify potentially useful MDRR agents from large libraries of compounds.