Computational analysis of structure-based interactions and ligand properties can predict efflux effects on antibiotics.

AcrA-AcrB-TolC efflux pumps extrude drugs of multiple classes from bacterial cells and are a leading cause for antimicrobial resistance. Thus, they are of paramount interest to those engaged in antibiotic discovery. Accurate prediction of antibiotic efflux has been elusive, despite several studies aimed at this purpose. Minimum inhibitory concentration (MIC) ratios of 32 β-lactam antibiotics were collected from literature. 3-Dimensional Quantitative Structure-Activity Relationship on the β-lactam antibiotic structures revealed seemingly predictive models (q(2)=0.53), but the lack of a general superposition rule does not allow its use on antibiotics that lack the β-lactam moiety. Since MIC ratios must depend on interactions of antibiotics with lipid membranes and transport proteins during influx, capture and extrusion of antibiotics from the bacterial cell, descriptors representing these factors were calculated and used in building mathematical models that quantitatively classify antibiotics as having high/low efflux (>93% accuracy). Our models provide preliminary evidence that it is possible to predict the effects of antibiotic efflux if the passage of antibiotics into, and out of, bacterial cells is taken into account--something descriptor and field-based QSAR models cannot do. While the paucity of data in the public domain remains the limiting factor in such studies, these models show significant improvements in predictions over simple LogP-based regression models and should pave the path toward further work in this field. This method should also be extensible to other pharmacologically and biologically relevant transport proteins.