Anti-mutant efficacy of antibiotic combinations: in vitro model studies with linezolid and daptomycin

Abstract Objectives To explore whether linezolid/daptomycin combinations can restrict Staphylococcus aureus resistance and if this restriction is associated with changes in the mutant prevention concentrations (MPCs) of the antibiotics in combination, the enrichment of resistant mutants was studied in an in vitro dynamic model. Methods Two MRSA strains, vancomycin-intermediate resistant ATCC 700699 and vancomycin-susceptible 2061 (both susceptible to linezolid and daptomycin), and their linezolid-resistant mutants selected by passaging on antibiotic-containing medium were used in the study. MPCs of antibiotics in combination were determined at a linezolid-to-daptomycin concentration ratio (1:2) that corresponds to the ratio of 24 h AUCs (AUC24s) actually used in the pharmacokinetic simulations. Each S. aureus strain was supplemented with respective linezolid-resistant mutants (mutation frequency 10−8) and treated with twice-daily linezolid and once-daily daptomycin, alone and in combination, simulated at therapeutic and sub-therapeutic AUC24s. Results Numbers of linezolid-resistant mutants increased at therapeutic and sub-therapeutic AUC24s, whereas daptomycin-resistant mutants were enriched only at sub-therapeutic AUC24 in single drug treatments. Linezolid/daptomycin combinations prevented the enrichment of linezolid-resistant S. aureus and restricted the enrichment of daptomycin-resistant mutants. The pronounced anti-mutant effects of the combinations were attributed to lengthening the time above MPC of both linezolid and daptomycin as their MPCs were lowered. Conclusions The present study suggests that (i) the inhibition of S. aureus resistant mutants using linezolid/daptomycin combinations can be predicted by MPCs determined at pharmacokinetically derived antibiotic concentration ratios and (ii) T>MPC is a reliable predictor of the anti-mutant efficacy of antibiotic combinations as studied using in vitro dynamic models.