Mechanism-based pharmacodynamic models of fluoroquinolone resistance in Staphylococcus aureus

Abstract

Pharmacodynamic modeling from earlier experiments in which two ciprofloxacin-susceptible Staphylococcus aureus strains and their corresponding resistant grlA mutants were exposed to a series of ciprofloxacin (J. J. Campion, P. J. McNamara, and M. E. Evans, Antimicrob. Agents Chemother. 49:209-219, 2005) and levofloxacin (J. J. Campion et al., Antimicrob. Agents Chemother. 49:2189-2199, 2005) pharmacokinetic profiles in an in vitro system indicated that the subpopulation-specific estimated maximal killing rate constants were similar for both agents, suggesting a common mechanism of action. We propose two novel pharmacodynamic models that assign mechanisms of action to fluoroquinolones (growth inhibition or death stimulation) and compare the abilities of these models and two other maximum effect models (net effect and MIC based) to describe and predict the changes in the population dynamics observed during our previous in vitro system experiments with ciprofloxacin. A high correlation between predicted and observed viable counts was observed for all models, but the best fits, as assessed by diagnostic tests, and the most precise parameter estimates were obtained with the growth inhibition and net effect models. All models, except the death stimulation model, correctly predicted that resistant subpopulations would not emerge when a high-density culture was exposed to a high initial concentration designed to rapidly eradicate low-level-resistant grlA mutants. Additional experiments are necessary to elucidate which of the proposed mechanistic models best characterizes the antibacterial effects of fluoroquinolone antimicrobial agents.

FIG. 1.

Fit of the growth inhibition model to experimental data from in vitro hollow-fiber system experiments. Observed colony counts of MRSA 8043 (filled symbols, top panel), MRSA 8043C0-1 (open symbols, top panel), MRSA 8282 (filled symbols, bottom panel), and MRSA 8282C0-1 (open symbols, bottom panel) following exposure to pharmacokinetic profiles produced by various simulated ciprofloxacin (CIP) dosage regimens are shown. Observed viable counts for growth control experiments are also indicated. The model-predicted viable count-versus-time profiles are shown by the solid (MRSA 8043 and MRSA 8282) and dashed (MRSA 8043C0-1 and MRSA 8282C0-1) lines. The legend for both panels appears in the top panel.

FIG. 2.

Abilities of the net effect, MIC-based, growth inhibition, and death stimulation pharmacodynamic models to predict MRSA 8043 and MRSA 8282 viable count profiles for (i) an experimental ciprofloxacin dosage regimen (single 6,250-mg dose followed by 400 mg every 12 h) designed to prevent the emergence of resistant subpopulations likely to be present in a culture of 1 × 10⁷ CFU/ml (top panel) and (ii) a conventional ciprofloxacin dosage regimen of 400 mg every 12 h with a culture of 1.0 × 10⁵ CFU/ml not likely to contain resistant subpopulations (bottom panel). The observed viable counts are shown as symbols (squares, MRSA 8043; diamonds, MRSA 8282). Predicted colony counts for MRSA 8043 and MRSA 8282 for all four models are shown as solid lines, with the exception of the death stimulation model for MRSA 8282 (dashed line). The reliable limit of detection in the experiments was 100 CFU/ml (horizontal dotted line).