Pharmacodynamic modeling of aminoglycosides against Pseudomonas aeruginosa and Acinetobacter baumannii: identifying dosing regimens to suppress resistance development.

To facilitate optimal dosing regimen design, we previously developed a mathematical model using time-kill study data to predict the responses of Pseudomonas aeruginosa to various pharmacokinetic profiles of meropenem and levofloxacin. In this study, we extended the model to predict the activities of gentamicin and amikacin exposures against P. aeruginosa and Acinetobacter baumannii, respectively. The input data were from a time-kill study with 10(7) CFU/ml of bacteria at baseline. P. aeruginosa ATCC 27853 was exposed to gentamicin (0 to 16x MIC; MIC = 2 mg/liter), and A. baumannii ATCC BAA 747 was exposed to amikacin (0 to 32x MIC; MIC = 4 mg/liter) for 24 h. Using the estimates of the best-fit model parameters, bacterial responses to various fluctuating aminoglycoside exposures (half-life, 2.5 h) over 72 h were predicted via computer simulation. The computer simulations were subsequently validated using an in vitro hollow-fiber infection model with similar aminoglycoside exposures. A significant initial reduction in the bacterial burden was predicted for all gentamicin exposures examined. However, regrowth over time due to resistance emergence was predicted for regimens with a maximum concentration of the drug (C(max))/MIC (dosing frequency) of 4 (every 8 h [q8h]), 12 (q24h), and 36 (q24h). Sustained suppression of bacterial populations was forecast with a C(max)/MIC of 30 (q12h). Similarly, regrowth and suppression of A. baumannii were predicted and experimentally verified with a three-dimensional response surface. The mathematical model was reasonable in predicting extended bacterial responses to various aminoglycoside exposures qualitatively, based on limited input data. Our approach appears promising as a decision support tool for dosing regimen selection for antimicrobial agents.