Effect of multidrug resistance-conferring mutations on the fitness and virulence of Pseudomonas aeruginosa.

BACKGROUND: Multidrug resistance has become a quandary in the treatment of bacterial infections. The effect of resistance mutations and the fitness cost on the pathogenicity of Pseudomonas aeruginosa is not well established. The objective of this study was to examine the impact of multidrug resistance on the fitness and virulence of P. aeruginosa. METHODS: Fourteen P. aeruginosa strains with various resistance mechanisms were used. In vitro growth of these isolates was investigated in full-strength and 0.25-strength Mueller-Hinton broth (MHB). Exponential growth rates were estimated from serial bacterial burden over 24 h. In vitro growth of two multidrug-resistant strains (PAO1ΔmexRΔoprD and PA9019) was studied when each was grown in co-culture with wild-type strain PAO1. In vivo growth was compared between PAO1 and PAO1ΔmexRΔopD using a murine pneumonia model; virulence over 10 days was studied in six isolates. RESULTS: Significant reduction in growth rate was observed in selected mutants (P < 0.01). PAO1 out-competed PAO1ΔmexRΔoprD and PA9019 in vitro, and in vivo growth of PAO1 was faster than PAO1ΔmexRΔoprD. Compared with PAO1, PAO1ΔmexR and PAO1ΔoprD showed a slight reduction in mortality rate; significantly lower mortality was seen in PAO1ΔmexRΔoprD (P < 0.01). However, virulence of PA9019 was not significantly different from that of PAO1. CONCLUSIONS: Specific resistance mutations were associated with fitness cost in P. aeruginosa, and accumulation of such mutations was associated with a reduction in virulence. However, it was difficult to predict the impact in clinical isolates. Knowledge of multidrug resistance mechanisms and compensatory mutations would likely be helpful.

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.

Impact of sample size on the performance of multiple-model pharmacokinetic simulations.

Monte Carlo simulations are increasingly used to predict pharmacokinetic variability of antimicrobials in a population. We investigated the sample size necessary to provide robust pharmacokinetic predictions. To obtain reasonably robust predictions, a nonparametric model derived from a sample population size of >/=50 appears to be necessary as the input information.

Comparative performance of different stochastic methods to simulate drug exposure and variability in a population.

Monte Carlo simulations (MCSs) are increasingly being used to predict the pharmacokinetic variability of antimicrobials in a population. However, various MCS approaches may differ in the accuracy of the predictions. We compared the performance of 3 different MCS approaches using a data set with known parameter values and dispersion. Ten concentration-time profiles were randomly generated and used to determine the best-fit parameter estimates. Three MCS methods were subsequently used to simulate the AUC(0-infinity) of the population, using the central tendency and dispersion of the following in the subject sample: 1) K and V; 2) clearance and V; 3) AUC(0-infinity). In each scenario, 10000 subject simulations were performed. Compared to true AUC(0-infinity) of the population, mean biases by various methods were 1) 58.4, 2) 380.7, and 3) 12.5 mg h L(-1), respectively. Our results suggest that the most realistic MCS approach appeared to be based on the variability of AUC(0-infinity) in the subject sample.

Comparative pharmacodynamics of gentamicin against Staphylococcus aureus and Pseudomonas aeruginosa.

Aminoglycosides are often used to treat severe infections with gram-positive organisms. Previous studies have shown concentration-dependent killing by aminoglycosides of gram-negative bacteria, but limited data are available for gram-positive bacteria. We compared the in vitro pharmacodynamics of gentamicin against Staphylococcus aureus and Pseudomonas aeruginosa. Five S. aureus strains were examined (ATCC 29213 and four clinical isolates). Time-kill studies (TKS) in duplicate (baseline inocula of 10(7) CFU/ml) were conducted to evaluate bacterial killing in relation to increasing gentamicin concentrations (0 to 16 times the MIC). Serial samples were obtained over 24 h to quantify bacterial burden. Similar TKS with P. aeruginosa ATCC 27853 were conducted, and the time courses of the all bacterial strains were mathematically modeled for quantitative comparison. A dose fractionation study (using identical daily doses of gentamicin) in an in vitro hollow-fiber infection model (HFIM) over 5 days was subsequently used for data validation for the two ATCC strains. Model fits to the data were satisfactory; r(2) values for the S. aureus and P. aeruginosa ATCC strains were 0.915 and 0.956, respectively. Gentamicin was found to have a partially concentration-dependent killing effect against S. aureus; concentrations beyond four to 8 times the MIC did not result in significantly faster bacterial killing. In contrast, a concentration-dependent profile was demonstrated in suppressing P. aeruginosa regrowth after initial decline in bacterial burden. In HFIM, thrice-daily gentamicin dosing appeared to be superior to once-daily dosing for S. aureus, but they were similar for P. aeruginosa. Different killing profiles of gentamicin were demonstrated against S. aureus and P. aeruginosa. These results may guide optimal dosing strategies of gentamicin in S. aureus infections and warrant further investigations.

Pharmacodynamics of polymyxin B against Pseudomonas aeruginosa.

Despite limited data, polymyxin B (PB) is increasingly used clinically as the last therapeutic option for multidrug-resistant (MDR) gram-negative bacterial infections. We examined the in vitro pharmacodynamics of PB against four strains of Pseudomonas aeruginosa. Clonal relatedness of the strains was assessed by random amplification of polymorphic DNA. Time-kill studies over 24 h were performed with approximately 10(5) and 10(7) CFU/ml of bacteria, using PB at 0, 0.25, 0.5, 1, 2, 4, 8, and 16x MIC. Dose fractionation studies were performed using an in vitro hollow-fiber infection model (HFIM) against a wild-type and a MDR strain. Approximately 10(5) CFU/ml of bacteria were exposed to placebo and three regimens (every 8 h [q8 h], q12 h, and q24h) simulating the steady-state unbound PB pharmacokinetics resulting from a daily dose of 2.5 mg/kg of body weight and 20 mg/kg (8 times the clinical dose). Samples were obtained over 4 days to quantify PB concentrations, total bacterial population, and subpopulation with reduced PB susceptibility (>3x MIC). The bactericidal activity of PB was concentration dependent, but killing was significantly reduced with a high inoculum. In HFIM studies, a significant reduction in bacterial load was seen at 4 h in all active regimens, but selective amplification of the resistant subpopulation(s) was apparent at 24 h with the clinical dose (both strains). Regrowth was eventually observed in all dosing regimens with the MDR strain, but its occurrence was prevented in the wild-type strain by using 8 times the clinical dose (regardless of dosing intervals). Our results suggested that the bactericidal activity of PB was concentration dependent and appeared to be related to the ratio of the area under the concentration-time curve to the MIC.