Optimizing outcomes with antimicrobial therapy through pharmacodynamic profiling

Abstract

In patients with infection, improving the probability of positive treatment outcomes depends on optimizing the interactions between the host, pathogen, and drug. In this setting, optimal regimens must be utilized which not only maximize effectiveness in a specific patient, but also minimize the development of microbial resistance. The probability of achieving a specifically targeted antimicrobial exposure can be assessed using Monte Carlo simulation, a technique which integrates an agent’s in vitro potency distribution (i.e., minimum inhibitory concentrations [MICs]) with the pharmacokinetic profile. The targeted pharmacodynamic parameters assessed by this technique include the ratio of peak concentration (C_max) to MIC (C_max : MIC); the ratio of the area under the plasma concentration-time curve (AUC) to MIC (AUC : MIC), and the time the drug concentration exceeds the MIC (T ≫ MIC). Some antimicrobials, e.g., the aminoglycosides, are most effective/bactericidal when they have a high C_max : MIC ratio; others, e.g., the fluoroquinolones, are more effective when the AUC : MIC ratio is high. In both of these scenarios, organism eradication is concentration-dependent, and the therapeutic goal is to maximize drug exposure. Like the fluoroquinolones, the efficacy of telithromycin, a newly developed ketolide, is most related to the AUC : MIC ratio. Outcome for other agents, such as the Β-lactams, is best predicted by the T ≫ MIC; in this case, organism eradication is time-dependent, and the therapeutic goal is to optimize the duration of antimicrobial exposure. This article discusses how the use of currently available antimicrobials can be optimized through an appreciation of pharmacodynamic profiling.