A Strategy for Dosing Vancomycin to Therapeutic Targets Using Only Trough Concentrations.

A Strategy for Dosing Vancomycin to Therapeutic Targets Using Only Trough Concentrations.

Clin Pharmacokinet. 2016 Jul 7;

Authors: Prybylski JP

Abstract
Effective treatment of complicated methicillin-resistant Staphylococcus aureus (MRSA) infections with vancomycin requires a 24-h area under the concentration-time curve (AUC24) to minimum inhibitory concentration (MIC) ratio of at least 400. To ensure goal AUC24 has been reached requires either dosing to concentrations strongly associated with nephrotoxicity, measurement of patient-specific pharmacokinetics, or use of Bayesian statistics. In this study, we show a method of determining patient-specific pharmacokinetics and dosing to therapeutic AUC24 while minimizing potentially toxic concentrations, guided by only trough measurements. A Monte-Carlo simulation of 10,000 patients with complicated MRSA infections was prepared from two-compartment pharmacokinetic parameters using patient data extracted from the literature. The proposed method of determining patient-specific pharmacokinetics using consecutive trough concentrations was found to be more accurate than the conventional peak-trough method for peaks measured up to 4 h after infusion. Simulated human error in trough timing was found to reduce accuracy of the consecutive trough method, but an approach to resolve timing errors during a loading sequence or at steady-state using iteration is proposed. Both the simulated minimized concentration strategy and trough-based dosing to 15-20 mg/L had a high probability of achieving AUC24 at least 400 mg·h/L, but conventional trough-based dosing was associated with higher probability of potentially toxic 24-h doses and trough concentrations. The proposed strategy must be validated in real patients, with outcomes assessed before it is used in daily practice, but the theoretical benefits found in the simulation suggest this simple strategy should be considered with other AUC24-based approaches.

PMID: 27389404 [PubMed - as supplied by publisher]