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

Two Innovative Approaches to Optimize Vancomycin Dosing Using Estimated AUC after First Dose: Validation Using Data Generated from Population PK Model Coupled with Monte-Carlo Simulation and Comparison with the First-Order PK Equation Approach

The revised consensus guidelines for optimizing vancomycin doses suggest that maintaining the area under the concentration-time curve to minimal inhibitory concentration ratio (AUC/MIC) of 400–600 mg·h/L is the target pharmacokinetic/pharmacodynamic (PK/PD) index for efficacy. AUC-guided dosing approach uses a first-order pharmacokinetics (PK) equation to estimate AUC using two samples obtained at steady state and one-compartment model, which can cause inaccurate AUC estimation and fail to achieve the effective PK/PD target early in therapy (days 1 and 2). To achieve an efficacy target from the third or fourth dose, two innovative approaches (Method 1 and Method 2) to estimate vancomycin AUC at steady state (AUCSS) using two-compartment model and three or four levels after the first dose are proposed. The feasibility of the proposed methods was evaluated and compared with another published dosing algorithm (Method 3), which uses two samples and a one-compartment approach. Monte Carlo simulation was performed using a well-established population PK model, and concentration-time profiles for virtual patients with various degrees of renal function were generated, with 1000 subjects per group. AUC extrapolated to infinity (AUC0–∞) after the first dose was estimated using the three methods, whereas reference AUC (AUCref) was calculated using the linear-trapezoidal method at steady state after repeated doses. The ratio of AUC0–∞: AUCref and % bias were selected as the indicators to evaluate the accuracy of three methods. Sensitivity analysis was performed to examine the influence of change in each sampling time on the estimated AUC0–∞ using the two proposed approaches. For simulated patients with various creatinine clearance, the mean of AUC0–∞: AUCref obtained from Method 1, Method 2 and Method 3 ranged between 0.98 to 1, 0.96 to 0.99, and 0.44 to 0.69, respectively. The mean bias observed with the three methods was −0.10% to −2.09%, −1.30% to −3.59% and −30.75% to −55.53%, respectively. The largest mean bias observed by changing sampling time while using Method 1 and Method 2 were −4.30% and −10.50%, respectively. Three user-friendly and easy-to-use excel calculators were built based on the two proposed methods. The results showed that our approaches ensured sufficient accuracy and achieved target PK/PD index early and were superior to the published methodologies. Our methodology has the potential to be used for vancomycin dose optimization and can be easily implemented in clinical practice.

Influence of Mechanical Ventilation on the Pharmacokinetics of Vancomycin Administered by Continuous Infusion in Critically Ill Patients

Pathophysiological changes involved in drug disposition in critically ill patients should be considered in order to optimize the dosing of vancomycin administered by continuous infusion, and certain strategies must be applied to reach therapeutic targets on the first day of treatment. The aim of this study was to develop a population pharmacokinetic model of vancomycin to determine clinical covariates, including mechanical ventilation, that influence the wide variability of this antimicrobial. Plasma vancomycin concentrations from 54 critically ill patients were analyzed simultaneously by a population pharmacokinetic approach. A nomogram for dosing recommendations was developed and was internally evaluated through stochastic simulations. The plasma vancomycin concentration-versus-time data were best described by a one-compartment open model with exponential interindividual variability associated with vancomycin clearance and the volume of distribution. Residual error followed a homoscedastic trend. Creatinine clearance and body weight significantly dropped the objective function value, showing their influence on vancomycin clearance and the volume of distribution, respectively. Characterization based on the presence of mechanical ventilation demonstrated a 20% decrease in vancomycin clearance. External validation (n = 18) was performed to evaluate the predictive ability of the model; median bias and precision values were 0.7 mg/liter (95% confidence interval [CI], -0.4, 1.7) and 5.9 mg/liter (95% CI, 5.4, 6.4), respectively. A population pharmacokinetic model was developed for the administration of vancomycin by continuous infusion to critically ill patients, demonstrating the influence of creatinine clearance and mechanical ventilation on vancomycin clearance, as well as the implications for targeting dosing rates to reach the therapeutic range (20 to 30 mg/liter).