A Population Pharmacokinetic Model for Vancomycin in Pediatric Patients and Its Predictive Value in a Naive Population

Abstract

The objectives of this study were to (i) construct a population pharmacokinetic (PK) model able to describe vancomycin (VAN) concentrations in serum in pediatric patients, (ii) determine VAN PK parameters in this population, and (iii) validate the predictive ability of this model in a naive pediatric population. Data used in this study were obtained from 78 pediatric patients (under 18 years old). PK analyses were performed using compartmental methods. The most appropriate model was chosen based on the evaluation of pertinent graphics and calculation of the Akaike information criterion test. The population PK analysis was performed using an iterative two-stage method. A two-compartment PK model using age, sex, weight, and serum creatinine as covariates was determined to be the most appropriate one to describe serum VAN concentrations. The quality of fit was very good, and the distribution of weighted residuals was found to be homoscedastic (Wilcoxon signed rank test). Fitted population PK parameters (mean ± standard deviation) were as follows: central clearance (0.1 ± 0.05 liter/h/kg), central volume of distribution (0.27 ± 0.07 liter/kg), peripheral volume of distribution (0.16 ± 0.07 liter/kg), and distributional clearance (0.16 ± 0.07 liter/kg). The predictive ability of the developed model (including the above-mentioned covariates) was evaluated in a naive population of 19 pediatric patients. The predictability was very good. Precision (±95% confidence interval [CI]) (peak, 4.1 [±1.4], and trough, 2.2 [±0.7]) and bias (±95% CI) (peak, −0.58 [±2.2], and trough, 0.63 [±1.1] mg/liter) were significantly ( P < 0.05) superior to those obtained using a conventional method (precision [±95% CI]: peak, 8.03 [±2.46], and trough, 2.7 [±0.74]; bias: peak, −7.1 [±2.9], and trough, −1.35 [±1.2] mg/liter). We propose the use of this population PK model to optimize VAN clinical therapies in our institution and others with similar patient population characteristics.