Abstract
The pharmacokinetic parameters of amikacin were determined in a population of 20 adults and 36 pediatric patients admitted into an intensive care unit. Amikacin was administered by repeated intravenous infusion over 0.5 h (600 to 1,350 mg for adults; 70 to 1,500 mg for children). The number of administrations ranged from 2 to 17, and the number of samples collected from each patient ranged from 2 to 70. The population enrolled in the study had large variabilities in age (0.5 to 85 years), weight (6 to 95 kg), height (72 to 187 cm), creatinine clearance rate (18 to 110 ml/min), blood urea nitrogen concentration (1.5 to 15 mmol/liter), and total protein concentration (30 to 91 g/liter). The mean population parameters and their interindividual variabilities were obtained for an initial group of 44 patients (16 adults and 28 children). A two-compartment model was fitted to the population data by using the computer program P-PHARM. Model selection was guided by evaluation of the minimum objective function and the weighted residuals. The population analysis has been performed with the complete set of the collected data, including the individual serum amikacin concentration together with the individual estimate of the creatinine clearance values. The potential sources of variability in the population parameters were investigated by using patients' age, height, weight, creatinine clearance, blood urea nitrogen concentration, and total protein concentration as covariables. A test group of 12 additional patients (4 adults and 8 children) was used to evaluate the predictive performances of the population parameters. The individual pharmacokinetic parameters were computed by a Bayesian fitting procedure. From the resulting individualized values of the parameters, the concentrations of amikacin in the serum of the patients were calculated. To evaluate the performance of the Bayesian estimation, the experimental concentrations were compared with the predicted ones. The Bayesian approached developed in the study accurately predicts amikacin concentrations in serum and allows for the estimation of amikacin pharmacokinetics parameters, minimizing the risk of bias in the prediction. This was demonstrated in patients with both stable and unstable renal functions.