Prediction of serum vancomycin concentrations using one-, two- and three-compartment models with implemented population pharmacokinetic parameters and with the Bayesian method

Abstract

Although previous studies have shown that vancomycin has a complicated pharmacokinetic profile requiring description using a two- or, better, three-compartment model, until recently predictions of serum vancomycin concentrations have been mainly based on one- or two-compartment models using computer software packages. In this study, we have predicted serum vancomycin concentrations in 59 patients using one-, two- and three-compartment models with implemented population pharmacokinetic parameters in the Abbott PKS program and by use of the Bayesian method. The percentage errors of predictions made using the one-compartment model were smaller when either the Bayesian method or implemented population pharmacokinetic parameters were used (medians of -8.61% and -9.49%, respectively). Predictions using the one-compartment model with the Bayesian method were less biased (median of -1.52 μg mL-1). The best predictions were those made using the three-compartment model with the Bayesian method - they were most accurate (median of 3.40 μg mL-1) and highly precise (median of 11.3 μg2 mL-1). The results suggest that predictions made using the one-compartment model with implemented population pharmacokinetic parameters are preferable if no samples are available, otherwise predictions made using the three-compartment model with the Bayesian method are preferable. The results also supported our previous argument that the greater the number of compartments involved in individualization, the better the predictions obtained using the Bayesian method.