Population pharmacokinetics and maximum a posteriori probability Bayesian estimator of abacavir: Application of individualized therapy in HIV-infected infants and toddlers

摘要

AIMS To develop a population pharmacokinetic model for abacavir in HIV-infected infants and toddlers, which will be used to describe both once and twice daily pharmacokinetic profiles, identify covariates that explain variability and propose optimal time points to optimize the area under the concentration-time curve (AUC) targeted dosage and individualize therapy. METHODS The pharmacokinetics of abacavir was described with plasma concentrations from 23 patients using nonlinear mixed-effects modelling (NONMEM) software. A two-compartment model with first-order absorption and elimination was developed. The final model was validated using bootstrap, visual predictive check and normalized prediction distribution errors. The Bayesian estimator was validated using the cross-validation and simulation-estimation method. RESULTS The typical population pharmacokinetic parameters and relative standard errors (RSE) were apparent systemic clearance (CL) 13.4lh -1 (RSE 6.3%), apparent central volume of distribution 4.94l (RSE 28.7%), apparent peripheral volume of distribution 8.12l (RSE14.2%), apparent intercompartment clearance 1.25lh -1 (RSE 16.9%) and absorption rate constant 0.758h -1 (RSE 5.8%). The covariate analysis identified weight as the individual factor influencing the apparent oral clearance: CL = 13.4 × (weight/12) 1.14. The maximum a posteriori probability Bayesian estimator, based on three concentrations measured at 0, 1 or 2, and 3h after drug intake allowed predicting individual AUC 0-t. CONCLUSIONS The population pharmacokinetic model developed for abacavir in HIV-infected infants and toddlers accurately described both once and twice daily pharmacokinetic profiles. The maximum a posteriori probability Bayesian estimator of AUC 0-t was developed from the final model and can be used routinely to optimize individual dosing.