Population pharmacokinetics model and limited sampling strategy for intravenous vinorelbine derived from phase I clinical trials

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Abstract
Aims a) To characterize the pharmacokinetics of intravenous vinorelbine, b) to use a population analysis for the identification of patient covariates that might appreciably influence its disposition and c) to define a limited sampling strategy for further Bayesian estimation of individual pharmacokinetic parameters. Methods All data were collected from 64 patients (99 courses) entered in three different phase I trials that have been previously reported. All patients received vinorelbine as a 20 min infusion with dose levels ranging from 20–45 mg m−2. The population pharmacokinetic model was built in a sequential manner on a subset of two-thirds of the data, starting with a covariate-free model then progressing to a covariate model using the nonlinear-mixed effect methodology. The remaining one-third of the data were used to validate several sparse sampling designs. Results A linear three-compartment model characterized vinorelbine blood concentrations (n=1228). Two primary pharmacokinetic parameters (total clearance and volume of distribution) were related to various combinations of covariates. The relationship for total clearance (CLtotal (l h−1)=29.2×BSA×(1−0.0090 Plt)+6.7×Wt/Crs) was dependent on the patient's body surface area (BSA), weight (Wt), serum creatinine (Crs) and platelet count before administration (Plt). The optimal limited sampling strategy consisted of a combination of three measured blood concentrations; the first immediately before the end of infusion or 20 min later, the second at either 1 h, 3 h or 6 h and the third at 24 h after drug administration. Conclusions A population pharmacokinetic model and a limited sampling strategy for intravenous vinorelbine have been developed. This is the first population analysis performed on the basis of a large phase I database that has identified clinical covariates influencing the disposition of i.v. vinorelbine. The model can be used to obtain accurate Bayesian estimates of pharmacokinetic parameters in situations where extensive pharmacokinetic sampling is not feasable.