Population Pharmacokinetic Model for Topotecan Derived From Phase I Clinical Trials

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Abstract
PURPOSE: To characterize the pharmacokinetics of topotecan in a population model that would identify patient variables or covariates that appreciably impacted on its disposition. PATIENTS AND METHODS: All data were collected from 82 patients entered in four different phase I trials that were previously reported as separate studies from 1992 to 1996. All patients received topotecan as a 30-minute constant-rate infusion on a daily-times-five schedule and were selected for this study because their daily dose did not exceed 2.0 mg/m2. Among the 82 patients were 30 patients classified as having renal insufficiency and 13 patients with hepatic dysfunction. The population pharmacokinetic model was built in sequential manner, starting with a covariate-free model and progressing to a covariate model with the aid of generalized additive modeling. RESULTS: A linear two-compartment model characterized total topotecan plasma concentrations (n = 899). Four primary pharmacokinetic parameters (total clearance, volume of the central compartment, distributional clearance, and volume of the peripheral compartment) were related to various combinations of covariates. The relationship for total clearance (TVCL [L/h] = 32.0 + [0.356(WT − 71) + 0.308(HT − 168.5) − 8.42(SCR − 1.1)] × [1 + 0.671 sex]) was dependent on the patients’ weight (WT), height (HT), serum creatinine (SCR), and sex and had a moderate ability to predict (r2 = 0.64) each patient’s individual clearance value. The addition of covariates to the population model improved the prediction errors, particularly for clearance. Removal of 10 outlying patients from the analysis improved the ability of the model to predict individual clearance values (r2 = 0.77). CONCLUSION: A population pharmacokinetic model for total topotecan has been developed that incorporates measures of body size and renal function to predict total clearance. The model can be used prospectively to obtain a revised and validated model that can then be used to design individualized dosing regimens.