PURPOSE: To develop a population pharmacokinetic (PK) model for cabazitaxel in patients with advanced solid tumors and examine the influence of demographic and baseline parameters. METHODS: One hundred and seventy patients who received cabazitaxel (10-30 mg/m(2), 1-h IV infusion) every 7 or 21 days in five Phase I-III studies were analyzed by non-linear mixed-effect modeling (NONMEM VI). Model evaluation comprised non-parametric bootstrap and visual predictive checks. RESULTS: Cabazitaxel PK was best described by a linear three-compartment model with: first-order elimination; interindividual variability on clearance (CL), central volume of distribution (V1), and all intercompartmental rate constants except K21; interoccasion variability in CL and V1; proportional residual error of 27.8%. Cabazitaxel CL was related to body surface area (BSA) and tumor type (breast cancer; finding confounded by study). Typical CL for a non-breast cancer patient with a BSA of 1.84 m(2) was 48.5 L/h, with V1 26.0 L, steady-state volume of distribution 4,870 L and alpha, beta, and gamma half-lives of 4.4 min, 1.6, and 95 h, respectively. Sex, height, weight, age, Caucasian race, renal/hepatic function, and cytochrome P450 inducer use did not significantly further explain the PK of cabazitaxel. Bootstrap and posterior predictive checks confirmed the adequacy of the model. CONCLUSIONS: Cabazitaxel PK appears unaffected by most baseline patient factors, and the influence of BSA on CL is addressed in practice by BSA-dependent doses. This analysis suggests consistent cabazitaxel PK and exposure across most solid tumor types, although the potential influence of breast cancer on CL requires further confirmation.
PURPOSE: To develop a population pharmacokinetic (PK) model for cabazitaxel in patients with advanced solid tumors and examine the influence of demographic and baseline parameters. METHODS: One hundred and seventy patients who received cabazitaxel (10-30 mg/m(2), 1-h IV infusion) every 7 or 21 days in five Phase I-III studies were analyzed by non-linear mixed-effect modeling (NONMEM VI). Model evaluation comprised non-parametric bootstrap and visual predictive checks. RESULTS:Cabazitaxel PK was best described by a linear three-compartment model with: first-order elimination; interindividual variability on clearance (CL), central volume of distribution (V1), and all intercompartmental rate constants except K21; interoccasion variability in CL and V1; proportional residual error of 27.8%. Cabazitaxel CL was related to body surface area (BSA) and tumor type (breast cancer; finding confounded by study). Typical CL for a non-breast cancerpatient with a BSA of 1.84 m(2) was 48.5 L/h, with V1 26.0 L, steady-state volume of distribution 4,870 L and alpha, beta, and gamma half-lives of 4.4 min, 1.6, and 95 h, respectively. Sex, height, weight, age, Caucasian race, renal/hepatic function, and cytochrome P450 inducer use did not significantly further explain the PK of cabazitaxel. Bootstrap and posterior predictive checks confirmed the adequacy of the model. CONCLUSIONS:Cabazitaxel PK appears unaffected by most baseline patient factors, and the influence of BSA on CL is addressed in practice by BSA-dependent doses. This analysis suggests consistent cabazitaxel PK and exposure across most solid tumor types, although the potential influence of breast cancer on CL requires further confirmation.
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