Matuzumab – A Population Pharmacokinetic Model and its Evaluation
K. Kuester(1,3), A. Kovar(2), B. Brockhaus(2), C. Kloft(1,3)
(1)Freie Universitaet Berlin, Dept Clinical Pharmacy, Berlin, Germany; (2)Merck KGaA, Dept Clinical Pharmacology and Pharmacokinetics, Darmstadt, Germany; (3)Martin-Luther-Universitaet Halle-Wittenberg, Dept Clinical Pharmacy, Halle, Germany
Objectives: Matuzumab is a humanised monoclonal antibody (mAb) of the immunoglobulin subclass IgG1 which targets the epidermal growth factor receptor (EGFR). A population pharmacokinetic (PK) model based on data from three phase I studies was to be developed including a covariate analysis and evaluated.
Methods: Matuzumab was administered as multiple 1 h iv infusions with 11 different dosing regimens ranging from 400 – 2000 mg, q1w-q3w. For model development 90 patients with 1256 serum concentrations were chosen. All data were fitted simultaneously using the software program NONMEM (ADVAN6, TRANS1, TOL5 and the FOCE INTERACTION estimation method).
Results: Serum concentration-time profiles were best described by a two compartment model. Within this model in addition to the linear clearance (CLL) a second elimination pathway as a non-linear process (Michaelis-Menten kinetics, CLNL) from the central compartment was included with the additional parameters Vmax and km. Total clearance as the sum of CLL (14.5 mL/h) and CLNL (114 mL/h) was in the expected range for mAbs. Due to the non-linearity the half-lives ranged between 1.3 d and 10.7 d at concentrations of 0.02 and 1000 mg/L, respectively. Central distribution volume of 3.72 L (V1) approximated serum volume. Peripheral distribution volume (V2) was estimated to be 1.84 L suggesting limited distribution throughout the body. Inter-individual variability could be established for CLL, V1, V2 and Vmax (22% - 62% CV). As random variation between the different infusions within one subject inter-occasion variability on CLL was successfully implemented (23% CV). All parameters were generally estimated with good precision (RSE < 39%). A covariate analysis was performed to reduce the interindividual variability of the base model. The covariates identified included an influence of weight on V1 and CLL. It should be recognised that our results do not suggest dose adjustments for sex, age or organ functions (liver or kidney). Model evaluation by visual predictive check, case deletion procedure and an external dataset is ongoing and results will be presented.
Conclusion: A final population pharmacokinetic model for matuzumab has been developed including nonlinear PK processes. In addition, relevant and plausible covariates have been incorporated. The developed model combined with PD data could serve as a tool to guide selection of optimal dose regimens for matuzumab, a highly promising “targeted” cancer therapy.
