OBJECTIVES: A developed population pharmacokinetic model of the humanized 
monoclonal antibody (mAb) matuzumab was evaluated by external evaluation. Based 
on the estimates of the final model, simulations of different dosing regimens 
and the covariate effect were performed.
METHODS: The development dataset included 90 patients, and the evaluation 
dataset included 81 patients; the two sets of patients were from three different 
studies. In all studies, the patients had different types of advanced carcinoma 
- mainly colon, rectal and pancreatic cancer. They received matuzumab as 
multiple 1-hour intravenous infusions in a wide range of dosing regimens 
(development dataset: from 400 mg every 3 weeks to 2000 mg in the first week 
followed by 1600 mg weekly; evaluation dataset: from 100 mg weekly to 800 mg 
weekly). In addition to 1256 serum mAb concentrations for model development, 
there were 1124 concentrations available for model evaluation. Serum 
concentration-time data were simultaneously fitted using NONMEM software. The 
developed two-compartment model - with the parameters central volume of 
distribution (V(1)) and peripheral volume of distribution (V(2)), 
intercompartmental clearance and linear clearance (CLL), an additional nonlinear 
elimination pathway (Michaelis-Menten constant: the concentration with the 
half-maximal elimination rate and V(max): the maximum elimination rate) and 
covariate relations - was evaluated by an external dataset. Different simulation 
scenarios were performed to demonstrate the impact of the incorporated covariate 
effect and the influence of different dosing regimens and dosing strategies on 
the concentration-time profiles.
RESULTS: The developed model included the covariate fat-free mass (FFM) on V(1) 
and on CLL. The evaluation did not support the covariate FFM on V(1) and, after 
deletion of this covariate, the model parameters of the refined model were 
estimated. The model showed good precision for all parameters: the relative 
standard errors (RSEs) were <42% for the development dataset and < or = 51% for 
the evaluation dataset (excluding the higher RSEs for the correlation between 
V(2) and V(max) and the interindividual variability on V(2) for the evaluation 
dataset). The model showed good robustness for the ability to estimate highly 
precise parameters for the combined dataset of 171 patients (RSE <29%). 
Simulations revealed that variability in concentration-time profiles for minimum 
and maximum steady-state concentrations was reduced to a marginal extent by a 
proposed dose adaptation.
CONCLUSION: The population pharmacokinetic model for matuzumab was improved by 
evaluation with an external dataset. The new model obtained precise parameter 
estimates and demonstrated robustness. After correlation with efficacy data 
simulation results in particular could serve as a tool to guide dose selection 
for this 'targeted' cancer therapy.