Prospective assessment of a D-optimal design for a population pharmacokinetic study of enoxaparin
Stephen Duffull and Bruce Green
School of Pharmacy, University of Queensland, Brisbane, Australia
Background Recently, methods for computing D-optimal designs for population pharmacokinetic experiments have become available. However there are few publications that have prospectively evaluated the benefits of D-optimal design in population or single-case settings. This study compared a population optimal design with an empirical design for estimating the baseline pharmacokinetic model for enoxaparin in a stratified randomized setting.
Methods The population pharmacokinetic D-optimal design for enoxaparin was estimated using the PFIM function (MATLAB version 220.127.116.11) developed by our group previously. The optimal design was based on a one-compartment model with lognormal between subject variability and proportional residual variability and consisted of a single design (0-30 mins, 1.5-5 hours and 11-12 hours post-dose) for all patients. The empirical design consisted of 9 windows representing the entire dose interval. Each patient was assigned to have one blood sample taken from 3 different windows. Windows for blood sampling times were also provided for the optimal design. Ninety six patients were recruited into the study who were currently receiving enoxaparin therapy. Patients were randomly assigned to either the optimal (n=50) or empirical (n=46) sampling design, stratified for body mass index. The exact times of blood samples and doses were recorded.
Results Analysis was undertaken using NONMEM (version 5). The empirical design supported a one compartment linear model with additive residual error, whilst the optimal design supported a two compartment linear model with additive residual error. The optimal model had the same structural and statistical form as the final baseline model estimated from the full data set. A posterior predictive check was performed where the models arising from the empirical and optimal designs were used to predict into the full data set. This revealed the ‘optimal’ and ‘empirical’ data derived models were similar to the full model in terms of bias and precision.
Conclusions The optimal design supported a more complex model than the empirical design strategy. Optimal design techniques may be useful in the future even when the optimized design was based on a model that was misspecified in terms of the structural and statistical models.