An Effective Approach for Obtaining Optimal Sampling Windows for Population Pharmacokinetic Experiments

Abstract

This paper describes an effective approach for optimizing sampling windows for population pharmacokinetic experiments. Sampling windows has been proposed for population pharmacokinetic experiments that are conducted in late phase drug development programs where patients are enrolled in many centers and out-patient clinic settings. Collection of samples under this uncontrolled environment at fixed times may be problematic and can result in uninformative data. A sampling windows approach is more practicable, as it provides the opportunity to control when samples are collected by allowing some flexibility and yet provide satisfactory parameter estimation. This approach uses D -optimality to specify time intervals around fixed D -optimal time points that results in a specified level of efficiency. The sampling windows have different lengths and achieve two objectives: the joint sampling windows design attains a high specified efficiency level and also reflects the sensitivities of the plasma concentration-time profile to parameters. It is shown that optimal sampling windows obtained using this approach are very efficient for estimating population PK parameters and provide greater flexibility in terms of when samples are collected.

Key Words:

• D-optimality
• Fisher information matrix
• Nonlinear mixed effects
• Optimal design
• Pharmacokinetics
• Sampling windows

ACKNOWLEDGMENTS

The research was sponsored by the Centre for Applied Pharmacokinetic Research (CAPKR is supported by the following consortium members; Eli Lilly, GlaxoSmithKline, Novartis, Pfizer and Servier). We also acknowledge Dr. Sergei Leonov, GSK, for the MATLAB code of the first-order algorithm.