ABSTRACT
Purpose
To develop and evaluate methods for conducting adaptive population pharmacokinetic bridging studies.
Methods
An adaptive D-optimal design based on optimization of the population Fisher information matrix was used to determine the best sampling schedule for a target-population. Recruitment of the target-population was divided into batches and patients are assumed to enrol by batch. A prior-population model was used to determine the optimal sampling schedule for the first batch and to stabilise the data analysis in the interim iteration. Simulation studies were performed under two scenarios (1) the prior- and target-populations have similar pharmacokinetic profiles and (2) the pharmacokinetic profiles diverge significantly. A design criterion to determine early full enrolment was also proposed.
Results
The target-population estimates obtained using the proposed method were compared to estimates obtained if the target-population was studied with a design optimized based on the prior-population model. The proposed method is shown to be not inferior in scenario (1) and superior in scenario (2). The criterion to determine early full enrolment was proven to be effective.
Conclusions
An adaptive optimal design method together with an early full enrolment criterion were evaluated and resulted in more accurate estimates for the target-population in bridging studies.
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ACKNOWLEDGMENTS & DISCLOSURES
Lee Kien Foo is supported by a University of Otago postgraduate scholarship. We acknowledge helpful discussions with James McGree and at the Population Optimal Design of Experiments meeting in Berlin 2010.
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ESM 1
Supplement: Boxplots of percentage relative error (%RE) for the simulation-estimation study to evaluate the estimation capability of NONMEM for the transit compartment model. The study design is an intensive design with fifteen samples per patient. Structural parameters clearance (CL), volume of distribution (V), mean transit time (MTT) and number of transit compartments (N). Statistical parameters between subject variability (BSV) for CL \( \left( {\omega_{CL}^2} \right) \), V \( \left( {\omega_V^2} \right) \) and MTT \( \left( {\omega_{MTT}^2} \right) \). Residual unexplained variability variance of the proportional error \( \left( {\sigma_{{\varepsilon_p}}^2} \right) \) and additive error \( \left( {\sigma_{{\varepsilon_a}}^2} \right) \). The horizontal line within each subplot is the zero percentage. (JPEG 106 kb)
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Foo, L.K., Duffull, S. Adaptive Optimal Design for Bridging Studies with an Application to Population Pharmacokinetic Studies. Pharm Res 29, 1530–1543 (2012). https://doi.org/10.1007/s11095-011-0659-3
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DOI: https://doi.org/10.1007/s11095-011-0659-3