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Optimal design of mixed-effects PK/PD models based on differential equations
Yi Wang1, Kent M Eskridge, Saralees Nadarajah
1Department of Statistics, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.
Journal of Biopharmaceutical Statistics
|December 30, 2011
Summary
This study optimizes trial designs for nonlinear mixed-effects models (NLMMs) without analytic solutions, crucial for pharmacokinetic studies. It introduces a method to efficiently compare population designs, enhancing model accuracy and robustness.
Area of Science:
- Pharmacometrics
- Mathematical modeling
- Clinical trial design
Background:
- Optimal design of nonlinear mixed-effects models (NLMMs) with analytic solutions is well-documented.
- Fewer studies address trial design for NLMMs lacking analytic solutions, particularly in population pharmacokinetics.
Purpose of the Study:
- To develop and apply methods for D-optimal design of experiments for NLMMs with nonanalytic solutions.
- To evaluate the efficiency of a Fisher information matrix-based criterion for comparing population designs.
- To assess the robustness of different designs against parameter misspecification.
Main Methods:
- Utilized the "direct" method to compute parameter sensitivities for ODE-defined models.
- Applied these sensitivities to determine D-optimal designs for a specific pharmacokinetic model (first-order absorption/elimination).
- Conducted two simulation studies to validate the design optimization criterion and assess robustness.
Main Results:
- The Fisher information matrix criterion effectively compares and optimizes population designs, reducing the need for extensive simulations.
- Sensitivity analysis revealed varying robustness of different population designs when parameters are misspecified.
Conclusions:
- The proposed "direct" method and Fisher information matrix criterion are valuable tools for optimizing trial designs in population pharmacokinetic studies with nonanalytic NLMMs.
- The findings provide guidance on selecting robust designs that account for potential parameter misspecification.
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