Interoccasion variability in population pharmacokinetic models: identifiability, influence, interdependencies and
Emily Behrens1, Sebastian G Wicha2
1Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Bundesstraße 45, 20146, Hamburg, Germany.
Journal of Pharmacokinetics and Pharmacodynamics
|April 11, 2025
Summary
Modeling interoccasion variability (IOV) in sparse pharmacokinetic studies is complex. This simulation found that including more occasions and trough samples improves parameter estimation and IOV detection, crucial for accurate clinical trial design.
Area of Science:
- Pharmacometrics
- Clinical Pharmacology
- Statistical Modeling
Background:
- Modeling interoccasion variability (IOV) in pharmacokinetic (PK) parameters presents challenges, particularly in sparse study designs.
- Understanding IOV's impact is crucial for accurate drug development and clinical trial design.
Purpose of the Study:
- To evaluate the influence of IOV on PK parameter estimation using stochastic simulation and estimation (SSE).
- To determine the minimal sample size required for IOV detection in clinical trials.
- To assess the consequences of neglecting IOV on parameter estimates and area under the concentration-time curve (AUC) calculations.
Main Methods:
- A simulation study using stochastic simulation and estimation (SSE) was conducted.
- Interoccasion variability (IOV) levels of 25% and 75% coefficient of variation (CV) were simulated.
- Two sampling schemes (with and without trough samples) across multiple occasions were compared.
Main Results:
- The power to detect IOV increased with the number of occasions (OCCs), while the type I error rate remained acceptable.
- Including trough samples significantly improved performance across various evaluations.
- Parameter estimates were more precise with more OCCs and higher IOV effect sizes.
- Neglecting true IOV led to substantial bias and imprecision in parameter estimates, especially for interindividual variabilities and residual error.
- A minimum of 10–50 patients across three OCCs were required to achieve ≥95% power in the investigated scenarios.
- Mis-specified models that did not account for IOV resulted in distorted AUC distributions.
Conclusions:
- Accurate modeling of interoccasion variability is essential for reliable pharmacokinetic parameter estimation in sparse designs.
- Increasing the number of occasions and incorporating trough samples enhance the detection and characterization of IOV.
- Failure to model IOV can significantly impact the accuracy of parameter estimates and derived metrics like AUC, potentially affecting clinical trial outcomes.
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