Evaluation of methods for estimating time to steady state with examples from phase 1 studies

Area of Science:

• Pharmacokinetics
• Clinical Pharmacology
• Statistical Modeling

Background:

• Accurate determination of time to steady state is crucial for Phase 1 multiple dose studies.
• Various statistical and modeling approaches exist, each with unique assumptions and applications.

Purpose of the Study:

• To provide an overview of methodologies for assessing time to steady state in Phase 1 multiple dose studies.
• To compare the advantages and disadvantages of different assessment methods.
• To evaluate the similarity of results obtained from various methodologies.

Main Methods:

• Overview of methods including NOSTASOT, Helmert contrasts, spline regression, effective half-life, nonlinear mixed effects modeling, and Bayesian Markov Chain Monte Carlo (MCMC).
• Discussion of distributional assumptions for pharmacokinetic (PK) parameters for each method.
• Application of selected methodologies to eight case studies.

Main Results:

• Spline regression and methods like NOSTASOT and Helmert contrasts do not require PK parameter distributional assumptions.
• Nonlinear mixed effects modeling and Bayesian hierarchical modeling require PK parameter distributional assumptions but allow for population and individual estimates.
• Estimates for time to steady state were generally similar across nonlinear mixed effects modeling, Bayesian hierarchical approach, effective half-life, and spline regression.

Conclusions:

• Different methodologies offer varying levels of assessment (average vs. individual) and have distinct data requirements.
• The choice of method depends on the specific study goals and assumptions regarding PK parameters.
• Convergent results from multiple methods increase confidence in the determined time to steady state.