Integrating Population Approaches With Physiologically Based Pharmacokinetic Models: A Novel Framework for Parameter...

Area of Science:

• Pharmacokinetics and Drug Development
• Computational Biology and Bioinformatics
• Systems Pharmacology

Background:

• Physiologically Based Pharmacokinetic (PBPK) modeling is crucial for predicting drug concentrations during development.
• Estimating parameters in PBPK models is challenging due to numerous parameters and limited data.
• Existing methods struggle to efficiently estimate inter-individual variability in physiologically relevant parameters.

Purpose of the Study:

• To introduce a novel population whole-body PBPK (popWB-PBPK) modeling approach for enhanced parameter estimation.
• To leverage individual patient data for more accurate PBPK model parameterization and variability assessment.
• To present an optimized Stochastic Approximation Expectation-Maximization (SAEM) algorithm for efficient PBPK parameter estimation.

Main Methods:

• Coupling whole-body PBPK (WB-PBPK) models with population estimation techniques.
• Implementing an optimized SAEM algorithm with adaptive parameter grid optimization and linear interpolation.
• Utilizing theophylline as a case study to estimate drug-specific parameters and covariate effects (e.g., smoking status).

Main Results:

• The popWB-PBPK approach accurately estimates drug-specific parameters like CYP1A2 clearance and lipophilicity.
• The optimized SAEM algorithm significantly reduces computational runtime compared to standard SAEM.
• The method successfully incorporates covariate effects, demonstrating its practical utility.

Conclusions:

• The developed popWB-PBPK framework provides an accessible R package (saemixPBPK) for robust PBPK parameter estimation.
• This approach enables simultaneous estimation of population parameters, variability, and uncertainty while preserving physiological relevance.
• Advancements in mechanistic modeling facilitate more reliable pharmacokinetic predictions using individual data.