Comparing the performance of FOCE and different expectation-maximization methods in handling complex population...

Area of Science:

• Pharmacometrics
• Computational Pharmacology
• Drug Development

Background:

• Population pharmacometric modeling relies on maximum likelihood estimation algorithms.
• While First-Order Conditional Estimation (FOCE) is widely used, Expectation-Maximization (EM) methods offer robustness for complex models and sparse data.
• Existing comparisons of these methods often focus on simpler models, leaving a gap in understanding their performance with complex physiologically based pharmacokinetic (PBPK) models.

Purpose of the Study:

• To compare the estimation accuracy and convergence speed of different EM algorithms (SAEM, IMP, QRPEM) against FOCE for complex PBPK models.
• To evaluate method performance using both sparse and rich PK data structures.
• To identify the most suitable EM method for complex population pharmacokinetic analyses.

Main Methods:

• Simulated pharmacokinetic data for everolimus based on published results.
• Evaluation of three popular EM methods (SAEM, IMP, QRPEM) and FOCE.
• Assessment of estimation accuracy and convergence speed across varying model complexity and data sparsity.

Main Results:

• FOCE outperformed EM methods for simple models.
• EM methods demonstrated superior robustness for complex models and/or sparse data.
• Estimation accuracy was comparable across EM methods, but convergence speed varied: QRPEM > IMP > SAEM.
• IMP provided the most realistic parameter standard error estimations, unlike SAEM and QRPEM which showed under- and over-estimation.

Conclusions:

• EM methods are more robust than FOCE for complex population pharmacokinetic models and sparse datasets.
• QRPEM is the fastest EM method, but IMP offers the most reliable parameter standard error estimates.
• The choice of EM algorithm depends on the specific model complexity, data structure, and the need for accurate standard error estimation.