Journal
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Volume 113, Issue 2, Pages 413-432Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2013.10.003
Keywords
Michaelis-Menten kinetic equation; Nonlinear models; Particle swarm optimization; Pharmacokinetics; Statistical identifiability
Categories
Funding
- NSF [DMS-1312603]
- NIH [R01 GMS 74217, U54 CA113001]
- NIH Center [P30 CA022453]
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The statistical identifiability of nonlinear pharmacokinetic (PK) models with the Michaelis-Menten (MM) kinetic equation is considered using a global optimization approach, which is particle swarm optimization (PSO). If a model is statistically non-identifiable, the conventional derivative-based estimation approach is often terminated earlier without converging, due to the singularity. To circumvent this difficulty, we develop a derivative-free global optimization algorithm by combining PSO with a derivative-free local optimization algorithm to improve the rate of convergence of PSO. We further propose an efficient approach to not only checking the convergence of estimation but also detecting the identifiability of nonlinear PK models. PK simulation studies demonstrate that the convergence and identifiability of the PK model can be detected efficiently through the proposed approach. The proposed approach is then applied to clinical PK data along with a two-compartmental model. (C) 2013 Elsevier Ireland Ltd. All rights reserved.
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