Statistical identifiability and convergence evaluation for nonlinear pharmacokinetic models with particle swarm optimization.
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.
Biostatistics Core, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA. Electronic address: kimse@karmanos.org.
Seongho
S
Kim
Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46032, USA. Electronic address: lali@iupui.edu.
Lang
L
Li
D000465:Algorithms; D006801:Humans; D006993:Hypnotics and Sedatives / Q000493:pharmacokinetics; D008874:Midazolam / Q000493:pharmacokinetics; D017711:Nonlinear Dynamics; D010599:Pharmacokinetics
D006993:Hypnotics and Sedatives; D008874:Midazolam
Michaelis–Menten kinetic equation; Nonlinear models; Particle swarm optimization; Pharmacokinetics; Statistical identifiability