Statistical identifiability and convergence evaluation for nonlinear pharmacokinetic models with particle swarm optimization

Seongho Kim, Lang Li

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)413-432
Number of pages20
JournalComputer Methods and Programs in Biomedicine
Issue number2
StatePublished - Feb 1 2014


  • Michaelis-Menten kinetic equation
  • Nonlinear models
  • Particle swarm optimization
  • Pharmacokinetics
  • Statistical identifiability

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Health Informatics

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