Abstract

Citation

Researchers should cite this work as follows:

• Khorasheh, F. (2023). 1999-Khorasheh-Ahmadi-Gerayeli-Application of Direct Search Optimization for Pharmacokinetic Parameter Estimation. SIMIODE, QUBES Educational Resources. doi:10.25334/GMPT-A991

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Article Context

Description

Khorasheh, Farhad , Amir Mahbod Ahmadi, and Abbas Gerayeli. 1999.  Application of Direct Search Optimization for Pharmacokinetic Parameter Estimation. J Pharm Pharmaceut Sci. 2 (3):92-98.

See https://europepmc.org/article/med/10953255 . Accessed 27 March 2023.

Purpose. For simple pharmacokinetic compartmental models, analytical solution to the governing differential equations along with common graphical methods provide a mean to evaluate the associated rate constants. These graphical methods, however, can not be used for the more complex multi-compartment models. Furthermore, parameter estimation using slope and intercept values from the graphical methods is often accompanied with error. In this study a numerical solution is applied for the solution of the governing differential equations and a simple direct search optimization procedure utilizing random numbers is used for pharmacokinetic parameter estimation.

Method. The methodology is demonstrated with reference to experimental literature data for ciprofloxacin and ofloxacin whose pharmacokinetic behavior has been reported in terms of a two-compartment model.

Results. Examination of the predicted drug concentrations from the graphical method and the optimization methodology indicate that both methods have comparable accuracy in predicting the drug concentrations. The graphical method, however, only shows good accuracy in the early stages both after i.v. and oral drug administration whereas the optimization procedure, due to the nature of the objective function formulation, provides good accuracy over the entire range of times after drug administration.

Conclusions. The methodology is simple, provides optimized parameters which accurately predict drug

This paper uses a direct search to estimate the parameters in a system of liner pharmacokinetics and while data is not offered plots of data with final models using best fit (lease squares sense) models is offered.

Keywords: pharmacokinetics, compartmental models, parameter estimation,  direct search, sum of squre errors, least squares, mionimization