Application of direct search optimization for pharmacokinetic parameter estimation.

Abstract

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 concentrations, and is flexible to include more weight on the early-time data or be extended to multi-compartment models.