An Evolutionary Search Algorithm for Covariate Models in Population Pharmacokinetic Analysis

Abstract

Building a covariate model is a crucial task in population pharmacokinetics. This study develops a novel method for automated covariate modeling based on gene expression programming (GEP), which not only enables covariate selection, but also the construction of nonpolynomial relationships between pharmacokinetic parameters and covariates. To apply GEP to the extended nonlinear least squares analysis, the parameter consolidation and initial parameter value estimation algorithms were further developed and implemented. The entire program was coded in Java. The performance of the developed covariate model was evaluated for the population pharmacokinetic data of tobramycin. In comparison with the established covariate model, goodness-of-fit of the measured data was greatly improved by using only 2 additional adjustable parameters. Ten test runs yielded the same solution. In conclusion, the systematic exploration method is a potentially powerful tool for prescreening covariate models in population pharmacokinetic analysis.

Keywords: automatic modeling; covariate models; gene expression programming; genetic algorithm; population pharmacokinetics.