Mixture models are applied in population pharmacometrics to characterize underlying population distributions that are not adequately approximated by a single normal or lognormal distribution. In addition to obtaining individualized maximum a posteriori Bayesian post hoc parameter estimates, the subpopulation to which an individual was classified can be determined. However, the accuracy of the classification of subjects to subpopulations is not well studied. We investigated the impact of several factors on the accuracy of classification in mixture models applied to pharmacokinetics using a simulation strategy. The availability of actual subject data allowed us to evaluate mixture model classification in a potentially common application, namely, the classification of clearance into poor metabolizer (PM) or extensive metabolizer (EM) subgroups with the known phenotype status in subjects receiving metoprolol. The factors explored in the simulation study were the magnitude of difference between the clearances in two subpopulations, the between subject variability in clearance, the mixing-fraction, and the population sample size. Populations were simulated at various levels of the above factors and analyzed with a mixture model using NONMEM. The population pharmacokinetics of metoprolol were modeled with the EM/PM phenotype as a known covariate, and without the phenotype covariate using a mixture model. Within the range of scenarios studied, the proportion of subjects classified into the correct subpopulation was high. The simulation-estimation study suggests that a greater separation between two subpopulations, a smaller variability in the parameter distribution, a larger sample size, and a smaller size subpopulation tend to be associated with a greater accuracy of subpopulation classification when a mixture model is applied to pharmacokinetic data. In a population pharmacokinetic analysis of metoprolol, a drug that undergoes polymorphic metabolism, it was possible to correctly identify phenotype status using a mixture model.
- Mixture models