Aims: The aim of this study was to evaluate a population model for epirubicin clearance using internal and external validation techniques.
Methods: Jackknife samples were used to identify outliers in the population dataset and individuals influencing covariate selection. Sensitivity analyses were performed in which serum aspartate transaminase (AST) values (a covariate in the population model) or epirubicin concentrations were randomly changed by +/-10%. Cross-validation was performed five times, on each occasion using 80% of the data for model development and 20% to assess the performance of the model. External validation was conducted by assessing the ability of the population model to predict concentrations and clearances in a separate group of 79 patients.
Results: Structural parameter estimates from all jackknife samples were within 7.5% of the final population estimates and examination of log likelihood values indicated that the selection of AST in the final model was not due to the presence of outliers. Alteration of AST or epirubicin concentrations by +/-10% had a negligible effect on population parameter estimates and their precision. In the cross-validation analysis, the precision of clearance estimates was better in patients with AST concentrations>150 U l-1. In the external validation, epirubicin concentrations were over-predicted by 81.4% using the population model and clearance values were also poorly predicted (imprecision 43%).
Conclusions: The results of internal validation of population pharmacokinetic models should be interpreted with caution, especially when the dataset is relatively small.