Validation of population pharmacokinetic models: a comparison of internal and external validation approaches for hydrochlorothiazide

Abstract

1. Model evaluation is an important issue in population analyses. Our aim was to perform and illustrate metrics and techniques for internal and external evaluation with an application to population pharmacokinetics of hydrochlorothiazide (HCTZ).

2. A nonlinear mixed effects model was used to study the pharmacokinetics of HCTZ. In addition, different types of internal assessment tools and external metrics were used for model evaluation. External evaluation was performed using an alternative dataset that included data from an independent group of subjects. For comparison, a previously published population pharmacokinetic model for HCTZ was applied to the same data.

3. A two-compartment model with first-order oral absorption using a constant time delay between administration and absorption and first-order elimination best described HCTZ pharmacokinetics. Age had a statistically significant effect on HCTZ clearance. The final model performed adequately in the internal and external assessment tests. The final model showed better predictive performance than the other previously published HCTZ model.

4. Finally, a robust population pharmacokinetic model for HCTZ in adults was constructed and validated internally and externally. Incorporating analytical assessment of nonlinear pharmacokinetics into the modelling may be a promising approach to improve the predictive power of the model.

Keywords:

• Population pharmacokinetics model
• evaluation
• external validation
• non-linear kinetics
• hydrochlorothiazide
• metrics

Acknowledgements

The authors thank Verisfield UK Ltd for providing the C-t data to conduct this computational study.

Disclosure statement

The authors report no declarations of interest.

Data availability statement

Data sharing is not applicable to this article.

Additional information

Funding

R.K. was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number: 261).