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Does Sample Size, Sampling Strategy, or Handling of Concentrations Below the Lower Limit of Quantification Matter When Externally Evaluating Population Pharmacokinetic Models?

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European Journal of Drug Metabolism and Pharmacokinetics Aims and scope Submit manuscript

Abstract

Background and Objectives

Precision dosing requires selecting the appropriate population pharmacokinetic model, which can be assessed through external evaluations (EEs). The lack of understanding of how different study design factors influence EE study outcomes makes it challenging to select the most suitable model for clinical use. This study aimed to evaluate the impact of sample size, sampling strategy, and handling of concentrations below the lower limit of quantification (BLQ) on the outcomes of EE for four population pharmacokinetic models using vancomycin and tobramycin as examples.

Methods

Three virtual patient populations undergoing vancomycin or tobramycin therapy were simulated with varying sample size and sampling scenarios. The three approaches used to handle BLQ data were to (1) discard them, (2) impute them as LLOQ/2, or (3) use a likelihood-based approach. EEs were performed with NONMEM and R.

Results

Sample size did not have an important impact on the EE results for a given scenario. Increasing the number of samples per patient did not improve predictive performance for two out of the three evaluated models. Evaluating a model developed with rich sampling did not result in better performance than those developed with regular therapeutic drug monitoring. A likelihood-based method to handle BLQ samples impacted the outcomes of the EE with lower bias for predicted troughs.

Conclusions

This study suggests that a large sample size may not be necessary for an EE study, and models selected based on TDM may be more generalizable. The study highlights the need for guidelines for EE of population pharmacokinetic models for clinical use.

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Authors and Affiliations

  1. Faculté de pharmacie, Université de Montréal, 2940 chemin de Polytechnique, Montréal, QC, H3T 1J4, Canada

    Mehdi El Hassani & Amélie Marsot

  2. Laboratoire de suivi thérapeutique pharmacologique et pharmacocinétique, Faculté de pharmacie, Université de Montréal, Montreal, QC, Canada

    Mehdi El Hassani & Amélie Marsot

  3. Department of Anaesthesiology, LMU University Hospital, LMU Munich, 81377, Munich, Germany

    Uwe Liebchen

Authors
  1. Mehdi El Hassani
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Corresponding author

Correspondence to Mehdi El Hassani.

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Author Contributions

ME and AM designed the research. ME performed research and analyzed data. ME wrote the manuscript. UL and AM reviewed the manuscript.

Funding

This work was supported by the Fonds de Recherche du Québec-Santé (FRQS); the Réseau Québécois de Recherche sur les Médicaments and Canada Foundation for Innovation.

Data Availability

The data that support the findings of this study are available on request from the corresponding author.

Code Availability

Available on request from the corresponding author.

Conflict of interest

Mehdi El Hassani, Uwe Liebchen and Amélie Marsot declare no conflicts of interest.

Ethical Approval

No ethics approval was necessary given that no clinical trial data were used for the simulations. We relied on published data and models to perform simulations.

Consent to Participate

Not applicable as this was a simulation study.

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Not applicable.

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El Hassani, M., Liebchen, U. & Marsot, A. Does Sample Size, Sampling Strategy, or Handling of Concentrations Below the Lower Limit of Quantification Matter When Externally Evaluating Population Pharmacokinetic Models?. Eur J Drug Metab Pharmacokinet (2024). https://doi.org/10.1007/s13318-024-00897-1

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