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External Evaluation of Population Pharmacokinetic Models for Precision Dosing: Current State and Knowledge Gaps

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A Letter to the Editor to this article was published on 23 June 2023

Abstract

Predicting drug exposures using population pharmacokinetic models through Bayesian forecasting software can improve individual pharmacokinetic/pharmacodynamic target attainment. However, selecting the most adapted model to be used is challenging due to the lack of guidance on how to design and interpret external evaluation studies. The confusion around the choice of statistical metrics and acceptability criteria emphasises the need for further research to fill this methodological gap as there is an urgent need for the development of standards and guidelines for external evaluation studies. Herein we discuss the scientific challenges faced by pharmacometric researchers and opportunities for future research with a focus on antibiotics.

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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, Montréal, Canada

    Mehdi El Hassani & Amélie Marsot

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  1. Mehdi El Hassani
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Correspondence to Mehdi El Hassani.

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Funding

Mehdi El Hassani received a scholarship from Université de Montréal, and Amélie Marsot acknowledges support from the Fonds de Recherche du Québec-Santé (FRQS) Research Scholars – Junior 1 (Young Researcher Establishment) Career Scholarship.

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Mehdi El Hassani and Amélie Marsot declare that they have no potential conflicts of interest that might be relevant to the contents of this manuscript.

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ME and AM conceptualised the manuscript. ME performed the literature search and wrote the manuscript. AM revised the manuscript.

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El Hassani, M., Marsot, A. External Evaluation of Population Pharmacokinetic Models for Precision Dosing: Current State and Knowledge Gaps. Clin Pharmacokinet 62, 533–540 (2023). https://doi.org/10.1007/s40262-023-01233-7

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