<p>Model-informed precision dosing relies on population pharmacokinetic models to personalize drug therapy and improve clinical outcomes. While hundreds of models are published, only a small fraction are externally evaluated and even fewer are applied in practice. This gap reflects a lack of clear standardized guidance on how to identify, assess, and implement published models in new clinical settings. This guidance provides a structured step-by-step framework for the external evaluation and selection of published population pharmacokinetic models to support their use in model-informed precision dosing. It outlines key considerations for defining the model’s intended use, assessing the characteristics of the available dataset, screening for suitable candidate models, and applying prediction- and simulation-based diagnostics. By addressing methodological and practical challenges, this framework supports more reproduceable use of published models in real-world settings to help bridge the gap between model development and clinical application.</p>

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Guidance for External Evaluation and Selection of Population Pharmacokinetic Models for Precision Dosing

  • Mehdi El Hassani,
  • Amélie Marsot

摘要

Model-informed precision dosing relies on population pharmacokinetic models to personalize drug therapy and improve clinical outcomes. While hundreds of models are published, only a small fraction are externally evaluated and even fewer are applied in practice. This gap reflects a lack of clear standardized guidance on how to identify, assess, and implement published models in new clinical settings. This guidance provides a structured step-by-step framework for the external evaluation and selection of published population pharmacokinetic models to support their use in model-informed precision dosing. It outlines key considerations for defining the model’s intended use, assessing the characteristics of the available dataset, screening for suitable candidate models, and applying prediction- and simulation-based diagnostics. By addressing methodological and practical challenges, this framework supports more reproduceable use of published models in real-world settings to help bridge the gap between model development and clinical application.