ArticleClinical pharmacokinetics2024
Selecting the Best Pharmacokinetic Models for a Priori Model-Informed Precision Dosing with Model Ensembling.
Article in Clinical pharmacokinetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Evaluation of two model-based Bayesian approaches for vancomycin dosing in general hospitalized patients.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Evaluation of a model averaging algorithm for model-informed precision dosing in the context of parameter misspecifications.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Bayesian Workflow for Minimal PBPK Models: Case Study of Dapagliflozin.CPT: pharmacometrics & systems pharmacology · 2026Article
- Model Ensembling and Machine Learning Approaches to Predict the First Dose of Amoxicillin in Intensive Care.CPT: pharmacometrics & systems pharmacology · 2026Article
- Methodologies for Population Pharmacokinetic Modeling of Target-Site Drug Exposure: A Narrative Review of Current Strategies.Clinical pharmacokinetics · 2026Review
- Article
- Therapeutic Drug Monitoring and Point-of-Care Technologies: Opportunities and Current Challenges.Therapeutic drug monitoring · 2026Review
- Machine Learning-Based Model Selection and Averaging Outperform Single-Model Approaches for a Priori Vancomycin Precision Dosing.CPT: pharmacometrics & systems pharmacology · 2025Article
- Leveraging Clinical Data to Enhance the Performance Evaluation of Ceftriaxone Population Pharmacokinetic Models in Children.Clinical pharmacokinetics · 2025Article
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Authors and funding
10 authors.
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Abstract
BACKGROUND AND
objectiveWhen utilizing population pharmacokinetic (popPK) models for a priori dosage individualization, selecting the best model is crucial to obtain adequate doses. We developed and evaluated several model-selection and ensembling methods, using external evaluation on the basis of therapeutic drug monitoring (TDM) samples to identify the best (set of) models per patient for a priori dosage individualization.
methodsPK data and models describing both hospitalized patients (n = 134) receiving continuous vancomycin (26 models) and patients (n = 92) receiving imatinib in an outpatient setting (12 models) are included. Target attainment of four model-selection methods was compared with standard dosing: the best model based on external validation, uninformed model ensembling, model ensembling using a weighting scheme on the basis of covariate-stratified external evaluation, and model selection using covariates in decision trees that were subsequently ensembled.
resultsOverall, the use of PK models improved the proportion of patients exposed to concentrations within the therapeutic window for both cohorts. Relative improvement of proportion on target for best model, unweighted, weighted, and decision trees were - 7.0%, 2.3%, 11.4%, and 37.0% (vancomycin method-development); 23.2%, 7.9%, 15.6%, and, 77.2% (vancomycin validation); 40.7%, 50.0%, 59.5%, and 59.5% (imatinib method-development); and 19.0%, 28.5%, 38.0%, and 23.8% (imatinib validation), respectively.
conclusionsThe best (set of) models per patient for a priori dosage individualization can be identified using a relatively small set of TDM samples as external evaluation. Adequately performing popPK models were identified while also excluding poor-performing models. Dose recommendations resulted in more patients within the therapeutic range for both vancomycin and imatinib. Prospective validation is necessary before clinical implementation.
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