ArticleClinical pharmacokinetics2024
Towards More Robust Evaluation of the Predictive Performance of Physiologically Based Pharmacokinetic Models: Using Confidence Intervals to Support Use of Model-Informed Dosing in Clinical Care.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Model-Based Virtual Clinical Trial Reveals Renal Impairment and Body Size as Key Determinants of Pharmacokinetic Variability and Drug-Drug Interaction Risk in Propranolol Therapy.Pharmaceutics · 2026Article
- Physiology-Based Pharmacokinetic Modeling for Prediction of Gentamicin Plasma Profile in Dogs with Renal Dysfunction.Pharmaceutics · 2026Article
- Validating Physiologically-Based Pharmacokinetic Models Using the Continuous Ranked Probability Score: Beyond Being Correct on Average.CPT: pharmacometrics & systems pharmacology · 2026Article
- An open-source framework for physiologically-based pharmacokinetic modeling of kinetics in the female reproductive tract.Frontiers in pharmacology · 2026Article
- An Open-Source Framework for Virtual Bioequivalence Modeling and Clinical Trial Design.CPT: pharmacometrics & systems pharmacology · 2025Article
- Clinical Ocular Exposure Extrapolation for an Ophthalmic Ointment Using PBPK Modeling and Simulation.The AAPS journal · 2025Article
- A multiscale molecular structural neural network for molecular property prediction.Molecular diversity · 2025Article
- PBPK Modeling of Lamotrigine and Efavirenz during Pregnancy: Implications for Personalized Dosing and Drug-Drug Interaction Management.Pharmaceutics · 2024Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveWith the rise in the use of physiologically based pharmacokinetic (PBPK) modeling over the past decade, the use of PBPK modeling to underpin drug dosing for off-label use in clinical care has become an attractive option. In order to use PBPK models for high-impact decisions, thorough qualification and validation of the model is essential to gain enough confidence in model performance. Currently, there is no agreed method for model acceptance, while clinicians demand a clear measure of model performance before considering implementing PBPK model-informed dosing. We aim to bridge this gap and propose the use of a confidence interval for the predicted-to-observed geometric mean ratio with predefined boundaries. This approach is similar to currently accepted bioequivalence testing procedures and can aid in improved model credibility and acceptance.
methodsTwo different methods to construct a confidence interval are outlined, depending on whether individual observations or aggregate data are available from the clinical comparator data sets. The two testing procedures are demonstrated for an example evaluation of a midazolam PBPK model. In addition, a simulation study is performed to demonstrate the difference between the twofold criterion and our proposed method.
resultsUsing midazolam adult pharmacokinetic data, we demonstrated that creating a confidence interval yields more robust evaluation of the model than a point estimate, such as the commonly used twofold acceptance criterion. Additionally, we showed that the use of individual predictions can reduce the number of required test subjects. Furthermore, an easy-to-implement software tool was developed and is provided to make our proposed method more accessible.
conclusionsWith this method, we aim to provide a tool to further increase confidence in PBPK model performance and facilitate its use for directly informing drug dosing in clinical care.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.