ArticleThe AAPS journal2020
Use of Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Drug-Food Interactions: an Industry Perspective.
Article in The AAPS journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 45 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed.
- Predicting and Confirming Bioequivalence of Alpelisib Oral Granules and Tablets for Patients With PIK3CA-Related Disorders.AAPS PharmSciTech · 2025Trial
- Effect of food on capsule and granule formulations of selumetinib.Clinical and translational science · 2022Trial
- Physiologically Based Pharmacokinetic Modeling for Mechanistic Analysis of Food Effects: A Case Study with Radotinib.Pharmaceutical research · 2026Article
- Best Practices in Physiologically Based Pharmacokinetic (PBPK) Modeling.CPT: pharmacometrics & systems pharmacology · 2026Review
- TIM-1 and Tiny-TIM as Robust In Vitro Models for Oral Biopharmaceutics: Evidence from an International Ring Study.Pharmaceutics · 2026Article
- PQRI Workshop: Model-Informed Drug Development (MIDD) Approaches in Pediatric Formulation Development.The AAPS journal · 2026Article
- Physiologically-Based Pharmacokinetic Modeling to Support Pediatric Clinical Development: An IQ Working Group Perspective on the Current Status and Challenges.CPT: pharmacometrics & systems pharmacology · 2026Review
- Pharmacometric modeling of opioid disposition in pregnancy: a systematic review of PopPK and PBPK approaches, model limitations, and future directions.Frontiers in pharmacology · 2026Review
- Machine learning to predict food effects during drug development: a comprehensive review.Journal of cheminformatics · 2025Review
- Integrating In Vitro BE Checker with In Silico Physiologically Based Biopharmaceutics Modeling to Predict the Pharmacokinetic Profiles of Oral Drug Products.Pharmaceutics · 2025Article
- Physiologically Based Pharmacokinetic Simulation of Tofacitinib in Humans Using Extrapolation from Single-Species Renal Failure Model.Pharmaceutics · 2025Article
- Development of a Physiologically Based Biopharmaceutics Model Report Template: Considerations for Improved Quality in View of Regulatory Submissions.Molecular pharmaceutics · 2025Review
- Assessing Impact of Food on Oral Drug Bioequivalence Supporting ICH M13 A with the Advancements of Physiologically Based Pharmacokinetic Modeling.Pharmaceutical research · 2025Article
- Formulation Strategy of BCS-II Drugs by Coupling Mechanistic In-Vitro and Nonclinical In-Vivo Data with PBPK: Fundamentals of Absorption-Dissolution to Parameterization of Modelling and Simulation.AAPS PharmSciTech · 2025Review
- Evaluation of Solubility-Limited Absorption as a Surrogate to Predicting Positive Food Effect of BCS II/IV Drugs.Clinical pharmacokinetics · 2025Article
- Application of Physiologically Based Pharmacokinetic Modeling in the Research of Anti-HIV Drugs.Current drug metabolism · 2025Review
- Physiologically based pharmacokinetic modeling of small molecules: How much progress have we made?Drug metabolism and disposition: the biological fate of chemicals · 2025Review
- Article
- Understanding the mechanisms of food effect on omaveloxolone pharmacokinetics through physiologically based biopharmaceutics modeling.CPT: pharmacometrics & systems pharmacology · 2024Article
- Tipifarnib physiologically-based pharmacokinetic modeling to assess drug-drug interaction, organ impairment, and biopharmaceutics in healthy subjects and cancer patients.CPT: pharmacometrics & systems pharmacology · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The effect of food on pharmacokinetic properties of drugs is a commonly observed occurrence affecting about 40% of orally administered drugs. Within the pharmaceutical industry, significant resources are invested to predict and characterize a clinically relevant food effect. Here, the predictive performance of physiologically based pharmacokinetic (PBPK) food effect models was assessed via de novo mechanistic absorption models for 30 compounds using controlled, pre-defined in vitro, and modeling methodology. Compounds for which absorption was known to be limited by intestinal transporters were excluded in this analysis. A decision tree for model verification and optimization was followed, leading to high, moderate, or low food effect prediction confidence. High (within 0.8- to 1.25-fold) to moderate confidence (within 0.5- to 2-fold) was achieved for most of the compounds (15 and 8, respectively). While for 7 compounds, prediction confidence was found to be low (> 2-fold). There was no clear difference in prediction success for positive or negative food effects and no clear relationship to the BCS category of tested drug molecules. However, an association could be demonstrated when the food effect was mainly related to changes in the gastrointestinal luminal fluids or physiology, including fluid volume, motility, pH, micellar entrapment, and bile salts. Considering these findings, it is recommended that appropriately verified mechanistic PBPK modeling can be leveraged with high to moderate confidence as a key approach to predicting potential food effect, especially related to mechanisms highlighted here.
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Registered trials
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.