ArticleThe AAPS journal2015
Food Effect in Humans: Predicting the Risk Through In Vitro Dissolution and In Vivo Pharmacokinetic Models.
Article in The AAPS journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- Machine learning to predict food effects during drug development: a comprehensive review.Journal of cheminformatics · 2025Review
- Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug-Food Interactions from Chemical Structures.Sensors (Basel, Switzerland) · 2023Article
- Food effect risk assessment in preformulation stage using material sparing μFLUX methodology.ADMET & DMPK · 2022Article
- Use of Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Drug-Food Interactions: an Industry Perspective.The AAPS journal · 2020Article
- Integrated Multi-stakeholder Systems Thinking Strategy: Decision-making with Biopharmaceutics Risk Assessment Roadmap (BioRAM) to Optimize Clinical Performance of Drug Products.The AAPS journal · 2020Article
- Food Effects on Oral Drug Absorption: Application of Physiologically-Based Pharmacokinetic Modeling as a Predictive Tool.Pharmaceutics · 2020Review
- Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In vitro and in vivo experimental models are frequently used to assess a new chemical entity's (NCE) biopharmaceutical performance risk for food effect (FE) in humans. Their ability to predict human FE hinges on replicating key features of clinical FE studies and building an in vitro-in vivo relationship (IVIVR). In this study, 22 compounds that span a wide range of physicochemical properties, Biopharmaceutics Classification System (BCS) classes, and food sensitivity were evaluated for biorelevant dissolution in fasted- and fed-state intestinal media and the dog fed/fasted-state pharmacokinetic model. Using the area under the curve (AUC) as a performance measure, the ratio of the fed-to-fasted AUC (FE ratio) was used to correlate each experimental model to FE ratio in humans. A linear correlation was observed for the in vitro dissolution-human IVIVR (R (2) = 0.66, % mean square error 20.7%). Similarly, the dog FE ratio correlated linearly with the FE ratio in humans (R (2) = 0.74, % mean square error 16.25%) for 15 compounds. Data points near the correlation line indicate dissolution-driven mechanism for food effect, while deviations from the correlation line shed light on unique mechanisms that can come into play such as GI physiology or unusual physicochemical properties. In summary, fed/fasted dissolution studies and dog PK studies show a reasonable correlation to human FE, hence are useful tools to flag high-risk NCEs entering clinical development. Combining kinetic dissolution, dog FE model and in silico modeling one can study FE mechanism and formulation strategies to mitigate the FE risk.
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Registered trials
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