ArticleThe AAPS journal2021
Predicting the Drug-Drug Interaction Mediated by CYP3A4 Inhibition: Method Development and Performance Evaluation.
Article in The AAPS journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- Machine Learning Enables Rapid Prediction of Acid-Reducing Agent Drug Interactions: A Streamlined Complement to PBPK Modeling.CPT: pharmacometrics & systems pharmacology · 2026Article
- Drug-drug interactions in targeted cancer therapies: a focus on tyrosine kinase inhibitors.Expert review of clinical pharmacology · 2025Review
- High-performance PBPK model for predicting CYP3A4 induction-mediated drug interactions: a refined and validated approach.Frontiers in pharmacology · 2025Article
- Editorial: Application of PKPD modeling in drug discovery and development.Frontiers in pharmacology · 2025Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of drug-drug interactions (DDIs) plays critical roles for the estimation of DDI risk caused by inhibition of CYP3A4. The aim of this paper is to develop a physiologically based pharmacokinetic (PBPK)-DDI model for prediction of the DDI co-administrated with ketoconazole in humans and evaluate the predictive performance of the model. The pharmacokinetic and biopharmaceutical properties of 35 approved drugs, as victims, were collected for the development of a PBPK model, which were linked to the PBPK model of ketoconazole for the DDI prediction. The PBPK model of victims and ketoconazole were validated by matching actual in vivo pharmacokinetic data. The predicted results of DDI were compared with actual data to evaluate the predictive performance. The percentage of predicted ratio of AUC (AUCR), C
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Registered trials
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