ArticleCPT: pharmacometrics & systems pharmacology2021
Prediction of the drug-drug interaction potential of the α1-acid glycoprotein bound, CYP3A4/CYP2C9 metabolized oncology drug, erdafitinib.
Article in CPT: pharmacometrics & systems pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- Mechanistic Physiologically Based Pharmacokinetic Modeling to Predict CYP3A4-Mediated Drug-Drug Interactions of Flumatinib as Both a Victim and a Perpetrator.Drug design, development and therapy · 2026Article
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- Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions of Soticlestat as a Victim of CYP Induction and Inhibition, and as a Perpetrator of CYP and P-Glycoprotein Inhibition.Clinical pharmacology in drug development · 2025Article
- Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs.Cancer chemotherapy and pharmacology · 2023Review
- The Blind Spot of Pharmacology: A Scoping Review of Drug Metabolism in Prematurely Born Children.Frontiers in pharmacology · 2022Article
- Prediction of the drug-drug interaction potential of the α1-acid glycoprotein bound, CYP3A4/CYP2C9 metabolized oncology drug, erdafitinib.CPT: pharmacometrics & systems pharmacology · 2021Article
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Authors and funding
11 authors at 3 institutions in 3 countries.
Funding
Abstract
Erdafitinib is a potent oral pan-fibroblast growth factor receptor inhibitor being developed as oncology drug for patients with alterations in the fibroblast growth factor receptor pathway. Erdafitinib binds preferentially to α1-acid glycoprotein (AGP) and is primarily metabolized by cytochrome P450 (CYP) 2C9 and 3A4. This article describes a physiologically based pharmacokinetic (PBPK) model for erdafitinib to assess the drug-drug interaction (DDI) potential of CYP3A4 and CYP2C9 inhibitors and CYP3A4/CYP2C9 inducers on erdafitinib pharmacokinetics (PK) in patients with cancer exhibiting higher AGP levels and in populations with different CYP2C9 genotypes. Erdafitinib's DDI potential as a perpetrator for transporter inhibition and for time-dependent inhibition and/or induction of CYP3A was also evaluated. The PBPK model incorporated input parameters from various in vitro and clinical PK studies, and the model was verified using a clinical DDI study with itraconazole and fluconazole. Erdafitinib clearance in the PBPK model consisted of multiple pathways (CYP2C9/3A4, renal, intestinal; additional hepatic clearance), making the compound less susceptible to DDIs. In poor-metabolizing CYP2C9 populations carrying the CYP2C9*3/*3 genotype, simulations shown clinically relevant increase in erdafitinib plasma concentrations. Simulated luminal and enterocyte concentration showed potential risk of P-glycoprotein inhibition with erdafitinib in the first 5 h after dosing, and simulations showed this interaction can be avoided by staggering erdafitinib and digoxin dosing. Other than a simulated ~ 60% exposure reduction with strong CYP3A/2C inducers such as rifampicin, other DDI liabilities were minimal and considered not clinically relevant.
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