ArticleCPT: pharmacometrics & systems pharmacology2024
Tipifarnib physiologically-based pharmacokinetic modeling to assess drug-drug interaction, organ impairment, and biopharmaceutics in healthy subjects and cancer patients.
Article in CPT: pharmacometrics & systems pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Assessing Cytochrome P450 Drug Interaction Risk for Dordaviprone Using Physiologically Based Pharmacokinetic Modeling.CPT: pharmacometrics & systems pharmacology · 2025Article
- 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
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A physiologically-based pharmacokinetic (PBPK) model for tipifarnib, which included mechanistic absorption, was built and verified by integrating in vitro data and several clinical data in healthy subjects and cancer patients. The final PBPK model was able to recover the clinically observed single and multiple-dose plasma concentrations of tipifarnib in healthy subjects and cancer patients under several dosing conditions, such as co-administration with a strong CYP3A4 inhibitor and inducer, an acid-reducing agent (proton pump inhibitor and H2 receptor antagonist), and with a high-fat meal. In addition, the model was able to accurately predict the effect of mild or moderate hepatic impairment on tipifarnib exposure. The appropriately verified model was applied to prospectively simulate the liability of tipifarnib as a victim of CYP3A4 enzyme-based drug-drug interactions (DDIs) with a moderate inhibitor and inducer as well as tipifarnib as a perpetrator of DDIs with sensitive substrates of CYP3A4, CYP2B6, CYP2D6, CYP2C9, and CYP2C19 in healthy subjects and cancer patients. The effect of a high-fat meal, acid-reducing agent, and formulation change at the therapeutic dose was simulated. Finally, the model was used to predict the effect of mild, moderate, or severe hepatic, and renal impairment on tipifarnib PK. This multipronged approach of combining the available clinical data with PBPK modeling-guided dosing recommendations for tipifarnib under several conditions. This example showcases the totality of the data approach to gain a more thorough understanding of clinical pharmacology and biopharmaceutic properties of oncology drugs in development.
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