Evidence map›Paper›PMID 38482980›Full record

ArticleCPT: pharmacometrics & systems pharmacology2024

Physiologically based pharmacokinetic modeling of imatinib and N-desmethyl imatinib for drug-drug interaction predictions.

Helena Leonie Hanae Loer, Christina Kovar, Simeon Rüdesheim, Fatima Zahra Marok, Laura Maria Fuhr, Dominik Selzer, Matthias Schwab, Thorsten Lehr

Open access · goldAbstract read
In one paragraph

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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
4.1field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Helena Leonie Hanae LoerClinical Pharmacy, Saarland University, Saarbrücken, Germany.
Christina KovarClinical Pharmacy, Saarland University, Saarbrücken, Germany.
Simeon RüdesheimClinical Pharmacy, Saarland University, Saarbrücken, Germany.ORCID 0000-0002-5741-2511
Fatima Zahra MarokClinical Pharmacy, Saarland University, Saarbrücken, Germany.
Laura Maria FuhrClinical Pharmacy, Saarland University, Saarbrücken, Germany.
Dominik SelzerClinical Pharmacy, Saarland University, Saarbrücken, Germany.
Matthias SchwabDr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.ORCID 0000-0002-9984-075X
Thorsten LehrClinical Pharmacy, Saarland University, Saarbrücken, Germany.ORCID 0000-0002-8372-1465
Saarland University · DEDr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The first-generation tyrosine kinase inhibitor imatinib has revolutionized the development of targeted cancer therapy and remains among the frontline treatments, for example, against chronic myeloid leukemia. As a substrate of cytochrome P450 (CYP) 2C8, CYP3A4, and various transporters, imatinib is highly susceptible to drug-drug interactions (DDIs) when co-administered with corresponding perpetrator drugs. Additionally, imatinib and its main metabolite N-desmethyl imatinib (NDMI) act as inhibitors of CYP2C8, CYP2D6, and CYP3A4 affecting their own metabolism as well as the exposure of co-medications. This work presents the development of a parent-metabolite whole-body physiologically based pharmacokinetic (PBPK) model for imatinib and NDMI used for the investigation and prediction of different DDI scenarios centered around imatinib as both a victim and perpetrator drug. Model development was performed in PK-Sim® using a total of 60 plasma concentration-time profiles of imatinib and NDMI in healthy subjects and cancer patients. Metabolism of both compounds was integrated via CYP2C8 and CYP3A4, with imatinib additionally transported via P-glycoprotein. The subsequently developed DDI network demonstrated good predictive performance. DDIs involving imatinib and NDMI were simulated with perpetrator drugs rifampicin, ketoconazole, and gemfibrozil as well as victim drugs simvastatin and metoprolol. Overall, 12/12 predicted DDI area under the curve determined between first and last plasma concentration measurements (AUC

Indexed as

Drug InteractionsImatinib MesylateModels, BiologicalAdultAntineoplastic AgentsComputer SimulationCytochrome P-450 CYP2C8Cytochrome P-450 CYP3AFemaleHumansKetoconazoleLeukemia, Myelogenous, Chronic, BCR-ABL PositiveMaleMiddle AgedProtein Kinase InhibitorsRifampinAntineoplastic AgentsCYP3A4 protein, humanCytochrome P-450 CYP2C8Cytochrome P-450 CYP3AImatinib MesylateKetoconazoleProtein Kinase InhibitorsRifampin

Identifiers

PMID38482980
PMCPMC11179706
OpenAlexW4392807478

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.