Evidence map›Paper›PMID 27225997›Full record

ArticleClinical pharmacokinetics2016

Evaluation of Cytochrome P450 3A4-Mediated Drug-Drug Interaction Potential for Cobimetinib Using Physiologically Based Pharmacokinetic Modeling and Simulation.

Nageshwar R Budha, Tao Ji, Luna Musib, Steve Eppler, Mark Dresser, Yuan Chen, Jin Y Jin

Registry-linked trialAbstract read
PubMed Publisher
In one paragraph

Article in Clinical pharmacokinetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05216614 (Fluvoxamine to Augment Olfactory Recovery For Long COVID-19 Parosmia), which is not on this map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
2.3field-weighted citation impact, top 12% 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.

NCT05216614 phase2withdrawnnot on this mapstarted 2021, after this paper: background citation

Fluvoxamine to Augment Olfactory Recovery For Long COVID-19 Parosmia (FluCOP Trial)

TypeinterventionalSponsorWashington University School of MedicineRan2021 to 2022Enrolled0ConditionsCOVID-19, Olfactory Disorder, ParosmiaArmsFluvoxamine, Placebo
3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.

  1. Pooled it
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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

7 authors at 1 institution in 1 country.

Nageshwar R BudhaClinical Pharmacology, Genentech, Inc., South San Francisco, CA, USA.
Tao JiClinical Pharmacology, Genentech, Inc., South San Francisco, CA, USA.
Luna MusibClinical Pharmacology, Genentech, Inc., South San Francisco, CA, USA.
Steve EpplerClinical Pharmacology, Genentech, Inc., South San Francisco, CA, USA.
Mark DresserClinical Pharmacology, Genentech, Inc., South San Francisco, CA, USA.
Yuan ChenDrug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA, 94080, USA. chen.yuan@gene.com.
Jin Y JinClinical Pharmacology, Genentech, Inc., South San Francisco, CA, USA.
Genesys (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesCobimetinib is eliminated mainly through cytochrome P450 (CYP) 3A4-mediated hepatic metabolism in humans. A clinical drug-drug interaction (DDI) study with the potent CYP3A4 inhibitor itraconazole resulted in an approximately sevenfold increase in cobimetinib exposure. The DDI risk for cobimetinib with other CYP3A4 inhibitors and inducers needs to be assessed in order to provide dosing instructions.

methodsA physiologically based pharmacokinetic (PBPK) model was developed for cobimetinib using in vitro data. It was then optimized and verified using clinical pharmacokinetic data and itraconazole-cobimetinib DDI data. The contribution of CYP3A4 to the clearance of cobimetinib in humans was confirmed using sensitivity analysis in a retrospective simulation of itraconazole-cobimetinib DDI data. The verified PBPK model was then used to predict the effect of other CYP3A4 inhibitors and inducers on cobimetinib pharmacokinetics.

resultsThe PBPK model described cobimetinib pharmacokinetic profiles after both intravenous and oral administration of cobimetinib well and accurately simulated the itraconazole-cobimetinib DDI. Sensitivity analysis suggested that CYP3A4 contributes ~78 % of the total clearance of cobimetinib. The PBPK model predicted no change in cobimetinib exposure (area under the plasma concentration-time curve, AUC) with the weak CYP3A inhibitor fluvoxamine and a three to fourfold increase with the moderate CYP3A inhibitors, erythromycin and diltiazem. Similarly, cobimetinib exposure in the presence of strong (rifampicin) and moderate (efavirenz) CYP3A inducers was predicted to decrease by 83 and 72 %, respectively.

conclusionThis study demonstrates the value of using PBPK simulation to assess the clinical DDI risk inorder to provide dosing instructions with other CYP3A4 perpetrators.

Indexed as

Models, BiologicalAdministration, OralAlkynesArea Under CurveAzetidinesBenzoxazinesComputer SimulationCyclopropanesCytochrome P-450 CYP3ACytochrome P-450 CYP3A InducersCytochrome P-450 CYP3A InhibitorsDiltiazemDrug InteractionsErythromycinFluvoxamineHumansAlkynesAzetidinesBenzoxazinescobimetinibCyclopropanesCYP3A4 protein, humanCytochrome P-450 CYP3ACytochrome P-450 CYP3A InducersCytochrome P-450 CYP3A InhibitorsDiltiazemefavirenzErythromycinFluvoxaminePiperidinesRifampin

Identifiers

PMID27225997
OpenAlexW2401259207

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.