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Record W3038254578 · doi:10.1002/cpdd.822

Effect of Itraconazole, a Potent CYP3A4 Inhibitor, on the Steady‐State Pharmacokinetics of Vemurafenib in Patients With <i>BRAF</i><sup>V600</sup> Mutation–Positive Malignancies

2020· article· en· W3038254578 on OpenAlexaff
Weijiang Zhang, Michael S. Mathisen, Grant R. Goodman, Harper Forbes, Yuyao Song, Enric Bertran, Lev Demidov, Sang Joon Shin

Bibliographic record

VenueClinical Pharmacology in Drug Development · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsRoche (Canada)
Fundersnot available
KeywordsVemurafenibItraconazoleMedicinePharmacokineticsDosingPharmacologyCrossover studyConfidence intervalArea under the curveInternal medicineAdverse effectUrologyGastroenterologyCancerDermatologyPathologyMetastatic melanoma

Abstract

fetched live from OpenAlex

mutation-positive metastatic malignancies received oral vemurafenib 960 mg twice daily on days 1 to 20 (period A) and oral vemurafenib 960 mg twice daily with oral itraconazole 200 mg once daily on days 21 to 40 (period B). A mixed-effects analysis of variance model was used to compare log-transformed area under the concentration-time curve during the dosing interval and maximum plasma concentration values for vemurafenib in 8 patients between period B (vemurafenib plus itraconazole) and period A (vemurafenib alone). Multiple doses of itraconazole increased steady-state exposure of vemurafenib by approximately 40%, with geometric least squares mean ratios (period B/period A) of 140% (90% confidence interval, 121-161) for both maximum plasma concentration and area under the concentration-time curve during the dosing interval. There was no apparent increase in incidence or severity of adverse events during coadministration of vemurafenib with itraconazole. In conclusion, coadministration of itraconazole with vemurafenib resulted in a modest increase in exposure of vemurafenib at steady state and was generally well tolerated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.278
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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