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Record W2029310284 · doi:10.1592/phco.24.16.1508.50958

The Influence of St. John's Wort on the Pharmacokinetics and Protein Binding of Imatinib Mesylate

2004· article· en· W2029310284 on OpenAlexaboutno aff
Patrick F. Smith, Julie Bullock, Brent M. Booker, Curtis E. Haas, Charles S. Berenson, William J. Jusko

Bibliographic record

VenuePharmacotherapy The Journal of Human Pharmacology and Drug Therapy · 2004
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsnot available
FundersNational Institute of General Medical Sciences
KeywordsImatinib mesylatePharmacokineticsPharmacologyMesylateMedicineChemistryImatinibInternal medicine

Abstract

fetched live from OpenAlex

STUDY OBJECTIVE: To determine the effect of St. John's wort on the pharmacokinetics of imatinib mesylate. DESIGN: Open-label, complete crossover, fixed-sequence, pharmacokinetic study. SETTING: Clinical research center. SUBJECTS: Ten healthy adult volunteers. INTERVENTION: Single 400-mg oral doses of imatinib were administered before and after 2 weeks of treatment with St. John's wort 300 mg 3 times/day. MEASUREMENTS AND MAIN RESULTS: The pharmacokinetics of imatinib were significantly altered by St. John's wort, with reductions of 32% in the median area under the concentration-time curve from time zero to infinity (p=0.0001), 29% in maximum observed concentration (p=0.005), and 21% in half-life (p=0.0001). Protein binding ranged from 97.7-90.3% (mean 94.9%), was concentration independent, and was not altered by St. John's wort. Therapeutic outcomes of imatinib have been shown to correlate with both dose and drug concentrations. CONCLUSION: Coadministration of imatinib with St. John's wort may compromise imatinib's clinical efficacy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.020
GPT teacher head0.330
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), 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

Citations148
Published2004
Admission routes1
Has abstractyes

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