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Record W2138020718 · doi:10.1093/jnci/94.16.1247

Effects of St. John's Wort on Irinotecan Metabolism

2002· article· en· W2138020718 on OpenAlexaboutno aff
Ron H.J. Mathijssen

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer therapeutics and mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsIrinotecanCYP3A4PharmacologyConfidence intervalCrossover studyHypericum perforatumMedicineActive metaboliteHypericumMetaboliteInternal medicineHypericinCYP2D6CancerPharmacokineticsCytochrome P450MetabolismTraditional medicineColorectal cancer

Abstract

fetched live from OpenAlex

St. John's wort (SJW), a widely used herbal product, has been implicated in drug interactions resulting from the induced expression of the cytochrome P450 CYP3A4 isoform. In this study, we determined the effect of SJW on the metabolism of irinotecan, a pro-drug of SN-38 and a known substrate for CYP3A4. Five cancer patients were treated with irinotecan (350 mg/m(2), intravenously) in the presence and absence of SJW (900 mg daily, orally for 18 days) in an unblinded, randomized crossover study design. The plasma levels of the active metabolite SN-38 decreased by 42% (95% confidence interval [CI] = 14% to 70%) following SJW cotreatment with 1.0 micro M x h (95% CI = 0.34 micro M x h to 1.7 micro M x h) versus 1.7 micro M x h (95% CI = 0.83 micro M x h to 2.6 micro M x h) (P =.033, two-sided paired Student's t test). Consequently, the degree of myelosuppression was substantially worse in the absence of SJW. These findings indicate that patients on irinotecan treatment should refrain from taking SJW because plasma levels of SN-38 were dramatically reduced, which may have a deleterious impact on treatment outcome.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.027
GPT teacher head0.286
Teacher spread0.259 · 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 designObservational
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

Citations447
Published2002
Admission routes1
Has abstractyes

Explore more

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