An update on the ability of St. John's wort to affect the metabolism of other drugs
Bibliographic record
Abstract
INTRODUCTION: Hypericum perforatum (HP), more commonly known as St. John's wort, is a popular medicinal herb used for the treatment of depression. HP affects the pharmacokinetics of many drugs by inducing cytochrome P450 (CYP) isozymes, such as CYP3A4, CYP2C19, CYP2C9, and the P-glycoprotein (P-gp) transporter. AREAS COVERED: This review focuses on drugs that are metabolized by CYP3A4, CYP2C19, CYP2C9 and P-gp as their plasma concentrations show the effects of concomitant use of HP. For the purpose of this review, all electronic databases such as PubMed, Scopus, Google Scholar and Cochrane library were searched to identify in vitro, in vivo or human studies about the effects of HP on the metabolism of drugs. Data collected were published between 1966 and January 2012. EXPERT OPINION: There are a number of drugs whose metabolism is reduced by HP. The authors point out that metabolic interactions between HP and drugs are not always unfavorable and sometimes have benefits (e.g., reduction of irinotecan toxicity and increase in clopidogrel responsiveness). HP does not have a significant influence on the kinetics of drugs such as carbamazepine, ibuprofen and theophylline. The use of HP preparations is not recommended in people who are taking immunosuppressants or cardiovascular drugs. With other medications, it is recommended that practitioners should only use HP preparations with a low hyperforin content and under careful monitoring. It is also recommended that because of the reduction in the bioavailability of oral contraceptives administered concurrently with HP, women who use HP preparations should use additional preventive methods to avoid unintended pregnancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".