An update on the ability of St. John's wort to affect the metabolism of other drugs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it