Clinical assessment of CYP2D6‐mediated herb–drug interactions in humans: Effects of milk thistle, black cohosh, goldenseal, kava kava, St. John's wort, and<i>Echinacea</i>
Bibliographic record
Abstract
Cytochrome P450 2D6 (CYP2D6), an important CYP isoform with regard to drug-drug interactions, accounts for the metabolism of approximately 30% of all medications. To date, few studies have assessed the effects of botanical supplementation on human CYP2D6 activity in vivo. Six botanical extracts were evaluated in three separate studies (two extracts per study), each incorporating 16 healthy volunteers (eight females). Subjects were randomized to receive a standardized botanical extract for 14 days on separate occasions. A 30-day washout period was interposed between each supplementation phase. In study 1, subjects received milk thistle (Silybum marianum) and black cohosh (Cimicifuga racemosa). In study 2, kava kava (Piper methysticum) and goldenseal (Hydrastis canadensis) extracts were administered, and in study 3 subjects received St. John's wort (Hypericum perforatum) and Echinacea (Echinacea purpurea). The CYP2D6 substrate, debrisoquine (5 mg), was administered before and at the end of supplementation. Pre- and post-supplementation phenotypic trait measurements were determined for CYP2D6 using 8-h debrisoquine urinary recovery ratios (DURR). Comparisons of pre- and post-supplementation DURR revealed significant inhibition (approximately 50%) of CYP2D6 activity for goldenseal, but not for the other extracts. Accordingly, adverse herb-drug interactions may result with concomitant ingestion of goldenseal supplements and drugs that are CYP2D6 substrates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".