Gauging the clinical significance of P‐glycoprotein‐mediated herb‐drug interactions: Comparative effects of St. John's wort, Echinacea, clarithromycin, and rifampin on digoxin pharmacokinetics
Bibliographic record
Abstract
Concomitant administration of botanical supplements with drugs that are P-glycoprotein (P-gp) substrates may produce clinically significant herb-drug interactions. This study evaluated the effects of St. John's wort and Echinacea on the pharmacokinetics of digoxin, a recognized P-gp substrate. Eighteen healthy volunteers were randomly assigned to receive a standardized St. John's wort (300 mg three times daily) or Echinacea (267 mg three times daily) supplement for 14 days, followed by a 30-day washout period. Subjects were also randomized to receive rifampin (300 mg twice daily, 7 days) and clarithromycin (500 mg twice daily, 7 days) as positive controls for P-gp induction and inhibition, respectively. Digoxin (Lanoxin 0.25 mg) was administered orally before and after each supplementation and control period. Serial digoxin plasma concentrations were obtained over 24 h and analyzed by chemiluminescent immunoassay. Comparisons of area under the curve (AUC)((0-3)), AUC((0-24)), elimination half-life, and maximum serum concentration were used to assess the effects of St. John's wort, Echinacea, rifampin, and clarithromycin on digoxin disposition. St. John's wort and rifampin both produced significant reductions (p < 0.05) in AUC((0-3)), AUC((0-24)), and C(max), while clarithromycin increased these parameters significantly (p < 0.05). Echinacea supplementation did not affect digoxin pharmacokinetics. Clinically significant P-gp-mediated herb-drug interactions are more likely to occur with St. John's wort than with Echinacea.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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 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".