Utility of Therapeutic Drug Monitoring in Identifying Clinically Significant Interactions Between St. John's Wort and Prescription Drugs
Bibliographic record
Abstract
BACKGROUND: The general population widely uses herbal medicines, as they are regarded as effective and safe. St. John's wort, which is an effective herbal antidepressant, exhibits both pharmacokinetic and pharmacodynamic interactions with several drugs. The aim of this review was to highlight the clinically significant interactions of St. John's wort with drugs that require to be monitored to assess their therapeutic effect. METHODS: Published literature was searched using electronic databases, such as MEDLINE, PubMed, and Elsevier ScienceDirect using terms such as "herbal medicine," "herbal toxicity," "legislation herbal medicine," "drug-herb interactions," "St. John's wort," and "St. John's wort-drug interactions." Searches were limited to the English language, and there was no restriction on the date of publication. RESULTS: St. John's wort exhibits a number of pharmacokinetic and pharmacodynamic interactions with drugs. The most dangerous interactions occurred when used concurrently with the immunosuppressants, cyclosporine, and tacrolimus (treatment failure or organ rejection) or warfarin (treatment failure resulting in thromboembolic events) or antiretroviral agents (treatment failure and the emergence of new viral variants that are resistant to conventional drugs). CONCLUSIONS: Patients should consult their health care providers before consuming herbal supplements, especially St. John's wort, to avoid potentially dangerous drug-herb interactions.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".