St. John's wort does not interfere with therapeutic drug monitoring of 12 commonly monitored drugs using immunoassays
Bibliographic record
Abstract
St. John's wort, a popular herbal remedy for depression, is known to interact with many Western drugs because of the ability of its components to induce liver enzymes. Lower concentrations of various drugs due to increased clearance have been reported. Because immunoassays are commonly used in clinical laboratories for therapeutic drug monitoring, we studied the potential interference of St. John's wort with commonly monitored therapeutic drugs. Drug-free serum pools were supplemented with St. John's wort to achieve in vitro St. John's wort concentrations mimicking in vivo concentrations after both recommended use and overdose. Concentrations of digoxin, tricyclic antidepressants (TCAs), phenytoin, carbamazepine, theophylline, valproic acid, quinidine, phenobarbital, procainamide, and N-acetyl procainamide were measured in serum. Pooled serum specimens from patients who were taking a particular drug were also supplemented in vitro with concentrations of St. John's wort to investigate whether observed concentrations changed after supplementation with St. John's wort. The effect of St. John's wort on cyclosporine and tacrolimus (FK 506) was studied in whole blood. We found no significant interference from St. John's wort with any assay studied. Moreover, when drug-free serum was supplemented with very high concentrations of hypericin (2 microg/mL) and hyperforin (2 microg/mL) pure standard, we observed no apparent drug level with any immunoassay. The presence of both hypericin and hyperforin was also confirmed by thin layer chromatography (TLC) in both preparations of St. John's wort. We conclude that immunoassays may be used to measure levels of therapeutic drugs in patients who self-medicate with St. John's wort.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".