Herbal Supplements and Therapeutic Drug Monitoring: Focus on Digoxin Immunoassays and Interactions With St. John's Wort
Bibliographic record
Abstract
Herbal supplements can affect concentrations of therapeutic drugs measured in biological fluids by different mechanisms. Herbal products can either directly interfere with the methodology used in the measurement of drugs or indirectly interfere by altering the pharmacokinetics of coadministered drugs. The active components of Chan Su, Lu-Shen-Wan, Dan Shen, Asian and Siberian ginseng, oleander containing supplements, and Ashwagandha interfere with digoxin measurements by immunoassays, especially the polyclonal antibody-based immunoassays. Herbal supplements are sometimes contaminated with Western drugs causing drug toxicity. A therapeutic drug monitoring (TDM) service is very helpful for diagnosis of drug toxicity in such patients. Herbal products such as St. John's wort, a popular herbal antidepressant, increase the clearance of certain drugs either by increasing the activity of liver or intestinal cytochrome P-450 mixed-function oxidase or through modulation of the P-glycoprotein efflux pump. Significantly reduced concentrations of various therapeutic drugs such as digoxin, theophylline, cyclosporine, tacrolimus, tricyclic antidepressants, warfarin, and protease inhibitors can be observed due to interaction of these drugs with St. John's wort, causing treatment failure. On the other hand, a few drugs such as carbamazepine, mycophenolic acid, and procainamide do not show any interaction with St. John's wort. Understanding the effect of herbal products on TDM methodologies and identification of interactions between herbal products and drugs by TDM are very important clinically.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".