Inhibition of Human Cytochrome P450 Metabolism by Blended Herbal Products and Vitamins
Bibliographic record
Abstract
PURPOSE: The use of supplements as herbal and micronutrient natural health products with conventional health products has become increasingly popular. It has been reported that some herbal products can inhibit the activity of cytochrome P450-mediated metabolism and drug disposition. This study was designed to investigate a case report of a severe adverse event to determine the potential interactions of femMED, Thyrosense and vitamins on cytochrome P450-mediated drug metabolism. METHODS: The effect of extracts from these commercially available herbal formulations, trans-ß-carotene, multivitamins, and vitamin D3 supplements on cytochrome P450-mediated drug metabolism of marker substrates was determined in vitro. RESULTS: The blended herbal products femMED and Thyrosense had a high potential to affect the safety and efficacy of many health products. Some vitamin and trans-β-carotene containing products also have the potential to affect drug disposition. The tBC content of various products was analyzed and significant discrepancies were found among them and between values indicated on product labels. Product extracts also exhibited a low to moderate capacity to inhibit cytochrome P450 2C9, 2C19 and 3A4-mediated metabolism. CONCLUSIONS: The findings of this study suggest that these herbal products and most vitamin products may have an inhibitory effect on cytochrome P450 activity that could contribute to development of an adverse event. Further work is warranted to determine how supplementation with these products may affect drug metabolism in an in vivo context.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".