The Inhibition of Human Cytochrome P450 by Ethanol Extracts of North American Botanicals
Bibliographic record
Abstract
High-throughput enzyme inhibition screening assays were used to quantify the effect of ethanol extracts of 2 accessions of 10 North American (NA) botanicals against the activity of the human cytochrome P450s: CYP3A4, CYP19, and CYP2C19. In addition, phytochemical biomarkers within each extract were identified and quantified using HPLC-MS or GC. Extracts containing uncharacterized phytochemicals were identified taxonomically. The overall objective was to describe the relationship between types and quantities of phytochemicals in ethanol extracts and their ability to inhibit CYP activity. The top three inhibitors of CYP3A4 were Gaultheria procumbens. L. leaf > Rhodiola rosea. L. root > Arctostaphylos uva-ursi. L. Spreng leaf; of CYP19 were R. rosea. root > Rhododendron groenlandicum. (Oeder) Kron & Judd leaf > A. uva-ursi. leaf; and of CYP2C19 were Achillea millefolium. L. leaf and flower > Vaccinium. sp. L. leaf > Polygala senega. L. root. Equisetum arvense. L. leaf, Arctium lappa. L. root, and P. senega. root had the least effect on CYP3A4 and CYP19 activity. These results suggest that North American botanicals have the potential to inhibit the metabolism of drug-specific CYPs in vivo., causing a direct shift in the availability of drugs and their pharmacokinetics in the body. Furthermore, the concentration of certain phytochemical markers varied significantly between accessions (i.e., rosarin and essential oils), suggesting that the extent of metabolic inhibition is directly dependent upon the concentration of bioactive constituents in an extract.
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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".