Cytochrome P450 2D6 and 3A4 enzyme inhibition by amine stimulants in dietary supplements
Bibliographic record
Abstract
A number of dietary supplements used for weight loss and athletic performance enhancement have been recently shown to contain a variety of stimulants, for which there is a lack of pharmacological and toxicological information. One concern for these emerging compounds is their potential to inhibit metabolic enzymes in the liver such as cytochromes P450 (CYP), which can lead to unexpected interactions among dietary supplements, drugs, and other xenobiotics. In this study, inhibition of human recombinant CYP2D6 and CYP3A4 by 27 amine stimulants associated with dietary supplements and their analogs was evaluated by luminescence assays. The strongest CYP2D6 inhibitors were coclaurine (IC50 = 0.14 ± 0.01 μM) and N-benzylphenethylamine (IC50 = 0.7 ± 0.2 μM), followed by several other relatively strong inhibitors (IC50 , 2-12 μM) including β-methylphenethylamine, N,β-dimethylphenethylamine (phenpromethamine), 1,3-dimethylamylamine (DMAA), N,α-diethylphenethylamine, higenamine (norcoclaurine) and N,N-diethylphenethylamine. Only nine compounds inhibited CYP3A4 by 20-55% at 100 μM. Results of this study illustrate that several amine stimulants associated with dietary supplements inhibit CYP2D6 and CYP3A4 in vitro, and these compounds may participate in adverse drug-dietary supplement interactions in vivo. Copyright © 2015 John Wiley & Sons, Ltd.
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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".