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Record W1748017913 · doi:10.1002/dta.1863

Cytochrome P450 2D6 and 3A4 enzyme inhibition by amine stimulants in dietary supplements

2015· article· en· W1748017913 on OpenAlexfundno aff
Yitong Liu, Michael F. Santillo

Bibliographic record

VenueDrug Testing and Analysis · 2015
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsCYP3A4PharmacologyCYP2D6ChemistryEnzymeCytochrome P450CYP1A2DrugDietary supplementIn vivoBiochemistryMedicineBiologyFood scienceBiotechnology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.137
GPT teacher head0.414
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2015
Admission routes1
Has abstractyes

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