Repurposing Resveratrol and Fluconazole To Modulate Human Cytochrome P450-Mediated Arachidonic Acid Metabolism
Bibliographic record
Abstract
Cytochrome P450 (P450) enzymes metabolize arachidonic acid (AA) to several biologically active epoxyeicosatrienoic acids (EETs) and hydroxyeicosatetraenoic acids (HETEs). Repurposing clinically-approved drugs could provide safe and readily available means to control EETs and HETEs levels in humans. Our aim was to determine how to significantly and selectively modulate P450-AA metabolism in humans by clinically-approved drugs. Liquid chromatography-mass spectrometry was used to determine the formation of 15 AA metabolites by human recombinant P450 enzymes, as well as human liver and kidney microsomes. CYP2C19 showed the highest EET-forming activity, while CYP1B1 and CYP2C8 showed the highest midchain HETE-forming activities. CYP1A1 and CYP4 showed the highest subterminal- and 20-HETE-forming activity, respectively. Resveratrol and fluconazole produced the most selective and significant modulation of hepatic P450-AA metabolism, comparable to investigational agents. Monte Carlo simulations showed that 90% of human population would experience a decrease by 6-22%, 16-39%, and 16-35% in 16-, 18-, and 20-HETE formation, respectively, after 2.5 g daily of resveratrol, and by 22-31% and 14-23% in 8,9- and 14,15-EET formation after 50 mg of fluconazole. In conclusion, clinically-approved drugs can provide selective and effective means to modulate P450-AA metabolism, comparable to investigational drugs. Resveratrol and fluconazole are good candidates to be repurposed as new P450-based treatments.
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 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".