Fatty acid amide hydrolase inhibition and N‐arachidonoylethanolamine modulation by isoflavonoids: A novel target for upcoming antidepressants
Bibliographic record
Abstract
Modulation of the endocannabinoid system (ECS) is a novel putative target for therapeutic intervention in depressive disorders. Altering concentrations of one of the principal endocannabinoids, N-arachidonoylethanolamine, also known as anandamide (AEA) can affect depressive-like behaviors through several mechanisms including anti-inflammatory, hormonal, and neural circuit alterations. Recently, isoflavonoids, a class of plant-derived compounds, have been of therapeutic interest given their ability to modulate the metabolism of the endogenous ligands of the ECS. To determine the therapeutic potential of isoflavonoids, we screened several candidate compounds (Genistein, Biochanin-A, and 7-hydroxyflavone) in silico to determine their binding properties with fatty acid amide hydrolase (FAAH), the primary degrative enzyme for AEA. We further validated the ability of these compounds to inhibit FAAH and determined their effects on depressive-like and locomotor behaviors in the forced swim test (FST) and open field test in male and female mice. We found that while genistein was the most potent FAAH inhibitor, 7-hydroxyflavone was most effective at reducing immobility time in the forced swim test. Finally, we measured blood corticosterone and prefrontal cortex AEA concentrations following the forced swim test and found that all tested compounds decreased corticosterone and increased AEA, demonstrating that isoflavonoids are promising therapeutic targets as FAAH inhibitors.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".