High-Throughput Screening for the Discovery of Inhibitors of Fatty Acid Amide Hydrolase Using a Microsome-Based Fluorescent Assay
Bibliographic record
Abstract
Fatty acid amide hydrolase (FAAH) is a membrane-associated enzyme that catalyzes the hydrolysis of several endogenous bioactive lipids, including anandamide (AEA), N-palmitoylethanolamine (PEA), oleamide, and N-oleoylethanolamine (OEA). These fatty acid amides participate in many physiological activities such as analgesia, anxiety, sleep modulation, anti inflammatory responses, and appetite suppression. Because FAAH plays an essential role in controlling the tone and activity of these endogenous bioactive lipids, this enzyme has been implicated to be a drug target for the therapeutic management of pain, anxiety, and other disorders. In an effort to discover FAAH inhibitors, the authors have previously reported the development of a novel fluorescent assay using purified FAAH microsomes as an enzyme source and a fluorogenic substrate, arachidonyl 7-amino, 4-methyl coumarin amide (AAMCA). Herein, the authors have adapted this assay to a high-throughput format and have screened a large library of small organic compounds, identifying a number of novel FAAH inhibitors. These data further verify that this fluorescent assay is sufficiently robust, efficient, and low-cost for the identification of FAAH inhibitory molecules and open this class of enzymes for therapeutic exploration.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.001 |
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".