Modulation of the anti‐nociceptive effects of 2‐arachidonoyl glycerol by peripherally administered FAAH and MGL inhibitors in a neuropathic pain model
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There are limited options for the treatment of neuropathic pain. Endocannabinoids, such as anandamide and 2-arachidonoyl glycerol (2-AG), are promising pain modulators and there is recent evidence of interactions between anandamide and 2-AG biosynthesis and metabolism. It has been clearly demonstrated that 2-AG degradation is mainly catalysed not only by monoacylglycerol lipase (MGL) but also by a fatty acid amide hydrolase (FAAH). Inhibitors specifically targeting these two enzymes have also been described: URB602 and URB597, respectively. However, the anti-nociceptive effects of the combination of peripherally injected 2-AG, URB602 and URB597 in a neuropathic pain model have not yet been determined. This was performed in the presence or absence of cannabinoid CB(1) (AM251) and CB(2) (AM630) receptor antagonists. EXPERIMENTAL APPROACH: Mechanical allodynia and thermal hyperalgesia were evaluated in 213 male Wistar rats allocated to 32 different groups. Drugs were injected subcutaneously in the dorsal surface of the hind paw (50 muL) 15 min before pain tests. KEY RESULTS: 2-AG, URB602 and URB597 significantly decreased mechanical allodynia and thermal hyperalgesia with ED50 of 1.6+/-1.5 and 127+/-83 mug for 2-AG and URB602, respectively. These effects were mediated locally and were mostly inhibited by the two cannabinoid antagonists. CONCLUSIONS AND IMPLICATIONS: The combination of the three compounds did not produce any greater anti-allodynic or anti-hyperalgesic effects, suggesting that FAAH inhibition could reduce or limit the anti-nociceptive effects of 2-AG. Peripheral administration of endocannabinoids or MGL/FAAH inhibitors is a promising analgesic approach requiring further investigation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".