Local application of the endocannabinoid hydrolysis inhibitor URB597 reduces nociception in spontaneous and chemically induced models of osteoarthritis
Bibliographic record
Abstract
The present study examined whether enhancement of endogenous cannabinoid levels by administration of the fatty acid amide hydrolase inhibitor URB597 could modulate joint nociception in 2 rodent models of osteoarthritis (OA). OA-like changes were induced in male Wistar rats by intra-articular injection of monoiodoacetate, while Dunkin-Hartley guinea pigs (age 9-12 months) develop OA naturally and were used as a model of spontaneous OA. Joint nociception was measured by recording electrophysiologically from knee joint primary afferents in response to noxious hyper-rotation of the joint before and after close intra-arterial injection of URB597 (0.03 mg; 0.1 mL bolus); the CB(1) receptor antagonist AM251 (1 mg/kg intraperitoneally) or the CB(2) receptor antagonist AM630 (1 mg/kg intraperitoneally). The effect of systemic URB597 administration (5 mg/kg) on joint pain perception in the monoiodoacetate model was determined by hindlimb incapacitance. Peripheral injection of URB597 caused afferent firing rate to be significantly reduced by up to 56% in the rat OA model and by up to 69% in the guinea pig OA model. Systemic co-administration of AM251, but not AM630, abolished the antinociceptive effect of URB597 in both models. URB597 had no effect in saline-injected control rat joints or in nonarthritic guinea pigs. Systemic URB597 administration significantly reduced hindlimb incapacitance in monoiodoacetate joints and co-administration of the CB(1) antagonist abolished this effect. Local injection of URB597 into OA knee joints reduces mechanonociception and pain, and this response is mediated by CB(1) receptors. Targeting endocannabinoid-metabolizing enzymes in the peripheral nervous system could offer novel therapeutic approaches for the treatment of OA pain.
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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) | 0.000 | 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) | 0.001 | 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".