A peripherally restricted cannabinoid receptor agonist produces robust anti-nociceptive effects in rodent models of inflammatory and neuropathic pain
Bibliographic record
Abstract
Cannabinoids are analgesic in man, but their use is limited by their psychoactive properties. One way to avoid cannabinoid receptor subtype 1 (CB1R)-mediated central side-effects is to develop CB1R agonists with limited CNS penetration. Activation of peripheral CB1Rs has been proposed to be analgesic, but the relative contribution of peripheral CB1Rs to the analgesic effects of systemic cannabinoids remains unclear. Here we addressed this by exploring the analgesic properties and site of action of AZ11713908, a peripherally restricted CB1R agonist, in rodent pain models. Systemic administration of AZ11713908 produced robust efficacy in rat pain models, comparable to that produced by WIN 55, 212-2, a CNS-penetrant, mixed CB1R and CB2R agonist, but AZ11713908 generated fewer CNS side-effects than WIN 55, 212-in a rat Irwin test. Since AZ11713908 is also a CB2R inverse agonist in rat and a partial CB2R agonist in mouse, we tested the specificity of the effects in CB1R and CB2R knock-out (KO) mice. Analgesic effects produced by AZ11713908 in wild-type mice with Freund's complete adjuvant-induced inflammation of the tail were completely absent in CB1R KO mice, but fully preserved in CB2R KO mice. An in vivo electrophysiological assay showed that the major site of action of AZ11713908 was peripheral. Similarly, intraplantar AZ11713908 was also sufficient to induce robust analgesia. These results demonstrate that systemic administration of AZ11713908, produced robust analgesia in rodent pain models via peripheral CB1R. Peripherally restricted CB1R agonists provide an interesting novel approach to analgesic therapy for chronic 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".