The M1/M4 preferring agonist xanomeline is analgesic in rodent models of chronic inflammatory and neuropathic pain via central site of action
Bibliographic record
Abstract
Summary The data presented show that the nonselective muscarinic agonist xanomeline is analgesic in models of persistent pain and suggest that the activation of supraspinal M1 receptors, and to a lesser extent supraspinal M4 receptors, contributes to that analgesia. The role of muscarinic receptor subtype-1 (M1) in chronic pain is unclear. In an attempt to gain an understanding of its role, we have tested xanomeline, an M1/M4-preferring agonist, together with nonselective (scopolamine and pirenzepine), and selective (MT-7 and MT-3) muscarinic receptor (M1 and M4, respectively) antagonists in a number of inflammatory and neuropathic pain models. Xanomeline potently and effectively reversed tactile allodynia and heat hyperalgesia associated with established neuropathic and inflammatory pain in both rat and mouse models. Scopolamine and pirenzepine completely blocked the analgesic response to xanomeline, confirming that the analgesic effect is mediated by the muscarinic system. The highly selective M1 receptor toxin, MT-7, almost completely abolished the analgesic response to xanomeline when administered supraspinally. However, the highly selective M4 receptor toxin, MT-3, only marginally reversed the analgesia when given supraspinally, and had no effect when given spinally. In conclusion, the data presented show that the nonselective muscarinic agonist xanomeline is analgesic in models of persistent pain and suggest that the activation of supraspinal M1 receptors, and to a lesser extent supraspinal M4 receptors, contributes to that analgesia.
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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) | 0.000 | 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 | 0.000 |
| 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".