Targeting Melatonin MT2 Receptors: A Novel Pharmacological Avenue for Inflammatory and Neuropathic Pain
Bibliographic record
Abstract
Melatonin (MLT) has been implicated in several pathophysiological states, including pain. MLT mostly activates two G-protein coupled receptors, MT1 and MT2. In this review, we present the analgesic properties of MLT in preclinical and clinical studies, giving particular emphasis to the effects mediated by MT2 receptors and to recent investigations demonstrating the analgesic effects of MT2 receptor partial agonists in chronic and acute/inflammatory pain conditions. MT2 receptors are localized in specific brain areas, including the reticular and the ventromedial nuclei of the thalamus (part of the ascending nociceptive pathway) and the ventrolateral periaqueductal grey matter (vlPAG) (part of the descending antinociceptive pathway). MLT displays analgesic properties in several animal paradigms of chronic, acute, inflammatory and neuropathic pain; importantly, these effects are mediated by MT2 receptors since they are blocked by selective MT2 antagonists. In different pain paradigms, UCM924 and UCM765, two selective MT2 receptor partial agonists, produce analgesic effects with higher potency than MLT, thus confirming the involvement of MT2 receptors in pain. Notably, these compounds do not induce sedation and motor impairments. Although their analgesic mechanism of action is not yet completely elucidated, they act on antinociceptive descending pathways by stimulating MT2 receptors on glutamatergic neurons of the vlPAG, which in turn activate OFF cells and inhibit ON cells of the rostral ventromedial medulla (RVM). Collectively, there is strong preclinical evidence suggesting the pharmacological potential of MT2 receptor partial agonists, which also have a favorable toxicological profile. These compounds may be further developed as novel analgesic drugs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".