PT628. First in class melatonin MT2 receptors agonists for neuropathic pain
Bibliographic record
Abstract
Abstract Neuropathic pain is a major public health problem for which only few treatments are available. Translational studies showed that melatonin, a neurohormone acting on MT1 and MT2 receptors, has analgesic properties, likely through MT2 receptor. Here, we elucidated the effects of the novel selective melatonin MT2 receptor partial agonist N-{2-[(3-bromophenyl)-4-fluorophenylamino]ethyl}acetamide (UCM924) on neuropathic pain animal models and its mechanism of action. In rat spinal L5-L6 nerve ligation (Kim and Chung’s method) and spared nerve injury models (SNI), UCM924 (20–40 mg/kg, s.c.) produce a prolonged antiallodynic effect that is 1) dose-dependent and blocked by the selective MT2 receptor antagonist 4P-PDOT, 2) superior to high doses of melatonin (150 mg/kg) and comparable to gabapentin (100 mg/kg), but 3) without noticeable motor-sedative effects in the rotarod test. Using double staining immunohistochemistry, we found that MT2 receptors are expressed by glutamatergic neurons in the rostral ventrolateral periaqueductal gray (vlPAG). Using in-vivo electrophysiology combined to tail flick, we observed that microinjection of UCM924 into the vlPAG decreases tail flick responses, depressing the firing activity of ON cells and activating the firing of OFF cells, an effect MT2 receptor-dependent. Moreover, UCM 924 showed also antinoceptive properties in the hot plate model and in the formalin test. Altogether, these data demonstrate, for the first time, that selective MT2 receptor partial agonists have analgesic properties through modulation of brainstem descending antinociceptive pathways and indicate that MT2 receptors may be a novel target in the treatment of neuropathic 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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".