NMP-7 Inhibits Chronic Inflammatory and Neuropathic Pain via Block of Cav3.2 T-type Calcium Channels and Activation of CB2 Receptors
Bibliographic record
Abstract
BACKGROUND: T-type calcium channels and cannabinoid receptors are known to play important roles in chronic pain, making them attractive therapeutic targets. We recently reported on the design, synthesis and analgesic properties of a novel T-type channel inhibitor (NMP-7), which also shows mixed agonist activity on CB1 and CB2 receptors in vitro. Here, we analyzed the analgesic effect of systemically delivered NMP-7 (intraperitoneal (i.p.) or intragstric (i.g.) routes) on mechanical hypersensitivity in inflammatory pain induced by Complete Freund's Adjuvant (CFA) and neuropathic pain induced by sciatic nerve injury. RESULTS: NMP-7 delivered by either i.p. or i.g. routes produced dose-dependent inhibition of mechanical hyperalgesia in mouse models of inflammatory and neuropathic pain, without altering spontaneous locomotor activity in the open-field test at the highest active dose. Neither i.p. nor i.g. treatment reduced peripheral inflammation per se, as evaluated by examining paw edema and myeloperoxidase activity. The antinociception produced by NMP-7 in the CFA test was completely abolished in CaV3.2-null mice, confirming CaV3.2 as a key target. The analgesic action of intraperitoneally delivered NMP-7 was not affected by pretreatment of mice with the CB1 antagonist AM281, but was significantly attenuated by pretreatment with the CB2 antagonist AM630, suggesting that CB2 receptors, but not CB1 receptors are involved in the action of NMP-7 in vivo. CONCLUSIONS: Overall, our work shows that NMP-7 mediates a significant analgesic effect in a model of persistent inflammatory and chronic neuropathic pain by way of T-type channel modulation and CB2 receptor activation. Thus, this study provides a novel therapeutic avenue for managing chronic pain conditions via mixed CB ligands/T-type channel blockers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".