Analgesic Effect of a Mixed T-Type Channel Inhibitor/CB<sub>2</sub> Receptor Agonist
Bibliographic record
Abstract
BACKGROUND: Cannabinoid receptors and T-type calcium channels are potential targets for treating pain. Here we report on the design, synthesis and analgesic properties of a new mixed cannabinoid/T-type channel ligand, NMP-181. RESULTS: NMP-181 action on CB1 and CB2 receptors was characterized in radioligand binding and in vitro GTPγ[35S] functional assays, and block of transiently expressed human Cav3.2 T-type channels by NMP-181 was analyzed by patch clamp. The analgesic effects and in vivo mechanism of action of NMP-181 delivered spinally or systemically were analyzed in formalin and CFA mouse models of pain. NMP-181 inhibited peak CaV3.2 currents with IC50 values in the low micromolar range and acted as a CB2 agonist. Inactivated state dependence further augmented the inhibitory action of NMP-181. NMP-181 produced a dose-dependent antinociceptive effect when administered either spinally or systemically in both phases of the formalin test. Both i.t. and i.p. treatment of mice with NMP-181 reversed the mechanical hyperalgesia induced by CFA injection. NMP-181 showed no antinocieptive effect in CaV3.2 null mice. The antinociceptive effect of intrathecally delivered NMP-181 in the formalin test was reversed by i.t. treatment of mice with AM-630 (CB2 antagonist). In contrast, the NMP-181-induced antinociception was not affected by treatment of mice with AM-281 (CB1 antagonist). CONCLUSIONS: Our work shows that both T-type channels as well as CB2 receptors play a role in the antinociceptive action of NMP-181, and also provides a novel avenue for suppressing chronic pain through novel mixed T-type/cannabinoid receptor ligands.
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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 | 0.001 |
| 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".