Functional Characterization and Analgesic Effects of Mixed Cannabinoid Receptor/T-Type Channel Ligands
Bibliographic record
Abstract
BACKGROUND: Both T-type calcium channels and cannabinoid receptors modulate signalling in the primary afferent pain pathway. Here, we investigate the analgesics activities of a series of novel cannabinoid receptor ligands with T-type calcium channel blocking activity. RESULTS: Novel compounds were characterized in radioligand binding assays and in vitro functional assays at human and rat CB1 and CB2 receptors. The inhibitory effects of these compounds on transient expressed human T-type calcium channels were examined in tsA-201 cells using standard whole-cell voltage clamp techniques, and their analgesic effects in response to various administration routes (intrathecally, intraplantarly, intraperitoneally) assessed in the formalin model. A series of compounds were synthesized and evaluated for channel and receptor activity. Compound NMP-7 acted as non-selective CB1/CB2 agonist while NMP4 was found to be a CB1 partial agonist and CB2 inverse agonist. Furthermore, NMP-144 behaved as a selective CB2 inverse agonist. All of these three compounds completely inhibited peak Cav3.2 currents with IC50 values in the low micromolar range. All compounds mediated analgesic effects in the formalin model, but depending on the route of administration, could differentially affect phase 1 and phase 2 of the formalin response. CONCLUSIONS: Our results reveal that a set of novel cannabinioid receptor ligands potently inhibit T-type calcium channels and show analgesic effects in vivo. Our findings suggest possible novel means of mediating pain relief through 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 | 0.000 |
| 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".