Characterization of Novel Cannabinoid Based T-Type Calcium Channel Blockers with Analgesic Effects
Bibliographic record
Abstract
Low-voltage-activated (T-type) calcium channels are important regulators of the transmission of nociceptive information in the primary afferent pathway and finding ligands that modulate these channels is a key focus of the drug discovery field. Recently, we characterized a set of novel compounds with mixed cannabinoid receptor/T-type channel blocking activity and examined their analgesic effects in animal models of pain. Here, we have built on these previous findings and synthesized a new series of small organic compounds. We then screened them using whole-cell voltage clamp techniques to identify the most potent T-type calcium channel inhibitors. The two most potent blockers (compounds 9 and 10) were then characterized using radioligand binding assays to determine their affinity for CB1 and CB2 receptors. The structure-activity relationship and optimization studies have led to the discovery of a new T-type calcium channel blocker, compound 9. Compound 9 was efficacious in mediating analgesia in mouse models of acute inflammatory pain and in reducing tactile allodynia in the partial nerve ligation model. This compound was shown to be ineffective in Cav3.2 T-type calcium channel null mice at therapeutically relevant concentrations, and it caused no significant motor deficits in open field tests. Taken together, our data reveal a novel class of compounds whose physiological and therapeutic actions are mediated through block of Cav3.2 calcium channels.
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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".