Differential regulation of Ca<sub>v</sub>3.2 and Ca<sub>v</sub>2.2 calcium channels by CB<sub>1</sub> receptors and cannabidiol
Bibliographic record
Abstract
Background and Purpose Cannabinoids are a promising therapeutic avenue for chronic pain. However, clinical trials often fail to report analgesic efficacy of cannabinoids. Inhibition of voltage gate calcium (Ca v ) channels is one mechanism through which cannabinoids may produce analgesia. We hypothesized that cannabinoids and cannabinoid receptor agonists target different types of Ca v channels through distinct mechanisms. Experimental Approach Electrophysiological recordings from tsA‐201 cells expressing either Ca v 3.2 or Ca v 2.2 were used to assess inhibition by HU‐210 or cannabidiol (CBD) in the absence and presence of the CB 1 receptor. Homology modelling assessed potential interaction sites for CBD in both Ca v 2.2 and Ca v 3.2. Analgesic effects of CBD were assessed in mouse models of inflammatory and neuropathic pain. Key Results HU‐210 (1 μM) inhibited Ca v 2.2 function in the presence of CB 1 receptor but had no effect on Ca v 3.2 regardless of co‐expression of CB 1 receptor. By contrast, CBD (3 μM) produced no inhibition of Ca v 2.2 and instead inhibited Ca v 3.2 independently of CB 1 receptors. Homology modelling supported these findings, indicating that CBD binds to and occludes the pore of Ca v 3.2, but not Ca v 2.2. Intrathecal CBD alleviated thermal and mechanical hypersensitivity in both male and female mice, and this effect was absent in Ca v 3.2 null mice. Conclusion and Implications Our findings reveal differential modulation of Ca v 2.2 and Ca v 3.2 channels by CB 1 receptors and CBD. This advances our understanding of how different cannabinoids produce analgesia through action at different voltage‐gated calcium channels and could influence the development of novel cannabinoid‐based therapeutics for treatment of chronic 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) | 0.000 | 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) | 0.001 | 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".