The terpenes camphene and alpha-bisabolol inhibit inflammatory and neuropathic pain via Cav3.2 T-type calcium channels
Bibliographic record
Abstract
T-type calcium channels are known molecular targets of certain phytocannabinoids and endocannabinoids. Here we explored the modulation of Cav3.2 T-type calcium channels by terpenes derived from cannabis plants. A screen of eight commercially available terpenes revealed that camphene and alpha-bisabolol mediated partial, but significant inhibition of Cav3.2 channels expressed in tsA-201 cells, as well as native T-type channels in mouse dorsal root ganglion neurons. Both compounds inhibited peak current amplitude with IC50s in the low micromolar range, and mediated an additional small hyperpolarizing shift in half-inactivation voltage. When delivered intrathecally, both terpenes inhibited nocifensive responses in mice that had received an intraplantar injection of formalin, with alpha-bisabolol showing greater efficacy. Both terpenes reduced thermal hyperalgesia in mice injected with Complete Freund's adjuvant. This effect was independent of sex, and absent in Cav3.2 null mice, indicating that these compounds mediate their analgesic properties by acting on Cav3.2 channels. Both compounds also inhibited mechanical hypersensitivity in a mouse model of neuropathic pain. Hence, camphene and alpha-bisabolol have a wide spectrum of analgesic action by virtue of inhibiting Cav3.2 T-type 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) | 0.003 | 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".