Evaluation of the potential of the phytocannabinoids, cannabidivarin (<scp>CBDV</scp>) and Δ<sup>9</sup>‐tetrahydrocannabivarin (<scp>THCV</scp>), to produce <scp>CB<sub>1</sub></scp> receptor inverse agonism symptoms of nausea in rats
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The cannabinoid 1 (CB1 ) receptor inverse agonists/antagonists, rimonabant (SR141716, SR) and AM251, produce nausea and potentiate toxin-induced nausea by inverse agonism (rather than antagonism) of the CB1 receptor. Here, we evaluated two phytocannabinoids, cannabidivarin (CBDV) and Δ(9) -tetrahydrocannabivarin (THCV), for their ability to produce these behavioural effect characteristics of CB1 receptor inverse agonism in rats. EXPERIMENTAL APPROACH: In experiment 1, we investigated the potential of THCV and CBDV to produce conditioned gaping (measure of nausea-induced behaviour) in the same manner as SR and AM251. In experiment 2, we investigated the potential of THCV and CBDV to enhance conditioned gaping produced by a toxin in the same manner as CB1 receptor inverse agonists. KEY RESULTS: SR (10 and 20 mg·kg(-1) ) and AM251 (10 mg·kg(-1) ) produced conditioned gaping; however, THCV (10 or 20 mg·kg(-1) ) and CBDV (10 or 200 mg·kg(-1) ) did not. At a subthreshold dose for producing nausea, SR (2.5 mg·kg(-1) ) enhanced lithium chloride (LiCl)-induced conditioned gaping, whereas Δ(9) -tetrahydrocannabinol (THC, 2.5 and 10 mg·kg(-1) ), THCV (2.5 or 10 mg·kg(-1) ) and CBDV (2.5 or 200 mg·kg(-1) ) did not; in fact, THC (2.5 and 10 mg·kg(-1) ), THCV (10 mg·kg(-1) ) and CBDV (200 mg·kg(-1) ) suppressed LiCl-induced conditioned gaping, suggesting anti-nausea potential. CONCLUSIONS AND IMPLICATIONS: The pattern of findings indicates that neither THCV nor CBDV produced a behavioural profile characteristic of CB1 receptor inverse agonists. As well, these compounds may have therapeutic potential in reducing nausea.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".