In vitro and in vivo pharmacological activity of minor cannabinoids isolated from Cannabis sativa
Bibliographic record
Abstract
Abstract The Cannabis sativa plant contains more than 120 cannabinoids. With the exceptions of ∆ 9 -tetrahydrocannabinol (∆ 9 -THC) and cannabidiol (CBD), comparatively little is known about the pharmacology of the less-abundant plant-derived (phyto) cannabinoids. The best-studied transducers of cannabinoid-dependent effects are type 1 and type 2 cannabinoid receptors (CB1R, CB2R). Partial agonism of CB1R by ∆ 9 -THC is known to bring about the ‘high’ associated with Cannabis use, as well as the pain-, appetite-, and anxiety-modulating effects that are potentially therapeutic . CB2R activation by certain cannabinoids has been associated with anti-inflammatory activities. We assessed the activity of 8 phytocannabinoids at human CB1R, and CB2R in Chinese hamster ovary (CHO) cells stably expressing these receptors and in C57BL/6 mice in an attempt to better understand their pharmacodynamics. Specifically, ∆ 9 -THC, ∆ 9 -tetrahydrocannabinolic acid (∆ 9 -THCa), ∆ 9 -tetrahydrocannabivarin (THCV), CBD, cannabidiolic acid (CBDa), cannabidivarin (CBDV), cannabigerol (CBG), and cannabichromene (CBC) were evaluated. Compounds were assessed for their affinity to receptors, ability to inhibit cAMP accumulation, βarrestin2 recruitment, receptor selectivity, and ligand bias in cell culture; and cataleptic, hypothermic, anti-nociceptive, hypolocomotive, and anxiolytic effects in mice. Our data reveal partial agonist activity for many phytocannabinoids tested at CB1R and/or CB2R, as well as in vivo responses often associated with activation of CB1R. These data build on the growing body of literature showing cannabinoid receptor-dependent pharmacology for these less-abundant phytocannabinoids and are critical in understanding the complex and interactive pharmacology of Cannabis -derived molecules.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".