Pharmacokinetics and pharmacodynamics of cannabigerol (CBG) in the C57BL/6Crl mouse
Bibliographic record
Abstract
Introduction Cannabis holds therapeutic potential; however, activation of the type 1 cannabinoid receptor (CB1R) via Δ 9- tetrahydrocannabinol (THC) is also responsible for the characteristic “high” induced by cannabis. The pharmacology of the less abundant phytocannabinoid, cannabigerol (CBG), is poorly established, though it has been shown to exhibit promising therapeutic properties such as potential anxiolytic effects. Methods We assessed the pharmacokinetics (PK) and pharmacodynamics (PD) of CBG in C57BL/6Crl mice, hypothesizing that CBG would produce fewer PD effects than we had previously observed with THC, even when accounting for PK differences. Following oral ( p.o .), intraperitoneal ( i.p .), and intravenous ( i.v. ) administration, the PK profile of CBG was assessed via blood sampling at specified time points (10 min, 30 min, 1 h, 3 h, 6 h, 12 h, 18 h, and 24 h). The blood concentrations of CBG were quantified by High-Performance Liquid Chromatography-Tandem Mass Spectrometry (HPLC-MS/MS). A separate cohort of mice was treated with CBG and tested for cataleptic, hypothermic, anti-nociceptive, and locomotor effects to correlate the PK profile of CBG with CBG’s observed PD effects. Results and Discussion Our data reveal that CBG was not intoxicating, even when accounting for the route of administration and blood concentration. Our findings support previous reports that CBG is not intoxicating and reveal that even if CBG were present at sufficiently high concentrations in cannabis products, it would not produce intoxicating effects like those of THC.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".