Modulation of type 1 cannabinoid receptor activity by cannabinoid by-products from Cannabis sativa and non-cannabis phytomolecules
Bibliographic record
Abstract
Cannabis sativa contains more than 120 cannabinoids and 400 terpene compounds (i.e., phytomolecules) present in varying amounts. Cannabis is increasingly available for legal medicinal and non-medicinal use globally, and with increased access comes the need for a more comprehensive understanding of the pharmacology of phytomolecules. The main transducer of the intoxicating effects of Cannabis is the type 1 cannabinoid receptor (CB1R). ∆9-tetrahydrocannabinolic acid (∆9-THCa) is often the most abundant cannabinoid present in many cultivars of Cannabis. Decarboxylation converts ∆9-THCa to ∆9-THC, which is a CB1R partial agonist. Understanding the complex interplay of phytomolecules—often referred to as “the entourage effect”—has become a recent and major line of inquiry in cannabinoid research. Additionally, this interest is extending to other non-Cannabis phytomolecules, as the diversity of available Cannabis products grows. Here, we chose to focus on whether 10 phytomolecules (∆8-THC, ∆6a,10a-THC, 11-OH-∆9-THC, cannabinol, curcumin, epigallocatechin gallate, olivetol, palmitoylethanolamide, piperine, and quercetin) alter CB1R-dependent signaling with or without a co-treatment of ∆9-THC. Phytomolecules were screened for their binding to CB1R, inhibition of forskolin-stimulated cAMP accumulation, and βarrestin2 recruitment in Chinese hamster ovary cells stably expressing human CB1R. Select compounds were assessed further for cataleptic, hypothermic, and anti-nociceptive effects on male mice. Our data revealed partial agonist activity for the cannabinoids tested, as well as modulation of ∆9-THC-dependent binding and signaling properties of phytomolecules in vitro and in vivo. These data represent a first step in understanding the complex pharmacology of Cannabis- and non-Cannabis-derived phytomolecules at CB1R and determining whether these interactions may affect the physiological outcomes, adverse effects, and abuse liabilities associated with the use of these compounds.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".