Cannabidiol is a negative allosteric modulator of the cannabinoid CB <sub>1</sub> receptor
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cannabidiol has been reported to act as an antagonist at cannabinoid CB1 receptors. We hypothesized that cannabidiol would inhibit cannabinoid agonist activity through negative allosteric modulation of CB1 receptors. EXPERIMENTAL APPROACH: Internalization of CB1 receptors, arrestin2 recruitment, and PLCβ3 and ERK1/2 phosphorylation, were quantified in HEK 293A cells heterologously expressing CB1 receptors and in the STHdh(Q7/Q7) cell model of striatal neurons endogenously expressing CB1 receptors. Cells were treated with 2-arachidonylglycerol or Δ(9)-tetrahydrocannabinol alone and in combination with different concentrations of cannabidiol. KEY RESULTS: Cannabidiol reduced the efficacy and potency of 2-arachidonylglycerol and Δ(9)-tetrahydrocannabinol on PLCβ3- and ERK1/2-dependent signalling in cells heterologously (HEK 293A) or endogenously (STHdh(Q7/Q7)) expressing CB1 receptors. By reducing arrestin2 recruitment to CB1 receptors, cannabidiol treatment prevented internalization of these receptors. The allosteric activity of cannabidiol depended upon polar residues being present at positions 98 and 107 in the extracellular amino terminus of the CB1 receptor. CONCLUSIONS AND IMPLICATIONS: Cannabidiol behaved as a non-competitive negative allosteric modulator of CB1 receptors. Allosteric modulation, in conjunction with effects not mediated by CB1 receptors, may explain the in vivo effects of cannabidiol. Allosteric modulators of CB1 receptors have the potential to treat CNS and peripheral disorders while avoiding the adverse effects associated with orthosteric agonism or antagonism of these receptors.
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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) | 0.002 | 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".