Allosteric and orthosteric pharmacology of cannabidiol and cannabidiol‐dimethylheptyl at the type 1 and type 2 cannabinoid receptors
Bibliographic record
Abstract
Background and Purpose We sought to understand why (−)‐cannabidiol (CBD) and (−)‐cannabidiol‐dimethylheptyl (CBD‐DMH) exhibit distinct pharmacology, despite near identical structures. Experimental Approach HEK293A cells expressing either human type 1 cannabinoid (CB 1 ) receptors or CB 2 receptors were treated with CBD or CBD‐DMH with or without the CB 1 and CB 2 receptor agonist CP55,940, CB 1 receptor allosteric modulator Org27569 or CB 2 receptor inverse agonist SR144528. Ligand binding, cAMP levels and βarrestin1 recruitment were measured. CBD and CBD‐DMH binding was simulated with models of human CB 1 or CB 2 receptors, based on the recently published crystal structures of agonist‐bound (5XRA) or antagonist‐bound (5TGZ) human CB 1 receptors. Key Results At CB 1 receptors, CBD was a negative allosteric modulator (NAM), and CBD‐DMH was a mixed agonist/positive allosteric modulator. CBD and Org27569 shared multiple interacting residues in the antagonist‐bound model of CB 1 receptors (5TGZ) but shared a binding site with CP55,940 in the agonist‐bound model of CB 1 receptors (5XRA). The binding site for CBD‐DMH in the CB 1 receptor models overlapped with CP55,940 and Org27569. At CB 2 receptors, CBD was a partial agonist, and CBD‐DMH was a positive allosteric modulator of cAMP modulation but a NAM of βarrestin1 recruitment. CBD, CP55,940 and SR144528 shared a binding site in the CB 2 receptor models that was separate from CBD‐DMH. Conclusion and Implications The pharmacological activity of CBD and CBD‐DMH in HEK293A cells and their modelled binding sites at CB 1 and CB 2 receptors may explain their in vivo effects and illuminates the difficulties associated with the development of allosteric modulators for CB 1 and CB 2 receptors. Linked Articles This article is part of a themed section on 8 th European Workshop on Cannabinoid Research. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.10/issuetoc
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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".