In vitro pharmacological activity of twenty-eight synthetic cannabinoid receptor agonists at the type 1 and 2 cannabinoid receptors
Bibliographic record
Abstract
-tetrahydrocannabinol (THC) is the primary intoxicating compound present in cannabis and is well-known to behave as a partial agonist at both the type 1 and 2 cannabinoid receptors (CB1R, CB2R). Unlike THC, the SCRAs characterized to date generally behave as CB1R and/or CB2R full agonists. The high potency and full agonism of these ligands are thought to drive the toxicity of SCRAs, including psychoses, emesis, and tachycardia. In this study, twenty-eight compounds (including the reference ligands CP55,940 and THC) were evaluated for binding affinity, Gi protein-dependent inhibition of cAMP, and βarrestin2 recruitment in Chinese hamster ovary (CHO-K1) cells stably expressing either receptor. Radioligand binding results demonstrate a general lack of selectivity between cannabinoid receptor subtypes. In signaling assays, most compounds displayed the anticipated full agonism with low nanomolar potency characteristic of SCRAs. Many compounds displayed bias for the inhibition of cAMP over the recruitment of βarrestin2, and this was especially true at CB2R, where several compounds were inactive in the βarrestin2 recruitment assay. However, no clear structure-activity relationship emerged among the tested SCRAs that could account for their selectivity, potency, efficacy, or bias, although potential patterns are discussed herein. Overall, our data support growing evidence that the cannabinoid receptors accommodate a diverse range of ligands, and that compound function may be dictated by factors that are not yet well characterized, such as binding kinetics.
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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 | 0.000 | 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 | 0.000 |
| 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".