Assessment of select synthetic cannabinoid receptor agonist bias and selectivity between the type 1 and type 2 cannabinoid receptor
Bibliographic record
Abstract
Abstract The first synthetic cannabinoid receptor agonists (SCRAs) were designed as tool compounds to study the endocannabinoid system’s two predominant cannabinoid receptors, CB1R and CB2R. Unfortunately, novel SCRAs now represent the most rapidly proliferating novel psychoactive substances (NPS) of abuse globally. Unlike ∆ 9 -tetrahydrocannabinol, the CB1R and CB2R partial agonist and the intoxicating constituent of Cannabis , many SCRAs characterized to date are full agonists of CB1R. Gaining additional insight into the pharmacological activity of these SCRAs is critical to assess and regulate NPSs as they enter the marketplace. The purpose of this study was to assess select SCRAs recently identified by Canadian police, border service agency, private companies and the illicit market as potential CB1R and CB2R agonists. To this end, fifteen SCRAs were screened for in vitro activity and in silico interactions at CB1R and CB2R. Several SCRAs were identified as being highly biased for cAMP inhibition or βarrestin2 recruitment and receptor subtype selectivity between CB1R and CB2R. The indazole ring and halogen-substituted butyl or pentyl moieties were identified as two structural features that may direct βarrestin2 bias. Two highly-biased SCRAs—JWH-018 2′-napthyl-N-(3-methylbutyl) isomer (biased toward cAMP inhibition) and 4-fluoro MDMB-BINACA (biased toward βarrestin2 recruitment) displayed unique and differential in vivo activity in mice. These data provide initial insight into the correlations between structure, signalling bias, and in vivo activity of the SCRAs.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".