Different classes of CB <sub>1</sub> ligands bias CB <sub>1</sub> ‐dependent signal transduction
Bibliographic record
Abstract
Biased agonism describes the ligand‐dependent selectivity of different signal transduction pathways by molecules that act as agonists at the same receptor. This functional selectivity may be exploited to alter the balance between signaling pathways downstream of a receptor. Agonists of the type 1 cannabinoid receptor (CB 1 ) are structurally diverse and include the aminoalkylindole WIN55,212–2, the endocannabinoid N‐ arachidonoyl ethanolamine (anadamide), and the Δ 9 ‐tetrahydrocannabinol (THC)‐like, CP55,940. Given the differences in potency and efficacy of these agonists, we sought to determine whether activation of CB 1 by these ligands resulted in biased signal transduction in a cell culture model of striatal neurons. We found that the THC‐like compound CP55,940 enhanced the interaction between CB 1 and β‐arrestin2 compared to vehicle, anandamide, and WIN55,212–2, which resulted in CB 1 internalization and persistent pERK signalling. In contrast, the endocannaibnoid anandamide and the aminoalkylindole WIN55,212–2 enhanced the interaction between CB 1 and G αi , and promoted a rapid and transient increase in pERK signaling compared to vehicle or CP55,940. Based on these data, the therapeutic efficacy of cannabinoid agonists may be maximized by selecting agents with specific potency, affinity, and signaling bias.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".