Characterization of cannabinoid receptor ligands in tissues natively expressing cannabinoid <scp>CB2</scp> receptors
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although cannabinoid CB₂ receptor ligands have been widely characterized in recombinant systems in vitro, little pharmacological characterization has been performed in tissues natively expressing CB₂ receptors. The aim of this study was to compare the pharmacology of CB₂ receptor ligands in tissue natively expressing CB₂ receptors (human, rat and mouse spleen) and hCB₂-transfected CHO cells. EXPERIMENTAL APPROACH: We tested the ability of well-known cannabinoid CB₂ receptor ligands to stimulate or inhibit [³⁵S]GTPγS binding to mouse, rat and human spleen membranes and to hCB₂-transfected CHO cell membranes. cAMP assays were also performed in hCB₂-CHO cells. KEY RESULTS: The data presented demonstrate that: (i) CP 55,940, WIN 55,212-2 and JWH 133 behave as CB₂ receptor full agonists both in spleen and hCB₂-CHO cells, in both [³⁵S]GTPγS and cAMP assays; (ii) JWH 015 behaves as a low-efficacy agonist in spleen as well as in hCB₂-CHO cells when tested in the [³⁵S]GTPγS assay, while it displays full agonism when tested in the cAMP assay using hCB₂-CHO cells; (iii) (R)-AM 1241 and GW 405833 behave as agonists in the [³⁵S]GTPγS assay using spleen, instead it behaves as a low-efficacy inverse agonist in hCB₂-CHO cells; and (iv) SR 144528, AM 630 and JTE 907 behave as CB₂ receptor inverse agonists in all the tissues. CONCLUSION AND IMPLICATIONS: Our results demonstrate that CB₂ receptor ligands can display differential pharmacology when assays are conducted in tissues that natively express CB₂ receptors and imply that conclusions from recombinant CB₂ receptors should be treated with caution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".