Species‐specific<i>in vitro</i>pharmacological effects of the cannabinoid receptor 2 (CB<sub>2</sub>) selective ligand AM1241 and its resolved enantiomers
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Racemic (R,S) AM1241 is a cannabinoid receptor 2 (CB(2))-selective aminoalkylindole with antinociceptive efficacy in animal pain models. The purpose of our studies was to provide a characterization of R,S-AM1241 and its resolved enantiomers in vitro and in vivo. EXPERIMENTAL APPROACH: Competition binding assays were performed using membranes from cell lines expressing recombinant human, rat, and mouse CB(2) receptors. Inhibition of cAMP was assayed using intact CB(2)-expressing cells. A mouse model of visceral pain (para-phenylquinone, PPQ) and a rat model of acute inflammatory pain (carrageenan) were employed to characterize the compounds in vivo. KEY RESULTS: In cAMP inhibition assays, R,S-AM1241 was found to be an agonist at human CB(2), but an inverse agonist at rat and mouse CB(2) receptors. R-AM1241 bound with more than 40-fold higher affinity than S-AM1241, to all three CB(2) receptors and displayed a functional profile similar to that of the racemate. In contrast, S-AM1241 was an agonist at all three CB(2) receptors. In pain models, S-AM1241 was more efficacious than either R-AM1241 or the racemate. Antagonist blockade demonstrated that the in vivo effects of S-AM1241 were mediated by CB(2) receptors. CONCLUSIONS AND IMPLICATIONS: These findings constitute the first in vitro functional assessment of R,S-AM1241 at rodent CB(2) receptors and the first characterization of the AM1241 enantiomers in recombinant cell systems and in vivo. The greater antinociceptive efficacy of S-AM1241, the functional CB(2) agonist enantiomer of AM1241, is consistent with previous observations that CB(2) agonists are effective in relief of pain.
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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 | 0.001 |
| 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".