A novel peripherally restricted cannabinoid receptor antagonist, AM6545, reduces food intake and body weight, but does not cause malaise, in rodents
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cannabinoid CB(1) receptor antagonists reduce food intake and body weight, but clinical use in humans is limited by effects on the CNS. We have evaluated a novel cannabinoid antagonist (AM6545) designed to have limited CNS penetration, to see if it would inhibit food intake in rodents, without aversive effects. EXPERIMENTAL APPROACH: Cannabinoid receptor binding studies, cAMP assays, brain penetration studies and gastrointestinal motility studies were carried out to assess the activity profile of AM6545. The potential for AM6545 to induce malaise in rats and the actions of AM6545 on food intake and body weight were also investigated. KEY RESULTS: AM6545 binds to CB(1) receptors with a K(i) of 1.7 nM and CB(2) receptors with a K(i) of 523 nM. AM6545 is a neutral antagonist, having no effect on cAMP levels in transfected cells and was less centrally penetrant than AM4113, a comparable CB(1) receptor antagonist. AM6545 reversed the effects of WIN55212-2 in an assay of colonic motility. In contrast to AM251, AM6545 did not produce conditioned gaping or conditioned taste avoidance in rats. In rats and mice, AM6545 dose-dependently reduced food intake and induced a sustained reduction in body weight. The effect on food intake was maintained in rats with a complete subdiaphragmatic vagotomy. AM6545 inhibited food intake in CB(1) receptor gene-deficient mice, but not in CB(1)/CB(2) receptor double knockout mice. CONCLUSIONS AND IMPLICATIONS: Peripherally active, cannabinoid receptor antagonists with limited brain penetration may be useful agents for the treatment of obesity and its complications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".