A novel peripherally restricted cannabinoid receptor antagonist, AM6545, reduces food intake and body weight, but does not cause malaise, in rodents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it