Targeting the Endocannabinoid CB1 Receptor to Treat Body Weight Disorders: A Preclinical and Clinical Review of the Therapeutic Potential of Past and Present CB1 Drugs
Bibliographic record
Abstract
Obesity rates are increasing worldwide and there is a need for novel therapeutic treatment options. The endocannabinoid system has been linked to homeostatic processes, including metabolism, food intake, and the regulation of body weight. Rimonabant, an inverse agonist for the cannabinoid CB1 receptor, was effective at producing weight loss in obese subjects. However, due to adverse psychiatric side effects, rimonabant was removed from the market. More recently, we reported an inverse relationship between cannabis use and BMI, which has now been duplicated by several groups. As those results may appear contradictory, we review here preclinical and clinical studies that have studied the impact on body weight of various cannabinoid CB1 drugs. Notably, we will review the impact of CB1 inverse agonists, agonists, partial agonists, and neutral antagonists. Those findings clearly point out the cannabinoid CB1 as a potential effective target for the treatment of obesity. Recent preclinical studies suggest that ligands targeting the CB1 may retain the therapeutic potential of rimonabant without the negative side effect profile. Such approaches should be tested in clinical trials for validation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".