Blocking cannabinoid CB<sub>1</sub>receptors for the treatment of nicotine dependence: insights from pre‐clinical and clinical studies
Bibliographic record
Abstract
Tobacco use is one of the leading preventable causes of death in developed countries. Since existing medications are only partially effective in treating tobacco smokers, there is a great need for improved medications for smoking cessation. It has been recently proposed that cannabinoid CB(1) receptor antagonists represent a new class of therapeutic agents for drug dependence, and notably, nicotine dependence. Here, we will review current evidence supporting the use of this class of drugs for smoking cessation treatment. Pre-clinical studies indicate that nicotine exposure produces changes in endocannabinoid content in the brain. In experimental animals, N-piperidinyl-5-(4-chlorophenyl)-1-(2,4-dichlorophenyl)-4-methylpyrazole-3-carboxamide (rimonabant, SR141716) and N-(piperidin-1-yl)-5-(4-iodophenyl)-1-(2,4-dichlorophenyl)-4-methyl-1H-pyrazole-3-carboxamide (AM251), two cannabinoid CB(1) receptor antagonists, block nicotine self-administration behavior, an effect that may be related to the blockade of the dopamine-releasing effects of nicotine in the brain. Rimonabant also seems efficacious in decreasing the influence of nicotine-associated stimuli over behavior, suggesting that it may act on two distinct neuronal pathways, those implicated in drug-taking behavior and those involved in relapse phenomena. The utility of rimonabant has been evaluated in several clinical trials. It seems that rimonabant is an efficacious treatment for smoking cessation, although its efficacy does not exceed that of nicotine-replacement therapy and its use may be limited by emotional side effects (nausea, anxiety and depression, mostly). Rimonabant also appears to decrease relapse rates in smokers. These findings indicate significant, but limited, utility of rimonabant for smoking cessation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".