Clozapine reduces nicotine self‐administration, blunts reinstatement of nicotine‐seeking but increases responding for food
Bibliographic record
Abstract
People with schizophrenia display significantly higher rates of smoking than the general population, which may be due to an interaction between nicotine and antipsychotic medication. While the conventional antipsychotic drug haloperidol sometimes increases cigarette smoking in patients with schizophrenia, there is some evidence suggesting that clozapine, an atypical antipsychotic drug, may reduce nicotine use in these patients. However, the effects of antipsychotic drugs like clozapine on aspects of nicotine self-administration and reinstatement have not been systematically examined. In the current study, we assessed the effect of clozapine on nicotine self-administration under fixed ratio and progressive ratio schedules of reinforcement, as well as reinstatement of nicotine-seeking following a period of abstinence. To determine the specificity of its effect on nicotine reward, we also tested the effect of clozapine on responding for food reinforcement under fixed ratio and progressive ratio schedules. For comparison, we also examined the effects of haloperidol, a first-generation antipsychotic drug, under some of the same behavioral conditions as clozapine. We show that clozapine inhibits nicotine self-administration and reinstatement of nicotine-seeking but also increases the amount of effort that rats will exert for food reward. In contrast, haloperidol at a wide range of doses attenuated responding for nicotine and food reward, suggestive of a non-specific reduction in reinforcer efficacy. These results show the potential utility of clozapine as a smoking cessation treatment for patients with schizophrenia, in addition to its antipsychotic properties.
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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) | 0.000 | 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 | 0.000 |
| 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 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".