The Serotonin 2C Receptor Agonist Lorcaserin Attenuates Intracranial Self-Stimulation and Blocks the Reward-Enhancing Effects of Nicotine
Bibliographic record
Abstract
Lorcaserin, a serotonin (5-hydroxytryptamine, 5-HT) 2C receptor agonist, was recently approved for the treatment of obesity. We previously suggested that 5-HT2C receptor agonists affect reward processes and reduce the rewarding effects of drugs of abuse. Here, we determined whether lorcaserin (1) decreases responding for brain stimulation reward (BSR) and (2) prevents nicotine from enhancing the efficacy of BSR. Rats were trained on the intracranial self-stimulation (ICSS) paradigm to nosepoke for BSR of either the dorsal raphé nucleus or left medial forebrain bundle. In Experiment 1, lorcaserin (0.3-1.0 mg/kg) dose-dependently reduced the efficacy of BSR. This effect was blocked by prior administration of the 5-HT2C receptor antagonist SB242084. In Experiment 2, separate groups of rats received saline or nicotine (0.4 mg/kg) for eight sessions prior to testing. Although thresholds were unaltered in saline-treated rats, nicotine reduced reward thresholds. An injection of lorcaserin (0.3 mg/kg) prior to nicotine prevented the reward-enhancing effect of nicotine across multiple test sessions. These results demonstrated that lorcaserin reduces the rewarding value of BSR and also prevents nicotine from facilitating ICSS. Hence, lorcaserin may be effective in treating psychiatric disorders, including obesity and nicotine addiction, by reducing the value of food or drug rewards.
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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 | 0.001 |
| 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".