Preclinical evidence for combining the 5‐<scp>HT<sub>2C</sub></scp> receptor agonist lorcaserin and varenicline as a treatment for nicotine dependence
Bibliographic record
Abstract
receptor agonist has been approved in some countries to treat obesity. Based on preclinical and preliminary clinical evidence, lorcaserin may have potential to treat nicotine dependence. These experiments examined in rats the effects of combining varenicline (0.5 or 1 mg/kg) and lorcaserin (0.3, 0.6 and 1 mg/kg) on nicotine self-administration, reinstatement of nicotine seeking, responding for food and impulsive action. Both drugs alone reduced nicotine self-administration. Combining varenicline and 0.6 mg/kg lorcaserin reduced responding to a greater extent than either drug alone. In a relapse model, extinguished nicotine seeking was reinstated by a priming injection of nicotine and nicotine-associated cues. Reinstatement was reduced by varenicline (1 mg/kg) and by lorcaserin (0.3 mg/kg). Combining lorcaserin (0.3 mg/kg) with varenicline (0.5 or 1 mg/kg) reduced reinstatement to a greater degree than either drug alone. Both drugs had minimal effects on responding for food, alone or in combination. In the five-choice serial reaction time test, varenicline (0.5 or 1 mg/kg) increased impulsivity, measured as increased premature responding. This effect was reduced by lorcaserin (0.3 mg/kg). Plasma levels of varenicline or lorcaserin were not altered by co-administration of the other drug. Varenicline and lorcaserin have additive effects on nicotine self-administration, and on nicotine seeking. Lorcaserin prevents impulsivity induced by varenicline. This pattern of effects suggests that co-administration of varenicline and lorcaserin has potential as a treatment for nicotine dependence that may exceed the value of either drug alone.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".