Effects of nicotinic receptor antagonism on nicotine and THC self-administration in a model of polysubstance use
Bibliographic record
Abstract
Abstract Background Most substance users are polysubstance users; however, little is known about how the combined use of different drugs affects the course of substance use disorder or effectiveness of treatment. Notably, co-use of cannabis and nicotine is very common, and we previously demonstrated that nicotine enhances the self-administration of a synthetic cannabinoid receptor agonist and the primary psychoactive phytocannabinoid in cannabis, Δ-9-tetrahydrocannabinol (THC). Methods Here we aimed to further investigate the patterns of nicotine and THC self-administration when available in a concurrent choice model, and to determine the effects of nicotinic acetylcholine receptor (nAchR) antagonists on nicotine-enhanced THC self-administration in male and female rats. Results During concurrent choice, nicotine availability increased THC self-administration in females without affecting THC metabolism, while THC availability decreased nicotine self-administration and preference in females relative to when nicotine and saline were concurrently available. In females, THC self-administration was reduced by the β2/4 subunit-containing nAchR antagonist dihydro-beta-erythroidine (DHβE) in both nicotine and saline concurrent availability groups; while nicotine self-administration was reduced in both sexes by the α7nAchR antagonist methylylcaconitine (MLA), but only in rats that had concurrent access to THC. The nonspecific nAchR antagonist mecamylamine had minimal effects in the concurrent choice model, but it prevented nicotine-induced enhancement of THC self-administration when nicotine was given prior to a single choice THC only self-administration session. Conclusions Thus, behavioral regulation of self-administration is differentially influenced by nAchR subtypes depending on the availability of other substances, which has implications for the efficacy of treatments in the context of polysubstance use. Significance Statement Polysubstance use is extremely common, but very understudied in both clinical and preclinical research. The results presented here highlight that pharmacological modulators of drug reinforcement can differ when multiple drugs are available simultaneously, highlighting the importance of investigating potential treatments for substance use disorders in the context of polysubstance use.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".