Effects of Varenicline on Cigarette Self Administration

Tobacco use is the leading preventable cause of death in the U.S.A. Every year 400,000 people die from cigarette smoking and in 2006, one out of every five deaths in the US were smoking related. Recent advances in laboratory studies of tobacco effects in humans and in understanding the effects of nicotine on the brain and behavior present an opportunity to advance medication development. The addictive properties of nicotine are thought to be a result of nicotine triggering the acute release of dopamine, a pleasurable event that a person seeks to repeat. Varenicline is a partial agonist of the nicotine receptors, therefore also triggering the release of dopamine but in a more sustained and moderate manner, which could counter the low dopamine levels arising from a lack of nicotine and therefore aid craving. Also, by binding to these nicotine receptors in advance of smoking, it could stop nicotine from binding and creating pleasurable effects. This study will assess the effect of acute treatment with varenicline and placebo on early tobacco withdrawal, acute effects of cigarettes and cigarette self-administration in cigarette-smoking volunteers. After overnight abstinence, participants will come into the lab and receive acute treatment with varying doses of varenicline or placebo and perform computer tests and fill out questionnaires. Then they will be given the opportunity to smoke under operational conditions (cigarette versus money choice). This study will employ a within-group, double-blind, randomized and counterbalanced design. The main goal of this project is to improve the current laboratory model of smoking cessation and study the mechanism involved in smoking maintenance. We hypothesize that varenicline will dose-dependently: 1) decrease nicotine withdrawal symptoms, 2) decrease acute effects of cigarettes and 3) decrease self-administration of cigarettes in the laboratory paradigm. Showing the effectiveness of varenicline in the proposed laboratory model will confirm the model's predictive validity to detect clinically effective medication.