Tobacco-attributable disease remains the largest potentially modi?able cause of mortality. Strategies to reduce smoking prevalence include developing more effective smoking cessation treatments. Nicotine metabolism and dependence are predictors of smoking behaviors, including response to smoking cessation treatments. The goal of this Phase II project is to develop prediction models of nicotine metabolism, nicotine dependence and smoking cessation from clinical and genomic data. An optimized set of models will be implemented in the ?SmokescreenTranslational (TL) Analysis Platform?, and applied to clinical cohorts of treatment-seeking smokers. We have previously designed SmokescreenGTA, a genome-wide array that deeply captures variation in over 1,000 addiction genes, including the most important loci for nicotine metabolism and nicotine dependence. We have identi?ed multiple metabolic and regulatory genes, that with relatively few markers, can predict an individual's nicotine metabolic activity. We will use existing cohorts and a clinical treatment trial of smokers to discover and test integrated models with the goal of providing estimates of nicotine metabolism, nicotine dependence and cessation probability. These models will incorporate ancestry, clinical, genomic and social vari- ables to maximize prediction of smoking cessation. We will develop a compact laboratory assay for genotyping DNA samples with speci?c markers and software to analyze clinical and genomic data. SmokescreenTL will be validated in smokers in clinical care. The results will be delivered in ?exible reporting formats. Ultimately, SmokescreenTL will be available for use by health care providers interested in helping treatment seeking smokers quit.
