Tobacco use is the largest preventable cause of death in the United States [1] and worldwide [2]. Most smokers wish to stop, and both behavioral counseling and pharmacotherapies increase abstinence rates two-to-three fold compared to placebo (PLA) abstinence rates in RCTs, though there are differences in therapy effectiveness [3]. Yet, the majority of smokers are not able to quit long-term with either behavioral therapy and/or pharmacotherapy. Thus, there is a critical need to enhance the effectiveness of smoking cessation treatments. One approach to improve cessation rates would be to identify factors that indicate which individuals will benefit most from which treatment and to develop algorithms to incorporate these factors into clinical practice. These factors could include gender, nicotine dependence, comorbidity, the rate of nicotine metabolism, pharmacogenetic variation, or combinations of factors [4,5,6,7,8,9,10,11].
