Abstinence-induced cognitive deficits that promote relapse are another key target in the area of medication development. Preclinical studies show that nicotine withdrawal impairs learning and suggest that relapse after smoking withdrawal may occur as an attempt to ameliorate selleck screening library learning-related deficits (Davis, James, Siegel, & Gould, 2005). Atomoxetine, used in the treatment of attention-deficit/hyperactivity disorder, reversed nicotine withdrawal�Cinduced deficits in learning in the mouse contextual fear conditioning model (Davis & Gould, 2007). Although atomoxetine did not alter abstinence-induced cognitive deficits in human smokers, it reduced subjective withdrawal symptoms and cravings to smoke in a subset of smokers (Ray et al., 2009).
New data also suggest that varenicline, an ��4��2 nicotinic acetylcholine receptor (nAChR) antagonist, blocks withdrawal-associated cognitive deficits in mice (Raybuck, Portugal, Lerman, & Gould, 2008). Consistent with this finding, varenicline enhances cognitive performance in abstinent smokers, an effect that may contribute to its efficacy in relapse prevention (Patterson et al., 2009). Other TTURC work has documented effects of nicotine and medications to treat nicotine dependence in a mouse event-related potential (ERP) paradigm (Metzger, Maxwell, Liang, & Siegel, 2007; Siegel et al., 2005). This work is being extended to human ERP models. Developing and validating new models for nicotine dependence medication development is a key goal of the TTURC. Some research focuses on improving models for early human screening (Perkins, Stitzer, & Lerman, 2006).
A recent experiment tested the sensitivity of a within-subject model of medication effects on abstinence and found that intrinsic, but not extrinsic, quit motivation of participants may enhance the validity of brief tests of medication efficacy for smoking cessation (Perkins et al., 2008). Additional studies are exploring the utility of neuroimaging as a tool for identifying smokers at high risk for abstinence-induced craving and to screen new medications for nicotine dependence (Wang et al., 2007). In addition to developing novel treatment approaches and screening models, research at the TTURC seeks to improve the efficacy of existing therapies by identifying likely responders and nonresponders based on genotype. Collaborative pharmacogenetic trials have identified genetic factors predicting treatment outcome, including drug-metabolizing enzymes (Lee et al., 2007; Malaiyandi et al., 2006; F. Patterson, R. A. Schnoll et al., 2008), genes involved in dopaminergic signaling (Colilla et al., 2005; Lerman et al., 2006), AV-951 and nAChRs (Conti et al., 2008).