The modeling approach makes it easier to analyze multiple dimensions of a specific policy and the potential impact of a policy or policies (including unintended consequences) before they are implemented. Modeling also helps to assess the complex array of factors that modulate overall selleck chemical impact. Tools for Assessing the Effects of Reduced Nicotine Tools for assessing the impact of RNC cigarettes include imaging studies, animal and human laboratory studies, clinical trials, and measurement of biomarkers of exposure and harm. The following describes the types of studies that are likely to contribute to the science base for reducing nicotine in cigarettes and other combustible products. Preclinical Animal Models Understanding the neurobiology of nicotine addiction has advanced significantly (D��Souza & Markou, 2011; Gotti, Zoli, & Clementi, 2006; Kuryatov, Berrettini, & Lindstrom, 2011; Saccone et al.

, 2009; Thorgeirsson et al., 2008; Tuesta, Fowler, & Kenny, 2011). However, there is little knowledge regarding how reducing the levels of nicotine in cigarettes would affect the developing brain or a brain that has been altered by chronic exposure to nicotine. Animal models allow investigation in these areas, and such basic research will be important to continue regardless of whether or not a nicotine reduction policy is implemented (Donny et al., 2012; Hatsukami, Perkins, et al., 2010). Animal studies on nicotine reduction also allow for controlled analysis of factors that might alter the functional relationship between nicotine reduction and outcomes of interest.

The FDA and the Drug Enforcement Agency recognize that specific animal models are particularly informative when assessing abuse liability (Food and Drug Administration, 2010). The following animal models and techniques would be particularly useful in evaluating effects of reducing Cilengitide levels of nicotine: (a) Drug self-administration models that provide estimates of threshold reinforcing nicotine doses in adolescents and adults and factors that moderate them; (b) demand curve analysis and growth-curve analysis that provide quantitative techniques to facilitate detection of factors that moderate reduction and acquisition of self-administration, respectively (Greenwald & Hursh, 2006; Hursh, Galuska, Winger, & Woods, 2005; Hursh & Silberberg, 2008; Lanza, Donny, Collins, & Balster, 2004); (c) drug discrimination models that can be used to screen understudied or novel constituents for their own abuse potential or capability of enhancing nicotine��s effects (Smith & Stolerman, 2009); (d) withdrawal models that allow for further delineation of the mechanisms underlying possible adverse consequences of reduction (e.g.