Biomarkers of Response to Smoking Cessation Pharmacotherapies: Progress to Date

Abstract

For the past thirty years, research examining predictors of successful smoking cessation treatment response has focused primarily on clinical variables, such as levels of tobacco dependence, craving, and self-efficacy. Recent research, however, has begun to determine biomarkers (such as genotype, nicotine and metabolite levels, and brain imaging findings) that may have utility in predicting smoking cessation. For genotype, genes associated with nicotinic acetylcholine receptors (nAChRs) and related proteins have been found to predict response to first line medications (e.g., nicotine replacement therapy [NRT], bupropion, or varenicline) or quitting over time without a controlled treatment trial. For nicotine and metabolite levels, function of the CYP 2A6 liver enzyme, which can be assessed with the nicotine metabolite ratio or via genotype, has been found to predict response, with slow nicotine metabolizers having less severe nicotine dependence and a greater likelihood of quitting with NRT than normal metabolizers. And for brain imaging, decreased activation of brain regions associated with emotion regulation and increased connectivity in emotion regulation networks, increased responsiveness to pleasant cues, and altered activation with the Stroop effect have been found in smokers who quit with the first-line medications listed above or counseling. In addition, our group recently demonstrated that lower pre-treatment brain nAChR density is associated with a greater chance of quitting smoking with NRT or placebo. Several of these studies found that specific biomarkers may provide additional information for predicting response beyond subjective symptom or rating scale measures, thereby giving an initial indication that biomarkers may, in the future, be useful for guiding smoking cessation treatment intensity, duration, and type.

Resting‐state functional connectivity and nicotine addiction: prospects for biomarker development

“…For one, self‐report measures provide little guidance about how treatment plans should differ among heterogeneous subgroups of smokers. As outlined above, recent evidence has shown that differences in potential treatment response can be identified and leveraged to provide treatment alternatives better suited to specific subgroups of smokers . In addition, specific changes in rsFC have been correlated with FTND, duration of chronic nicotine exposure, smoking cue reactivity, and subjective craving during nicotine withdrawal .…”

“…Thus, rsFC measures have the potential to predict treatment outcomes as well as to fractionate individual smokers based on the specific network dysfunctions present, which would facilitate differential diagnosis of smokers based on specific network dysfunctions. Recent evidence has shown that personalized treatment for nicotine addiction is possible, and rsFC measures may thus serve to improve outcomes by identifying ideal treatment options for individuals. Thus, measures of chronic use and—perhaps more importantly—measures that track disease severity and trajectory are important missing pieces in the clinical arsenal of addiction medicine today …”

“…Among the explanations for this difficulty in maintaining long‐term abstinence are a poor understanding of the neurobiological underpinnings of the disease, an inability to quantify the severity of addiction beyond behavioral reports of cigarettes per day and usage patterns, and an inability to proactively identify the most appropriate therapeutic plan for an individual (though see Refs. and for recent advances).…”

Individual Variations in the Mechanisms of Nicotine Seeking: A Key for Research on Nicotine Dependence

“…This supports the notion that the two types of factors (PF/NPF) can vary independently. Fast nicotine metabolism is another risk factor for heavy smoking and low cessation rates (Mamoun et al, 2015). Fast nicotine metabolizers are more sensitive to withdrawal than their slow metabolizer counterparts, but differently from the CHRNA5 case, they are also more responsive to drug cues (Falcone et al, 2016); a difference possibly due to their specific nicotine pharmacodynamics that might favor cue conditioning.…”

Pathways to precision medicine in smoking cessation treatments

“…Different neuroimaging markers have also been examined [111]. For example, lower pre-treatment brain nAChR density has been shown as associated with a greater chance of quitting smoking with NRT or placebo [112]. Cigarette smoking leads to upregulation of nicotinic acetylcholine receptors (nAChRs) in the human brain, including the common α4β2* nAChR subtype.…”