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Pathways to precision medicine in smoking cessation treatments.

ABSTRACT: Cigarette smoking is highly addictive and modern genetic research has identified robust genetic influences on nicotine dependence. An important step in translating these genetic findings to clinical practice is identifying the genetic factors affecting smoking cessation in order to enhance current smoking cessation treatments. We reviewed the significant genetic variants that predict nicotine dependence, smoking cessation, and response to cessation pharmacotherapy. These data suggest that genetic risks can predict smoking cessation outcomes and moderate the effect of pharmacological treatments. Some pharmacogenetic findings have been replicated in meta-analyses or in multiple smoking cessation trials. The variation in efficacy between smokers with different genetic markers supports the notion that personalized smoking cessation intervention based upon genotype could maximize the efficiency of such treatment while minimizing side effects, thus influencing the number needed to treat (NNT) and the number needed to harm. In summary, as precision medicine is revolutionizing healthcare, smoking cessation may be one of the first areas where genetic variants may identify individuals at increased risk. Current evidence strongly suggests that genetic variants predict cessation failure and that cessation pharmacotherapy effectiveness is modulated by biomarkers such as nicotinic cholinergic receptor ?5 subunit (CHRNA5) genotypes or nicotine metabolism ratio (NMR). These findings strengthen the case for the development and rigorous testing of treatments that target patients with different biological risk profiles.

SUBMITTER: Chen LS

PROVIDER: S-EPMC5115988 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Pathways to precision medicine in smoking cessation treatments.

Chen Li-Shiun LS Horton Amy A Bierut Laura L

Neuroscience letters 20160518

Cigarette smoking is highly addictive and modern genetic research has identified robust genetic influences on nicotine dependence. An important step in translating these genetic findings to clinical practice is identifying the genetic factors affecting smoking cessation in order to enhance current smoking cessation treatments. We reviewed the significant genetic variants that predict nicotine dependence, smoking cessation, and response to cessation pharmacotherapy. These data suggest that geneti ...[more]

PMID: 27208830

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Project description:BackgroundTwo treatments for smoking cessation--varenicline and bupropion--carry Boxed Warnings from the U.S. Food and Drug Administration (FDA) about suicidal/self-injurious behavior and depression. However, some epidemiological studies report an increased risk in smoking or smoking cessation independent of treatment, and differences between drugs are unknown.MethodologyFrom the FDA's Adverse Event Reporting System (AERS) database from 1998 through September 2010 we selected domestic, serious case reports for varenicline (n = 9,575), bupropion for smoking cessation (n = 1,751), and nicotine replacement products (n = 1,917). A composite endpoint of suicidal/self-injurious behavior or depression was defined as a case with one or more Preferred Terms in Standardized MedDRA Query (SMQ) for those adverse effects. The main outcome measure was the ratio of reported suicide/self-injury or depression cases for each drug compared to all other serious events for that drug.ResultsOverall we identified 3,249 reported cases of suicidal/self-injurious behavior or depression, 2,925 (90%) for varenicline, 229 (7%) for bupropion, and 95 (3%) for nicotine replacement. Compared to nicotine replacement, the disproportionality results (OR (95% CI)) were varenicline 8.4 (6.8-10.4), and bupropion 2.9 (2.3-3.7). The disproportionality persisted after excluding reports indicating concomitant therapy with any of 58 drugs with suicidal behavior warnings or precautions in the prescribing information. An additional antibiotic comparison group showed that adverse event reports of suicidal/self-injurious behavior or depression were otherwise rare in a healthy population receiving short-term drug treatment.ConclusionsVarenicline shows a substantial, statistically significant increased risk of reported depression and suicidal/self-injurious behavior. Bupropion for smoking cessation had smaller increased risks. The findings for varenicline, combined with other problems with its safety profile, render it unsuitable for first-line use in smoking cessation.

Project description:IntroductionBy improving our understanding of the neurobiological mechanisms underlying addiction, neuroimaging research is helping to identify new targets for personalized treatment interventions. When trying to quit, smokers with larger electrophysiological responses to cigarette-related, compared with pleasant, stimuli ("C > P") are more likely to relapse than smokers with the opposite brain reactivity profile ("P > C").Aim and methodThe goal was to (1) build a classification algorithm to identify smokers characterized by P > C or C > P neuroaffective profiles and (2) validate the algorithm's classification outcomes in an independent data set where we assessed both smokers' electrophysiological responses at baseline and smoking abstinence during a quit attempt. We built the classification algorithm applying discriminant function analysis on the event-related potentials evoked by emotional images in 180 smokers.ResultsThe predictive validity of the classifier showed promise in an independent data set that included new data from 177 smokers interested in quitting; the algorithm classified 111 smokers as P > C and 66 as C > P. The overall abstinence rate was low; 15 individuals (8.5% of the sample) achieved CO-verified 12-month abstinence. Although individuals classified as P > C were nearly 2.5 times more likely to be abstinent than smokers classified as C > P (12 vs. 3, or 11% vs. 4.5%), this result was nonsignificant, preliminary, and in need of confirmation in larger trials.ConclusionThese results suggest that psychophysiological techniques have the potential to advance our knowledge of the neurobiological underpinnings of nicotine addiction and improve clinical applications. However, larger sample sizes are necessary to reliably assess the predictive ability of our algorithm.ImplicationsWe assessed the clinical relevance of a neuroimaging-based classification algorithm on an independent sample of smokers enrolled in a smoking cessation trial and found those with the tendency to attribute more relevance to rewards than cues were nearly 2.5 times more likely to be abstinent than smokers with the opposite brain reactivity profile (11% vs. 4.5%). Although this result was not statistically significant, it suggests our neuroimaging-based classification algorithm can potentially contribute to the development of new precision medicine interventions aimed at treating substance use disorders. Regardless, these findings are still preliminary and in need of confirmation in larger trials.

Project description:Introduction:Varenicline doubles cessation over nicotine replacement therapy (NRT) patch for "normal," but not "slow," nicotine metabolizers, as assessed by the nicotine metabolite ratio (NMR). Metabolism-informed care (MIC) could improve outcomes by matching normal metabolizers with non-nicotine medication (e.g., varenicline) and slow metabolizers with NRT patch. Methods:We conducted a feasibility randomized controlled trial of MIC versus guideline based care (GBC) among 81 outpatient adult daily smokers with medical comorbidity. Participants reported perceptions of MIC, underwent blood draw for NMR, and received expert cessation counseling. For MIC participants, medication selection was informed by NMR result (normal (?0.31) vs. slow (< 0.31)). The primary outcome was MIC feasibility, reflected by attitudes toward MIC and by match rates between NMR and medication. Secondary endpoints (cessation confidence, medication use, smoking status) were assessed over 6 months to inform future studies. Results:Participants were median age 53 years, 46% female, 28% black, and ~90% endorsed MIC. Despite high varenicline prescription rates (~60%) in both arms, NMR-medication matching was higher in MIC (84%) versus GBC (58%) participants (p=0.02); unadjusted odds ratio (OR) 3.67, 95% confidence interval [1.33, 11.00; p-value=0.02]. Secondary endpoints were similar at 1, 3, and 6 months. Conclusions:MIC, an NMR-based precision approach to smoking cessation, was acceptable to 90% of smokers and improved NMR-medication match rates more than 3-fold compared to GBC, even with generally high use of varenicline. These data support the feasibility of MIC, which could maximize efficacy of smoking cessation medication while minimizing side effects and cost. Implications:Among treatment-seeking daily smokers with medical comorbidity, most viewed metabolism-informed care (MIC), guided by the nicotine metabolism ratio (NMR), favorably, and were willing to accept MIC-guided medication. Compared to GBC participants (58%), more MIC participants (84%) were prescribed NMR-matched medication (i.e., normal metabolizers received varenicline; slow metabolizers received NRT patch). MIC increased the odds of optimized matching between NMR and medication more than 3-fold over GBC. Because the number needed to treat (NNT) to help one normal metabolizer quit smoking is only 4.9 for varenicline versus 26 for patch, broad implementation of MIC will improve drug efficacy in normal metabolizers as well as minimize side effects in slow metabolizers.

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Pathways to precision medicine in smoking cessation treatments.

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Pathways to precision medicine in smoking cessation treatments.

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