123

Chapter 3

New Biological Insights into Smoking Cessation

Introduction 125

Cell and Molecular Biology of Nicotine Addiction 125

Literature Review Methods 125

Neurobiology of Nicotine Addiction 125

Nicotinic Acetylcholine Receptors 126

Nicotine Reward 128

Nicotine Withdrawal and Relapse 128

Animal Models of Nicotine Addiction 129

Molecular Targets of Current Pharmacotherapies 129

Novel Targets for Smoking Cessation 131

Glutamatergic Signaling 131

Neuropeptide Systems 132

The Habenulo-Interpeduncular Pathway 135

Vaccines and Other Immunotherapies as Treatments for Nicotine Addiction 137

Literature Review Methods 137

Design and Mechanism of Action 137

Examining Data from Animals to Confirm Vaccine Activity 138

Clinical Trials of Nicotine Vaccines 138

Next-Generation Vaccines 138

Combining Vaccines with Medications 139

Passive Immunization with Monoclonal Antibodies or Gene Transfer 139

Summary 139

Insights into Smoking Cessation from the Field of Neurobiology 139

Literature Review Methods 140

Methodology of Neuroimaging Studies 141

Differences in Brain Circuitry and Cognitive Constructs in Nicotine Dependence 141

Smoking Cues and Craving Provocation 142

Reward 143

Cognition and Cognitive Control 143

Insights from Neuroimaging for Antismoking Messages 144

Neuronal Circuits and Networks 144

Molecular Imaging 146

Summary 147

Genetic Studies of Smoking Phenotypes 147

Literature Review Methods 148

Candidate Gene Studies 148

Genomewide Association Studies 148

Examples of Biologically Promising Candidate Genes (DRD2 and DAT1) 148

Associations Between the DRD2 and DAT1 Genes and Smoking Behavior 149
124

The Moderating Effect of DRD2 and DAT1 on the Efficacy of Treatment for Smoking Cessation 149

Genetic Risk Scores 149

Examples of Biologically Promising Genes That May Help Optimize Treatment 150

The CHRNA5-A3-B4 Gene Cluster 150

Associations with Nicotine Dependence and Smoking Intensity 150

CHRNA5-A3-B4 Variants and Smoking Cessation in Absence of Treatment 151

CHRNA5-A3-B4 Variants and Smoking Cessation with Pharmacotherapy 151

The CYP2A6 Gene and the Nicotine Metabolite Ratio 153

Nicotine Metabolite Ratio and Smoking Behavior 153

Nicotine Metabolite Ratio and Smoking Cessation in Absence of Treatment 154

Smoking Cessation in Treatment Seekers 154

Summary 154

Summary of the Evidence 155

Conclusions 157

References 158
New Biological Insights into Smoking Cessation 125

Smoking Cessation

Introduction

The 1988 Surgeon General’s report on nicotine 

addiction was the first in this series to conclude that 

“[n]icotine is the drug in tobacco that causes addiction” (U.S. Department of Health and Human Services 

[USDHHS] 1988, p. 9). The biologic mechanisms underlying nicotine addiction continue to be a subject of great 

research interest, and several promising pharmacotherapeutic targets have emerged. For example, acquisition of 

basic knowledge about the function of nicotinic acetylcholine receptors (nAChRs) led to the development of targeted smoking cessation medications currently in use, and 

research would benefit from an additional understanding 

of molecular mechanisms (USDHHS 2010). The 2010 

Surgeon General’s report on how tobacco causes disease 

described the pharmacokinetics of nicotine, the behavioral pharmacology of nicotine addiction, and the known 

genotypes and receptor subtypes that contribute to nicotine addiction (USDHHS 2010). This chapter focuses on 

how biology can influence smoking cessation and reviews 

four areas of intensive research since the publication of 

the 2010 Surgeon General’s report.

1. Cell and molecular biology of nicotine addiction

focuses on the nAChRs as the primary target of currently available medications and on the following 

potential targets for medication development: glutamatergic signaling, neuropeptide systems, habenulointerpeduncular pathway, and noradrenergic system. 

This section describes the preclinical basis for 

understanding nicotine addiction and the ways that 

this knowledge could be used to enhance smoking 

cessation.

2. Vaccines and other immunotherapies as treatments for tobacco addiction focuses on the conceptual basis of vaccine treatment, vaccine mechanistic 

design, and vaccine animal studies; progress made 

and barriers encountered with the early generation 

vaccines; and approaches to next-generation treatments and passive immunization.

3. Insights into smoking cessation from the field of 

neurobiology describes the brain circuitry involved 

in nicotine dependence, as understood primarily 

through advances in brain imaging techniques; the 

role of stress, craving, and reward; and changes in 

cognitive control. Findings provide insight into the 

effects of smoking on the brain and the potential to 

identify new types of targets for smoking cessation.

4. Genetic studies of smoking phenotypes focuses on 

the further mechanistic understanding gained from 

the interindividual differences that genetics creates 

and from some of the methodologic approaches that 

can be used to examine genetics in humans. Findings 

provide insight into distinct classes of genes that represent potential targets for novel smoking cessation 

therapeutics and optimizing choice of treatment.

Cell and Molecular Biology of Nicotine Addiction

Literature Review Methods

For this section of the chapter, PubMed was searched 

in January 2017 for studies published between 2010 and 

2017 that focused on the neurobiologic mechanisms 

underlying nicotine addiction in model organisms and in 

human subjects. Such search teams included “nACh” and 

“nicotinic receptor,” and these terms were combined with 

such terms as “addiction” and “behavior.” Studies about 

nicotinic acetylcholine receptor mechanisms that were 

cited in these articles were also reviewed to identify primary research articles. These studies and a current search 

of clinical trials websites were used to identify molecular 

targets for the development of novel smoking cessation 

aids and ongoing clinical trials of relevant therapeutic 

agents. One reviewer conducted a full review and identified 76 articles for this section. The cited references for 

preclinical work represent a compilation of the current 

knowledge base obtained from rodent studies, but the base 

cannot be considered completely comprehensive because 

of the large volume of studies in this area.

Neurobiology of Nicotine Addiction

Nicotine, the main addictive constituent of cigarette smoke, binds to nAChRs, a class of ligand-gated ion 

channels that, following the binding of acetylcholine or 
A Report of the Surgeon General

126 Chapter 3

nicotine, open and allow the trafficking of cations (positive 

ions [e.g., Ca++, Na+, K+]) (USDHHS 2010). nAChRs play 

an important role in transmitter release, cell excitability, 

and neuronal integration. Through these processes, nicotine stimulates the release of many different neurotransmitters throughout the brain. In particular, nicotine activates the mesocorticolimbic dopamine system, which can 

induce both reward or aversion (USDHHS 2010).

The mesocorticolimbic system, which is characterized by the ventral tegmental area (VTA) located 

in the midbrain, transmits dopamine to two main targets: one cortical, the prefrontal cortex (PFC); and one 

limbic, the nucleus accumbens (NAc) in the ventral striatum (Figure 3.1). Nicotine increases extracellular dopamine in all of these structures but mainly in the NAc. 

The reward associated with the release of dopamine is 

one of the underlying mechanisms of the development of 

nicotine dependence. In fact, the dopaminergic pathway 

is targeted by existing pharmacotherapies for smoking 

cessation. At present, the approved pharmacologic treatments in the United States or Europe are nicotine replacement therapy (NRT), varenicline, and bupropion (U.S. 

Food and Drug Administration [FDA] 2016). Varenicline 

(trade names: Chantix, Champix) partially blocks the 

α4β2 nAChRs, and bupropion (trade names: Wellbutrin, 

Zyban) is a norepinephrine/dopamine reuptake inhibitor 

that also can decrease the function of nAChRs by acting 

as an antagonist of the receptors (Mansvelder et al. 2007). 

These two medications act indirectly and directly on the 

dopamine pathway.

Nicotinic Acetylcholine Receptors

nAChRs are ion channels that normally are activated 

by the neurotransmitter acetylcholine, but the nicotine 

in tobacco products “hijacks” nAChRs. In humans, these 

receptors are assembled from combinations of 17 known 

subunits, 12 of which are expressed in the brain (α2–α10 

and β2–β4) (Picciotto et al. 2008; Picciotto and Kenny 

2013). Importantly, co-assembly of specific combinations 

of subunits results in a set of nAChR subtypes that vary in 

their properties, location in the brain, and sensitivity to nicotine (Figure 3.2). For example, α7 can form a functional 

nAChR on its own [(α7)5,], while all other nAChRs contain 

at least one α subunit and one β subunit [e.g., (α4)2(β2)3]. 

The α4 and β2 subunits, which are expressed throughout 

the brain and body in many types of cells, nearly always 

assemble together, sometimes with additional subunits, 

and their interface forms a high-affinity nicotine binding 

site (Kutlu and Gould 2016). Activation of these α4- and 

β2-containing receptors is required for many of the neurobiologic and behavioral effects associated with nicotine 

reward. The α6 subunit also can associate selectively with 

these receptors in dopamine and norepinephrine neurons 

(Kutlu and Gould 2016).

Nicotine and the endogenous ligand acetylcholine 

bind to the extracellular interface between two nAChR 

subunits. Upon binding of either nicotine or acetylcholine, 

the receptors undergo a structural change that causes the 

ion channel to open, permitting the influx of cations and 

membrane depolarization. Cellular responses to nicotine depend on the composition of nAChR subunits and 

their subcellular localization. For example, activation of 

nAChRs located on nerve terminals stimulates the release 

of neurotransmitters, and activation of cell body receptors increases neuronal excitability and can induce action 

potentials. Nicotine also binds to intracellular receptors in 

the endoplasmic reticulum and promotes their assembly 

and trafficking. Long-term exposure to nicotine increases 

the surface expression of nAChRs, particularly the highaffinity α4- and β2-containing receptors. Cells in the 

brains of smokers, therefore, have an increased capacity 

for nicotine binding, which may result in altered neuronal 

signaling once nicotine is cleared from the brain and these 

nAChRs become available for acetylcholine signaling. In 

fact, heightened expression of nAChRs is observed in 

the brains of smokers for weeks following cessation; this 

might contribute to craving and withdrawal symptoms 

(Cosgrove et al. 2012). Although low levels of nicotine 

activate nAChRs, leading to nicotine reinforcement, continued exposure to nicotine desensitizes the receptors, 

which contributes to tolerance. The extent of desensitization varies with the composition of receptors and concentration of nicotine. β2 subunit-containing nAChRs, 

which are required for the rewarding effects of nicotine, 

desensitize rapidly in response to very low concentrations 

of nicotine (Picciotto et al. 2008). α7 receptors, however, 

will continue to respond in the presence of sustained low 

concentrations of nicotine.

 The physiologic consequences of nAChR desensitization are complex and not entirely understood, but chronic 

exposure to nicotine in the brains of users of tobacco 

products likely results in phases of activation and desensitization of nAChRs that contribute to nicotine reinforcement and tolerance, respectively. The variability in this 

balance also may contribute to individual differences in 

susceptibility to nicotine addiction. In addition, receptors 

are reactivated once nicotine is removed from the system. 

Thus, increases in the number of nAChRs and receptor 

reactivation when nicotine is cleared from the system that 

last at least 4 weeks after cessation (Cosgrove et al. 2012) 

result in robust potentiation of nAChR signaling following 

abstinence, which then contributes to withdrawal symptoms (Millar and Harkness 2008; Picciotto et al. 2008; 

Changeux 2010).
New Biological Insights into Smoking Cessation 127

Smoking Cessation

Figure 3.1 Stages of the addiction cycle

Source: From Volkow and colleagues (2016, p. 365). Copyright © 2016 Massachusetts Medical Society. Reprinted with permission 

from Massachusetts Medical Society. 

Notes: “Binge and intoxication” and “feeling euphoric” are not relevant to nicotine. “During intoxication, drug-induced activation of

the brain’s reward regions (in blue) is enhanced by conditioned cues in areas of increased sensitization (in green). During withdrawal, 

the activation of brain regions involved in emotions (in pink) results in negative mood and enhanced sensitivity to stress. During preoccupation, the decreased function of the prefrontal cortex leads to an inability to balance the strong desire for the drug with the will 

to abstain, which triggers relapse and reinitiates the cycle of addiction. The compromised neurocircuitry reflects the disruption of the 

dopamine and glutamate systems and the stress-control systems of the brain, which are affected by corticotropin-releasing factor and 

dynorphin. The behaviors during the three stages of addiction change as a person transitions from drug experimentation to addiction 

as a function of the progressive neuroadaptations that occur in the brain” (Volkow et al. 2016, p. 365).
A Report of the Surgeon General

128 Chapter 3

Nicotine Reward

As for all drugs of abuse, the primary reinforcing 

(i.e., initial rewarding or addictive) effects of nicotine 

are driven by its activation of the mesolimbic dopamine 

system, commonly known as the brain’s reward circuit. 

Nicotine promotes phasic firing of dopamine neurons in 

the VTA through several nAChR-mediated mechanisms 

(USDHHS 2010). Activation of α4- and β2-containing 

nAChRs on dopamine cell bodies increases their excitability and is required for the reinforcing properties of nicotine. Nicotine also acts through α7 nAChRs located on 

glutamatergic terminals in the VTA to promote glutamate 

release onto dopamine neurons, further enhancing their 

excitation (USDHHS 2010). Similarly, nicotine stimulation of nAChRs made of the α4, β2, and α6 subunits that 

are found on dopamine terminals promotes the release of 

dopamine in NAc and other regions (Picciotto and Kenny 

2013; Wickham et al. 2013; Picciotto and Mineur 2014).

Nicotine Withdrawal and Relapse

Chronic nicotine use can induce a physical dependence severe enough that cessation induces a series of 

negative withdrawal symptoms in humans and in laboratory animals (Picciotto et al. 2008; USDHHS 2010). Thus, 

Figure 3.2 Structure and properties of nAChRs

Source: Created by Marina Picciotto and Megan Miller, Yale University School of Medicine, for this Surgeon General’s report.

Notes: α = alpha; β = beta; nAChR = nicotinic acetylcholine receptor. nAChRs in the brain assemble as pentameric (5-member) 

structures from 12 subunits: α2–α10 and β2–β4. The most common combinations are formed as homomers (all subunits the same) 

of the α7 subunit, or heteromers of the α4 and β2, or α3 and β4 subunits. Many subunit combinations with different properties are 

possible, with variability particularly at the fifth position in the receptor (indicated in grey as a choice of α or β subunit in this figure). 

Assembled receptors form a channel through the membrane, with a pore that is closed under resting conditions. The neurotransmitter 

acetylcholine normally binds to interfaces between subunits in the assembled nAChRs, activating the receptors and allowing ions to 

flow through the opened pore into cells expressing them. Nicotine binds to the same site in the nAChR as acetylcholine and can open 

the channel, although with different open times and likelihood of desensitization.
New Biological Insights into Smoking Cessation 129

Smoking Cessation

in addition to being drawn to the primary reinforcing 

properties of nicotine, many persons return to smoking 

to avoid negative effects of abstinence, such as irritability, 

anxiety, depression, insomnia, and difficulty concentrating. Additionally, environmental cues (sights, sounds, 

or other sensations) associated with nicotine often elicit 

drug cravings that can be sufficient to induce relapse to 

regular smoking after a quit attempt (USDHHS 2010). 

For example, former smokers who used to have a cigarette with their morning coffee may experience intense 

nicotine cravings at the smell of coffee, which could 

trigger relapse to smoking (Bevins and Palmatier 2004). 

Importantly, drug-paired cues (things in the environment that are associated with nicotine being on board) 

can become themselves reinforcing after repeated pairings, and this conditioned reinforcement may be at least 

partially responsible for continuing drug use and relapse. 

Mechanistically, perseverative drug use and high relapse 

rates happen because of persistent neurobiologic adaptations (tolerance), particularly within the mesocorticolimbic dopamine system. Thus, although developing 

therapies aimed at reducing the reinforcing properties 

of nicotine itself is reasonable, this strategy is unlikely to 

be completely effective in combating relapse to smoking 

(USDHHS 2010). For this reason, several research efforts 

have focused on elucidating the neurobiologic underpinnings of relapse.

Animal Models of Nicotine Addiction

Studies of animal models of disease have contributed to much of our understanding of the neurobiologic basis of nicotine addiction. Although animal models 

cannot capture the full range of human addiction, mice 

and rats do develop addiction-like behaviors, and several 

reliable paradigms have been established to measure specific aspects of the disease in animals. The drugs that animals self-administer correspond well with drugs that have 

high abuse liability in humans (Carter and Griffiths 2009). 

As described in detail below, nicotine-dependent animals 

will work to obtain nicotine and to relieve nicotine withdrawal symptoms (Koob and Simon 2009). Therefore, 

animal models are useful for measuring the abuse liability 

of addictive drugs, such as nicotine, and identifying pharmacotherapies that make addictive drugs less reinforcing 

or that mitigate withdrawal symptoms.

Modeling Nicotine Reward

The conditioned place preference (CPP) and selfadministration paradigms are two common models used 

to evaluate nicotine reinforcement and drug-seeking 

behavior. CPP is established by repeatedly pairing nicotine 

administration with exposure to a particular environmental 

context. Over time, the animal learns to associate the context with nicotine and develops a preference for that environment over an adjacent, similar environment that is not 

paired with nicotine. The development of such a preference is considered to be an indication of the rewarding 

effects of the drug.

In the self-administration model, animals are trained 

to complete an operant task, such as pressing a lever to 

receive an infusion of nicotine. Once the task is learned, 

changes in operant behavior are thought to indicate 

changes in drug reinforcement or craving. Variations of 

this task also can be used to measure motivation (i.e., how 

hard an animal is willing to work for nicotine), extinction, 

and relapse. Interestingly, self-administration of nicotine 

is more robust if infusion is paired with a cue versus with 

the drug alone (Caggiula et al. 2001).

Modeling Nicotine Withdrawal and Relapse

Human smokers often relapse in response to one of 

three stimuli: exposure to environmental cues associated 

with nicotine, aversive or stressful life events, or a small 

amount of the drug (i.e., a “lapse”) (USDHHS 2010). Each of 

these types of stimuli is also sufficient to induce reinstatement of nicotine-seeking behavior in rodents after forced 

extinction of the behavior. In the cue-induced reinstatement model, animals are trained to self-administer nicotine that is paired with an innocuous cue, such as a light 

or a tone. After self-administration of nicotine is acquired, 

the operant behavior can be extinguished by placing the 

animals in the same context but in the absence of the drug 

and the associated cue. Following extinction, animals will 

resume responding to the cue alone, even in the absence of 

nicotine. Similar paradigms have been developed to model 

stress-induced reinstatement and drug-induced reinstatement in animals, all of which may be valid for nicotine relapse in humans (Mantsch et al. 2016). Preclinical 

studies using these paradigms have been useful in identifying cellular and molecular processes that contribute to 

drug reinstatement, as discussed in this section.

Molecular Targets of Current 

Pharmacotherapies

As a consequence of our understanding of the neurobiology of nicotine addiction, several successful pharmacotherapies have been developed to aid in smoking cessation 

(Table 3.1) (Cochrane Tobacco Addiction Group n.d.), most 

of which alter nAChR signaling (Cahill et al. 2013, 2016). 

These include varenicline (a partial agonist of nAChRs) 

and bupropion (an atypical antidepressant with the ability 

to block nAChRs). Various forms of NRT—including 
A Report of the Surgeon General

130 Chapter 3

the patch, gums, lozenges, and nasal sprays—also act 

on nAChRs. Varenicline activates nAChRs, although to 

a lesser extent than nicotine, and blocks the binding of 

nicotine from tobacco to the nAChR, thereby resulting 

in reduced withdrawal symptoms and less reward from 

a lapse to smoking. Although not currently approved for 

use in the United States, cytisine is another nAChR partial 

agonist and has been used as an herbal smoking cessation aid for decades in Eastern European countries and 

Canada (Gómez-Coronado et al. 2018). Repeated efficacy 

studies, including a Phase 3 clinical trial in New Zealand, 

have found cytisine to be effective for smoking cessation 

at levels similar to varenicline (Etter 2006). Because cytisine is a naturally occurring compound, it is less expensive than currently available cessation aids, making it a 

potentially promising tool for reducing smoking rates in 

certain populations, including low-income individuals. 

With withdrawal-induced negative affect a major problem 

for smokers trying to quit, antidepressants are often prescribed, and several of these drugs have shown efficacy 

Table 3.1 Current pharmacotherapies for smoking cessation

Line Trade name(s) Target Action

FDA approved 

for smoking 

cessation: Yes/no Other information

First-line

Bupropion • 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

Wellbutrin

Elontril

Zyban

Catecholemine 

system/nAChRs 

(multiple subtypes)

Norepinephrine or 

dopamine reuptake 

inhibitor/nAChR 

antagonist

Yes Atypical antidepressant; 

also approved for 

ADHD and obesity

NRT Nicoderm

Commit

Nicorette

Others

nAChRs (multiple 

subtypes)

Agonist Yes —

Varenicline Chantix

Champix

nAChRs (multiple 

subtypes)

Partial agonist Yes —

Second-line

Nortriptyline Sensoval

Aventyl

Pamelor

Norpress

Allegron

Noritren

Nortrilen

Serotonin and 

norepinephrine 

systems

Serotonin or 

norepinephrine 

reuptake inhibitor

No Tricyclic antidepressant

Clonidine Catapres

Kapvay

Duraclon

Nexiclon

Adrenergic 

receptors

Agonist No Also indicated for high 

blood pressure, ADHD, 

anxiety, migraine,

withdrawal (opiates, 

alcohol, and nicotine), 

and other

Others

Cytisine Tabex nAChR Partial agonist No Popular in Eastern 

Europe but not

available in the United 

States; relatively

inexpensive

Naltrexone Revia

Vivitrol

Opioid receptors 

(μ, κ)

Antagonist No Commonly used to 

treat alcoholism and 

opioid dependence

Notes: κ = kappa; μ = mu; ADHD = attention-deficit/hyperactivity disorder; FDA = U.S. Food and Drug Administration; 

nAChR = nicotinic acetylcholine receptor; NRT = nicotine replacement therapy.
New Biological Insights into Smoking Cessation 131

Smoking Cessation

in reducing smoking (Hughes et al. 2014). Bupropion 

can alleviate withdrawal symptoms and reduce the 

severity of nicotine cravings. Overall, its efficacy for cessation is about double that of placebo (Wu et  al. 2006). 

Notably, bupropion is also an nAChR antagonist that alters 

nicotine-mediated dopamine responses, which likely contributes to its efficacy in reducing smoking (Mansvelder 

et al. 2007). Although it has not been approved by FDA for 

smoking cessation, nortriptyline (trade names: Sensoval, 

Pamelor, Aventyl, and others), a tricyclic antidepressant 

and serotonin/norepinephrine reuptake inhibitor, also has 

shown off-label efficacy in improving rates of smoking 

cessation (Hughes et al. 2005).

Novel Targets for Smoking Cessation

Glutamatergic Signaling

Although enhanced dopamine signaling is critical 

for the initial reinforcing properties of nicotine, both 

the maintenance and reinstatement of nicotine-seeking 

behavior require long-lasting alterations in the actions of 

glutamate, the major excitatory neurotransmitter in the 

brain (Knackstedt and Kalivas 2009; Li et al. 2014; Marchi 

et al. 2015). Glutamate levels are elevated in both the NAc 

and the VTA after exposure to nicotine, and glutamate 

inputs to the VTA mediate the increases in the activity 

of dopamine neurons in response to nicotine. Repeated 

exposure to nicotine results in a long-term potentiation 

(or long-lasting increase in activation) of these synapses, 

which contributes to elevated excitability of dopamine 

neurons. Furthermore, sustained low levels of nicotine, 

as would be observed in the brains of smokers, can desensitize nAChRs located on inhibitory nerve terminals in 

the VTA. This may reduce the inhibition of dopamine 

neurons, further shifting the excitatory–inhibitory balance in the VTA. Nicotine dependence also is associated 

with long-term potentiation of glutamate synapses in the 

NAc, and disruption of glutamate signaling in this region 

alters nicotine-mediated physiology and behavior. Thus, 

chronic use of nicotine causes long-lasting changes to the 

mesolimbic dopamine system, many of which are driven 

by alterations in glutamate transmission. Behaviorally, 

these adaptations sustain drug cravings and contribute 

to a vulnerability to relapse. Glutamate binds to and activates two types of receptors: ionotropic, which are ion 

channels that allow current to pass through and activate 

cell membranes; and metabotropic, which are G-proteincoupled receptors that activate downstream cell signaling 

cascades. Neuroadaptive mechanisms in the glutamate 

system, perhaps on both types of glutamate receptors, 

may be targets for pharmacologic intervention.

Ionotropic Glutamate Receptors

Glutamate signaling through the ionotropic glutamate receptors N-methyl-D-aspartate (NMDA) and α-amino3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) is 

implicated in the neurobiologic mechanisms of nicotine 

dependence (Li et al. 2014; D’Souza 2015). Pharmacologic 

blockade of both NMDA and AMPA receptors in the VTA 

attenuates nicotine-induced dopamine release in the NAc, 

and inhibition of NMDA receptors impairs nicotine-seeking 

behaviors (Kenny et al. 2009; Mao et al. 2011). Conversely, 

blockade of NMDA receptors in the shell region of the NAc 

increases the self-administration of nicotine, suggesting 

that glutamatergic transmission in this region may offset 

the rewarding effects of nicotine (D’Souza and Markou 

2014). The mechanisms underlying this effect are not fully 

understood, but one hypothesis is that medium spiny neurons in the shell region of the NAc are activated by glutamate, and these medium spiny neurons project to and 

inhibit dopamine neurons in the VTA (Yang et al. 2018). 

Regardless, glutamatergic signaling in mesocorticolimbic 

regions clearly contributes to nicotine reinforcement.

Gipson and colleagues (2013) demonstrated that 

long-lasting changes in glutamate signaling are central 

to post-withdrawal reinstatement of nicotine-seeking 

behavior in rats. Long-term potentiation of glutamatergic 

synapses in the NAc was apparent after 2 weeks of nicotine withdrawal, with further strengthening observed following cue-induced reinstatement of nicotine seeking. 

Furthermore, blocking the function of NMDA receptors 

in the core region of the NAc prevented cue-induced reinstatement of nicotine-seeking behavior. Similar observations have been made with other drugs of abuse, such as 

cocaine and alcohol. These data suggest that dampening 

mesolimbic glutamate signaling, potentially by inhibiting the function of NMDA receptors in the core of the 

NAc, may be a useful strategy for reducing vulnerability to 

smoking relapse in humans.

Although blockade of ionotropic glutamate receptors is effective in reducing addiction-like behaviors in 

animal models, systemic use of these drugs in humans is 

likely not feasible using current pharmacologic agents, 

given the crucial role of glutamate in the function of the 

nervous system. Also, because glutamate plays different 

roles in different regions of the brain, a more targeted, 

region-specific approach is warranted.

Metabotropic Glutamate Receptors

Metabotropic glutamate receptors (mGluRs) are 

widely expressed, G-protein-coupled receptors that use 

second-messenger systems (key distributors of an external 

signal) to modulate neuronal excitability. Two of these 

receptors, mGluR5 and mGluR2, have been implicated in 
A Report of the Surgeon General

132 Chapter 3

the neurobiology of nicotine addiction. Because pharmacologic manipulation of metabotropic glutamate signaling 

may have a more subtle effect on the function of the nervous 

system than do ionotropic drugs, targeting these receptors 

may be a more feasible clinical approach for smoking cessation (D’Souza 2015; Mihov and Hasler 2016; Acri et al. 

2017; Chiamulera et al. 2017). Table 3.2 summarizes novel 

pharmacologic targets for smoking cessation.

mGluR5. mGluR5 is localized postsynaptically 

where it signals through the excitatory G-protein Gαs

, 

to enhance neuronal excitability. Reducing the function 

of mGluR5 with MPEP (2-methyl-6-(phenylethynyl pyridine), a negative allosteric modulator (NAM) in the NAc 

shell, attenuates nicotine self-administration and dopamine release (Paterson et al. 2003; Tronci et al. 2010), suggesting a role for this receptor in the primary reinforcing 

properties of nicotine. Additionally, both drug- and cueinduced reinstatement of nicotine-seeking behavior 

are reduced in animals that have been pretreated with 

mGluR5 antagonists (Bespalov et al. 2005). In humans, 

studies using selective mGluR5 radiotracers have revealed 

a significant reduction of binding sites in the brains of persons addicted to nicotine, which is normalized after cessation (Akkus et al. 2013; Hulka et al. 2014). The mechanism 

for this reduction is not entirely understood, but it may be 

a compensatory action meant to limit aberrant glutamate 

signaling in the brains of smokers.

The preclinical efficacy of mGluR5 NAMs in reducing 

drug-seeking behavior is well documented, but higher 

doses of the same drugs also have been reported to impair 

food-seeking behaviors in animals (Mihov and Hasler 2016). 

Although the curbing of appetite during smoking cessation 

may seem like an appealing side effect, such overly generalized effects may be dangerous or undesirable. mGluR5 

NAMs also have been shown to increase the severity of 

nicotine withdrawal symptoms (Chiamulera et  al. 2017), 

which could limit their feasibility for clinical use.

mGluR2. In contrast to mGluR5, mGluR2 is 

expressed on presynaptic glutamate terminals, acting as an 

autoreceptor that inhibits the release of this neurotransmitter. Therefore, dopamine neurons can be inhibited 

by the activation of mGluR2 receptors on glutamatergic 

inputs to the VTA.

Stimulation with the nonselective mGluR2/3 agonist LY379268 can reduce drug- and cue-induced reinstatement of nicotine responding, and these effects can be 

blocked by an mGluR2 antagonist (Justinova et al. 2016). 

mGluR2/3 stimulation also can reduce the primary reinforcing properties of nicotine, but these effects are smaller 

and less consistent than the effects on reinstatement. In 

nicotine-experienced rats, LY379268 reduced nicotineinduced increases in dopamine levels in the NAc only in 

the presence of drug-predicting cues (D’Souza et al. 2011), 

supporting the hypothesis that mGluR2/3 stimulation is 

more effective at reducing the conditioned effects of nicotine than its primary reinforcing effects.

Although a selective mGluR2 positive allosteric 

modulator (PAM) was not shown to improve schizophrenia symptoms in a Phase 2 clinical trial, it has been 

repurposed as a possible therapy for nicotine addiction 

because of its good safety profile and preclinical efficacy in 

reducing nicotine reinstatement (Justinova et al. 2015). 

A  Phase  2 clinical trial of this drug for smoking cessation was completed in January 2017, but results are not 

yet available. This study enrolled 210 female cigarette 

smokers and evaluated abstinence from nicotine as a primary endpoint. Although GluR5 and GluR2 have been 

linked to addiction-like behaviors in animals, Acri and 

colleagues (2017) argued that mGluR2 may be a more feasible drug target because of its relatively mild side-effect 

profile compared with mGluR5 antagonists.

Glutamate Transporters

Alterations in the function of glutamate transporters also contribute to nicotine-mediated disruptions 

in the excitatory–inhibitory balance. Mesocorticolimbic 

expression of glutamate transporter 1 (GLT-1), the cystine/

glutamate exchanger, and excitatory amino acid transporter  3 are all decreased after chronic administration 

of nicotine in rodents (Knackstedt and Kalivas 2009; 

Knackstedt et al. 2009; Yoon et al. 2014). In addition, reinstatement of nicotine-seeking behavior is associated with 

decreased expression of GLT-1 and elevated concentration 

of extracellular glutamate (Gipson et al. 2013). In mice, 

upregulation of GLT-1 with ceftriaxone had no effect on 

CPP acquisition but reduced withdrawal symptoms and 

significantly attenuated nicotine-primed reinstatement 

of nicotine CPP (Alajaji et al. 2013). Stimulating cystine/

glutamate exchanger activity with N-acetylcysteine also 

may be effective in reducing nicotine consumption. An 

open-label pilot study of a combination therapy of varenicline and N-acetylcysteine showed a favorable safety 

profile. Although the study was not designed to evaluate 

differences in cessation efficacy, patients receiving both 

therapies smoked fewer cigarettes than those receiving 

only varenicline (McClure et  al. 2015). In addition, a 

double-blind, randomized controlled trial (RCT) found 

that, in combination with group behavioral therapy, 

N-acetylcysteine was effective in reducing the number of 

cigarettes smoked and in increasing quit rates versus a 

placebo control group (Prado et al. 2015).

Neuropeptide Systems

Neuropeptides are a class of short-chain polypeptides that serve as neurotransmitters (Table 3.2). Acting 
New Biological Insights into Smoking Cessation 133

Smoking Cessation

Table 3.2 Novel pharmacologic targets for smoking cessation

Target Pharmacology

Expected 

neurobiologic 

effect

Expected 

behavioral 

outcome

Stage of drug 

development Other information

Glutamate system

mGluR5 NAM • 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

Decreased Glu 

transmission

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

• 

Decreased 

nicotine intake

Decreased relapse 

vulnerability

Preclinical —

mGluR2 PAM Decreased Glu 

transmission

Decreased relapse 

vulnerability

Decreased 

nicotine intake

Phase 2 —

GLT-1 Agonist Decreased Glu 

transmission

Decreased relapse 

vulnerability

Preclinical —

xCT Agonist 

(N-acetylcysteine)

Decreased Glu 

transmission

Decreased 

nicotine intake

Phase 2 —

Neuropeptides

CRF-1 Antagonist 

(Paxacerfont)

Decreased 

reactivity to 

withdrawal

Decreased 

dopamine 

response

Decreased relapse 

vulnerability

Decreased 

nicotine intake

Preclinical Failed Phase 2 anxiety 

trial

DOR Antagonist Decreased 

dopamine 

response

Decreased 

nicotine intake

Preclinical Naltrexone is a nonselective opioid receptor 

antagonist used to treat 

alcoholism and opioid 

dependence

KOR Antagonist Decreased 

reactivity to 

withdrawal

Decreased relapse 

vulnerability

Preclinical Naltrexone is a nonselective opioid receptor

antagonist used to treat 

alcoholism and opioid 

dependence

MHb-IPN pathway

α5 PAM Increased 

nicotinemediated MHbIPN activation

Decreased 

nicotine intake

Preclinical —

Noradrenergic system

α1 Antagonist Decreased 

norepinephrine 

signaling

Decreased 

nicotine intake

Decreased relapse 

vulnerability

— —

α2 Agonist (clonidine) Decreased 

norepinephrine 

signaling

Decreased relapse 

vulnerability

Off-label use Potent side effects 

include sedation and 

low blood pressure

Notes: α = alpha; CRF = corticotropin-releasing factor; DOR = delta (δ) opioid receptor; GLT = glutamate transporter; KOR = kappa 

(κ) opioid receptor; mGluR = metabotropic glutamate receptor; MHb-IPN = medial habenulo-interpeduncular nucleus; NAM = negative allosteric modulator; PAM = positive allosteric modulator; xCT = cystine/glutamate exchanger.
A Report of the Surgeon General

134 Chapter 3

on designated G-protein-coupled receptors, these molecules can modulate neuronal activities. As outlined in the 

upcoming sections of this chapter, a substantial amount of 

preclinical evidence suggests that multiple neuropeptide 

systems can contribute to the development of nicotine 

dependence. Additionally, because several neuropeptides 

can modulate mood, manipulating these systems may be 

an effective strategy for improving success rates for cessation by reducing the severity of negative withdrawal symptoms. Although at least a dozen neuropeptides have been 

linked to nicotine dependence, this section focuses on two 

primary promising targets: corticotropin-releasing factor 

(CRF) and the opioid system.

Corticotropin-Releasing Factor

CRF is a peptide hormone known best for its role 

in the stress response. Chronic nicotine administration increases CRF levels in the VTA of rats, and genetic 

knockdown of this peptide attenuates self-administration 

of nicotine (Grieder et al. 2014). In addition, blockade of 

the peptide’s receptor, CRF1, in rats prevented the normally observed increase in nicotine self-administration 

following a period of forced abstinence and prevented 

the aversive effects of withdrawal (Cohen et al. 2015). In 

an intracranial self-stimulation paradigm, the sensitivity 

of the brain reward pathway can be assessed by measuring the intensity of a stimulus required to elicit selfstimulation behavior, such that higher stimulation thresholds indicate a less sensitive reward system. Exposure to 

nicotine (or other drugs of abuse) causes animals to perform for much less intense stimulation (i.e., they have 

lower thresholds), indicating a drug-induced potentiation 

of the reward system. Conversely, a period of abstinence 

from a drug elicits a large increase in the intracranial selfstimulation threshold, indicating reduced excitability of 

the reward system and signifying a depression-like brain 

state (reflected in elevations of brain reward thresholds) 

(Stoker et al. 2012).

In nicotine-dependent animals, withdrawal-induced 

increases in the intracranial self-stimulation threshold 

are absent in animals treated systemically with CRF1 

receptor antagonists, or only in the central amygdala, 

a brain region known to regulate mood (Marcinkiewcz 

et al. 2009; Bruijnzeel et al. 2012). Similarly, withdrawalinduced, anxiety-like behavior is exacerbated by infusion of 

CRF into the interpeduncular nucleus (IPN), and blockade 

of CRF1 alleviates this behavior (Zhao-Shea et al. 2015). 

Thus, CRF signaling, particularly in the amygdala and IPN, 

contributes to the negative affect associated with nicotine 

withdrawal. Lastly, inhibition of the CRF1 receptor can 

block both stress-induced potentiation of nicotine CPP and 

stress-induced reinstatement of self-administration (Zislis 

et  al. 2007). Together, these studies suggest that CRF 

signaling is central to changes in nicotine-seeking behavior 

in response to stress. Although clinical data regarding the 

role of CRF in smoking behavior are not available, many 

studies in animal models of nicotine dependence suggest 

that CRF antagonists may be useful for reducing smoking 

in humans (Bruijnzeel 2017).

Notably, several small-molecule CRF ligands can 

cross the blood–brain barrier. Although most are being 

used only for preclinical research, several have been evaluated clinically to treat anxiety and depression. In a clinical 

trial of 260 patients, Paxacerfont (a CRF1 receptor agonist) was no more effective than placebo for treating generalized anxiety disorder (Coric et al. 2010); however, this 

drug has not been evaluated for smoking cessation.

The Opioid System

Mounting evidence has implicated the endogenous opioid system in both neurobiologic and behavioral 

responses to nicotine. The opioid system consists of three 

G-protein-coupled opioid receptors that are activated by 

endogenous peptide ligands. Delta (δ) opioid receptors 

(DORs) are activated primarily by enkephalins; kappa 

(κ) opioid receptors (KORs) are activated by dynorphins; 

and mu (μ) opioid receptors (MORs) are activated by 

β-endorphins. Each of these receptor–ligand pairs appears 

to play a role in nicotine addiction. Nicotine-induced 

dopamine release is attenuated in mice lacking DORs, 

and these animals do not acquire a CPP for nicotine 

(Berrendero et al. 2012). Genetic ablation or pharmacologic blockade of DORs with naltrindole also substantially 

reduces self-administration of nicotine (Berrendero et al. 

2012). Although DORs do not appear to play an important role in the somatic responses to nicotine (Berrendero 

et  al. 2012), animals treated with the KOR antagonist 

JDTic have diminished physical and affective nicotine 

withdrawal symptoms (Jackson et al. 2010a).

Interestingly, KOR activity does not appear to be 

necessary for the initial reinforcing properties of nicotine (Jackson et al. 2010a), but pharmacologic blockade 

of the receptor reduces the anxiogenic effects of nicotine 

withdrawal and prevents stress-induced reinstatement of 

nicotine-seeking behavior (Jackson et  al. 2010a; Nygard 

et al. 2016). In addition, withdrawal-mediated activation 

of the amygdala was reduced in mice pretreated with 

the KOR antagonist norbinaltorphimine (Nygard et  al. 

2016). Together, these data suggest that DORs and KORs 

play discrete roles in the physiological and behavioral 

responses to nicotine. Although DOR contributes to dopamine release and nicotine reinforcement, KOR appears 

to be more involved in the physiologic effects of nicotine 

withdrawal.

In humans, MORs have been linked to craving 

and addiction severity among smokers. Compared with 
New Biological Insights into Smoking Cessation 135

Smoking Cessation

nonsmoking controls, smokers had fewer available 

MOR-binding sites in the basal ganglia and thalamus, and 

the number of binding sites in the basal ganglia was negatively associated with baseline craving levels (Nuechterlein 

et al. 2016). Additionally, the availability of MOR-binding 

sites in both the basal ganglia and temporal cortex was 

inversely correlated with the severity of physical dependence on nicotine, as assessed by the Fagerström Test 

for Nicotine Dependence (FTND) (Kuwabara et al. 2014; 

Nuechterlein et al. 2016). Interestingly, a MOR gene variant 

(OPRM1 A118G) was found to be potentially associated 

with reduced availability of MOR binding (Nuechterlein 

et al. 2016).

Naltrexone (trade names: Revia, Vivitrol), a nonselective opioid receptor antagonist, is commonly used to 

treat alcoholism and opioid dependence. A clinical trial 

of 121  smokers found that combining naltrexone with 

bupropion was associated with higher rates of abstinence 

from smoking after 7 weeks of treatment compared with 

bupropion alone, but these rates did not differ significantly between the bupropion-plus-placebo group and the 

bupropion-plus-naltrexone group at 6  months (Mooney 

et al. 2016). Similarly, a Cochrane review of eight trials 

showed no effect of naltrexone alone or as an adjunct to 

NRT (David et al. 2013a).

Finally, preclinical studies have implicated orexin/

hypocretin peptides, originally thought to be involved 

mainly in feeding and arousal but now shown to modulate the rewarding effects of nicotine, as potential therapies for smoking cessation (Plaza-Zabala et  al. 2010; 

Hollander et  al. 2012). An orexin/hypocretin receptor  2 

polymorphism has been associated with nicotine dependence in human smokers (Nishizawa et al. 2015), and in 

rats the selective receptor 2 antagonist (2-SORA 18) can 

block both cue-induced reinstatement of nicotine selfadministration and motivation to respond to nicotine cues, 

as determined by a progressive ratio experiment in which 

animals had to press a lever exponentially more times to 

receive each successive nicotine-paired cue (Uslaner et al. 

2014). Similarly, the orexin/hypocretin receptor 1 antagonist SB-334867 decreased the reward-enhancing effects 

of nicotine in rats, as well as their cue-induced reinstatement of nicotine-seeking behaviors (Hollander et  al. 

2008; Plaza-Zabala et al. 2013). Interestingly, stimulation 

of nAChRs increased the activity of orexin/hypocretin 

neurons (Zhou et al. 2015), suggesting that stimulation 

of this system may contribute to the physiologic effects 

of nicotine.

Summary

Neuropeptide systems play a role in multiple stages 

of the addiction process. Experiments in animals have 

shown that CRF and the opioid system, neuropeptide Y, 

hypocretin, galanin, ghrelin, and vasopressin and additional peptides not discussed here are associated with nicotine dependence (Bruijnzeel 2017). Thus, modulating the 

function of neuropeptides may effectively reduce smoking 

behavior in humans. Even so, the role that neuropeptide 

systems play in human addiction should be investigated 

further. Several drugs targeting neuropeptide receptors 

are already in use for treatment of other disorders, but 

none are approved by FDA for use in smoking cessation.

The Habenulo-Interpeduncular Pathway

Aversive Effects of Nicotine

As discussed previously, nicotine stimulates dopamine pathways to generate the rewarding effects that 

contribute to addiction. At the same time, activation of 

nAChRs in the brain and elsewhere results in highly aversive effects, such as nausea, dizziness, and irregular heartbeat. In fact, most first-time smokers report a largely 

unpleasant experience with nicotine, and sensitivity to 

the aversive effects of cigarette smoke is inversely correlated with the likelihood of developing habitual smoking 

(Sartor et al. 2010).

Animal studies of nicotine withdrawal and aversion have identified a crucial role for the habenulointerpeduncular pathway in mediating these responses. 

The medial habenula (MHb) is composed mostly of cholinergic neurons that also express Substance P and co-release 

glutamate. MHb neurons project to the IPN, and activation 

of this circuit is required for many of the negative effects 

associated with exposure to nicotine, including the sedative effects induced by high concentrations of this chemical and negative symptoms of withdrawal. Furthermore, 

stimulation of the MHb or IPN reduces the reinforcing 

properties of nicotine, but disrupting neuronal signaling 

in these connected brain regions has the opposite effect—

resulting in increased self-administration of nicotine in 

rodents (Fowler and Kenny 2014).

Potential Molecular Targets

Nicotinic receptors are highly expressed on MHb 

and IPN neurons, and these regions have the highest 

expressions of α3, β4, and α5 nAChR subunits in the brain. 

Several human genomewide association studies (GWAS) 

have linked variants in the CHRNA3-CHRNA5-CHRNB4 

gene cluster (genes that encode the α3, α5, and β4 

nAChR subunits, respectively) to susceptibility to tobacco 

use, and preclinical studies in rodents have revealed an 

important role for these subunits in moderating nicotine 

intake. α5 knockout mice lacking the α5 nAChR subunit 

acquired a CPP for high doses of nicotine, but such doses 

were aversive to their wild-type littermates (Jackson et al. 
A Report of the Surgeon General

136 Chapter 3

2010b). Similarly, α5 knockout animals failed to titrate 

their responses in a self-administration paradigm when 

increasing doses of nicotine were offered, and this effect 

was rescued by expression of α5 in MHb (Fowler et  al. 

2011). Interestingly, α5 knockout mice were indistinguishable from controls at low doses of nicotine in both 

CPP and self-administration paradigms (Jackson et  al. 

2010b; Fowler et al. 2011), indicating that the α5 nAChR 

subunit is not required for the rewarding properties of 

nicotine. Furthermore, overexpression of the β4 nAChR 

subunit in MHb resulted in increased aversion to nicotine 

(Frahm et  al. 2011). Mice overexpressing β4 nAChRs—

with or without a β4 mutation, which is associated with 

decreased risk of smoking in humans—displayed larger 

nicotine-evoked current amplitudes and enhanced aversive behavior (Slimak et al. 2014). Together, these studies 

suggest that α5- and β4-containing nAChRs in the MHb 

are essential for encoding the aversive properties of nicotine, and they likely serve to limit nicotine intake.

Characterization of the MHb to IPN aversive circuit 

offers a novel and intriguing approach to addiction pharmacotherapy, in which the goal is to enhance the aversive 

effects of nicotine rather than to reduce its reinforcing 

effects. α5- and β4-containing nAChRs are obvious targets. Unfortunately, continuous stimulation of this aversive pathway likely will warrant the use of full agonists 

of these receptors and is, therefore, clinically unrealistic, because the β4-containing nAChRs are also highly 

expressed in the autonomic ganglia and a full agonist 

would likely be poorly tolerated. Instead, the use of PAMs 

that would enhance signaling only in the presence of an 

agonist may be more feasible (Fowler and Kenny 2014). 

Recent findings show that galantamine, which acts as a 

positive allosteric modulator of α5 subunit-containing 

nAChRs at low doses, can reduce nicotine intake in rats 

and smoking in humans (Ashare et al. 2016), supporting 

the rationale for developing α5 PAMs as novel smoking 

cessation agents. Notably, other brain regions and neuronal systems, including the mesocorticolimbic dopamine 

pathway and autonomic nervous system, also contribute 

to the aversive effects of nicotine. However, the specific 

mechanisms by which aversive pathways communicate 

with reward pathways are uncertain.

The Noradrenergic System

Norepinephrine (also known as noradrenaline) is a 

monoamine neurotransmitter that signals through α1, 

α2, and β G-protein-coupled adrenoceptors. Like other 

neuromodulators, norepinephrine receptors are found 

throughout the brain, and norepinephrine is well known 

for its role in arousal and the stress response. The noradrenergic system has also been implicated in neurobiologic 

responses to nicotine, contributing to both nicotine 

reward and reinstatement (Fitzgerald 2013). Nicotine 

increases activity of adrenergic neurons in the locus coeruleus, resulting in increased levels of norepinephrine in 

the brain. In animal models of nicotine addiction, blocking 

the transmission of norepinephrine with prazosin, the α1 

receptor antagonist, reduced nicotine-induced dopamine 

signaling and attenuated nicotine self-administration 

and reinstatement (Forget et al. 2010). In other studies, 

reducing the tone of norepinephrine by stimulating α2, an 

inhibitory autoreceptor, with clonidine or dexmedetomidine diminished stress-induced reinstatement of nicotineseeking behavior in rats (Zislis et  al. 2007; Yamada and 

Bruijnzeel 2011).

In humans, long-term smoking is associated with 

reduced expression of α2- and β-adrenergic receptors, 

which normalize after a period of abstinence (Klimek et al. 

2001). In addition, guanfacine, the α2 agonist, reduced 

stress-induced nicotine craving and smoking in a study 

of 33 smokers (McKee et al. 2015). Thus, both clinical and 

preclinical evidence suggest that nicotine increases noradrenergic activity and that correction of this increase 

may be an effective strategy for reducing smoking.

Clonidine (trade names: Catapres, Kapvay, Nexiclon), 

the α2a receptor agonist, has consistently shown some 

efficacy in improving cessation rates by alleviating negative withdrawal symptoms (Gourlay et al. 2004), but clonidine is not an FDA-approved cessation aid, and prominent 

adverse side effects, mainly sedation and low blood pressure, limit its practicality. Notably, bupropion and nortriptyline, the antidepressant smoking cessation aids, are 

norepinephrine reuptake inhibitors.

Summary

Although current pharmacotherapies are effective 

in reducing smoking in some persons, many are unable to 

maintain abstinence. With continued interest in the neurobiologic mechanisms of addiction, preclinical advances 

have improved considerably our understanding of the 

pathophysiology of nicotine dependence, withdrawal, and 

relapse. Correspondingly, dozens of novel targets for pharmacologic intervention have emerged, and further investigation into the role of these targets in human smoking 

is warranted.

Moving forward, the need to develop individualized, multifaceted approaches to smoking cessation is 

becoming apparent. For instance, drugs that reduce the 

initial rewarding properties of nicotine are unlikely to 

normalize the long-lasting neuroadaptations associated 

with persistent drug use, which underlie craving, withdrawal, and vulnerability to relapse. Another approach 

may be combination therapy that targets multiple aspects 
New Biological Insights into Smoking Cessation 137

Smoking Cessation

of addiction behavior, such as a combination of bupropion with NRT or varenicline, which has been successful 

in human clinical trials. Current evidence is conclusive 

that current pharmacotherapies for smoking cessation, 

including such combination therapies as bupropion with 

NRT or varenicline, improve quit rates (see Chapter 6) but 

many persons still relapse to smoking (see Chapter 2).

Finally, the pathophysiology underlying addiction to 

other drugs of abuse, particularly stimulants like cocaine, 

is similar to that of nicotine. Thus, research that leads to 

improved smoking cessation therapies also may benefit 

the treatment of other addictions. The literature should 

be mined to identify novel targets for interventions that 

promote smoking cessation.

Vaccines and Other Immunotherapies as Treatments for 

Nicotine Addiction

Nicotine vaccines are a new class of medication 

being developed for smoking cessation; interest in these 

vaccines stems from their novel mechanism of action. 

Unlike existing cessation medications that act on neurotransmitter receptors in the brain to reduce the reinforcement or withdrawal associated with the use of 

tobacco products, vaccines act directly on nicotine, the 

principal addictive constituent of tobacco (Pentel and 

LeSage 2014). Vaccines stimulate the immune system 

to produce antibodies that can bind and retain nicotine 

in the blood, thereby reducing or slowing its delivery 

to the brain (LeSage et  al. 2006b; Esterlis et  al. 2013). 

Interrupting nicotine delivery to its site of action blocks 

or reduces its behavioral effects (Jefferson et  al. 2004; 

Goniewicz and Delijewski 2013; Maglione et al. 2014). If it 

proves feasible for nicotine vaccines to produce very high 

levels of antibodies in blood, efficacy for this approach to 

smoking cessation should be possible. Because vaccines 

act in a different manner than existing medications for 

smoking cessation, such as varenicline or bupropion, 

combining a nicotine vaccine with those medications to 

enhance overall efficacy may be possible. An additional 

potential benefit of nicotine vaccines is that their effects 

last for many months (Cornuz et  al. 2008; Hatsukami 

et al. 2011), avoiding the need to take a medication each 

day or, for some products, even more often (Prochaska 

and Benowitz 2016).

Literature Review Methods

For this section of the chapter, PubMed was searched 

in January 2017 for studies published between January 

1966 and January 2017 about active or passive immunization against nicotine in vitro in animals or humans. The 

following terms were searched alone or in combination: 

nicotine, tobacco, smoking, cigarette, vaccine, vaccination, immunogen, immunization, antibody, linker, hapten, 

conjugate, adjuvant, addiction, dependence, cessation, 

and monoclonal. Articles identified in this manner were 

also reviewed to find additional primary references. One 

reviewer conducted a full review and identified 35 articles 

for this section.

Design and Mechanism of Action

The human immune system can recognize foreign 

(nonhuman) proteins present on infectious agents, such as 

bacteria or viruses, and can form antibodies to help defend 

against them. Nicotine is a much smaller molecule than 

a protein and lacks the structure needed to be recognized 

as foreign. Even so, nicotine can be chemically linked to a 

foreign carrier protein to stimulate the production of antibodies against it (Pentel et al. 2000; Isomura et al. 2001; 

Maurer et al. 2005). This nicotine–protein immunogen is 

typically administered with an adjuvant, a chemical or mix 

of chemicals that generally enhances immune responsiveness. Administration of such a vaccine results in the production of antibodies that circulate in the blood and bind 

nicotine tightly and with high specificity. Because these 

antibodies do not bind appreciably to anything other than 

nicotine, they might not disrupt the actions of other drugs 

or medications, and they might not interfere with normal 

physiologic functions.

Nicotine vaccines have not shown any serious side 

effects in animals and humans (Hatsukami et  al. 2005; 

Fahim et al. 2013). Autoimmune reactions from vaccinegenerated antibodies have not been observed (Hatsukami 

et al. 2005). Nicotine-specific antibodies do not bind acetylcholine (the endogenous ligand that nicotine mimics), 

and nicotine itself is a small molecule that should not be 

able to cross-link antibodies and form immune complexes 

(Pentel et al. 2000).

Nicotine-specific antibodies in blood cannot enter 

the brain because of their large size (Satoskar et al. 2003). 

In addition, nicotine that binds to an antibody cannot enter 

the brain to interact with the receptors that mediate its 
A Report of the Surgeon General

138 Chapter 3

actions. As a consequence, vaccination can attenuate many 

of the effects of nicotine, provided a sufficient amount of 

antibody is present (Lindblom et  al. 2002; LeSage et  al. 

2006a). After vaccination, levels of nicotine-specific antibodies in blood decline slowly, over months, and periodic 

booster doses of vaccine are needed to maintain high levels 

of antibody (Cornuz et  al. 2008; Hatsukami et  al. 2011). 

Because smoking cessation medications generally are 

required for only 3–6 months, vaccine efficacy should be 

obtainable after an initial three or four monthly doses of 

vaccine to achieve high serum antibody concentrations and 

perhaps a booster dose 3–6 months after that (Hatsukami 

et al. 2011).

After vaccination, nicotine in blood exists as an equilibrium between a large amount of nicotine bound to antibody and a much smaller amount that remains unbound. 

Nicotine that is bound to antibody cannot be metabolized, 

but the unbound nicotine is metabolized normally. As the 

concentration of unbound nicotine in blood is reduced by 

metabolism, bound nicotine dissociates from the antibody 

to re-establish equilibrium and is, in turn, metabolized. In 

this manner, nicotine can be eliminated even in the presence of antibody, albeit more slowly than otherwise. For 

example, in rats, immunization doubled the elimination 

half-life of nicotine from 1 hour in controls to 2 hours in 

rats vaccinated against nicotine (Keyler et al. 2005). This 

process frees the antibody of its bound nicotine so that it 

is once again available to bind newly delivered nicotine 

(e.g., from the next cigarette).

Examining Data from Animals to 

Confirm Vaccine Activity

In rats and mice, nicotine vaccination reduces by 

up to 80% the delivery of single doses of clinically relevant nicotine (equivalent to one or two cigarettes) to the 

brain (Cerny et  al. 2002; Maurer et al. 2005; Pravetoni 

et al. 2011). Vaccine efficacy is lower with chronic doses 

of nicotine that approximate regular smoking, but the 

entry of nicotine into the brain is still slowed (Hieda 

et al. 2000). In rats, which are thought to provide the best 

animal models for smoking behavior in humans, vaccination markedly reduces addiction-relevant behaviors, such 

as nicotine self-administration (Lindblom et  al. 2002; 

LeSage et  al. 2006a). Animal studies consistently show 

that vaccine efficacy is greatest when the level of nicotinespecific antibodies in the blood is high, maximizing the 

nicotine-binding capacity provided in relation to the 

amount of nicotine present (Maurer et al. 2005; Pravetoni 

et  al. 2011). For the same reason, vaccination is more 

effective in blocking the effects of fewer or lower doses of 

nicotine than against regular or higher doses (Keyler et al. 

1999). Extrapolating these findings to humans, it appears 

that nicotine vaccines will be most useful for preventing 

relapse, which is often triggered by taking just a few puffs 

or smoking just a few cigarettes, and may be less effective for encouraging smoking cessation among regular 

smokers who are not motivated to quit.

Clinical Trials of Nicotine Vaccines

Several nicotine vaccines have progressed through 

Phase  2 or  3 clinical trials (i.e.,  have been tested for 

safety, efficacy, and effectiveness relative to other treatments), in  combination with standard behavioral counseling (Cornuz et al. 2008; Hatsukami et al. 2011; Fahim 

et al. 2013; Tonstad et al. 2013). All of these studies provide preliminary evidence of safety, but levels of antibody 

in the blood have been substantially lower than those 

achieved in rats or mice. Mean levels of antibody in participants in human studies have reached approximately 

40 micrograms per milliliter (μg/mL), but levels of 200–

500 μg/mL can be produced in mice or rats (Maurer et al. 

2005; Keyler et  al. 2008). Part of this difference comes 

from the ability to administer higher doses of immunogens and stronger adjuvants in animals than would be 

tolerated in humans without producing side effects. Not 

surprising, therefore, is that the overall efficacy of vaccines for enhancing smoking cessation has not been demonstrated. In several studies, however, participants with 

the highest levels of serum antibody also had higher rates 

of smoking cessation compared with those who received 

a placebo vaccine (Cornuz et  al. 2008; Hatsukami et  al. 

2011). This key observation suggests that the vaccine 

strategy has merit and has the potential to be effective. 

At this time, FDA has not approved any nicotine vaccines.

Next-Generation Vaccines

Next-generation vaccines hold promise for producing higher levels of antibody than those studied to 

date; several approaches are being evaluated:

• Improving the way in which nicotine is attached 

to its carrier protein to provide tighter binding to 

the immune cells that initiate antibody production 

(Moreno et al. 2012);

• Using more immunogenic carrier proteins or 

designing and synthesizing carrier proteins that are 

optimized to enhance the interaction of nicotine 
New Biological Insights into Smoking Cessation 139

Smoking Cessation

with immune cells (McCluskie et al. 2013; Rosenberg 

et al. 2013; Miller et al. 2014; Jacob et al. 2016);

• Mixing or combining the nicotine–protein immunogen with newer adjuvants (e.g., CpG oligonucleotides, water/lipid emulsions) that enhance the production of antibodies by activating novel molecular 

pathways, or using combinations of adjuvants that 

provide additive efficacy (McCluskie et  al. 2013; 

Jacob et al. 2016); and

• Attaching nicotine to synthetic nanoparticle scaffolds that are designed to more precisely control 

and optimize interactions between nicotine and 

the immune system (Lockner et al. 2013; Desai and 

Bergman 2015; Liu et al. 2016).

Combining Vaccines with 

Medications

Nicotine vaccines can be designed to display different surfaces of the nicotine molecule to the immune 

system. Because the immune system sees each surface as 

a distinct stimulus, two or three suitably designed nicotine vaccines can be co-administered to get an additive 

antibody response (Keyler et  al. 2008; de Villiers et  al. 

2013). Nicotine vaccines also can be combined with smallmolecule medications because those drugs act by separate 

mechanisms. For example, nicotine-specific antibodies 

can be combined with mecamylamine, a nicotine antagonist that blocks the action of nicotine on its receptors in 

the brain and has been used experimentally to promote 

smoking cessation. This combination is more effective 

in rats than either of these treatments alone for blocking 

nicotine discrimination, a measure of whether the animal 

recognizes that it has received nicotine (LeSage et  al. 

2012). However, a clinical trial of a nicotine vaccine combined with another drug for smoking cessation, varenicline, found no additional effect from vaccination compared with the drug alone (Hoogsteder et al. 2014).

Passive Immunization with 

Monoclonal Antibodies or 

Gene Transfer

The amount of antibody produced by vaccination is 

limited by the capacity of the immune system. Thus, it 

could be possible to produce nicotine-specific monoclonal 

antibodies in bacterial cultures or other in vitro systems 

and bypass the need for vaccination by administering the 

preformed antibodies directly (passive immunization). In 

animals, this approach mimics vaccination, but greater 

efficacy is possible because very large doses of antibody 

can be safely administered (Carrera et  al. 2004; Keyler 

et  al. 2005). The main limitations to this approach in 

humans are its high cost and the likely need to administer 

the antibodies intravenously (Skolnick 2015). An alternative approach to passive immunization is to administer a 

harmless virus (not capable of replication) that contains 

DNA coding for the production of the desired antibody. 

This virus can take up temporary residence in tissues and 

produce nicotine-specific antibodies that are independent 

of the host’s immune system. In rodents, extremely high 

levels of antibody have been achieved using this strategy 

for periods of up to several months (Hicks et  al. 2012). 

This approach holds promise for human therapies if measures to ensure its safety can be established.

Summary

Animal studies and early clinical trials have provided proof-of-principle that drug-specific antibodies 

can block the addictive effects of nicotine and serve as an 

adjunct to smoking cessation. The main benefit of this 

approach may be preventing relapse. Anticipated progress in vaccine design and enhancement of the immune 

response should (a) provide substantially more effective 

vaccines and other approaches to providing nicotinespecific antibodies and (b) create opportunities to better 

explore their therapeutic potential.

Insights into Smoking Cessation from the Field of Neurobiology

Smokers trying to quit often can maintain abstinence for short periods, ranging from days to weeks. 

However, quitting smoking usually requires several 

attempts (USDHHS 2000, 2010; García-Rodríguez et  al. 

2013). Evidence shows that smokers often require multiple quit attempts (even more than 20, depending on the 

metrics used) and many years to obtain long-term (greater 

than 1  year) smoking abstinence (Chaiton et  al. 2016). 

This clinical observation highlights the often-mistaken 

assumption made by both practitioners and smokers 

trying to quit that the absence of the behavior (smoking) 

reflects the absence of the disease (dependence). Thus, to 
A Report of the Surgeon General

140 Chapter 3

enhance treatment outcomes, a better understanding of 

the neurobiologic basis of the disease is required. Until 

the development of noninvasive brain imaging (initially 

positron emission tomography [PET] and more recently 

and prominently, functional magnetic resonance imaging 

[fMRI]), such an understanding of affected humans has 

been difficult to obtain. In contrast, considerable preclinical data (Leslie et al. 2013) have convincingly supported 

the proposition that chronic self-administration of nicotine—like that of other dependence-producing drugs, 

including stimulants and opiates—alters specific longterm regional neurobiologic processes that have been 

hypothesized to explain the high rates of recidivism in 

persons who are trying to quit smoking (Sutherland and 

Stein 2018).

During the past two decades, noninvasive brain 

imaging has repeatedly demonstrated differences in 

brain structure and function in smokers compared with 

matched, never-smoking, healthy persons. Thus, it is 

plausible that such differences might be applied usefully 

and clinically to develop better behavioral interventions 

and pharmacologic treatment strategies to improve the 

current rates of cessation. There are, however, no currently available brain-based neuroimaging biomarkers of 

treatment outcome, and much of the historic behavioral 

and personality characterizations that have been shown 

to differ between smokers and nonsmokers have failed to 

serve as accurate predictors of treatment success.

Why, after consistent demonstrations of differences 

in brain and behavior between groups, have these data 

not been effective in predicting treatment outcomes? One 

working hypothesis is that the differences are not a result 

of the addiction process, but rather that they reflect a predispositional trait that preceded drug use and dependence 

and are more likely to reflect risk factors for addiction than 

consequences of drug use. If so, it would seem unlikely 

that differences identified from cross-sectional population studies would or should signal outcome changes in 

brain circuits.

The alternative hypothesis is that the aforementioned brain differences are indeed caused by chronic 

drug use and reflect dependence-induced, neuroplastic 

brain changes. If so, this would suggest that longitudinal, 

within-participant neuroimaging data collected along the 

trajectory from the onset of treatment through short- and 

long-term recovery might serve as a biomarker of current 

disease severity and, importantly, be predictive of disease 

remission. Such a biomarker also could determine the 

possible liability risk for addiction of potential novel pharmacologic agents and help match treatment options with 

the highest probability of aiding the individual smoker. 

A review of the neuroimaging literature reveals a miniscule number of studies performed on former smokers 

(Neuhaus et al. 2006; Nestor et al. 2011, 2018a,b; Krönke 

et al. 2015; Zanchi et al. 2015, 2016; Weywadt et al. 2017; 

Ono et al. 2018), leaving mostly unknown the answer to 

the question of what a former smoker’s brain actually 

looks like.

Once the data become available in greater numbers, 

noninvasive brain imaging could:

• Identify differences in brain structure and function 

between smokers and nonsmokers;

• Follow persons along the course of treatment to 

identify brain circuits and networks that uniquely 

change in those whose treatments induce prolonged 

abstinence versus those who relapse (i.e.,  whether 

the above-group differences return to a [presumed] 

pre-addicted state vs. whether other neurobiological 

systems strengthen to compensate for the dysregulated brain system and networks);

• Make post hoc predictions of treatment outcomes 

by using pretreatment data and posttreatment 

outcomes;

• Develop brain-based biomarkers in clinical trials 

that predict treatment outcomes;

• Identify intermediate phenotypes of brain circuits 

and networks that can be used to fractionate the 

phenotype of the individual smoker to allow for personalized medicine and identify treatments with the 

highest probability of successful outcomes.

The ultimate goal of this strategy is to develop a 

system to individualize predictions of health outcomes 

on the basis of a model developed from group studies 

(Gabrieli et al. 2015).

Literature Review Methods

For this section of the chapter, PubMed was searched 

in January 2017 for articles that were published between 

2014 and 2017 about studies that focused on the intersection of human neuroimaging and nicotine addiction. 

The following terms were searched: fMRI, PET, MRI, nicotine, and nicotine addiction. The references cited represent publications in this domain since the 2014 Surgeon 

General’s report. From these articles, some studies conducted between the publication of the 2010 and 2014 

Surgeon General’s reports were also included. One 

reviewer conducted a full review and identified 77 articles 

for this section. Articles were omitted if the studies were 
New Biological Insights into Smoking Cessation 141

Smoking Cessation

considered to be underpowered or if quality could not be 

assessed because of incomplete descriptions.

Methodology of Neuroimaging 

Studies

In contrast to PET technology, which is best suited 

to identify molecular changes in neurotransmitter systems (for a review, see Lameka et al. 2016), MRI can be 

used to study brain structure, including gray matter density and cortical thickness, and the microstructure and 

integrity of white matter tracts (diffusion tensor imaging). 

MRI also can measure certain biochemical constituents of 

the brain using magnetic resonance spectroscopy. Finally, 

fMRI measures changes in brain activity (as inferred from 

changes in blood flow, blood volume, and oxygenation). 

The strength of fMRI is that it can measure brain activity 

while persons perform various cognitive and emotionally 

laden tasks, linking the behavioral performance of such 

nicotine addiction-related processes as working memory, 

attention, cue reactivity, and inhibitory (cognitive) control to the localization and magnitude of brain activity 

(for a review, see Huettel et al. 2014).

Data from fMRI also can be acquired in the absence 

of a directed task (i.e., the participant is at rest) (Biswal 

et al. 1995). Studies using resting-state fMRI have demonstrated that specific brain connections (i.e., circuits and 

networks) are apparent in the absence of a directed task, 

with the strength of connections at rest sufficient to predict the strength of subsequent task activation and behavioral performance (Kelly et  al. 2008; Baldassarre et  al. 

2012). Differences in resting-brain circuits may reflect 

neuropsychiatric disease, including nicotine dependence 

(Fedota and Stein 2015).

Despite their increasing applicability, neuroimaging 

studies are inherently correlative. Nevertheless, designs 

that include a pharmacologic intervention and incorporate a parametric manipulation of the task or drug (doseresponse) enable more precise interpretations. Finally, 

the advent of noninvasive brain stimulation (NIBS) 

(e.g., transcranial magnetic stimulation and transcranial 

direct [or alternating] current stimulation) may enable 

more direct probes of and interventions directed at putative neural circuit plasticity. The rationale for applying 

NIBS in addiction is that it could enhance circuits related 

to cognitive control or weaken circuits that are sensitive 

to provocations from cues. Although these circuits are 

also targets for many of the behavioral therapies applied in 

addiction (e.g., cognitive behavioral therapy), brain electrical stimulation has the potential to improve the efficacy 

of the treatment intervention by directly engaging the 

affected circuits. Having achieved some modest success, 

transcranial magnetic stimulation, an FDA-approved treatment for depression, has been proposed as a treatment for 

addiction in general (Barr et al. 2008; Gorelick et al. 2014; 

Dunlop et al. 2017) and for smoking in particular (Fraser 

and Rosen 2012; Li et al. 2013b; Dinur-Klein et al. 2014; 

Pripfl et al. 2014). However, the data for NIBS are too preliminary to evaluate its efficacy in smoking cessation.

Differences in Brain Circuitry and 

Cognitive Constructs in Nicotine 

Dependence

The neuroimaging studies reviewed in this section 

have examined the effects of chronic cigarette smoking, 

acute versus extended abstinence, treatment interventions, 

and smoking cessation on the major cognitive and affective 

constructs hypothesized to be involved in nicotine addiction (for a general review of addiction neurobiology, see 

earlier discussion, Koob and Volkow 2016, and USDHHS 

2010). Although different drugs of abuse initially bind to 

receptors specific to that drug’s pharmacology (e.g., opiate 

receptors [opioids]; psychostimulants [monoamine transporters]; tobacco [various nicotinic receptor subtypes]), 

the “downstream” neurobiologic circuits and mechanisms 

generally are believed to share a common substrate across 

all (or most) addictions. The cyclic nature of addiction and 

the underlying circuitry and neuroplastic consequences of 

chronic drug administration provide a theoretical framework to discuss the circuitry of nicotine addiction (Koob and 

Volkow 2016; Volkow et al. 2016). A better understanding of 

these neurobiologic mechanisms may yield more effective 

tools to aid in smoking cessation and also may be achievable using many fewer participants than are necessary in 

a behavior-only-based clinical trial, because the effect size 

of a brain response, which is more proximal to the causative mechanism, is significantly greater than the more 

distal behavioral response (Rasetti and Weinberger 2011). 

A review of the literature by Menossi and colleagues (2013) 

summarized the role of neuroimaging in pharmacologic 

treatment for smoking and nicotine dependence. They identified multiple brain regions—including the anterior and 

posterior cingulate cortex, orbitofrontal cortex, ventral striatum, amygdala, thalamus, and insula—that are involved 

in both the maintenance of smoking and processes related 

to nicotine withdrawal, such that two reasonably efficacious 

drugs used to treat nicotine dependence, varenicline and 

bupropion, modulated activity in these areas. In contrast, 

although NRT improves cognitive symptoms related to 

withdrawal, it does not generally alter the activity of neural 

circuits that are associated with nicotine addiction.
A Report of the Surgeon General

142 Chapter 3

Smoking Cues and Craving 

Provocation

Exposure to cues related to smoking is thought to 

activate brain circuits related to the salience (i.e., of immediate relevance) of the stimuli and to engage memory, affective, and cognitive processes that promote drug seeking 

and, in most cases, drug taking. Moreover, smoking cues 

can directly interfere with the abstinent person’s ability to 

concentrate and to focus attention on performing a task 

or on a therapeutic intervention that involves behavioral 

change (Luijten et al. 2011). Accordingly, a better understanding of the brain circuits and neurobiologic mechanisms engaged by cues might lead to novel targets for 

treatment interventions and potentially the development 

of a biomarker of outcome efficacy. For example, treatment 

with bupropion is associated with improved ability to resist 

cue-induced cravings and a reduction in cue-induced activation of limbic and prefrontal brain regions, including the 

ventral striatum, medial orbitofrontal cortex (OFC), and 

anterior cingulate cortex (ACC) (Culbertson et al. 2011). 

Similarly, responses to varenicline in the medial OFC (as a 

function of reward) and in the lateral OFC (during reward 

evaluation) may play a role in a diminished response to 

smoking cues, which may contribute to the drug’s clinical 

efficacy (Franklin et al. 2011). Consistent with these findings, Hartwell and colleagues (2013) found that successful 

smoking cessation with varenicline was associated with 

increased activation, before a quit attempt, in brain areas 

related to attentiveness and memory while the person 

resisted the urge to smoke, suggesting the drug may exert 

its effects by reducing craving and enhancing resistance to 

urges to smoke during cue-elicited craving.

More mechanistically, activation in the amygdala—

a structure long associated with stress processing, reinforcement learning, and risk of relapse—is dampened by 

both varenicline and nicotine, but a report by Sutherland 

and colleagues (2013b) found that this was only in a subset 

of smokers who appeared most susceptible to the negative 

consequences of nicotine abstinence for behavioral performance (in this case, forced choice reaction time). This 

finding on individual difference may provide a useful step 

toward fractionating the smoker phenotype by discrete 

neurobiologic characteristics, which in turn could lead 

to differential treatment algorithms. Furthermore, the 

functional connectivity between the amygdala and insula 

and, in turn, of the insula to components of the default 

mode network (DMN) (which is composed of the ventromedial PFC, parahippocampal gyrus, and posterior cingulate cortex [PCC] and is thought to process interoceptive 

states, ruminations, reflective thoughts, and similar phenomena) is downregulated by both varenicline and nicotine in abstinent (but not sated) smokers, and the circuit 

reduction is linked to reduced symptoms of nicotine withdrawal, which may help to promote cessation (Sutherland 

et al. 2013a).

Consistent with a role for the amygdala and insula in 

cessation, 3 months of mindfulness treatment was found 

to reduce both behavioral reactivity and responsivity in 

both brain regions and to predict successful cessation 

(Kober et al. 2017). In another study, 2 weeks of meditation 

training (vs.  a relaxation control) resulted in an average 

60% reduction in smoking that correlated with increased 

activity in the ACC and PFC, which are brain areas related 

to self-control (Tang et  al. 2013). Taken together, these 

studies suggest that reducing DMN-insula-amygdala circuit 

activity (via pharmacologic or behavioral interventions) 

may promote abstinence by modulating the interoceptive, 

negative affective, and ruminatory consequences (i.e., cravings) of cessation and point toward reduced strength of discrete circuit connectivity, contributing in turn to the amelioration of subjective withdrawal symptoms.

Sutherland and colleagues (2012) hypothesized that 

the balance between various large-scale brain networks 

modulates both normal and addiction-related behaviors. 

The three major large-scale brain networks in this model 

were (1) the DMN; (2) the executive control network, primarily composed of the dorsolateral prefrontal cortex 

(dlPFC) and posterior parietal cortex and thought to be 

engaged during the cognitive processing of exteroceptive 

signals; and (3) the salience network, which is anchored by 

the dorsal anterior cingulate cortex (dACC) and anterior 

insula and is thought to attribute salience to stimuli and 

the selection of action during times of conflict. In a test 

of this hypothesis, Lerman and colleagues (2014) demonstrated that the dynamic interrelationship among these 

three major large-scale networks is altered during acute 

24-hour abstinence (vs. satiety) and predicts the (a) difference in abstinence-induced changes in craving to smoke 

and (b) reduced cognitive performance and brain activation seen during a working memory task. Independently, 

a study by Zhang and colleagues (2011) positively correlated cue-elicited activity in the dlPFC with the strength 

of functional connectivity between the dlPFC and rostral 

anterior cingulate cortex.

If acute abstinence in fact modulates the circuits 

and networks described above, intervention strategies 

aimed at changing their activities might prove efficacious. 

One such potential cessation treatment uses real-time 

feedback of the fMRI signal to facilitate volitional control 

over regions of the brain that regulate craving. In a proofof-concept study, modulating the strength of functional 

connectivity between the ACC and medial PFC via feedback was associated with a reduction in craving among 

heavy smokers (Kim et al. 2015). Furthermore, feedback 

from the ACC but not the dorsomedial PFC (dmPFC), 
New Biological Insights into Smoking Cessation 143

Smoking Cessation

which is thought to be more involved in resisting craving, 

reduced activation to smoking cues, especially in persons 

with less severe nicotine dependence (Canterberry et al. 

2013; Hanlon et al. 2013; Hartwell et al. 2016).

The amount of nicotine presented to the brain via 

smoking is directly related to the severity of nicotine 

dependence, which in turn is linked to the severity of cravings during abstinence. Emergent data suggest a genetic 

link between the rate of nicotine metabolism, success 

in smoking cessation, pharmacologic efficacy, and brain 

activity (see “Genetic Studies of Smoking Phenotypes” 

later in this chapter for a discussion about the influence of 

nicotine metabolism on dependence). For example, compared with slow metabolizers, persons who are fast nicotine 

metabolizers demonstrate significantly greater responses 

to cigarette cues in the amygdala, hippocampus, striatum, 

insula, and cingulate cortex—supporting the importance of cue-induced craving in recidivism and helping to 

explain why fast metabolizers have lower cessation rates 

(Tang et al. 2012). In one study, greater activation in the 

caudate and frontal pole in fast versus normal metabolizers predicted abstinence-induced subjective cravings 

in response to smoking cues, suggesting that adjunctive 

behavioral cessation treatment, such as desensitization to 

repeated exposures to cues, may be useful in faster metabolizing persons (Falcone et al. 2016).

Reward

Like other abused drugs, nicotine, by virtue of its 

ability to interact with components of the mesocorticolimbic system and to enhance levels of dopamine (Volkow 

et  al. 2015), modulates reward processes in ways that 

may help perpetuate smoking and limit successful cessation. For example, 24-hour abstinence is associated 

with increased striatal activation during anticipation of a 

smoking reward and decreased activation in anticipation 

of a monetary reward, and greater abstinence-induced decrements in striatal activation during monetary reward are 

associated with a greater likelihood of relapse (Sweitzer 

et  al. 2016b). Consistently, administration of nicotine 

during abstinence reduces activity in the ventral striatum 

when the person is anticipating a win or loss (i.e., reward 

valence) and increases activity in the dorsal striatum when 

the person is anticipating the magnitude of a rewarded outcome (Rose et al. 2013; Fedota et al. 2015), suggesting a 

mechanism influencing the observed continued motivation to smoke and difficulty with cessation when trying 

to quit. Importantly, chronic dependence on nicotine, but 

not acute nicotine administration (i.e., NRT), reduced the 

ventral striatal temporal difference error signal (a learning 

mechanism construct related to dopamine release) in a 

classical conditioning reward paradigm, which is consistent 

with the inability of NRT to alter reward-related functional 

properties and perhaps explains its only modest ability to 

aid in smoking cessation (Rose et  al. 2012). In contrast, 

varenicline blunts the magnitude of mesocorticolimbic 

dopamine activity when a smoker is processing a reward, 

likely contributing to the drug’s greater efficacy as pharmacotherapy for smoking cessation (Fedota et al. 2015).

Practically speaking, smokers who show lower prequit brain reactivity to pleasant stimuli than to cigaretterelated cues are less likely to be abstinent 6 months after 

their quit attempt. Therefore, an important factor underlying relapse may be the lack of alternative forms of reinforcement when someone is deprived of nicotine (Versace 

et  al. 2014). Indeed, ambivalence about treatment negatively correlates with cue-related activation in brain 

areas linked to reward processing, motivation, and attention—including the rostral ACC, medial PFC, and caudate nucleus—thus, supporting the importance of both 

motivation to quit and expectancy to smoke (Wilson et al. 

2012, 2013).

Cognition and Cognitive Control

Cognitive performance and control processes have 

long been known to regulate so-called top-down control over behaviors, such as the ability to resist the drugseeking drive following cue presentation, subsequent drug 

craving, and ultimately drug taking. Such processes may 

serve as potential markers of sustained abstinence and 

treatment efficacy. For example, in a study by Krönke and 

colleagues (2015), former smokers exhibited less Stroop 

interference, indicating superior cognitive control, compared with current smokers. (Stroop interference is a 

behavioral task designed to induce a conflict in cognitive 

processing that leads to a reduction in reaction time to 

perform the task. One example of this effect requires individuals to identify the color of a word that is incongruent 

with the word itself [e.g., the word green written in red 

ink] [Stroop 1935].) Furthermore, when more demanding 

incongruent trials were contrasted with easier congruent 

trials in this study, former smokers showed stronger 

activity in the superior frontal gyrus and ACC than current smokers, suggesting successful smoking cessation 

may be mediated by enhanced cognitive control (Krönke 

et al. 2015). Elsewhere, in a study by Froeliger and colleagues (2017), differences in baseline corticothalamic 

function were predictive of inhibitory control processing 

and vulnerability to smoking relapse. In another study, 

greater activation in the inferior frontal gyrus, presupplementary motor area, and basal ganglia during a response 

inhibition task at pretreatment baseline was associated 
A Report of the Surgeon General

144 Chapter 3

with an attenuated association between cravings and subsequent smoking (Berkman et al. 2011).

Externalizing tendencies and/or compromised error 

processing among subsets of smokers may be relevant 

factors for the success of smoking cessation. Specifically, 

higher externalizing tendencies correlated with more performance errors and predicted less recruitment of the 

insula and dACC following the commission of errors in 

smokers, and smaller error-related insula activity and less 

dACC activity correlated with higher craving during abstinence (Carroll et al. 2015). In support of these regional 

alterations, reduced density of gray matter in the dlPFC of 

smokers, a structure long implicated in working memory, 

was associated with cue-elicited activity in the same brain 

area, suggesting a neurobiologic mechanism for the 

impaired cognitive control associated with chronic drug 

use (Zhang et al. 2011). Finally, smoking is associated with 

a diffuse cortical thinning that accelerates normal ageinduced thinning and cognitive decline, which requires 

approximately 25  years post-cessation for complete cortical recovery (Karama et al. 2015). Although the amount 

of cortical thinning was related to the amount of nicotine 

used, as an association, the causation of the thinning is not 

known. Similarly, Power and colleagues (2015) observed a 

dose-dependent relationship between smoking and white 

matter hyperintensities.

Working memory is a sensitive biomarker of nicotine dependence and acute withdrawal (Loughead et  al. 

2010). Relapse to smoking was highly predictive by 

decreased dlPFC and increased PCC activation during 

acute abstinence versus smoking satiety (Loughead et al. 

2015). Moreover, acute smoking abstinence was sufficient 

to reduce dmPFC activity and performance on a working 

memory task, and because smoking a low-nicotine cigarette did not ameliorate the deficit, NRT may be sufficient 

to resolve cognitive function during smoking abstinence. 

In contrast, an attempt to improve withdrawal-induced 

cognitive deficits by using tolcapone (to inhibit dopamine 

metabolism) only modestly improved the performance of 

working memory (Ashare et al. 2013). Similarly, a nicotine 

vaccine that blocks binding to nicotinic receptors in the 

brain did not block effectively either cue responsivity or 

brain activity during a working memory task (Havermans 

et al. 2014). Thus, like most vaccines, a nicotine vaccine 

may prove more effective in preventing a disease (i.e., nicotine addiction) because brain circuits that have been 

modified or dysregulated as a result of nicotine dependence are not likely to return to their pre-addiction state 

simply by blocking new nicotine from reaching the brain. 

Indeed, that smoking relapses occur months or even years 

after smoking cessation suggests that the absence of nicotine alone is insufficient to reverse dependence-induced 

circuit neuroadaptations.

Insights from Neuroimaging for 

Antismoking Messages

In addition to providing a salient stimulus to seek 

out or enhance drug use (Wang et  al. 2013), smoking 

cues could serve, together with appropriate messaging, 

as a negative reinforcement. For example, analyses using 

neuroimaging of responses to antismoking ads that were 

intended to change attitudes toward smoking appeared to 

predict the severity of subsequent smoking and treatment 

outcomes (Camenga and Klein 2016). Most persons begin 

using nicotine and often become nicotine dependent 

during adolescence (USDHHS 2012; Camenga and Klein 

2016). Compared with adult smokers, adolescent smokers 

exhibited greater craving reduction and greater blunted 

recruitment of insula and dlPFC in response to package 

warning labels (Do and Galván 2015). Furthermore, 

greater dlPFC regulation of limbic regions predicted 

cigarette craving. These data underscore the prominent 

role of frontoinsular circuitry in predicting the efficacy 

of graphic warning labels for reducing craving in adult 

and adolescent smokers. In adult smokers, activation in 

the dmPFC in response to persuasive advertisements predicted urine cotinine levels 1  month later (Wang et  al. 

2013). In smokers trying to quit, the amygdala’s response 

to smoking cessation messages was modulated by genetic 

variation in the serotonin transporter and was predictive 

of quitting outcome (Jasinska et al. 2012). Genetic alterations in the dopamine D4 receptor also modulated responsiveness of the amygdala to cues (Xu et al. 2014). A study 

by Chua and colleagues (2011) supports the hypothesis 

that tailored health interventions are more effective at 

eliciting positive behavior change than generic interventions. For example, messages tailored to the individual 

increased activation of the dmPFC, a region known to be 

involved in self-related processing, and predicted quitting 

during a 4-month follow-up. Taken together, these data 

suggest that fMRI may aid the prerelease evaluation of 

televised public health ads.

Neuronal Circuits and Networks

Studies of resting-state functional connectivity have 

revealed that the ACC, PCC, medial and lateral OFC, ventral 

striatum, amygdala, thalamus, and insula are all heavily 

involved in the maintenance of smoking and nicotine withdrawal (Figure 3.3). Varenicline and bupropion modulate 

activities in these brain areas, providing mechanistic support for their abilities to alleviate withdrawal symptoms 

and help with smoking cessation. For example, among nonlapsed smokers who were making a 3-week quit attempt, 
New Biological Insights into Smoking Cessation 145

Smoking Cessation

Sweitzer and colleagues (2016a) observed abstinenceinduced increases in connectivity strength between the 

ventral striatum and a network of regions implicated in 

addictive disorders, including the insula, superior temporal gyrus, and ACC; the opposite pattern was observed 

for those who later lapsed. Also in this study, following 

24-hour abstinence, decreased connectivity between the 

dorsal striatum and the medial PFC, PCC, hippocampus, 

and supplemental motor area was observed across both 

successful and unsuccessful cessation groups. These findings suggest that modulation of striatal connectivity with 

the cingulo-insular network during early withdrawal may 

be associated with outcomes for smoking cessation.

This potential association is particularly important because a high density of nAChRs has been found in 

the cingulo-insula network (Picard et al. 2013), and this 

salience network has been implicated in the switching of 

cognitive resources during abstinence (vs. satiety) toward 

more internal bodily processing and nicotine craving 

(Sutherland et  al. 2012; Lerman et  al. 2014). MoranSanta Maria and colleagues (2015) found a psychophysiological interaction between the anterior insula and the 

Figure 3.3 Neuronal mechanisms involved in nicotine addiction: A model

Source: Changeux (2010, p. 391), with permission.

Notes: α = alpha; β = beta; HB–IPN = habenula–interpeduncular; LDTg = laterodorsal tegmental nucleus; NAc = nucleus accumbens; 

nAChR = nicotinic acetylcholine receptor; PPTg = pedunculopontine tegmental nucleus; SNpc = substantia nigra pars compacta; 

VTA = ventral tegmental area. “Many brain areas contain nAChR subunits and are involved in nicotine addiction. First, the somata of 

the dopaminergic neurons that contribute to nicotine intake and reinforcement are in the VTA of the midbrain: they project to the 

prefrontal cortex and to limbic areas, in particular the hippocampus and NAc in the striatum [Balfour et al. 2000; Di Chiara 2000; 

Maskos et al. 2005; Balfour 2009]. These VTA neurons receive cholinergic innervation from the PPTg and the adjacent LDTg [Picciotto 

and Corrigall 2002; Maskos 2008]. Second, the emergence of a negative emotional state and withdrawal syndrome following smoking 

cessation—or nicotine deprivation—mobilizes distinct neural circuits that can include the extended amygdala and brain stress systems 

[Koob 2008], the hypothalamus, hippocampus [Davis and Gould 2009], SNpc, and/or the HB–IPN system [Salas et al. 2009]. Third, 

the ‘switch’ from voluntary nicotine use to compulsive drug use may represent a global top-down ‘gating’ transition from control 

by a prefrontal (cortical and insular) global neuronal workspace (BOX 1) to subcortical (striatal) control [Grace 2000; Changeux and 

Dehaene 2008; Naqvi and Bechara 2009]” (Changeux 2010, p. 391).
A Report of the Surgeon General

146 Chapter 3

precuneus (a part of the DMN)—which are regions known 

to be involved in self-awareness and interoception, or the 

sense of internal bodily states—during the presentation of 

smoking cues. According to Zelle and colleagues (2017), 

connectivity strength between the anterior insula and 

dlPFC following provocation from smoking cues predicts 

the ability to resist smoking after acute abstinence.

Vulnerability to relapse after a quit attempt was 

associated with weaker connectivity between the posterior 

insula and primary sensorimotor cortex, suggesting that 

greater connectivity in this network improves the ability 

to inhibit a motor response to cigarette cravings when 

those cravings conflict with the goal to remain abstinent 

(Addicott et  al. 2015). Elsewhere, research has consistently shown that the insula and basal ganglia play a role 

in addiction to smoking, as revealed by localized stroke 

lesions in these regions (Naqvi and Bechara 2010; Gaznick 

et al. 2014), and that local connectivity coherence within 

the PCC, a key DMN region, can predict the success of cessation (Wang et al. 2017).

In contrast to the insula-based circuits related to the 

state of nicotine withdrawal and the positive effects of NRT 

on cognitive processing, NRT does not alter the activity 

in an ACC-ventral striatal neural circuit that is associated 

with the severity of trait nicotine addiction (Hong et al. 

2009). Further speaking to the role of the ACC and striatum 

in trait addiction, slow nicotine metabolizers, which presumably have relatively higher nicotine levels in the brain, 

showed greater functional connectivity in the dACC and 

ventral striatum, which is negatively associated with the 

severity of nicotine dependence (Li et al. 2017). Critically, 

the dACC and ventral striatum are biased by inputs from 

the insula. Moreover, a similar gene–environment reduction was seen in the dACC and ventral striatum during 

smoking abstinence when study participants performed a 

cognitive control response inhibition task and a reward 

task to probe their function, which were both normalized 

following NRT. These data suggest that the inherited rate 

of nicotine metabolism fundamentally changes brain circuits and function, which may, in turn, influence the outcomes of smoking cessation (Li et al. 2017).

The findings that both nicotine trait addiction (long 

standing) and current state (transient) engage distinct 

neural mechanisms (dACC and ventral striatum) and circuits (amygdala, insula, and DMN) and that NRT appears 

to improve cognitive symptoms related to withdrawal but 

does not alter a measure of disease severity (the FTND), 

suggest that both nicotinic and non-nicotinic pharmacotherapy may reduce smoking via distinct neural mechanisms of action and thereby endorse the potential value of 

neuroimaging in the development of new medications and 

discovery of brain-based biomarkers of early therapeutic 

response in cigarette smokers (Menossi et al. 2013).

Molecular Imaging

PET imaging has contributed to a better understanding 

of the biochemical and molecular alterations in nicotine 

addiction and smoking cessation. Clearly, understanding 

the mechanisms of action of effective pharmacotherapies 

for nicotine dependence is critical to the development of 

better treatments. A PET study using [(11)C]-(+)-PHNO 

demonstrated that varenicline, the most effective pharmacotherapy currently available, increases levels of striatal dopamine, much as smoking does (Cosgrove et al. 2014), which 

may contribute to the drug’s efficacy (Di Ciano et al. 2016).

An important public health question is whether documented changes in brain structure and function in persons who are dependent on nicotine can be reversed or normalized following extended abstinence. Notably, smoking 

cessation is accompanied by a decrease in the density of 

α4β2* nAChRs across the brain, suggesting a normalization of the receptors that primarily bind nicotine following 

intake (Brody et al. 2013). Additionally, smokers with less 

upregulation of α4β2* were found to have a greater probability of quitting smoking than those with greater upregulation, providing a potential biomarker of cessation success (Brody et al. 2014). In a different study (Akkus et al. 

2016), compared with recent former smokers, long-term 

former smokers showed higher mGluR5 binding, most 

prominently in the frontal cortex and thalamus, suggesting that downregulation of these receptors may be a 

mechanism underlying nicotine dependence and the high 

rate of relapse seen in those previously exposed to nicotine. Accordingly, mGluR5 receptor binding may serve 

as an effective smoking biomarker and a potential target 

for future medications (Akkus et  al. 2016). In contrast, 

binding at the GABA(A) receptor, a component of the principal brain inhibitory system, does not seem to normalize 

with sustained abstinence (Stokes et al. 2013).

Sex differences in smoking behavior and brain molecular mechanisms have been reported (Sieminska and Jassem 

2014). Consistent with the notions that men smoke cigarettes for their reinforcing properties and women smoke for 

such reasons as mood regulation and cue reactivity (Perkins 

et al. 2001; Xu et al. 2008), Cosgrove and colleagues (2014) 

found, in an analysis of smoking in a PET scanner, that 

smoking resulted in rapid increases in dopamine in the ventral striatum of men, while dopamine release in women was 

faster than in men in a subregion of the dorsal putamen. 

Moreover, smoking-induced alterations in nAChR binding 

appeared to differ by sex, with receptor upregulation seen in 

male but not female smokers (vs. nonsmokers, respectively). 

In contrast, nAChRs are negatively correlated with levels 

of progesterone, which in turn are positively correlated 

with symptoms of depression and intensities of cigarette 

craving and withdrawal (Cosgrove et al. 2012). These data 
New Biological Insights into Smoking Cessation 147

Smoking Cessation

suggest that female smokers may be best treated by medications that do not interact directly with nicotinic mechanisms. Additionally, a study using fMRI indicated higher 

reactivity to smoking cues (vs. neutral cues) in males compared with females in specific reward-related regions of the 

brain (the ventral striatum/ventral pallidum and the ventral medial prefrontal cortex) (Dumais et al. 2017). Brain 

activation during smoking cues correlated positively with 

cue-induced subjective craving in males but not in females. 

These data suggest that, compared with women, men have 

greater reward-related brain activation to drug cues.

Although these small studies may have been underpowered to definitively distinguish smoking-related, sexspecific differences in the neurochemistry and circuitry in 

the brain, they add to a growing and important base of 

literature on sex differences in nicotine addiction. They 

also underscore the need for more research on sex-specific 

neurobiology of the etiology and treatment of nicotine 

dependence.

Summary

The data presented in this section highlight new 

biologic insights into smoking cessation gained from multiple neuroimaging modalities, including PET and fMRI. 

These studies highlight the neurobiologic complexities of 

nicotine dependence and, in their totality, are sufficient 

to support the multiple cognitive and affective systems 

that are dysregulated in persons with this disease, suggesting why persons who are addicted to nicotine are so 

resistant to treatment even with multiple FDA-approved 

medications. On a more positive note, these neuroimaging findings have begun to reveal neurobiologic mechanisms and cognitive constructs that may serve as novel 

targets for future therapeutic developments, including 

reward processing, cognitive control, and executive functions (such as working memory and inhibitory control 

processes and affective responses to internal and external 

cues and stressors). These studies are suggestive of dysregulated brain regions, including various prefrontal and 

cingulate cortical regions, and their corresponding circuits and interactions with various striatal and insula 

loci. Almost all studies were cross-sectional—not longitudinal. Therefore, specific causal relationships are difficult 

to infer in the absence of repeated measurements within 

subjects. Nevertheless, outcomes for smoking cessation 

may be improved by using pre- and posttreatment, multimodal neuroimaging measures that are coupled with 

recent computational advances (e.g., machine learning) to 

create objective, quantifiable biomarkers that can be used 

to assess disease severity and treatment efficacy.

Genetic Studies of Smoking Phenotypes

Studies of twins suggest that smoking behaviors are 

moderately to highly heritable. For example, according 

to earlier studies, genetic factors explain an estimated 

46–84% of the variability in smoking initiation and 

smoking persistence, up to 75% of the variability in nicotine dependence (Kendler et  al. 1999; Vink et  al. 2005), 

and 50–58% of the variability in smoking cessation (Xian 

et al. 2003; Broms et al. 2006). Two broad approaches to 

molecular genetics exist: Candidate gene studies identify a 

specific gene to investigate, on the basis of biologic plausibility, and test the association between the selected genetic 

variants and the phenotype of interest. In contrast, GWAS 

are not restricted to individual genes. Instead, they assess 

the association between hundreds of thousands of variants 

(and, more recently, several million variants) across the 

genome with the phenotype of interest.

The 2010 Surgeon General’s report summarized 

studies of candidate genes involved in the dopamine 

pathway, which at the time was considered a promising 

target for genetic dissection, with the DRD2 Taq1A polymorphism being one focus of interest (USDHHS 2010). 

Early studies suggested that the A1 allele at this locus was 

associated with increased short-term effectiveness of NRT 

and bupropion. Subsequent studies, however, have not 

confirmed an association with smoking status (Tobacco 

and Genetics Consortium 2010) or with response to pharmacotherapy for smoking cessation (Choi and Shin 2015). 

The 2010 Surgeon General’s report also reviewed studies 

of candidate genes (e.g., CYP2A6 and CYP2E1) involved in 

nicotine metabolism in relation to smoking phenotypes, 

but it concluded that findings were not consistent, possibly 

because of differences in samples across studies (USDHHS 

2010). Two later studies used the nicotine metabolite ratio 

(NMR), which is the ratio of 3’-hydroxycotinine (the product 

of CYP2A6 activity) to cotinine, as a phenotypic biomarker 

for CYP2A6 activity and concluded that NMR predicts the 

outcomes of treatment for smoking (Kaufmann et al. 2015; 

Lerman et al. 2015). This conclusion likely results from 

better measurement of nicotine metabolism activity gained 

using a phenotype instead of a genotype, as this gene locus is 

very complicated and results can be inconsistent because of 

the different variants being tested. Since the 2010 Surgeon 

General’s report, considerable progress has been made in 

understanding the genetic basis of smoking phenotypes, 
A Report of the Surgeon General

148 Chapter 3

particularly through GWAS. Candidate gene studies and 

GWAS have identified variants in the CHRNA5-CHRNA3-

CHRNB4 region as promising targets for the study of nicotine dependence and smoking intensity.

Literature Review Methods

For this section of the chapter, MEDLINE was 

searched for articles that were published between 2000 

and 2018 about studies that focused on genetic associations with smoking behavior (including cessation). A combination of controlled vocabulary and keyword terms was 

used for each of the concepts: smoking cessation, smoking 

behavior, smoking phenotype, genetics, and precision 

medicine. Studies were excluded if they did not focus 

on the underlying biology of smoking behavior and/or 

smoking cessation. Conclusions were formulated from 

evidence cited in the 2014 Surgeon General’s report on 

smoking and any newly available evidence. Search results 

were limited to studies published in English and to original research. Duplicates were deleted, and unique hits 

were screened. Two independent reviewers conducted a 

full review and identified 47 articles for this section. From 

these articles, seven more articles about studies conducted 

in the 1990s were also included.

Candidate Gene Studies

Candidate gene approaches require some theoretical 

knowledge of the biologic mechanism underlying the phenotype of interest that points to specific genes. Typically, 

these approaches focus on genetic variants that result in 

functional changes (Kwon and Goate 2000). The selected 

variant is tested for its occurrence in cases and controls 

(e.g.,  assigned by smoking status) or for its association 

with a continuously distributed trait (e.g., nicotine dependence) (Patnala et al. 2013).

Findings from candidate gene studies are difficult 

to reproduce. This is likely because of the typically small 

samples used in these studies, the small effect sizes associated with common genetic variants and complex behavioral traits, and the relatively liberal alpha threshold used 

(Chang et al. 2014). Despite these limitations, candidate 

gene studies have produced some robust associations, 

as discussed later in this section.

Genomewide Association Studies

GWAS adopt the same approach as candidate gene 

studies, but rather than testing the association of one or 

a small number of genetic variants with a phenotype of 

interest, GWAS simultaneously test hundreds of thousands 

of genetic variants (typically single nucleotide polymorphisms [SNPs]) across the genome. The multiple testing 

burden implicit in GWAS has led to a consensus that signals have to achieve a very stringent threshold for statistical significance (typically p  <5  ×  10−8). This, in turn, 

requires very large samples or the pooling of data across 

multiple studies to achieve the necessary sample size to 

robustly identify the small effects associated with the 

common genetic variants. Most GWAS also report results 

from discovery and replication datasets. This combination 

of large sample sizes, statistical stringency, and replication means that GWAS have been extremely successful 

in identifying genetic variants associated with a range of 

complex phenotypes, including variants that would not 

have been considered previously on the basis of biological 

function. GWAS have identified novel genetic associations 

with smoking behaviors, such as BDNF for smoking initiation, the CHRNA5-A3-B4 gene cluster for intensity of 

smoking, and DBH for smoking cessation (Berrettini et al. 

2008; Bierut et al. 2008; Thorgeirsson et al. 2008; Tobacco 

and Genetics Consortium 2010).

As would be expected, one of the limitations of 

GWAS is their limited ability to detect low-frequency variants. For example, Lindquist and colleagues (2013) estimated the first GWAS to have detected less than 20% of 

all independent GWAS-detectable SNPs in chronic diseases. More recent GWAS have employed imputation to 

expand genomic coverage to better capture low-frequency 

variants. To impute genotypes, data for the microarray are 

matched to a genome reference panel, which consists of 

densely sequenced genomic data from multiple persons 

(e.g., 1,000 Genomes Project Consortium et al. 2010).

Microarrays designed in this manner cover a large 

portion of all SNPs in the human genome by directly 

measuring high- and low-frequency variants and by measuring SNPs in linkage disequilibrium (Lindquist et  al. 

2013). Even so, another limitation of GWAS is that the 

phenotypes are relatively crude because they are tested in 

large samples and in the case of smoking behavior, often 

rely on retrospective self-reports. Carefully defined and 

well-characterized phenotypes offer greater precision of 

measurement, increase the genetic signal, and improve 

the likelihood of replication (Munafò et al. 2012).

Examples of Biologically Promising Candidate 

Genes (DRD2 and DAT1)

The mesolimbic dopamine system is particularly 

important in addictive behaviors and is activated by nicotine. As a consequence, genes encoding proteins involved 

in the neurotransmission of dopamine have been considered plausible candidate genes for nicotine dependence 
New Biological Insights into Smoking Cessation 149

Smoking Cessation

and smoking cessation, and they have been widely investigated in candidate gene studies. Variations of the dopamine receptor D2 (DRD2) and the dopamine transporter 

SLC6A3 (also known as DAT1) genes have received particular attention (Sullivan and Kendler 1999; Dani 2003; 

Duan et al. 2003; Li et al. 2003; Dahl et al. 2006; Lerman 

et al. 2006a; Schnoll et al. 2007).

Associations Between the DRD2 and DAT1 Genes 

and Smoking Behavior

In the DRD2 gene, rs1800497 (Taq1A) is one polymorphism that is located downstream and in the neighboring ankyrin repeat and kinase domain containing 

1 (ANKK1) gene (Neville et al. 2004). This polymorphism, 

which is involved in inhibiting the synthesis and release 

of dopamine, leads to decreased density of the dopamine 

receptor (Noble et al. 1991, 1997; Pohjalainen et al. 1998; 

Jonsson et  al. 1999) and, therefore, reduced dopamine 

binding in the brain (Thompson et  al. 1997). Various 

studies have reported that the A1 allele of the DRD2 Taq1A 

polymorphism is associated with being a former or current 

smoker (Noble et al. 1994; Morton et al. 2006); with age of 

smoking initiation and duration of abstinence (Comings 

et  al. 1996); and with smoking intensity (Connor et  al. 

2007). In addition, meta-analyses have reported suggestive 

evidence of an association of the A1 allele with increased 

likelihood of smoking persistence (Munafò 2004; Munafò 

et al. 2004, 2009). Other studies, however, did not yield 

similar findings (Batra et al. 2000; Bierut et al. 2000).

Other studies have investigated whether DAT1 variants are associated with smoking behavior. DAT1 has a 

polymorphic variable number of tandem repeats sequence 

that varies from 3 to 11 copies, of which only the 9- and 

10-repeat alleles are common (Chen and Reith 2000). DAT1

plays a key role in regulating the transport of dopamine by 

regulating its reuptake (Choi and Shin 2015). Timberlake 

and colleagues (2006) reported that the absence of the 

9-repeat allele in DAT1 (DAT-9) was associated with being 

less likely to be a smoker; other studies have suggested 

that this association is stronger if the person was also carrying the DRD2 A2 allele (Lerman et al. 1999), had a later 

onset of smoking (Lerman et al. 1999; Schmid et al. 2009), 

had longer quitting attempts (Lerman et  al. 1999), or 

had formally tried smoking cessation (Sabol et al. 1999). 

However, these associations have not been found in other 

studies (Bierut et al. 2000; Jorm et al. 2000; Vandenbergh 

et al. 2002).

Meta-analyses of GWAS conducted by the Tobacco 

and Genetic Consortium (2010), using data from three 

GWAS of smoking consortia to evaluate a number of phenotypes, did not find evidence of an association between 

loci in either DRD2 or DAT1 and smoking behavior. Despite 

these equivocal results, several pharmacogenetic studies 

have suggested an association between genes involved 

in the dopaminergic pathway and response to pharmacotherapy that is aimed at smoking cessation (David and 

Munafò 2008).

The Moderating Effect of DRD2 and DAT1 on the 

Efficacy of Treatment for Smoking Cessation

Some studies have found that the A1 allele of the 

DRD2 gene is associated with better response to NRT 

(Johnstone et al. 2004; Yudkin et al. 2004; Lerman et al. 

2006b), and others have found an association between 

A2 and better response to bupropion for specific nicotine withdrawal symptoms (David et al. 2003; Swan et al. 

2005; David et al. 2007). In contrast, Berlin and colleagues 

(2005) did not find an association between the DRD2

genotype and smoking cessation. Additionally, Choi and 

Shin (2015) did not find an association between DRD2 

polymorphisms and response to therapy for smoking 

cessation. Finally, in a randomized, placebo-controlled, 

smoking cessation study of bupropion, Lerman and colleagues (2003) did not find an association between the 

DRD2 and DAT1 genotypes and either the abstinence rate 

or response to treatment.

These findings suggest that many genes likely play 

a role in the efficacy of treatment for smoking cessation (David et  al. 2013b). Each genetic variant probably 

explains only a small fraction of the variation in response 

to medication and success in quitting, and most studies 

have investigated only a single variant or just a small 

number of them. A combination of genetic variants in 

a single genetic risk score may reveal stronger associations with the outcomes of therapies for smoking cessation and support personalized therapy on the basis of a 

person’s score.

Genetic Risk Scores

Additive genetic scores (AGS) are an alternative approach to evaluate the effects of multiple susceptible SNPs for a single phenotype. These scores take into 

account the collective impact of several variants, on the 

basis of theoretical knowledge of those included, and provide greater statistical power than single-variant studies 

(David et al. 2013b). Early approaches developed AGS on 

the basis of candidate genes of theoretical interest, and 

recent approaches have generated scores from variants 

identified via GWAS.

In two randomized clinical trials of bupropion for 

smoking cessation, David and colleagues (2013b) used an 

AGS from genes in the dopaminergic system, including 

COMT, DRD2, DRD4, and DAT1. The score was calculated 

on the basis of the number of alleles considered to promote 

smoking cessation through bupropion and was estimated 
A Report of the Surgeon General

150 Chapter 3

for each participant. The score was not associated with 

the number of days to first lapse, but evidence from this 

study indicated that bupropion (vs. placebo) counteracts 

the propensity to lapse in persons with a higher additive 

genetic efficacy score.

Uhl and colleagues (2014) studied smokers by using 

the “v1.0 score,” which is based on 12,058 SNPs (Uhl et al. 

2010). Using a randomized controlled clinical trial in 

which dose of NRT was matched to the smoking intensity 

of each participant, the study found that the v1.0 score can 

predict success of quitting.

More recently, Chenoweth and Tyndale (2017) suggested that including environmental effects (e.g.,  use 

of estrogen-containing hormonal therapy) into AGS 

approaches would improve the ability to predict the outcomes of treatment for smoking cessation. At the same 

time, evaluative tools, such as biomarkers, could lead to 

tailored or personalized treatment (Bough et  al. 2013). 

Regardless, early approaches to AGS, which used candidate genes, need to be treated with caution in light of the 

poor reproducibility of many findings for candidate genes.

Examples of Biologically Promising Genes That 

May Help Optimize Treatment

Both genetic and metabolic biomarkers have the 

potential to predict outcomes for different treatments 

for smoking cessation and individual responses to medication. Particularly promising genetic variants include 

those in the CHRNA5-A3-B4 gene cluster on chromosome  15 (at  15q25) that encodes 3  (α3, α5, β4) of the 

11  (α2–α7, α9, α10, β2–β4) neuronal nAChR subunits 

(Gold and Lerman 2012). Multiple candidate gene studies 

and GWAS have verified the small but robust association 

of this cluster of genes with smoking intensity and nicotine dependence (Saccone et al. 2007; Bierut et al. 2008; 

Thorgeirsson et al. 2010). Importantly, smoking intensity 

and nicotine dependence predict the success of cessation 

(Piper et al. 2006; Transdisciplinary Tobacco Use Research 

Center on Tobacco and Dependence et al. 2007), and thus 

the relationship between the CHRNA5-A3-B4 gene cluster 

and cessation phenotypes has been investigated (Munafò 

et al. 2011; Bergen et al. 2013; Tyndale et al. 2015).

NMR is a metabolic predictive biomarker that captures activity of the CYP2A6 gene. CYP2A6 plays an important role in nicotine metabolism; up to 80% of nicotine is 

inactivated to cotinine by the hepatic enzyme cytochrome 

P450 (CYP) 2A6, with a small contribution (10%) from 

CYP2B6. Most of the cotinine is further metabolized to 

3’-hydroxycotinine. NMR is used as a proxy of CYP2A6 

activity and is preferred over assessing the gene itself 

because CYP2A6 is characterized by dozens of polymorphisms. A faster NMR reflects higher CYP2A6 activity and 

is associated with several smoking phenotypes.

The CHRNA5-A3-B4 Gene Cluster

Associations with Nicotine Dependence and 

Smoking Intensity

Saccone and colleagues (2007) authored the first 

candidate gene study to report an association between 

the SNP rs16969968 in CHRNA5 and nicotine dependence. The following year, GWAS conducted separately by 

Berrettini and colleagues (2008) and Thorgeirsson and colleagues (2008) reported that rs1051730 at the same locus 

but in CHRNA3 (and strongly correlated with rs16969968 

in samples of European ancestry) was associated with the 

number of cigarettes smoked per day. CHRNA5 was not 

considered a strong candidate gene at the time, given 

what was then known about the neurobiology of nicotine 

dependence. Animal experiments had implicated the α4 

and β2 nicotinic receptor subunits as critical to nicotine’s 

reinforcing effects (Picciotto et  al. 1998; Tapper et  al. 

2004), and α4β2* partial agonists are now known to be 

one of the most effective treatments available for smoking 

cessation (Fowler and Kenny 2014). Findings from GWAS 

have made variants in the CHRNA5-A3-B4 region promising targets for the study of nicotine dependence and 

smoking intensity, given their association with response 

to nicotine and its consequent consumption.

In particular, the rs1051730 SNP in CHRNA3 is 

a coding-synonymous variant that does not result in an 

altered protein, and thus it likely does not have any functional significance. However, the highly correlated variant 

rs16969968 in CHRNA5 is functional and presents with a 

missense mutation that results in an amino acid substitution of aspartate to asparagine in the α5 subunit protein. Both in vitro and in vivo studies have further characterized the role of the rs16969968 variant. In in vitro 

studies, α5 receptor complexes featuring the aspartic acid 

variant, when exposed to a nicotine agonist, have exhibited a substantially greater maximal response than the 

α5 receptor complexes containing the asparagine variant 

(i.e., the risk variant associated with the number of cigarettes smoked per day and nicotine dependence) (Bierut 

et al. 2008). A series of animal studies has established the 

role of the α5 nAChR subunit by investigating the phenotype via an α5 knockout mouse model, which is analogous 

to a reduced α5 receptor function in humans (i.e., carriers 

of the rs16969968 risk allele) (Salas et  al. 2009; Fowler 

et al. 2011; Frahm et al. 2011). Salas and colleagues (2009) 

showed that α5 knockout mice, when exposed to chronic 

infusions of nicotine, exhibited withdrawal symptoms comparable to those of saline-infused mice (i.e., a lack of withdrawal symptoms relative to wild-type mice). In the experiment conducted by Fowler and colleagues (2011), both 

wild-type and mutant mice were trained to press a lever 
New Biological Insights into Smoking Cessation 151

Smoking Cessation

to obtain nicotine intravenously. All the mice showed the 

expected inverted U-shaped dose-response curve, with the 

difference that knockout mice responded more vigorously 

at high doses. Knockout mice consumed a greater amount 

of nicotine, and the wild-type mice appeared to titrate the 

delivery of nicotine to achieve a desired level. Although 

knockout mice appeared to experience rewarding effects 

of nicotine similar to those experienced by wild-type mice, 

the inhibitory effects of the high doses of nicotine on the 

activity of the rewarding circuitry seemed to be largely 

altered. The injection of a lentivirus vector into the MHb 

in α5 knockout mice rescued the expression of α5 subunits 

in this region and the phenotype.

Similarly, a study by Jackson and colleagues (2010b) 

showed differential effects of nicotine dose on reward 

between α5 knockout and wild-type mice using a CPP task. 

Later, in a study of humans, Jensen and colleagues (2015) 

found an attenuated aversive response to nicotine administered intravenously in overnight-abstinent smokers who 

were carriers of the CHRNA5 rs16969968 risk allele genotype. In summary, high doses of nicotine seem to stimulate the MHb–IPN tract through nAChRs containing α5 

subunits and elicit aversion, limiting further intake. This 

does not happen when the α5 signaling is deficient and, 

consequently, the negative effects of nicotine are attenuated. Similarly, smokers carrying the rs16969968 risk 

allele are more likely to smoke more heavily than their 

counterparts without the risk allele.

Evidence from in vitro and in vivo studies further 

indicates that the MHb acts as a gatekeeper for nicotine 

intake. Frahm and colleagues (2011) manipulated the 

concentration of α5 and β4 subunits in vitro, while α3 was 

kept constant. Nicotine-evoked currents in MHb neurons 

of wild-type and transgenic Tabac mice (characterized by 

an overexpression of β4) led to a dramatically higher firing 

rate in the neurons of the Tabac mice. Those mice exhibited a reduced nicotine intake and a strong preference for 

water rather than low-nicotine-concentration solutions in 

a two-bottle choice test that compared them with wildtype mice presented with the same volumes of water and 

the low-nicotine solution. When the expression of the α5 

risk variant was elicited by injecting a lentivirus vector 

into MHb neurons in the Tabac mice, the latter restored 

their nicotine consumption and their two-bottle choice 

behavior to a level comparable to that of the wild-type 

mice. These animal studies show that α5 and β4 play an 

important role and compete in regulating nicotine intake.

In humans, Hong and colleagues (2010) used 

resting-state functional connectivity to understand the 

mechanistic link between variation at the CHRNA5-A3-B4

locus and nicotine addiction. Their study identified a circuit between the dorsal anterior cingulate and the ventral 

striatum/extended amygdala that distinguished smokers 

from nonsmokers and predicted nicotine dependence. Both 

smokers and nonsmokers with the risk allele had a weaker 

circuit than those with the more common allele (although 

the circuit strength was even weaker in smokers), suggesting a trait-like circuit biomarker. A nearly identical 

circuit was described previously in smokers (Hong et al. 

2009) as a function of nicotine dependence. Critically, in 

that study, circuit strength did not change following NRT, 

suggesting that it reflected chronic dependence.

CHRNA5-A3-B4 Variants and Smoking Cessation 

in Absence of Treatment

The genetic risk variants associated with nicotine 

dependence and smoking intensity also were associated 

with smoking cessation. Interestingly, persons who smoke 

a greater number of cigarettes per day seem to quit at a 

later age (Chen et  al. 2015). Some studies have shown 

that CHRNA5, in particular the rs16969968 risk variant, 

has potential clinical significance in predicting delayed 

smoking cessation. Chen and colleagues (2015) conducted 

a large meta-analysis to investigate whether rs16969968 

plays a role in the age of smoking cessation among smokers 

without smoking-related disease and patients with lung 

cancer, chronic obstructive pulmonary disease, or coronary heart disease. Results from 24 datasets in their study 

showed evidence for an association only for the smokers 

without a smoking-related disease and the rs16969968 risk 

allele, with a median delay of 4 years. The heterogeneity of 

the studies in this meta-analysis shows that a number of 

factors may moderate genetic risk, such as the presence of 

disease, use of medication, and environmental risk factors 

(e.g., having a partner or friend who smokes).

Freathy and colleagues (2009) assessed smoking cessation in a large cohort of women of European ancestry, 

over the course of their pregnancies. Carriers of the risk 

variant rs1051730 showed a reduced likelihood of stopping smoking. The effect did not appear to be solely mediated by intensity of smoking, as adjusting the analysis for 

that variable did not affect the results, although this may 

have been because the number of cigarettes smoked per 

day does not fully capture intensity of smoking (e.g., given 

interindividual differences in depth of smoke inhalation 

and other measures of smoking topography). Thorgeirsson 

and Stefansson (2010) replicated this finding in a retrospective study of pregnant women, which found an association between the risk variant rs1051730 and continuing 

smoking during pregnancy.

CHRNA5-A3-B4 Variants and Smoking Cessation 

with Pharmacotherapy

Several studies have examined whether personalized smoking cessation treatments based on genotype 
A Report of the Surgeon General

152 Chapter 3

can improve cessation success. Such treatments require 

knowledge of whether genetic variants moderate the effects 

of the available pharmacotherapies for smoking cessation.

Baker and colleagues (2009) studied the effect of 

haplotypes on the basis of five tagging SNPs (rs680244, 

rs569207, rs16969968, rs578776, and rs1051730) in 

the CHRNA5-CHRNA3-CHRNB4 locus. For participants 

receiving either bupropion or placebo, the haplotypes were 

associated with tolerance, craving, and loss of control, but 

only among persons who had started smoking early in life. 

Elsewhere, Munafò and colleagues (2011) found 

evidence for a weak association between the same locus, 

looking at the risk variant rs1051730 in CHRNA3 and at 

the short-term ability to quit smoking in heavy smokers 

receiving either the placebo or NRT. Interestingly, the 

effect size reported in this study was comparable to the 

effects found in the studies of pregnant women (Freathy 

et  al. 2009; Thorgeirsson and Stefansson 2010) and the 

study by Baker and colleagues (2009).

Chen and colleagues (2012) conducted a large 

study to examine genetic associations with age of cessation. CHRNA5-A3-B4 risk haplotypes (rs16969968 and 

rs680299, both in CHRNA5) were associated with the 

number of cigarettes smoked per day and a later quitting 

age; the latter was no longer associated with the haplotypes 

when the analysis was adjusted for the number of cigarettes smoked per day. This study suggested that intensity 

of smoking, measured as the number of cigarettes smoked 

per day, impedes cessation. Furthermore, carriers of the 

medium- to high-risk haplotypes found abstinence more 

difficult, but if carriers received pharmacologic treatment 

(e.g.,  nicotine patch, nicotine lozenge, bupropion), they 

showed an increased rate of quitting success.

A meta-analysis by Bergen and colleagues (2013), 

which included eight RCTs, found that 6 months after a 

quit attempt, the risk allele rs1051730 was associated with 

higher rates of abstinence in the NRT group compared 

with the placebo group. The authors of this study assessed 

the association of four SNPs with smoking cessation and 

response to medication at the end of the treatment (8- to 

12-weeks post-quit) and after 6 months. The genetic variants were rs1051730, rs578776, and rs588765 in CHRNA5 

and CHRNA3, and rs2072661 in CHRNB2. CHRNB2 has 

been associated with a number of smoking cessation phenotypes, such as abstinence, FTND, and nausea among 

treatment-seeking smokers randomized to behavioral 

therapies and prescribed varenicline (Ehringer et al. 2007; 

Conti et al. 2008; Wessel et al. 2010; Swan et al. 2012). 

The eight RCTs considered in the meta-analysis employed 

placebo, NRT, bupropion, varenicline, or a combination of 

NRT and bupropion (along with a variety of counseling 

options). Although rs2072661 and rs578776 were not 

associated with smoking cessation, rs1051730 and, to a 

lesser degree, rs588765 were associated with quitting 

success in persons randomized to NRT and in those who 

received the placebo. Participants in the placebo conditions were less likely to be abstinent after 6 months, but 

those who received NRT were more likely to achieve abstinence after that time. Mediation analysis indicated that 

rs1051730 increased nicotine dependence—a variable 

that decreases the success of abstinence—and that a further mechanism (speculated to be abstinence-induced 

impairment in cognitive function) increased abstinence 

in the NRT group at the 6-month follow-up from the end 

of drug administration.

Two subsequent studies—a meta-analysis of four 

studies and a clinical trial—did not confirm these findings. The meta-analysis revealed no evidence at the end of 

NRT that rs16969968 or rs1051730 were associated with 

cessation (Leung et al. 2015). The clinical trial, conducted 

by Tyndale and colleagues (2015), examined the association between CHRNA5-A3-B4 haplotypes and smoking 

abstinence, finding no associations between rs16996968, 

rs578776, and rs588765 and abstinence at 6- or 12-month 

follow-up in participants who received placebo, NRT, 

or varenicline.

An important factor in smoking cessation is adherence to treatment. Ware and colleagues (2015), who 

studied this phenotype in a secondary analysis of data 

from an RCT of smoking cessation, found an association 

between rs1051730 and adherence to NRT after 7 days of 

the quit attempt but not after 28 days. Each copy of the 

minor allele corresponded to a 2.9% decrease in adherence to the prescribed dose of NRT over 7  days. This 

association was robust to adjustments made for age, sex, 

socioeconomic status, trial condition, body mass index at 

baseline, and daily cigarette consumption at baseline.

Most studies to date have used samples of European 

ancestry, but a few have examined samples from other 

populations, including African Americans. For example, 

in a small deep-sequencing discovery study of African 

Americans, Hamidovic and colleagues (2011) reported 

an association between rs12915366 in CHRNA5 and 

rs12914385 in CHRNA3 and smoking persistence. David 

and colleagues (2012), who performed a genomewide 

meta-analysis of 13  studies of African Americans, found 

that rs2036527, which is in linkage disequilibrium with 

rs1051730, was significantly associated genomewide with 

the number of cigarettes smoked per day. In another study 

of African Americans, Zhu and colleagues (2014) failed 

to find an association between rs16969968 and smoking 

abstinence in either the placebo or NRT group. In contrast, the minor allele of rs588765 was associated with 

lower abstinence in the placebo group and greater abstinence in the group receiving NRT during treatment but 

not after 6 months.
New Biological Insights into Smoking Cessation 153

Smoking Cessation

The study by Zhu and colleagues (2014) also reported 

an association, both during and at the end of treatment, 

between the risk allele of rs2036527 in CHRNA5 and lower 

smoking abstinence in those who received NRT but not in 

the placebo group. Interestingly, adjusting the analyses for 

the number of cigarettes smoked per day had a negligible 

effect. The rs2036527 SNP was in high linkage disequilibrium with rs1051730, and these findings are consistent 

with the association reported by Munafò and colleagues 

(2011) for rs1051730 and short-term smoking cessation in 

their study of a European population. These findings suggest that linkage disequilibrium structures differ between 

European and African American populations.

Overall, although the association between the 

CHRNA5-A3-B4 gene cluster and smoking intensity is 

robust, its role in smoking cessation needs further investigation, and currently no clear evidence exists that it 

influences responses to specific pharmacotherapies. Some 

of the inconsistent results may be due to differences in 

methods and sampling or to environmental factors that 

influence each study. AGS could be employed to explore 

the collective genetic influence of several variants that 

may exert a role in complex phenotypes, such as smoking 

behaviors, but more work is required to understand the 

role of these genes in ethnic groups other than those of 

European ancestry.

The CYP2A6 Gene and the Nicotine 

Metabolite Ratio

Nicotine from cigarette smoke is distributed in the 

body via the bloodstream (Benowitz et al. 2009). Its elimination half-life is around 2 hours, and up to 90% of nicotine 

is converted to cotinine, mainly by the metabolic enzyme 

CYP2A6, which, in turn, is solely responsible for the 

metabolism of cotinine to 3’-hydroxycotinine (Benowitz 

and Jacob 3rd 1994; Tanner and Tyndale 2017). Nicotine is 

also metabolized to more minor metabolites by additional 

enzymes, including FMO3 and UGT2B10 (Benowitz et al. 

2009). NMR is the ratio of 3’-hydroxycotinine to cotinine; 

studies of twins have estimated that about 60% of the variation in NMR is due to genetic factors (Swan et al. 2004). 

Importantly, CYP2A6 enzyme activity is reflected by NMR 

(Dempsey et al. 2004; Johnstone et al. 2006; Malaiyandi 

et al. 2006a). CYP2A6 is a highly polymorphic gene (with 

>30  genetic variants), and its numerous variants have 

an impact on NMR. Grouping variants, however, is possible according to the impact of CYP2A6 on the rate of 

NMR (i.e., faster or slower). Importantly, NMR also captures environmental influences (e.g.,  hormonal therapies and body mass index). Furthermore, NMR values are 

stable across time and exhibit high test-retest reliability 

when measured 2 to 3 weeks apart (Hamilton et al. 2015). 

Despite no consensus on the cut-off point between faster 

and slower metabolizers, several studies have used the 

lowest 25–50% of the NMR distribution to classify slower 

metabolizers (Lerman et al. 2006b; Ray et al. 2009; Schnoll 

et al. 2009; Dubroff et al. 2015).

Nicotine Metabolite Ratio and Smoking Behavior

The GWAS by Thorgeirsson and colleagues (2010) 

found an association between reduced smoking quantity, measured as the number of cigarettes smoked per 

day, and variants in or near CYP2A6 that reduce the enzymatic activity of CYP2A6 (in particular, rs4105144). Later, 

Loukola and colleagues (2015) conducted a GWAS metaanalysis of current smokers using data from three Finnish 

cohorts and identified novel genetic variants associated 

with NMR. Their study detected three strong independent signals in the immediate vicinity of CYP2A6: SNPs 

rs56113850, rs113288603, and rs2663194. Although 

the functional consequences of the first two SNPs are 

unknown, the third one is associated with a decreased 

clearance rate, and the three SNPs captured up to 31% of 

the total variance in NMR.

NMR has been assessed in several studies to further characterize smoking behavior. In one study, slower 

metabolizers smoked an average of 6  to 7  fewer cigarettes per day and had an earlier smoking onset by about 

1 year (Schoedel et al. 2004). Other studies found slower 

metabolizers to be less dependent on nicotine, as measured by the FTND (Malaiyandi et  al. 2006b; Wassenaar 

et al. 2011; Sofuoglu et al. 2012), and slower metabolizers 

took longer to become dependent on nicotine (AudrainMcGovern et al. 2007; Al Koudsi et al. 2010). Fast metabolizers exhibited a higher total cigarette puff volume 

(Strasser et al. 2011). This finding is consistent with the 

observation that fast metabolizers require higher levels of 

nicotine intake than those with a slower nicotine clearance, which is consistent with self-titration by smokers to 

achieve the desired circulating level of nicotine (Strasser 

et  al. 2007). Adolescents who were slow metabolizers, 

however, had a higher risk of becoming nicotine dependent compared with fast metabolizers (Chenoweth et al. 

2013, 2016). It is not clear if adolescent smokers titrate 

their level of nicotine intake according to their NMR to 

maintain desired levels, but Chenoweth and colleagues 

(2013) found that once adolescents who were slow metabolizers became dependent on nicotine, they smoked fewer 

cigarettes and were more likely to become adult smokers 

(Chenoweth et al. 2013). In fact, slow metabolizers who 

were adults were more likely than fast metabolizers to 

successfully quit smoking in the absence of pharmacotherapy (Gu et al. 2000; Patterson et al. 2008; Chenoweth 

et al. 2013).
A Report of the Surgeon General

154 Chapter 3

In a study of mice, Bagdas and colleagues (2014) 

used an inhibitor of CYP2A5, the mouse ortholog of human 

CYP2A6, to mimic the slower nicotine metabolism of 

humans. The effects of this manipulation were illustrated 

using a CPP task. A low dose of nicotine administrated on 

one side of a box, versus saline administrated on the other 

side, did not induce a CPP in mice in the control group. In 

contrast, mice treated with the CYP2A5/6 inhibitor before 

being exposed to nicotine developed a CPP for the nicotine 

side and showed increased levels of plasma nicotine. Thus, 

it appears that the treated mice had become more sensitive to the effects of nicotine. Li and colleagues (2013a) 

reported similar results from a study that measured CPP 

in CYP2A4/5 knockout mice that were exposed to nicotine. In addition, Bagdas and colleagues (2014) administered nicotine to naïve mice across 5 days and pretreated 

half of the mice with the CYP2A5/6 inhibitor; they then 

tested the somatic signs of withdrawal after nicotine abstinence. The pretreated mice showed a potentiation of the 

intensity of somatic signs of withdrawal and higher levels 

of plasma nicotine. In summary, the mice tested in these 

studies experienced a decrease of nicotine clearance, similar to human slow metabolizers, and a greater exposure 

to nicotine in these mice enhanced nicotine dependence 

and affected nicotine withdrawal behaviors.

Nicotine Metabolite Ratio and Smoking 

Cessation in Absence of Treatment

Gu and colleagues (2000), who compared the likelihood of quitting smoking between slow and fast metabolizers, found that slow metabolizers were almost twice 

as successful in quitting smoking. Later, in a prospective 

cohort of adolescents, Chenoweth and colleagues (2016) 

also assessed the hypothesis that slow metabolizers are 

more likely to quit smoking than fast metabolizers and 

found a linear relationship between CYP2A6 activity and 

quit rate: slow metabolizers were more than twice as likely 

as fast metabolizers to quit smoking.

Smoking Cessation in Treatment Seekers

Compared with slow metabolizers, fast metabolizers have a higher NMR and inactivate nicotine quickly. 

A higher NMR results in lower levels of nicotine in the 

blood. Lerman and colleagues (2006b) found that a lower 

NMR was associated with increased odds of abstinence, 

both at the end of treatment and after 6 months, in persons who received a nicotine patch but not in those who 

received nicotine in the form of nasal spray, suggesting 

that, in contrast with transdermal nicotine (for which the 

dose is fixed), users of nicotine nasal spray may titrate 

their intake of nicotine. Furthermore, cravings for cigarettes after 1 week of abstinence were more severe in fast 

metabolizers who received the transdermal patch. A subsequent study by Lerman and colleagues (2010) found 

that slow metabolizers benefitted from using the transdermal nicotine patch for an extended period of time 

(i.e., 6 months vs. the standard 8 weeks).

Some evidence suggests that bupropion enhances 

the quit success of fast metabolizers and that the nicotine patch enhances the quit success of slow metabolizers. Patterson and colleagues (2008) assessed the baseline NMR in smokers who subsequently participated in 

a 10-week randomized trial of bupropion versus placebo 

with counseling support. With placebo, quit rates were 

lower among fast metabolizers than slow metabolizers, 

but with bupropion, quit rates were similar between fast 

and slow metabolizers.

Because slow metabolizers showed no difference in 

the likelihood of relapse in either the placebo or bupropion conditions, Lerman and colleagues (2015) conducted 

an NMR-stratified, placebo-controlled, randomized trial of 

nicotine patch versus varenicline to test whether varenicline had a superior effect compared with placebo. On the 

basis of evidence for an interaction of NMR by treatment, 

fast metabolizers receiving varenicline had higher odds of 

being abstinent. These studies suggest that NMR may be a 

predictive biomarker that can be used to personalize treatments for smoking cessation.

Summary

This section examined the role in smoking cessation played by candidate genes in the dopamine system 

(dopamine receptor D2, DRD2, and the dopamine transporter, DAT1) and variants in the CHRNA5-A3-B4 gene 

cluster and the CYP2A6 gene. Despite early evidence for 

associations between genetic variation in DRD2 or DAT1 

and smoking cessation and response to smoking cessation therapy, subsequent studies have failed to replicate 

these findings. In contrast, the small but robust association between the CHRNA5-A3-B4 gene cluster and 

smoking intensity and nicotine dependence has been replicated in several candidate gene studies and GWAS, and 

smoking intensity and nicotine dependence predict the 

success of cessation. Whether variants in this gene cluster 

influence responses to specific pharmacotherapies is still 

not clear. Investigating polygenic risk scores may better 

capture the quitting success and variations in responses 

to medication.

More consistent results have been provided by 

studies assessing CYP2A6 or related biomarkers, such 

as NMR, and smoking cessation (both with and without 

pharmacologic treatment). A linear relationship exists 

between CYP2A6 activity and quit rate: slow nicotine 
New Biological Insights into Smoking Cessation 155

Smoking Cessation

metabolizers are more likely than fast metabolizers to 

quit smoking. In addition, studies suggest that bupropion 

and varenicline enhance the quit success of fast metabolizers, and the nicotine patch enhances the quit success of 

slow metabolizers.

Schuit and colleagues (2017) published the first 

Cochrane systematic review and meta-analyses of pharmacogenetic biomarkers for smoking cessation, which 

included clinical trials with available genetic or NMR data 

for all approved smoking cessation pharmacotherapies, 

all genomewide significant SNPs for number of cigarettes 

smoked per day or smoking cessation, non-SNP polymorphisms with replication, and NMR. Data were available 

for 18 clinical trials and the following gene variants: nine 

SNPs (rs1051730 [CHRNA3]; rs16969968, rs588765, and 

rs2036527 [CHRNA5]; rs3733829 and rs7937 [in EGLN2, 

near CYP2A6]; rs1329650 and rs1028936 [LOC100188947]; 

and rs215605 [PDE1C]), two variable number tandem 

repeats (DRD4 and SLC6A4), and the NMR biomarker.

The meta-analyses indicated that genotype groups 

within certain ethnic groups may benefit more from NRT 

than from placebo (non-Hispanic Black individuals at 

6-months with rs169969968 GG genotype, slow metabolizers, non-Hispanic White and non-Hispanic Black individuals at the end of treatment with rs1051730 GA or AA 

genotype, and rs169969968 GG genotype) and from NRT 

(non-Hispanic Black individuals with rs2036527 GG genotype), or may benefit less from a combination of bupropion 

with NRT (non-Hispanic White individuals with rs1329650 

TT genotype and non-Hispanic Black individuals with 

rs3733829 AG or GG genotype). These results should be 

interpreted with caution because none of the statistically 

significant meta-analyses from placebo-controlled trials 

included more than two trials per genotype comparison, 

many confidence intervals were wide, and the quality of 

this evidence was generally moderate. Although evidence 

existed of superior NRT efficacy for NMR of normal versus 

slow metabolizers, the authors could not conclude that 

NRT is more effective in slow metabolizers. Given the 

number of trials and investigators who did not provide or 

publish meta-analyzable data, access to additional data is 

needed, particularly for comparisons of different pharmacotherapies to improve the reliability of meta-analysis and 

the potential clinical utility of genomic testing to guide 

treatment choice for smoking cessation.

Benefits may be derived from personalized precision 

tailoring of interventions based on genetic approaches. 

The efficacy of treatment could be improved by assigning 

patients to a specific treatment based on the results of 

genetic or biomarker testing. However, for a pharmacogenetic approach to be cost-effective, the effect size must be 

substantially larger in one stratum compared with another 

stratum. Other considerations, such as the proportion of 

the population that falls into each stratum, are also relevant. In particular, before pharmacogenetic or biomarker 

stratification becomes routine in clinical practice, an RCT 

should be conducted to determine whether this approach 

improves overall cessation outcomes. Ideally, the RCT 

would also include a health economic analysis to help 

determine the cost-effectiveness of this approach.

Summary of the Evidence

Although current pharmacotherapies are effective in increasing quitting, many current smokers want 

to quit but have been unable to sustain abstinence, so 

smoking remains one of the leading causes of preventable disease and death globally. Decades of preclinical 

advances have improved our understanding of the neurobiologic mechanisms underlying nicotine addiction. 

Although more remains to be understood, this information has identified dozens of novel and promising targets 

for pharmacologic intervention that remain to be evaluated in humans. Preclinical studies suggest that targeting 

multiple stages of addiction may be the most effective way 

to reduce smoking.

Immunotherapies for nicotine dependence offer an 

alternative therapeutic mechanism, producing antibodies 

that bind nicotine in blood and reduce nicotine delivery 

to the brain (see “Vaccines and Other Immunotherapies 

as Treatments for Tobacco Addiction”). This approach 

involves targeting the drug rather than the brain, potentially reducing the side effects of existing medications to 

treat nicotine dependence and perhaps treating a limited repertoire of smoking behaviors (see “Insights into 

Smoking Cessation from the Field of Neurobiology”). 

Immunotherapies are highly effective in animal models 

for blocking nicotine reinforcement, but they have not yet 

been effective in Phase 3 clinical trials for smoking cessation, at least in part because of insufficient and variable 

antibody concentrations in humans.

Multiple cognitive systems (e.g., attention, reward, 

inhibitory control) and affective processes (negative and 

positive emotion) are dysregulated in nicotine dependence, which might help to explain poor treatment outcomes. Regions of the brain involved in the maintenance 

of smoking and nicotine withdrawal include the anterior 

and posterior cingulate, amygdala, insula, striatum, and 

orbitofrontal cortex. Large-scale brain networks altered as 
A Report of the Surgeon General

156 Chapter 3

a result of nicotine dependence include the default mode, 

salience, and executive control networks. Circuit and network connections may serve as predictive biomarkers to 

personalize treatment choices and as predictors of the 

outcomes of cessation treatment. More longitudinal neuroimaging studies are needed to understand brain alterations as a function of sustained abstinence. Neuroimaging 

and genetic analyses to fractionate the nicotine addiction 

phenotype would help to identify novel therapeutic targets. Transcranial magnetic stimulation, an FDA-approved 

treatment for depression, has been proposed as a treatment for addiction in general, but further evaluation is 

needed to determine its efficacy for smoking cessation.

Large GWAS are identifying molecular genetic influences on smoking phenotypes. The greater sensitivity of 

these large studies allows signals to be identified that may 

inform the search for potential therapeutic targets, but the 

studies require somewhat blunt phenotypes. The strongest 

evidence on potential therapeutic targets to date points to 

variants related to nAChRs (CHRNA5-A3-B4 gene cluster) 

and nicotine metabolism (CYP2A6  gene). Variation in 

these genes influences intensity of smoking and nicotine 

dependence, and an increasing amount of evidence suggests that such variation may influence smoking cessation 

and be useful for personalized optimization of therapeutic 

choice. Genetic variants associated with smoking behaviors also provide tools that can be used to support stronger 

causal inference in observational studies—for example, 

by treating these genetic variants as instrumental variables 

(a method known as Mendelian randomization, which is 

predicated on the assumption that because genotype is 

assigned randomly at meiosis it should not be associated 

with potential confounders at a population level) (Gage 

et al. 2016). Emerging evidence suggests that genetic variants may influence responses to smoking cessation treatments, offering the potential for personalized or stratified approaches to treatment. However, this approach 

requires a randomized clinical trial to determine its efficacy and cost-effectiveness. Future research should focus 

on assessing smoking cessation outcomes prospectively 

(e.g.,  by routinely collecting genetic data at baseline in 

RCTs of smoking cessation interventions) and using intermediate phenotypes (e.g., brain circuits that are relevant to 

nicotine dependence) through modern genetic approaches. 

Research should also investigate genetic predictors of 

responses to behavioral and pharmacologic interventions.

From a public health perspective, interventions to 

achieve smoking cessation must be developed that are 

more effective than the current options. The development 

of biologically based biomarkers for diseases involving 

organ systems has led to the development of successful 

therapies for a variety of these diseases. However, such 

biomarker research lags behind in the fields of addiction 

(in  general) and of nicotine dependence (in particular). 

It will be important to invest in continued efforts to translate findings and observations from animal models of nicotine addiction and apply them to clinical settings to provide novel, mechanistically sound therapies for humans.

Limited ecologic validity and questions about 

subsequent predictability are limitations to almost all 

studies summarized in this chapter. Smoking is frequently comorbid with other neuropsychiatric diseases, 

including schizophrenia, depression, and anxiety disorders. Moreover, persons who abuse nicotine also use other 

drugs, including alcohol and marijuana. And yet, most 

research cohorts involving drugs are only on the basis of 

smoking. Therefore, a better understanding of the connections between nicotine dependence and neuropsychiatric comorbidity dual-drug dependence is warranted. 

Similarly, responses to smoking pharmacotherapies 

clearly differ by sex, but to date, little work has focused on 

these differences, whether in basic neurobiology or in the 

interactions with pharmacogenetics. For example, some 

studies suggest that female smokers may be best treated 

by medications that do not interact directly with nicotinic mechanisms; this should be explored further. Sex 

differences also should be evaluated further in the pathophysiology of nicotine addiction and be considered when 

treating patients. A shift toward developing individualized, 

multifaceted approaches to smoking cessation is critical.
New Biological Insights into Smoking Cessation 157

Smoking Cessation

Conclusions

1. The evidence is suggestive but not sufficient to infer 

that increasing glutamate transport can alleviate 

nicotine withdrawal symptoms and prevent relapse. 

2. The evidence is suggestive but not sufficient to infer 

that neuropeptide systems play a role in multiple 

stages of the nicotine addiction process, and that 

modulating the function of certain neuropeptides 

can reduce smoking behavior in humans.

3. The evidence is suggestive but not sufficient to 

infer that targeting the habenulo-interpeduncular 

pathway with agents that increase the aversive properties of nicotine are a useful therapeutic target for 

smoking cessation.

4. The evidence is suggestive but not sufficient to infer 

that vaccines generating adequate levels of nicotinespecific antibodies can block the addictive effects of 

nicotine and aid smoking cessation.

5. The evidence is suggestive but not sufficient to infer 

that dysregulated brain circuits, including prefrontal 

and cingulate cortical regions and their connections 

with various striatal and insula loci, can serve as 

novel therapeutic targets for smoking cessation.

6. The evidence is suggestive but not sufficient to infer 

that the effectiveness of nicotine replacement therapy 

may vary across specific genotype groups.
A Report of the Surgeon General

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