Rutter JL. Symbiotic Relationship of Pharmacogenetics and Drugs of Abuse. AAPS Journal. 2006; 8(1): E174-E184. DOI:  10.1208/aapsj080121

Correspondence to:
Joni L. Rutter
Tel: (301) 443-1887
Fax: (301) 594-6043
Email: jrutter@mail.nih.gov

Received: December 8, 2005;  Accepted: January 18, 2006;  Published: March 24, 2006

Abstract

Pharmacogenetics/pharmacogenomics is the study of how genetic variation affects pharmacology, the use of drugs to treat disease. When drug responses are predicted in advance, it is easier to tailor medications to different diseases and individuals. Pharmacogenetics provides the tools required to identify genetic predictors of probable drug response, drug efficacy, and drug-induced adverse events—identifications that would ideally precede treatment decisions. Drug abuse and addiction genetic data have advanced the field of pharmacogenetics in general. Although major findings have emerged, pharmacotherapy remains hindered by issues such as adverse events, time lag to drug efficacy, and heterogeneity of the disorders being treated. The sequencing of the human genome and high-throughput technologies are enabling pharmacogenetics to have greater influence on treatment approaches. This review highlights key studies and identifies important genes in drug abuse pharmacogenetics that provide a basis for better diagnosis and treatment of drug abuse disorders.

Keywords: Pharmacogenomics, addiction, treatment, psychiatric disease, SNP

Introduction

Pharmacogenetics/pharmacogenomics is the study of how genetic variation among individuals affects their capacity to metabolize drugs (pharmacokinetics) and the drugs’ effects on the individuals (pharmacodynamics). Pharmacogenetics emerged several decades ago, but only recently have the tools been in place for the field to flourish, led in part by the availability of the human genome sequence and the improving genotyping technologies. Pharmacogenetics provides the foundation for scientists to identify biological predictors of drug response, drug efficacy, and drug-induced adverse events to enable clinicians to use the information to make the best treatment decisions. The understanding of genetic variation may hold the most promise in clinically evaluating and predicting a drug’s effects, and successfully treating a patient.

Substance abuse is a unique psychiatric disorder given that genetic vulnerability can lead to disease only if the substance (licit or illicit) is readily available and used. In addition to the public health burden, addiction disorders cost society over $500 billion per year.¹ The gene-environment interaction of substance abuse and addiction may provide scientific building blocks to advance the field of pharmacogenetics. Understanding the genetics of the pharmacokinetics and pharmacodynamics of drugs, whether or not they are abused, can provide relevant crossover concepts for tailoring drug treatments. Drug use triggers the onset of the addictive process and maintains the neuroplastic changes after chronic use.² Thus, the genetics of substance abuse and addiction, by nature, is pharmacogenetic. The current impetus is to predict actual individual differences in risk of drug abuse vulnerability, drug response, and response to treatments for drug abuse based upon knowledge about specific genes. Genetic differences could contribute to acute drug responses (aversive vs nonaversive) and may also predict drug use and/or drug response.

Environmental interventions are likely to be most effective in reducing the number of people who start to use drugs, including alcohol and tobacco; however, these interventions may not be as effective in helping those already addicted. To unravel and point to biological targets highlighting genetic variants important for understanding addiction, drug treatment, and drug response, genetic and neurobiological studies are needed; they will help clarify the complex nature of addiction, which has a 40% to 60% genetic variance.^3-5 Although genetic studies are critical to advancing the field, some misconceptions are that genetic information will be used solely to identify those at high risk of addiction by screening people for susceptibility alleles,⁶ or that susceptibility alleles will be “replaced” through gene therapy.⁷ These approaches are not likely to be effective or efficient. Instead, studies to understand gene variants that confer vulnerability and predict treatment response are needed to provide a comprehensive approach to treating addiction effectively and to inform prevention strategies that will have a significant public health impact.

This review highlights some of the research from the field of drug use, abuse, and addiction and the importance of pharmacogenetics/pharmacogenomics in working toward better treatments for these types of psychiatric disorders.

Pharmacogenetics of Metabolism, Drugs of Abuse, and Addiction Genetics

Pharmacogenetics of Drug Metabolism: Therapy Versus Toxicity

Drug treatment can result in effective therapy, can fail, and/or can create toxicities and other side effects. Administering the same dose of a given medication to individuals with different drug-metabolizing genotypes gives rise to differences in drug response phenotypes—namely, the drug’s therapeutic and toxic effects.⁸ Genetic variation in metabolism genes alters the activation of a drug, which can result in more potent metabolites, different metabolic patterns with varying half-lives, or inactive metabolites (eg, nicotine metabolized to inactive cotinine by CYP2A6). If certain drugs are not metabolized appropriately or are given at the wrong dose, they can cause liver damage, triggering certain enzymes normally found in the liver to be secreted into the bloodstream, signifying distress in liver function. Although plasma blood tests are used to assess high liver enzymes that indicate toxicity, this approach is crude since it does not indicate potential toxicities affecting other organs or take into account the reasons for the toxicity. Once liver toxicity is identified, the remedy often results in discontinued use of a needed medication, or a change in the medication to one that may be less effective.

Most drugs are metabolized in the liver by the cytochrome P450 (CYP450) system, with metabolizing enzymes CYP3A4 and CYP2D6 responsible for the largest contribution. The CYP450 enzymes are expressed in most cells and are particularly abundant in the liver.⁸ Although the roles of these enzymes are known (eg, metabolism of compounds), there is little understanding of how genetic variation in metabolizing enzymes, transporters, and receptor targets can be used in the clinical environment. One of the central aspects of pharmacogenetics involves understanding the genetic variations within the CYP450 system and how the variations affect drug metabolism and ultimately contribute to treatment response.

Extrahepatic organs such as the brain, kidney, and lung also express CYP450 enzymes that metabolize drugs. For psychiatric diseases and drugs that act centrally, in the central nervous system, the brain is an important organ to consider. It is a heterogeneous organ, so drug metabolism in different cell types may create microenvironments with differing drug and metabolic levels, which would not necessarily be predicted by plasma drug monitoring. This concept is important for toxicity, since hepatic cells can regenerate but neurons cannot. Thus, single nucleotide polymorphisms (SNPs) or other types of genetic variations in metabolism genes as well as other genes involved in drug receptor/transporter pathways may account for variations in responses to centrally acting drugs and may affect receptor adaptation, toxicities, altered drug effects, and cross-tolerance.⁹ The following Web page has a list of SNPs in the metabolism genes: http://www.genome.utah.edu/genesnps/cgi-bin/query.cgi?FunctionClass=2. Further pharmacogenetic studies are needed to establish connections between CYP450 expression in the liver and in specific brain regions to determine the consequences on drug effects and brain function.

Smoking

Nicotine is a psychoactive substance responsible for establishing and maintaining tobacco dependence. There are more than 1.1 billion smokers worldwide, each dictating nicotine intake through markedly different behavioral patterns.¹⁰ Smoking is a highly regulated behavior; those addicted are precise in maintaining steady-state brain levels of nicotine.¹¹ A recent prospective study examining the health consequences of smoking concludes that smoking as little as 1 to 4 cigarettes per day engenders significantly higher risk for all causes of mortality, most notably, heart disease and lung cancer.¹² Relative risks associated with increased cigarette consumption are unclear.^13-15 However, evidence indicates that smoking fewer cigarettes does not provide much, if any, protection.

When heavy smokers (at least 20 cigarettes per day) reduced their nicotine intake to 5 cigarettes per day, so that they more closely resembled “chippers” (smokers who average 1-5 cigarettes a day but do not become dependent), the heavy smokers compensated by increasing their nicotine intake 3-fold by lengthening their puff duration, but the chippers did not.¹⁶ Pinpointing the genetic variance accounting for the level of self-regulation among smokers is an important step toward understanding the risks of becoming addicted, the consequences of being addicted, and the treatment options for those already addicted. In addition to prevention efforts, smoking cessation therapies with tailored drug treatments are important in fighting the health consequences of smoking. This paradigm may also be applied to other drugs of abuse as well as to treatment medications for other disorders. The National Institute on Drug Abuse (NIDA) has initiated an emphasis on this important avenue of smoking cessation research.

Drug prevention efforts will continue to be a public health need, with emphasis on targeting adolescents through school programs. However, even though some of these programs have shown reductions in cigarette use rates of up to 20%, less than 10% of the school districts are using the programs.¹⁷ Much of the success in decreasing the smoking prevalence since the 1950s has been realized through these types of preventive/educational programs (Figure 1). The steady decrease in prevalence from 1970 to 1990 appears to have reached a plateau and looks to have fluctuated little since 1990 (Figure 1 inset). This leveling off may indicate a significant preventive/educational role in decreasing the prevalence of smoking for a proportion of the population. The plateau may suggest that prevention may not be as effective in the actively smoking population, indicating that current smokers who cannot quit may represent a more genetically at-risk population. Understanding the genetics and pharmacogenetics of smoking may provide insight into the best treatments for those already addicted.

Figure 1. Historical Events Affecting Smoking in the United States, 1900-2000. Adapted from the US Department of Agriculture and the US Department of Health and Human Services (DHHS), 1986 Surgeon General’s Report and Morbidity and Mortality Weekly Report (www.cdc.gov/tobacco/news/achievements99.htm) 1999.

Smoking Pharmacogenetics

Data from twin studies provide consistent and strong evidence for the heritability of smoking addiction (estimates range from 0.4 to 0.6).^18-20 Although the precise genetic variants responsible are not clearly delineated, research has focused on genes in the dopamine, serotonin, glutamatergic, and norepinephrine receptor pathways, and the CYP450 metabolism pathways, such as DRD2-4, DAT1, TPH1, GABAB2, 5 HTT/SERT, MAO-A, CYP2A6, CYP2D6, CHRNA4, and ANKK1 (Table 1).^21-31

Approximately 70% to 80% of nicotine is metabolized to an inactive metabolite by CYP2A6. There are several genetic polymorphisms within CYP2A6 that have been associated with nicotine metabolism and consumption. Since each individual receives 2 copies of every gene—maternal and paternal—each individual may have different combinations of forms of the gene, depending on the genetic variation that was inherited. Several studies have shown that polymorphisms in CYP2A6 account for high enzyme activity.^41,42 Individuals receiving 2 copies of this allele are able to metabolize nicotine rapidly, while individuals receiving only 1 copy of the allele or no copies are intermediate or slow metabolizers, respectively.⁴¹

Effective smoking cessation pharmacologic intervention strategies are currently in use, such as nicotine replacement therapies (NRTs) and some nonnicotine agents (eg, antidepressant medications like bupropion).⁷⁴ Nevertheless, up to 80% of cessation attempts using these medications result in relapse within the year.⁷⁵

NRTs such as nicotine gum, patches, inhalers, and nasal sprays can significantly increase smoking cessation rates compared with behavioral counseling⁷⁶ and are the standard treatment for smoking dependence. NRTs seem to be most effective for treating the withdrawal symptoms accompanying cessation but are not as effective in reducing the craving for cigarettes, which often renders the quit attempt unsuccessful.^77,78 However, some NRTs work well, in part because of the genotypic profile of the individual taking them. Lerman et al examined the mu-opioid receptor variant OPRM1 A118G (discussed in further detail below) in either the transdermal nicotine patch or nicotine nasal spray in 320 smokers.⁶⁴ Smokers who were given the transdermal nicotine and who had the G allele were more likely to be abstinent by the end of the treatment period (odds ratio [OR] = 2.4; 95% confidence interval [CI] = 1.14-5.06).⁶⁴ Although the sample size was small, this finding gives credence to future studies attempting to identify gene variants for predicting successful treatment.

Of other nonnicotine pharmacologic agents for smoking cessation, bupropion seems to be the most successful to date, offering further support for genetic profile–tailored agents, with some caveats. Swan et al indicate that women with at least one A1 allele of the DRD2 gene reported early termination of the bupropion because of side effects (OR = 1.91, 95% CI = 1.01-3.60), but this result was not seen in men.²⁹ In 2 randomized clinical trials, Lerman et al examined DRD2 variants in subjects given NRT or bupropion, respectively, for tobacco dependence. The bupropion trial showed that subjects homozygous for the ins C variant of the -141 C ins/del SNP responded better to bupropion compared with those carrying the del C allele (OR = 4.99; 95% CI = 1.42-17.62; P = 0.01).⁴⁸ The NRT trial, on the other hand, suggests that individuals carrying the ins C allele had fewer quit attempts than those with the del C (OR = 0.44; 95% CI = 0.25-0.79; P = .006). Thus, NRT may be most beneficial for those with the del C allele and bupropion best for those with the ins C allele.⁴⁸

Newer drugs are showing early promise for more successful smoking cessation, including rimonabant and varenicline.⁷⁹ Rimonabant is a selective cannabinoid receptor 1 (CB1) blocker. CB1 is expressed in brain and adipose tissue and is known to regulate food intake. Upon chronic stimulation with nicotine, the endocannabinoid system (and CB1) become dysregulated and imbalanced.^36,37 By blocking CB1, rimonabant stabilizes the endocannabinoid system. In clinical trials, patients receiving up to 20 mg of rimonabant were twice as likely to quit smoking and stay abstinent for significantly longer (36.2%) than the placebo group (20.6%). Making the drug even more attractive is that on average, patients on rimonabant lost over a half-pound, while those on placebo gained 2.5 pounds.⁸⁰

Varenicline is a partial nicotine agonist and selective nicotinic receptor modulator to the α4β2 nicotinic receptor. Genetic variants within the CHRNA4 gene encoding the α4β2-containing nicotinic acetylcholine receptor seem to play a role in modulating the effects of nicotine and alcohol in mouse strains.³⁵ Varenicline appears to act on α4β2 receptors to remove feelings of reward from smoking and to prevent withdrawal symptoms.⁷⁹ Current NIDA-supported studies will further examine genetic variants known in metabolism and other targeted pathways of these medications. Although years away, a major goal is to determine a genetic profile that will result in high success rates for people given these medications.

Another treatment for smoking cessation is the nicotine vaccine. One vaccine, NicVAX, confers active immunity by inducing the formation of antibodies to nicotine.^81,82 The vaccine binds to nicotine prior to its crossing the blood-brain barrier and prevents it from entering the brain. In rats, NicVAX is effective in reducing nicotine’s access to the brain more than it reduces nicotine’s access to other tissues.⁸¹ The vaccine had a good safety profile in a recent clinical trial. Healthy smokers receiving the vaccine did not smoke more cigarettes during the study period and did not experience cravings or withdrawal symptoms.⁸³ As the vaccine proves successful in some addicted smokers, pharmacogenetic studies may provide the evidence needed to prevent smoking addiction and expedite smoking cessation in vulnerable individuals.

NRTs and bupropion inhibit smoking for some smokers. However, many relapse, indicating that maintaining long-term abstinence is more difficult.^76,84 Although these 2 treatments are available, genetic information is not currently being used to pinpoint which treatment modality would be most effective. Emerging pharmacogenetic data, such as the DRD2 -141C ins/del SNP and treatment response to NRT or bupropion, seem to support the notion that practitioners will eventually be able to individualize the choice of pharmacotherapies to be prescribed based on the genotypes of their patients who smoke. Perhaps this tailored treatment approach, along with the newer medications, will reduce the high rates of relapse.

Heroin and Other Opioid Substances

Heroin is an illicit opioid that is commonly abused. Genetic studies have revealed that heroin addiction disorder is highly heritable.^32,85-87 Several genes are associated with opioid addiction (DRD2-4, CYP2D6, DAT1, 5HTT/SERT, GABRG2, OPRM1, OPRD1, OPRK1, CNR1; see Table 1).^1,32 One recent study identified DRD2 as a strong susceptibility gene for heroin dependence in Chinese subjects (OR = 52.8; 95% CI = 7.2-382.5), but in a German sample this gene was associated with a low risk of heroin dependence.⁴⁹ These findings suggest that DRD2 is a risk gene specific to heroin dependence in certain populations, as it has not been associated with disorders involving use of other substances, including alcohol and cocaine, in varying populations.^50-54,56 Also, the DRD2 variant may be in linkage disequilibrium with another variant that is the actual functional variant. One possibility is the ANKK1 gene, which is a serine/threonine kinase involved in signal transduction pathways.³¹ Consequently, further clarification of the actual role in heroin addiction and potential roles in other drug and alcohol addictions is needed, including more dense SNP maps to reveal the underlying genetic architecture for DRD2, ANKK1, and other addiction candidate genes.

The OPRM1 susceptibility gene is the primary site of action of the most commonly used opiates, including heroin, morphine, and most drugs used to treat opiate dependence, such as methadone, LAAM (levo-alpha-acetylmethadol), and buprenorphine. A polymorphism in OPRM1 changes the more common allele at position 118 from an A to a G (A118G) and results in a functionally relevant coding change in the protein from an asparagine residue at amino acid position 40 to an aspartate residue. The literature is inconsistent regarding associations of the A118G polymorphism with addictions, possibly because of differences in minor allele frequencies among different populations,^32,65,66 the need for larger sample sizes to detect associations of small effect, or linkage disequilibrium associations with another allele. It has been suggested that the mu-opioid receptors encoded by the aspartate variant bind beta-endorphin and activate receptors more potently,⁶⁷ causing a gain of function, but this mechanism has not been replicated.^68-70 Zhang et al instead have shown by allele-specific expression in OPRM1 mRNA that the G-allele is 1.5- to 2.5-fold less abundant than the A-allele, translating to ~10-fold lower protein levels.⁷⁰ These results suggest a loss of function rather than a gain of function. Either way, the functional data presented provide strong evidence supporting the importance of A118G for abuse and addiction susceptibility.⁷⁰

Opioid Receptor Gene Variants in Opioid Pharmacogenetics

The A118G variant may be pharmacogenetically relevant as well. In a recent randomized, placebo-controlled clinical trial, Oslin et al⁷¹ examined the A118G polymorphism among patients treated for alcohol dependence with either naltrexone or placebo. Naltrexone is an opioid receptor antagonist that was originally used to treat dependence on opioid drugs but has been beneficial for treating alcohol dependence. In survival analysis for time to relapse, naltrexone-treated subjects carrying a 118G allele showed significantly longer time to relapse (OR = 3.52; 95% CI = 1.03-11.96, P = .04).⁷¹ These data, although preliminary, suggest that genotyping may be useful for identifying patients who may benefit from naltrexone more than they would from other treatment options. Since naltrexone’s effects are mediated by the mu-opioid receptor, investigating the outcomes of subjects treated with other medications acting through this receptor will be valuable, as the A118G variant may be a key predictor of response to other μ-opioid receptor-mediated drugs.

Opioid Receptors and Pain

There is a large array of individual variability in sensitivity and perception of pain.^34,88 Morphine is the most commonly prescribed opioid, although only 10% to 30% of chronic pain patients do not respond or have intolerable side effects,⁸⁹ especially those with renal disease who have an accumulation of active metabolite that is normally cleared by the kidney. Opioid treatment has been a conundrum for many treatment providers trying to strike a balance between pain management on the one hand, and abuse and addiction liability on the other. Dose escalation based upon patient feedback has resulted in discrepancies in pain management that have often resulted in under- or overmedication. A small (10%) portion of morphine is metabolized to morphine-6-glucuronide (M6G), an opioid agonist that contributes to morphine’s clinical effects.⁹⁰ In a study examining the effects of morphine and MG6 by measuring pupil diameters, heterozygous and homozygous individuals with the A118G variant had reduced M6G potency in constricting pupils, but this effect was not seen in variant carriers who were administered morphine.⁷² This finding indicates that A118G carriers are at lower risk for renal side effects of morphine therapy,⁷² especially in individuals with impaired renal function.

Heritability of pain perception has been reported to be in the range of 10% to 46%.^73,91 Other genes, such as OPRD1,⁶³COMT,³⁸CGRP,³⁴ and MCR1,^61,62 were shown to be associated with certain types of pain responses. COMT polymorphisms are associated with altering downstream responses of the mu-opioid neurotransmitter system, likely through changes in receptor concentration, binding affinity for beta-endorphin, or allele-specific expression, each of which can modulate pain perception.⁹²

Work by Fillingim et al⁷³ supports the role of the dopamine system and the opioid receptor pathway in pain perception. Healthy subjects with at least 1 rare allele (118G) exhibited lower sensitivity to pressure pain compared with subjects homozygous for the common allele (A118). Ross et al found no association with the A118G variant in cancer patients with chronic pain who were unable to tolerate morphine after dose escalation and switched to other opioid analgesics.⁸⁹ An association was found with beta-arrestin, a protein involved with desensitizing the mu-opioid receptor and internalized receptor trafficking.⁸⁹ These data suggest that other genes such as those involved with receptor signaling, receptor density, and receptor trafficking, in addition to those involved in the complex process of pain perception, require further study.

Pharmacogenetics has the potential to affect pain management by providing information on genetic variants that are associated with clinical effects and possibly by elucidating biological correlates of prescription opioid addiction risk. Research in this area is ongoing, and more studies are needed.

Amphetamines, Cocaine, and Other Stimulants

Abuse of amphetamine, especially methamphetamine, is a growing trend in the United States, with persistent abuse often causing a paranoid psychotic state.^93,94 Methamphetamine increases levels of dopamine in the brain and elicits euphoria, contributing to its addictive properties.⁹⁵ Heritability estimates for psychostimulants are around 66%.^86,96,97 Many of the genetic studies have concentrated on the contribution of variants within the dopamine system genes (DAT1, DRD2, and DBH; see Table 1) and GABA genes⁵⁷ to determine the association with abuse and treatment of cocaine and methamphetamine, but further studies focusing on other abused stimulants are needed. Glutamatergic transmission seems to be important for cocaine’s effects of tolerance and withdrawal. The genes HOMER1 and HOMER2 have recently been identified as mediators of glutamate transmission and may be good targets for future genetic and pharmacogenetic study.⁶⁰

Chen et al⁵⁵ examined the DRD2, DRD3, and DRD4 gene variants in 851 methamphetamine subjects with and without psychosis (416 methamphetamine abusers and 435 controls). Results showed that the 7-repeat (exon III) DRD4 polymorphism occurred more frequently in the methamphetamine abusers than in the controls (2% vs 0.6%; respectively, OR = 3.4; 95% CI = 1.2-9.4).⁵⁵ No other differences were noted. In a follow-up study, Li et al³⁹ examined the DRD4 gene variants in combination with the Val158Met COMT gene variant. There was a modest interaction among the high-activity allele of COMT and DRD4 genotypes (OR = 1.45; 95% CI = 1.1-1.8) among the methamphetamine abusers.³⁹ Another study showed significant main effects of these 2 genes but no interaction⁴⁰ suggesting that further studies are needed. If the interaction is upheld, it suggests that the lower-activity allele of COMT may be protective against methamphetamine abuse and that DRD4 alleles may also contribute to the protective effects.³⁹

The DAT1 gene is associated with the effects of amphetamine and cocaine. Several studies support the notion that genetic variations in DAT1 may contribute to the individual variability in response to these drugs. Gelernter et al demonstrated that the 9 allele of the DAT1 gene was present in more subjects with cocaine-induced paranoia than without cocaine-induced paranoia (P = .047).⁴³ The 9-repeat allele results in reduced expression of DAT1,⁴⁴ but it is not yet clear how or whether the reduced expression explains the effect of cocaine-induced paranoia. The 9-repeat allele of DAT1 contributes to reduced responsiveness to acute amphetamine administration,⁴⁵ suggesting that with amphetamine, the 9-repeat allele seems to protect individuals from dependence.⁴⁵ Indirectly supporting this finding is the finding that individuals with the 9-repeat allele showed a significantly reduced responsiveness to methylphenidate, an amphetamine-like compound used to treat attention deficit hyperactivity disorder.⁴⁶ If this finding is replicated, the 9-repeat allele may be an effective pharmacogenetic marker to predict who will not respond well to methylphenidate.

Mice with mutated dat1 have shown reduced binding to cocaine and methylphenidate but not amphetamine or methamphetamine, indicating that the binding sites for cocaine and methylphenidate may overlap but are distinct from those for amphetamine and methamphetamine.⁴⁷ Clinical trials examining drugs that target DAT1 have been somewhat disappointing, but some data suggest that genotyping for DAT1 may help clinicians select appropriate treatments for psychostimulant addictions.

In a Japanese population study, the GSTP1 I105V gene variant was associated with methamphetamine abusers with psychosis compared with controls (OR = 1.84; 95% CI = 1.13-2.97), but it was not associated with spontaneous relapse.⁵⁸ This variant results in a 30% decrease in enzyme activity,⁵⁹ which may contribute to reduced metabolism of methamphetamine and higher abuse risk.

Butyrylcholinesterase (BChE), an enzyme involved in cocaine hydrolysis, has genetic variants in the BCHE gene that affect the rate of cocaine detoxication.³³ The variant D70G in BCHE had a 10-fold lower binding affinity for cocaine, and individuals with this variant may not be able to metabolize cocaine as fast, resulting in severely toxic or fatal outcomes.³³ A pharmacogenetic approach would be to screen for this genetic variant in cocaine overdose victims so that treatment action for those with the G variant could be to provide them with exogenous BChE to attenuate the cocaine toxicity.

Conclusions

Pharmacogenetics may not yet be able to conclusively predict adverse events or ensure the most effective treatment, but it is contributing to the much-needed development of tailored care. In the future, an important part of the process of assigning an effective medication at an appropriate dose will be incorporating pharmacogenetic data that provide an understanding of how an individual’s profile of genetic variation will best predict treatment response and outcome. For example, taking the medication less frequently, taking dosages adjusted for the rates of metabolism inferred from a genetic profile, or changing the medication to one better tailored to a patient’s genetic makeup may prevent the onset of liver toxicities or other untoward side effects.

Addiction is a chronic brain disease⁹⁸ that is driven by 2 critical elements, genes and environment; neither can be ignored. Genetic studies have been invaluable in identifying addiction vulnerability loci and genetic variants that are important for understanding the neurobiology of the disease, pointing to potential drug targets, and identifying alleles that may be useful for tailored treatment interventions. Social and environmental elements such as family, peer, community, and social attitudes and beliefs are also important psychobehavioral domains that contribute to the complexity of addiction. Treatment and prevention interventions for substance abuse disorders and other complex diseases will have maximum benefit when multifaceted approaches, which incorporate genetic, pharmacologic, and environmental influences, are taken into account.⁹⁹ Management of drug abuse and addiction must be individualized according to the drugs involved and the specific problems of the individual patient. Effective treatment options for psychiatric diseases, which often include a high incidence of comorbidity or co-occurring disease, are still not well understood.^100,101 Many chronic disorders, such as addictions, diabetes, heart disease, and asthma, require behavioral intervention and/or long-term medication, but identifying the best treatment may involve trying several medications to determine which is most effective and has the fewest side effects. Some pharmacogenetics research suggests that pharmacogenetics may be a powerful tool for informing treatment options for those needing long-term pharmacotherapy.

This review highlighted many genetic targets for addiction vulnerability and treatment. However, the review is neither exhaustive nor complete. The genes reviewed in the article, listed in Table 1, are related to common pathways of addiction, such as dopamine, serotonin, glutamate, and metabolism. Many of the genes reviewed have overlapping roles for each class of drug discussed. Some of the genes may be associated with addiction in general, while some may be drug-specific.^102,103 When pharmacogenetic strategies to individualize the treatment of drug abuse and addiction are being designed, all of these genes, gene systems, and cell-type-specific expression of the genes must be considered.

The next step is to incorporate into the clinical setting those data that are reliably consistent. To this end, the US Food and Drug Administration is facilitating the use of pharmacogenetic data in drug development¹⁰⁴ and has issued several guidelines for industry as well as voluntary genomic data submissions from clinical trials (http://www.fda.gov/cder/genomics/regulatory.htm). Furthermore, analogous to the National Institutes of Health (NIH) Roadmap initiative, the neuroscience institutes within NIH have implemented the Blueprint Initiative (http://neuroscienceblueprint.nih.gov). One initiative is the NIH Blueprint Microarray Consortium, consisting of 4 microarray centers to serve the expression profiling and SNP genotyping needs of grantees within the neuroscience institutes. This resource will provide the most updated tools and allow investigations of many pharmacogenomic questions.

Other consortiums are in place and are making progress in incorporating pharmacogenetics into many areas of biological research. The Pharmacogenetics Research Network and Knowledge Base (http://www.pharmgkb.org), for example, is a relatively new initiative working toward correlating drug response phenotypes with genetic variation in various areas (eg, nicotine addiction and treatment, metabolism, transport, cancer and cancer treatment, asthma, cardiovascular disease). The idea is to create a valuable knowledge base populated with reliable information that links phenotypes to genotypes, and to promote interactive science to elevate the field of pharmacogenetics with knowledge, tools, and resources. For a more thorough mining of pharmacogenetic data in drug abuse, one can go to http://pharmdemo.stanford.edu/pharmdb/main.spy and search by drug name (eg, “heroin” or “cocaine”).¹⁰⁵

The NIDA Genetics Consortium (http://zork.wustl.edu/nida) is a group of investigators collecting samples (currently over 20 000) and comprehensive diagnostic information from individuals with smoking, cocaine, opioid, and polysubstance abuse and addictions. These resources are used to increase the understanding of addiction vulnerability and addiction treatment response, and most are available to the broad scientific community for further study.

Genetic data to date have pointed to pharmacodynamic genes involved in drug abuse and addiction, which have in turn elucidated genetic variants that may be helpful in identifying appropriate treatment medications for different individuals. These combined investments should begin to elucidate the importance of human genome variation on clinical treatment of addiction.

References

1. Uhl GR, Grow RW.  The burden of complex genetics in brain disorders. Arch Gen Psychiatry. 2004;61:223-229.
PubMed  DOI: 10.1001/archpsyc.61.3.223

2. Lichtermann D, Franke P, Maier W, Rao ML.  Pharmacogenomics and addiction to opiates. Eur J Pharmacol. 2000;410:269-279.
PubMed  DOI: 10.1016/S0014-2999(00)00820-7

3. Berrettini W, Bierut L, Crowley T, et al.  Letter—Setting priorities for genomic research. Science. 2004;304:1445-1447.
PubMed  DOI: 10.1126/science.304.5676.1445c

4. Goldman D, Oroszi G, Ducci F.  The genetics of addictions: uncovering the genes. Nat Rev Genet. 2005;6:521-532.
PubMed  DOI: 10.1038/nrg1635

5. Lessov CN, Swan GE, Ring HZ, Khroyan TV, Lerman C.  Genetics and drug use as a complex phenotype. Subst Use Misuse. 2004;39:1515-1569.
PubMed  DOI: 10.1081/JA-200033202

6. Hall WD.  Will nicotine genetics and a nicotine vaccine prevent cigarette smoking and smoking-related diseases? PLoS Med. 2005;2:e266.
PubMed  DOI: 10.1371/journal.pmed.0020266

7. Merikangas KR, Risch N.  Setting priorities for genomic research. Science. 2003;302:599-601.
PubMed  DOI: 10.1126/science.1091468

8. Evans WE, Relling MV.  Pharmacogenomics: translating functional genomics into rational therapeutics. Science. 1999;286:487-491.
PubMed  DOI: 10.1126/science.286.5439.487

9. Miksys S, Tyndale RF.  Drug-metabolizing cytochrome P450s in the brain. J Psychiatry Neurosci. 2002;27:406-415.
PubMed 

10. Peto R.  Smoking and death: the past 40 years and the next 40. BMJ. 1994;309:937-939.
PubMed 

11. Benowitz NL.  Drug therapy: pharmacologic aspects of cigarette smoking and nicotine addiction. N Engl J Med. 1988;319:1318-1330.
PubMed 

12. Bjartveit K, Tverdal A.  Health consequences of smoking 1-4 cigarettes per day. Tob Control. 2005;14:315-320.
PubMed  DOI: 10.1136/tc.2005.011932

13. Kawachi I, Colditz GA, Stampfer MJ, et al.  Smoking cessation and time course of decreased risks of coronary heart disease in middle-aged women. Arch Intern Med. 1994;154:169-175.
PubMed  DOI: 10.1001/archinte.154.2.169

14. Rosengren A, Wilhelmsen L, Wedel H.  Coronary heart disease, cancer and mortality in middle-aged light smokers. J Intern Med. 1992;231:357-362.
PubMed 

15. Prescott E, Scharling H, Osler M, et al.  Importance of light smoking and inhalation habits on risk of myocardial infarction and all cause mortality: a 22-year follow-up of 12,149 men and women in the Copenhagen City heart study. J Epidemiol Community Health. 2002;56:702-706.
PubMed  DOI: 10.1136/jech.56.9.702

16. Shiffman S, Fischer LB, Zettler-Segal M, Benowitz NL.  Nicotine exposure among nondependent smokers. Arch Gen Psychiatry. 1990;47:333-336.
PubMed 

17. Ellickson PL, McCaffrey DF, Ghosh-Dastidar B, Longshore DL.  New inroads in preventing adolescent drug use: results from a large-scale trial of project ALERT in middle schools. Am J Public Health. 2003;93:1830-1836.
PubMed 

18. Hall W, Madden P, Lynskey M.  The genetics of tobacco use: methods, findings and policy implications. Tob Control. 2002;11:119-124.
PubMed  DOI: 10.1136/tc.11.2.119

19. Tyndale RF.  Genetics of alcohol and tobacco use in humans. Ann Med. 2003;35:94-121.
PubMed  DOI: 10.1080/07853890310010014

20. Li MD, Cheng R, Ma JZ, Swan GE.  A meta-analysis of estimated genetic and environmental effects on smoking behavior in male and female adult twins. Addiction. 2003;98:23-31.
PubMed  DOI: 10.1046/j.1360-0443.2003.00295.x

21. Bierut LJ, Rice JP, Edenberg HJ, et al.  Family-based study of the association of the dopamine D2 receptor gene (DRD2) with habitual smoking. Am J Med Genet. 2000;90:299-302.
PubMed  DOI: 10.1002/(SICI)1096-8628(20000214)90:4<299::AID-AJMG7>3.0.CO_2-Y

22. Comings DE, Ferry L, Bradshaw-Robinson S, Burchette R, Chiu C, Muhleman D.  The dopamine D2 receptor (DRD2) gene: a genetic risk factor in smoking. Pharmacogenetics. 1996;6:73-79.
PubMed 

23. Beuten J, Ma JZ, Payne TJ, et al.  Single- and multilocus allelic variants within the GABA_B receptor subunit 2 (GABAB2) gene are significantly associated with nicotine dependence. Am J Hum Genet. 2005;76:859-864.
PubMed  DOI: 10.1086/429839

24. Vandenbergh DJ, Kozlowski LT, Bennett CJ, et al.  DAT’s not all, but it may be more than we realize. Nicotine Tob Res. 2002;4:251-252.
PubMed  DOI: 10.1080/14622200210141248

25. Lerman C, Shields PG, Audrain J, et al.  The role of the serotonin transporter gene in cigarette smoking. Cancer Epidemiol Biomarkers Prev. 1998;7:253-255.
PubMed 

26. Shields PG, Lerman C, Audrain J, et al.  Dopamine D4 receptors and the risk of cigarette smoking in African-Americans and Caucasians. Cancer Epidemiol Biomarkers Prev. 1998;7:453-458.
PubMed 

27. Sabol SZ, Nelson ML, Fisher C, et al.  A genetic association for cigarette smoking behavior. Health Psychol. 1999;18:7-13.
PubMed  DOI: 10.1037/0278-6133.18.1.7

28. Sullivan PF, Jiang Y, Neale MC, Kendler KS, Straub RE.  Association of the tryptophan hydroxylase gene with smoking initiation but not progression in nicotine dependence. Am J Med Genet. 2001;105:479-484.
PubMed  DOI: 10.1002/ajmg.1433

29. Swan GE, Valdes AM, Ring HZ, et al.  Dopamine receptor DRD2 genotype and smoking cessation outcome following treatment with bupropion SR. Pharmacogenomics J. 2005;5:21-29.
PubMed  DOI: 10.1038/sj.tpj.6500281

30. McKinney EF, Walton RT, Yudkin P, et al.  Association between polymorphisms in dopamine metabolic enzymes and tobacco consumption in smokers. Pharmacogenetics. 2000;10:483-491.
PubMed  DOI: 10.1097/00008571-200008000-00001

31. Neville MJ, Johnstone EC, Walton RT.  Identification and characterization of ANKK1: a novel kinase gene closely linked to DRD2 on chromosome band 11q23.1. Hum Mutat. 2004;23:540-545.
PubMed  DOI: 10.1002/humu.20039

32. Kreek MJ, Bart G, Lilly C, Laforge KS, Nielsen DA.  Pharmacogenetics and human molecular genetics of opiate and cocaine addictions and their treatments. Pharmacol Rev. 2005;57:1-26.
PubMed  DOI: 10.1124/pr.57.1.1

33. Xie W, Altamirano CV, Bartels CF, Speirs RJ, Cashman JR, Lockridge O.  An improved cocaine hydrolase: the A328Y mutant of human butyrylcholinesterase is 4-fold more efficient. Mol Pharmacol. 1999;55:83-91.
PubMed 

34. Mogil JS, Miermeister F, Seifert F, et al.  Variable sensitivity to noxious heat is mediated by differential expression of the CGRP gene. Proc Natl Acad Sci USA. 2005;102:12938-12943.
PubMed  DOI: 10.1073/pnas.0503264102

35. Owens JC, Balogh SA, McClure-Begley TD, et al.  Alpha 4 beta 2* nicotinic acetylcholine receptors modulate the effects of ethanol and nicotine on the acoustic startle response. Alcohol Clin Exp Res. 2003;27:1867-1875.
PubMed  DOI: 10.1097/01.ALC.0000102700.72447.0F

36. Cohen C, Kodas E, Griebel G.  CB1 receptor antagonists for the treatment of nicotine addiction. Pharmacol Biochem Behav. 2005;81:387-395.
PubMed  DOI: 10.1016/j.pbb.2005.01.024

37. Castane A, Berrendero F, Maldonado R.  The role of the cannabinoid system in nicotine addiction. Pharmacol Biochem Behav. 2005;81:381-386.
PubMed  DOI: 10.1016/j.pbb.2005.01.025

38. Zubieta JK, Heitzeg MM, Smith YR, et al.  COMT val158met genotype affects mu-opioid neurotransmitter responses to a pain stressor. Science. 2003;299:1240-1243.
PubMed  DOI: 10.1126/science.1078546

39. Li T, Chen CK, Hu X, et al.  Association analysis of the DRD4 and COMT genes in methamphetamine abuse. Am J Med Genet B Neuropsychiatr Genet. 2004;129:120-124.
PubMed  DOI: 10.1002/ajmg.b.30024

40. Vandenbergh DJ, Rodriguez LA, Hivert E, et al.  Long forms of the dopamine receptor (DRD4) gene VNTR are more prevalent in substance abusers: no interaction with functional alleles of the catechol-o-methyltransferase (COMT) gene. Am J Med Genet. 2000;96:678-683.
PubMed  DOI: 10.1002/1096-8628(20001009)96:5<678::AID-AJMG15>3.0.CO_2-8

41. Tyndale RF, Sellers EM.  Variable CYP2A6-mediated nicotine metabolism alters smoking behavior and risk. Drug Metab Dispos. 2001;29:548-552.
PubMed 

42. Pianezza ML, Sellers EM, Tyndale RF.  Nicotine metabolism defect reduces smoking. Nature. 1998;393:750.
PubMed  DOI: 10.1038/31623

43. Gelernter J, Kranzler HR, Satel SL, Rao PA.  Genetic association between dopamine transporter protein alleles and cocaine-induced paranoia. Neuropsychopharmacology. 1994;11:195-200.
PubMed  DOI: 10.1038/sj.npp.1380106

44. Fuke S, Suo S, Takahashi N, Koike H, Sasagawa N, Ishiura S.  The VNTR polymorphism of the human dopamine transporter (DAT1) gene affects gene expression. Pharmacogenomics J. 2001;1:152-156.
PubMed 

45. Lott DC, Jr, Kim S-J, Jr, Cook EH, Jr, de Wit H.  Dopamine transporter gene associated with diminished subjective response to amphetamine. Neuropsychopharmacology. 2005;30:602-609.
PubMed  DOI: 10.1038/sj.npp.1300637

46. Stein MA, Waldman ID, Sarampote CS, et al.  Dopamine transporter genotype and methylphenidate dose response in children with ADHD. Neuropsychopharmacology. 2005;30:1374-1382.
PubMed  DOI: 10.1038/sj.npp.1300787

47. Chen R, Han DD, Gu HH.  A triple mutation in the second transmembrane domain of mouse dopamine transporter markedly decreases sensitivity to cocaine and methylphenidate. J Neurochem. 2005;94:352-359.
PubMed  DOI: 10.1111/j.1471-4159.2005.03199.x

48. Lerman C, Jepson C, Wileyto EP, et al.  Role of Functional Genetic Variation in the Dopamine D2 Receptor (DRD2) in Response to Bupropion and Nicotine Replacement Therapy for Tobacco Dependence: Results of Two Randomized Clinical Trials. Neuropsychopharmacology. 2006;31:231-242.
PubMed 

49. Xu K, Lichtermann D, Lipsky RH, et al.  Association of specific haplotypes of D2 dopamine receptor gene with vulnerability to heroin dependence in 2 distinct populations. Arch Gen Psychiatry. 2004;61:597-606.
PubMed  DOI: 10.1001/archpsyc.61.6.597

50. Noble EP, Zhang X, Ritchie TL, Sparkes RS.  Haplotypes at the DRD2 locus and severe alcoholism. Am J Med Genet. 2000;96:622-631.
PubMed  DOI: 10.1002/1096-8628(20001009)96:5<622::AID-AJMG7>3.0.CO_2-5

51. Gelernter J, Kranzler H.  D2 dopamine receptor gene (DRD2) allele and haplotype frequencies in alcohol dependent and control subjects: no association with phenotype or severity of phenotype. Neuropsychopharmacology. 1999;20:640-649.
PubMed  DOI: 10.1016/S0893-133X(98)00110-9

52. Sander T, Ladehoff M, Samochowiec J, Finckh U, Rommelspacher H, Schmidt LG.  Lack of an allelic association between polymorphisms of the dopamine D2 receptor gene and alcohol dependence in the German population. Alcohol Clin Exp Res. 1999;23:578-581.
PubMed  DOI: 10.1097/00000374-199904001-00002

53. Gelernter J, Kranzler H, Satel SL.  No association between D2 dopamine receptor (DRD2) alleles or haplotypes and cocaine dependence or severity of cocaine dependence in European- and African-Americans. Biol Psychiatry. 1999;45:340-345.
PubMed  DOI: 10.1016/S0006-3223(97)00537-4

54. Goldman D, Urbanek M, Guenther D, Robin R, Long JC.  Linkage and association of a functional DRD2 variant [Ser311Cys] and DRD2 markers to alcoholism, substance abuse and schizophrenia in Southwestern American Indians. Am J Med Genet. 1997;74:386-394.
PubMed  DOI: 10.1002/(SICI)1096-8628(19970725)74:4<386::AID-AJMG9>3.0.CO_2-N

55. Chen CK, Hu X, Lin SK, et al.  Association analysis of dopamine D2-like receptor genes and methamphetamine abuse. Psychiatr Genet. 2004;14:223-226.
PubMed  DOI: 10.1097/00041444-200412000-00011

56. Chang FM, Ko HC, Lu RB, Pakstis AJ, Kidd KK.  The dopamine D4 receptor gene (DRD4) is not associated with alcoholism in three Taiwanese populations: six polymorphisms tested separately and as haplotypes. Biol Psychiatry. 1997;41:394-405.
PubMed  DOI: 10.1016/S0006-3223(96)00248-X

57. Nishiyama T, Ikeda M, Iwata N, et al.  Haplotype association between GABAA receptor gamma2 subunit gene (GABRG2) and methamphetamine use disorder. Pharmacogenomics J. 2005;5:89-95.
PubMed  DOI: 10.1038/sj.tpj.6500292

58. Hashimoto T, Hashimoto K, Matsuzawa D, et al.  A functional glutathione S-transferase P1 gene polymorphism is associated with methamphetamine-induced psychosis in Japanese population. Am J Med Genet B Neuropsychiatr Genet. 2005;135:5-9.
PubMed 

59. Zimniak P, Nanduri B, Pikula S, et al.  Naturally occurring human glutathione S-transferase GSTP1-1 isoforms with isoleucine and valine in position 104 differ in enzymic properties. Eur J Biochem. 1994;224:893-899.
PubMed  DOI: 10.1111/j.1432-1033.1994.00893.x

60. Szumlinski KK, Abernathy KE, Oleson EB, et al.  Homer isoforms differentially regulate cocaine-induced neuroplasticity. Neuropsychopharmacology. 2005;14.Epub ahead of print.

61. Mogil JS, Ritchie J, Smith SB, et al.  Melanocortin-1 receptor gene variants affect pain and mu-opioid analgesia in mice and humans. J Med Genet. 2005;42:583-587.
PubMed  DOI: 10.1136/jmg.2004.027698

62. Mogil JS, Wilson SG, Chesler EJ, et al.  The melanocortin-1 receptor gene mediates female-specific mechanisms of analgesia in mice and humans. Proc Natl Acad Sci USA. 2003;100:4867-4872.
PubMed  DOI: 10.1073/pnas.0730053100

63. Kim H, Neubert JK, San MA, et al.  Genetic influences on variability in human acute experimental pain sensitivity associated with gender, ethnicity and psychological temperament. Pain. 2004;109:488-496.
PubMed  DOI: 10.1016/j.pain.2004.02.027

64. Lerman C, Wileyto EP, Patterson F, et al.  The functional mu opioid receptor (OPRM1) Asn40Asp variant predicts short-term response to nicotine replacement therapy in a clinical trial. Pharmacogenomics J. 2004;4:184-192.
PubMed  DOI: 10.1038/sj.tpj.6500238

65. Gelernter J, Kranzler H, Cubells J.  Genetics of two μ opioid receptor gene (OPRM1) exon 1 polymorphisms: population studies, and allele frequencies in alcohol- and drug-dependent subjects. Mol Psychiatry. 1999;4:476-483.
PubMed  DOI: 10.1038/sj.mp.4000556

66. Bart G, Heilig M, LaForge KS, et al.  Substantial attributable risk related to a functional mu-opioid receptor gene polymorphism in association with heroin addiction in central Sweden. Mol Psychiatry. 2004;9:547-549.
PubMed  DOI: 10.1038/sj.mp.4001504

67. Bond C, LaForge KS, Tian M, et al.  Single-nucleotide polymorphism in the human mu opioid receptor gene alters beta-endorphin binding and activity: possible implications for opiate addiction. Proc Natl Acad Sci USA. 1998;95:9608-9613.
PubMed  DOI: 10.1073/pnas.95.16.9608

68. Befort K, Filliol D, Decaillot FM, Gaveriaux-Ruff C, Hoehe MR, Kieffer BL.  A single nucleotide polymorphic mutation in the human mu-opioid receptor severely impairs receptor signaling. J Biol Chem. 2001;276:3130-3137.
PubMed  DOI: 10.1074/jbc.M006352200

69. Beyer A, Kock T, Schroder H, Schulz S, Hollt V.  Effect of the A118G polymorphism on binding affinity, potency and agonist-mediated endocytosis, desensitization, and resensitization of the human mu-opioid receptor. J Neurochem. 2004;89:553-560.
PubMed  DOI: 10.1111/j.1471-4159.2004.02340.x

70. Zhang Y, Wang D, Johnson AD, Papp AC, Sadee W.  Allelic expression imbalance of human mu opioid receptor (OPRM1) caused by variant A118G. J Biol Chem. 2005;280:32618-32624.
PubMed  DOI: 10.1074/jbc.M504942200

71. Oslin DW, Berrettini W, Kranzler HR, et al.  A functional polymorphism of the μ-opioid receptor gene is associated with naltrexone response in alcohol-dependent patients. Neuropsychopharmacology. 2003;28:1546-1552.
PubMed  DOI: 10.1038/sj.npp.1300219

72. Lotsch J, Skarke C, Grosch S, Darimont J, Schmidt H, Geisslinger G.  The polymorphism A118G of the human mu-opioid receptor gene decreases the pupil constrictory effect of morphine-6-glucuronide but not that of morphine. Pharmacogenetics. 2002;12:3-9.
PubMed  DOI: 10.1097/00008571-200201000-00002

73. Fillingim RB, Kaplan L, Staud R, et al.  The A118G single nucleotide polymorphism of the mu-opioid receptor gene (OPRM1) is associated with pressure pain sensitivity in humans. J Pain. 2005;6:159-167.
PubMed  DOI: 10.1016/j.jpain.2004.11.008

74. Fiore MC.  Treating tobacco use and dependence: an introduction to the US Public Health Service Clinical Practice Guideline. Respir Care. 2000;45:1196-1199.
PubMed 

75. National Institute on Drug Abuse. Research Report Series: Nicotine Addiction. NIH Publ No 01-4342. 2001.  Available at: http://www.nida.nih.gov/researchreports/nicotine/nicotine.html. Accessed February 23, 2006. 

76. Fiore MC, Smith SS, Jorenby DE, Baker TB.  The effectiveness of the nicotine patch for smoking cessation: a meta-analysis. JAMA. 1994;271:1940-1947.
PubMed  DOI: 10.1001/jama.271.24.1940

77. Paoletti P, Fornai E, Maggiorelli F, et al.  Importance of baseline cotinine plasma values in smoking cessation: results from a double-blind study with nicotine patch. Eur Respir J. 1996;9:643-651.
PubMed  DOI: 10.1183/09031936.96.09040643

78. Pickworth WB, Fant RV, Butschky MF, Henningfield JE.  Effects of transdermal nicotine delivery on measures of acute nicotine withdrawal. J Pharmacol Exp Ther. 1996;279:450-456.
PubMed 

79. Foulds J, Burke M, Steinberg M, Williams JM, Ziedonis DM.  Advances in pharmacotherapy for tobacco dependence. Expert Opin Emerg Drugs. 2004;9:39-53.
PubMed  DOI: 10.1517/14728214.9.1.39

80. Anthenelli RM. Rimonabant helps for smoking cessation, weight loss. ACC 53rd Annual Scientific Session: Late-Breaking Clinical Trials; March 9, 2004; New Orleans, LA. 

81. Heading CE.  NicVAX Nabi Biopharmaceuticals. IDrugs. 2003;6:1178-1181.
PubMed 

82. Cerny T.  Anti-nicotine vaccination: where are we now? Recent Results Cancer Res. 2005;166:167-175.
PubMed 

83. Hatsukami DK, Rennard S, Jorenby D, et al.  Safety and immunogenicity of a nicotine conjugate vaccine in current smokers. Clin Pharmacol Ther. 2005;78:456-467.
PubMed  DOI: 10.1016/j.clpt.2005.08.007

84. Dale LC, Glover ED, Sachs DP, et al.  Bupropion for smoking cessation: predictors of successful outcome. Chest. 2001;119:1357-1364.
PubMed  DOI: 10.1378/chest.119.5.1357

85. Merikangas KR, Stolar M, Stevens DE, et al.  Familial transmission of substance use disorders. Arch Gen Psychiatry. 1998;55:973-979.
PubMed  DOI: 10.1001/archpsyc.55.11.973

86. Tsuang MT, Lyons MJ, Meyer JM, et al.  Co-occurrence of abuse of different drugs in men: the role of drug-specific and shared vulnerabilities. Arch Gen Psychiatry. 1998;55:967-972.
PubMed  DOI: 10.1001/archpsyc.55.11.967

87. Regier DA, Farmer ME, Rae DS, et al.  Comorbidity of mental disorders with alcohol and other drug abuse: results from the Epidemiology Catchment Area (ECA) study. JAMA. 1990;264:2511-2518.
PubMed  DOI: 10.1001/jama.264.19.2511

88. Ikeda K, Soichiro I, Han W, Hayashida M, Uhl GR, Sora I.  How individual sensitivity to opiates can be predicted by gene analyses. Trends Pharmacol Sci. 2005;26:311-317.
PubMed  DOI: 10.1016/j.tips.2005.04.001

89. Ross JR, Rutter D, Welsh K, et al.  Clinical response to morphine in cancer patients and genetic variation in candidate genes. Pharmacogenomics J. 2005;5:324-336.
PubMed  DOI: 10.1038/sj.tpj.6500327

90. Tiseo PJ, Thaler HT, Lapin J, Inturrisi CE, Portenoy RK, Foley KM.  Morphine-6-glucuronide concentrations and opioid-related side effects: a survey in cancer patients. Pain. 1995;61:47-54.
PubMed  DOI: 10.1016/0304-3959(94)00148-8

91. MacGregor AJ, Griffiths GO, Baker J, Spector TD.  Determinants of pressure pain threshold in adult twins: evidence that shared environmental influences predominate. Pain. 1997;73:253-257.
PubMed  DOI: 10.1016/S0304-3959(97)00101-2

92. Rainville P, Duncan GH, Price DD, Carrier B, Bushnell MC.  Pain affect encoded in human anterior cingulate but not somatosensory cortex. Science. 1997;277:968-971.
PubMed  DOI: 10.1126/science.277.5328.968

93. Snyder SH.  Amphetamine psychosis: a “model” schizophrenia mediated by catecholamines. Am J Psychiatry. 1973;130:61-67.
PubMed 

94. Sato M, Chen CC, Akiyama K, Otsuki S.  Acute exacerbation of paranoid psychotic state after long-term abstinence in patients with previous methamphetamine psychosis. Biol Psychiatry. 1983;18:429-440.
PubMed 

95. Volkow ND. Message from the Director on Amphetamine Abuse.   Available at: http://www.nida.nih.gov/about/welcome/messagemeth405.html. Accessed February 23, 2006. 

96. Kendler KS, Karkowski LM, Neale MC, Prescott CA.  Illicit psychoactive substance use, heavy use, abuse, and dependence in a US population-based sample of male twins. Arch Gen Psychiatry. 2000;57:261-269.
PubMed  DOI: 10.1001/archpsyc.57.3.261

97. Tsuang MT, Lyons MJ, Eisen SA, et al.  Genetic influences on DSM-III-R drug abuse and dependence: a study of 3,372 twin pairs. Am J Med Genet. 1996;67:473-477.
PubMed  DOI: 10.1002/(SICI)1096-8628(19960920)67:5<473::AID-AJMG6>3.0.CO_2-L

98. Leshner AI.  Addiction is a brain disease, and it matters. Science. 1997;278:45-47.
PubMed  DOI: 10.1126/science.278.5335.45

99. Merikangas KR, Risch N.  Will the genomics revolution revolutionize psychiatry? Am J Psychiatry. 2003;160:625-635.
PubMed  DOI: 10.1176/appi.ajp.160.4.625

100. Croghan TW, Tomlin M, Pescosolido BA, et al.  American attitudes toward and willingness to use psychiatric medications. J Nerv Ment Dis. 2003;191:166-174.
PubMed  DOI: 10.1097/00005053-200303000-00005

101. Nunes EV, Levin FR.  Treatment of depression in patients with alcohol or other drug dependence: a meta-analysis. JAMA. 2004;291:1887-1896.
PubMed  DOI: 10.1001/jama.291.15.1887

102. Kendler KS, Jacobson KC, Prescott CA, Neale MC.  Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. Am J Psychiatry. 2003;160:687-695.
PubMed  DOI: 10.1176/appi.ajp.160.4.687

103. Bierut LJ, Rice JP, Goate A, et al.  A genomic scan for habitual smoking in families of alcoholics: common and specific genetic factors in substance dependence. Am J Med Genet A. 2004;124:19-27.
PubMed  DOI: 10.1002/ajmg.a.20329

104. Frueh FW, Goodsaid F, Rudman A, Huang S-M, Lesko LJ.  The need for education in pharmacogenomics: a regulatory perspective. Pharmacogenomics J. 2005;5:218-220.
PubMed  DOI: 10.1038/sj.tpj.6500316

105. Rubin DL, Thorn C, Klein TE, Altman RB.  A statistical approach to scanning the biomedical literature for pharmacogenetics knowledge. J Am Med Inform Assoc. 2005;12:121-129.
PubMed  DOI: 10.1197/jamia.M1640