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Breaking Barriers in the Genomics and Pharmacogenetics of Drug Addiction

Centre for Addiction & Mental Health, Department of Psychiatry, Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada.
Clinical Pharmacology &#38 Therapeutics (Impact Factor: 7.39). 10/2010; 88(6):779-91. DOI: 10.1038/clpt.2010.175
Source: PubMed

ABSTRACT Drug addiction remains a substantial health issue with limited treatment options currently available. Despite considerable advances in the understanding of human genetic architecture, the genetic underpinning of complex disorders remains elusive. On the basis of our current understanding of neurobiology, numerous candidate genes have been implicated in the etiology and response to treatment for different addictions. Genome-wide association (GWA) studies have also identified novel targets. However, replication of these studies is often lacking, and this complicates interpretation. The situation is expected to improve as issues such as phenotypic characterization, the apparent "missing heritability," the identification of functional variants, and possible gene-environment (G × E) interactions are addressed. In addition, there is growing evidence that genetic information can be useful in refining the choice of addiction treatment. As genetic testing becomes more common in the practice of medicine, a variety of ethical and practical challenges, some of which are unique to drug addiction, will also need to be considered.

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    • "1977; Morihisa and Glick. 1977; Seidel et al. 1979; Mickley et al. 1990; Bryant et al. 2009; Hodgson et al. 2010) and its behavioral pattern can be expressed as a conditioned opioid-like placebo response (Bryant et al. 2009). Our observations indicate that mice expressing drug-free conditioned opioid reward behave in a manner that is similar to having received an opioid injection. "
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    ABSTRACT: Drug liking versus drug disliking is a subjective motivational measure in humans that assesses the addiction liability of drugs. Variation in this trait is hypothesized to influence vulnerability versus resilience toward substance abuse disorders and likely contains a genetic component. In rodents and humans, conditioned place preference (CPP) / aversion (CPA) is a Pavlovian conditioning paradigm whereby a learned preference for the drug-paired environment is used to infer drug liking whereas a learned avoidance or aversion is used to infer drug disliking. C57BL/6 inbred mouse substrains are nearly genetically identical, yet demonstrate robust differences in addiction-relevant behaviors, including locomotor sensitization to cocaine and consumption of ethanol. Here, we tested the hypothesis that B6 substrains would demonstrate differences in the rewarding properties of the mu opioid receptor agonist oxycodone (5 mg/kg, i.p.) and the aversive properties of the opioid receptor antagonist naloxone (4 mg/kg, i.p.). Both substrains showed similar degrees of oxycodone-induced CPP; however, there was a three-fold enhancement of naloxone-induced CPA in agonist-naïve C57BL/6J relative to C57Bl/6NJ mice. Exploratory factor analysis of CPP and CPA identified unique factors that explain variance in behavioral expression of reward versus aversion. “Conditioned Opioid-Like Behavior” was a reward-based factor whereby drug-free locomotor variables resembling opioid treatment co-varied with the degree of CPP. “Avoidance and Freezing” was an aversion-based factor, whereby the increase in the number of freezing bouts co-varied with the degree of aversion. These results provide new insight into the behavioral architecture of the motivational properties of opioids. Future studies will use quantitative trait locus mapping in B6 substrains to identify novel genetic factors that contribute to the marked strain difference in NAL-CPA.
    Frontiers in Behavioral Neuroscience 12/2014; accepted. DOI:10.3389/fnbeh.2014.00450 · 4.16 Impact Factor
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    • "As a case scenario, evidence for the OPRM1 A118G polymorphism as a moderator of naltrexone response is discussed. However, these considerations will be relevant for other gene–drug response associations under study in the context of alcohol treatments, summaries of which are provided elsewhere [23-25]. "
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    ABSTRACT: Despite advances in characterizing genetic influences on addiction liability and treatment response, clinical applications of these efforts have been slow to evolve. Although challenges to clinical translation remain, stakeholders already face decisions about evidentiary thresholds for the uptake of pharmacogenetic tests in practice. There is optimism about potential pharmacogenetic applications for the treatment of alcohol use disorders, with particular interest in the OPRM1 A118G polymorphism as a moderator of naltrexone response. Findings from human and animal studies suggest preliminary evidence for the clinical validity of this association; on this basis, arguments for clinical implementation can be made in accordance with existing frameworks for the uptake of genomic applications. However, generating evidence-based guidelines requires evaluating the clinical utility of pharmacogenetic tests. This goal will remain challenging, largely due to minimal data to inform clinical utility estimates. The pace of genomic discovery highlights the need for clinical utility and implementation research to inform future translation efforts. Near-term implementation of promising pharmacogenetic tests can help expedite this goal, generating an evidence base to enable efficient translation as additional gene-drug associations are discovered.
    Addiction science & clinical practice 09/2014; 9(1):20. DOI:10.1186/1940-0640-9-20
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    • "A particular focus was put on pharmacogenetic candidate genes, which may contribute to inter-individual variations in opioid effects and opioid-induced behaviours (i.e. genes encoding proteins directly involved in the pharmacokinetic and pharmacodynamic action of commonly abused opioids) [1,6,9]. A well-phenotyped sample of long-term addicts undergoing opioid maintenance treatment (OMT) and a well-matched sample of healthy controls, both recruited according to stringent exclusion criteria, were investigated. "
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    ABSTRACT: It is becoming increasingly evident that genetic variants contribute to the development of opioid addiction. An elucidation of these genetic factors is crucial for a better understanding of this chronic disease and may help to develop novel therapeutic strategies. In recent years, several candidate genes were implicated in opioid dependence. However, most study findings have not been replicated and additional studies are required before reported associations can be considered robust. Thus, the major objective of this study was to replicate earlier findings and to identify new genetic polymorphisms contributing to the individual susceptibility to opioid addiction, respectively. Therefore, a candidate gene association study was conducted including 142 well-phenotyped long-term opioid addicts undergoing opioid maintenance therapy and 142 well-matched healthy controls. In both study groups, 24 single nucleotide polymorphisms predominantly located in pharmacogenetic candidate genes have been genotyped using an accurate mass spectrometry based method. The most significant associations with opioid addiction (remaining significant after adjustment for multiple testing) were observed for the rs948854 SNP in the galanin gene (GAL, p = 0.001) and the rs2236861 SNP in the delta opioid receptor gene (OPRD1, p = 0.001). Moreover, an association of the ATP binding cassette transporter 1 (ABCB1) variant rs1045642 and the Mu Opioid receptor (OPRM1) variant rs9479757 with opioid addiction was observed. The present study provides further support for a contribution of GAL and OPRD1 variants to the development of opioid addiction. Furthermore, our results indicate a potential contribution of OPRM1 and ABCB1 SNPs to the development of this chronic relapsing disease. Therefore it seems important that these genes are addressed in further addiction related studies.
    PLoS ONE 09/2013; 8(9):e75359. DOI:10.1371/journal.pone.0075359 · 3.23 Impact Factor
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