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Some patients respond well to medications designed to help people quit smoking, whereas as others do not. The reasons why some smokers respond to treatment and others do not are poorly understood. The research field called pharmacogenetics seeks DNA markers that can predict which medicines will work well for a particular patient, and which medicines will not work or will cause side effects. We have DNA and clinical information from a study of 301 smokers who were treated with a nicotine replacement patch and the medication bupropion, both of which are approved for smoking cessation. We are currently conducting a second study in which 400 additional subjects are receiving nicotine patches and bupropion. 209 DNA samples have been obtained so far from this study. In addition, we have stared a third study with 230 smokers to examine the effects of selegiline, another drug that may be useful in helping people quit cigarettes. This combined sample of approximately 900 patients is particularly valuable for pharmacogenetics because all of the clinical evaluations are done at the same treatment center, which increases the uniformity of the data. We now propose to use this unique DNA and clinical resource to identify additional genetic markers for nicotine patch, bupropion, and selegiline treatment efficacy and side effects. Our 14 target genes are chosen based on current understanding of the way nicotine, bupropion, and selegiline work in the brain. The genes we will study make proteins involved in the action of the neurotransmitters dopamine, acetylcholine, serotonin, and norepinephrine, all of which are known to play a role in nicotine addiction. These brain chemicals are key in producing the pleasurable effects of smoking, as well as the unpleasant withdrawal symptoms that occur when a smoker quits suddenly. We aim to study all relevant variation in these 14 genes for pharmacogenetic prediction of treatment outcomes. Because public and proprietary databases of human DNA variation contain numerous errors, we will only study DNA variants that we have verified. To do this, we have determined the sequences of our target genes in 24 DNA samples, and a large number of potential DNA predictor markers have been identified. To prioritize them, will use bioinformatic analysis to identify markers that change the function of the protein a gene makes, or change how much protein a gene makes. Then, we will test all 900 patient DNA samples for our highest priority, verified DNA markers. Within genes, we will consider not only individual DNA variants, but combinations of variants that together could change the function of the protein made by the gene. The clinical outcome measures will be abstinence from smoking, relapse, craving and withdrawal symptoms, medication side effects, weight gain, and change in mood. In our analyses we will take into account important factors such as patient compliance with treatment, the dose of medication received, gender, age and ethnicity, and how many cigarettes a day were smoked before starting treatment. This work will provide a comprehensive pharmacogenetic analysis in smokers of DNA markers important in the treatment of nicotine addiction. Eventually, these markers may be useful in identifying patients likely to benefit from medications for smoking cessation, as well as those likely to experience side effects. |
