Smokescreen Translational (TL) Analysis Platform

烟幕转化 (TL) 分析平台

• 批准号: 9791133
• 负责人: ANDREW W BERGEN
• 金额: $ 79.47万
• 依托单位: BIOREALM
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9791133
• 关键词: African American; Biological; Biological Assay; Biological Markers; CYP2A7 gene; Cancer Patient; Cardiovascular Diseases; Clinical; Clinical Data; Clinical Treatment; Code; Cohort Studies; Communities; Computer software; Cotinine; DNA; Dependence; Development; Disease; Enrollment; European; Genes; Genetic Transcription; Genomic Segment; Genomics; Genotype; Glucuronides; Goals; Health Personnel; Healthcare; Individual; Japanese Population; Joints; Laboratories; Laboratory Study; Latino; Lung diseases; Malignant Neoplasms; Measures; Metabolic; Metabolic Diseases; Metabolism; Modeling; National Heart, Lung, and Blood Institute; National Institute of Drug Abuse; Native Hawaiian; Nicotine; Nicotine Dependence; Outcome; Oxides; Participant; Pathway interactions; Pharmacogenomics; Pharmacology; Phase; Population; Probability; Randomized Clinical Trials; Regulator Genes; Reporting; Sample Size; Sampling; Scientific Advances and Accomplishments; Series; Smoker; Smoking Behavior; Smoking Cessation Intervention; Statistical Models; Technology; Testing; Tobacco; Tobacco smoking behavior; Tobacco use; Translating; United States National Institutes of Health; Variant; addiction; biosignature; cancer risk; cigarette smoking; clinical care; cohort; design; flexibility; genome wide association study; genome-wide; genomic biomarker; genomic data; health care service organization; hydroxycotinine; interest; mortality; nicotine cessation; novel; personalized medicine; pharmacokinetics and pharmacodynamics; predicting response; predictive modeling; predictive test; response; smoking abstinence; smoking cessation; smoking prevalence; social; statistics; therapy outcome; tobacco control; tobacco products; treatment trial

Project Summary Tobacco-attributable disease remains the largest potentially modifiable cause of mortality. Strategies to reduce smoking prevalence include developing more effective smoking cessation treatments. Nicotine metabolism and dependence are predictors of smoking behaviors, including response to smoking cessation treatments. The goal of this Phase II project is to develop prediction models of nicotine metabolism, nicotine dependence and smoking cessation from clinical and genomic data. An optimized set of models will be implemented in the “Smokescreen®Translational (TL) Analysis Platform”, and applied to clinical cohorts of treatment-seeking smokers. We have previously designed Smokescreen®GTA, a genome-wide array that deeply captures variation in over 1,000 addiction genes, including the most important loci for nicotine metabolism and nicotine dependence. We have identified multiple metabolic and regulatory genes, that with relatively few markers, can predict an individual's nicotine metabolic activity. We will use existing cohorts and a clinical treatment trial of smokers to discover and test integrated models with the goal of providing estimates of nicotine metabolism, nicotine dependence and cessation probability. These models will incorporate ancestry, clinical, genomic and social vari- ables to maximize prediction of smoking cessation. We will develop a compact laboratory assay for genotyping DNA samples with specific markers and software to analyze clinical and genomic data. Smokescreen®TL will be validated in smokers in clinical care. The results will be delivered in flexible reporting formats. Ultimately, Smokescreen®TL will be available for use by health care providers interested in helping treatment seeking smokers quit.

项目概要 烟草引起的疾病仍然是最大的潜在可改变的死亡原因。 吸烟率的提高包括更有效的戒烟治疗。 和依赖是吸烟行为的预测因素，包括对戒烟治疗的反应。 该二期项目的目标是开发尼古丁代谢、尼古丁依赖的预测模型 将根据临床和基因组数据实施一组优化的戒烟模型。 “Smokescreen®翻译（TL）分析平台”，并应用于寻求治疗的临床队列 吸烟者。 我们之前设计了Smokescreen®GTA，这是一种全基因组阵列，可以深度捕获基因组中的变异 超过 1,000 个成瘾基因，包括尼古丁代谢和尼古丁依赖性最重要的基因座。 我们已经确定了多个代谢和调节基因，只需相对较少的标记，就可以预测 我们将使用现有的队列和吸烟者的临床治疗试验。 发现和测试集成模型，目标是提供尼古丁代谢、尼古丁代谢的估计 这些模型将结合血统、临床、基因组和社会变量。 能够最大限度地预测戒烟情况。我们将开发一种紧凑的基因分型实验室检测方法。 带有特定标记的 DNA 样本和用于分析临床和基因组数据的软件将。 最终，结果将以灵活的报告格式在吸烟者中进行验证。 Smokescreen®TL 可供有兴趣帮助寻求治疗的医疗保健提供者使用 吸烟者戒烟。