EAGER: Collaborative Research: Computational Public Drug Surveillance

EAGER：合作研究：计算公共药物监测

• 批准号: 1236983
• 负责人: Donald Adjeroh
• 金额: $ 8万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1236983&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Computational; Public

Adverse drug reactions (ADR) (undesired or excessive responses drugs) have been linked with significant morbidity and mortality, and account for as much as 5% of all admissions. A drug-drug interaction (DDI) is a type of ADR involving two or more drugs. Reports suggest that 50% percent of the drugs withdrawn in the U.S. by the Food and Drug Administration (FDA) from 1999 to 2003 were linked with significant DDIs. The ADR profile of a given drug is rarely complete at the time the drug is approved by FDA. Hence, after a drug has been in use by the general population (with significant diversity in race, gender, age, lifestyle), often previously unidentified DDIs are discovered. To complicate matters, certain populations of patients, e.g., psychiatric patients, are often concurrently treated with multiple medications. The potential interactions between multiple drugs are neither well understood nor completely characterized. Voluntary reporting, the basic mechanism used by the FDA to monitor new drugs, suffers from underreporting, delayed reporting, uneven quality of reports, and even lack of reports of rare DDIs.Against this background, this collaborative project aims to explore the feasibility of a novel computational approach to the problem of drug-drug interaction surveillance. It seeks to develop new methods for predicting molecular level interactions between drugs from data gleaned from online sources and digital social media. The project aims to test the hypothesis that such online data, in combination with with data from traditional drug related databases can be used to reliably predict potential DDIs much sooner than possible using current methods. The effectiveness of the approach is assessed through verification of predictions against future reports. If successful, the project could lead to effective, proactive computational approaches to drug interaction surveillance, with benefits to federal, local and public health agencies, drug companies, clinical practitioners, the patients, and the public at large. Early detection of adverse DDIs could lead to improved patient care, and significant reduction in healthcare costs and lawsuits involving DDIs. The project offers enhanced opportunities for collaboration among investigators with expertise in computational and health sciences. It also offers research-based training opportunities to students at West Virgina University and the University of Virginia. Results of the research will be freely disseminated to the broader academic and research community.

不良药物反应（ADR）（不希望或过度反应药物）与大量发病率和死亡率有关，占所有入院的5％。药物相互作用（DDI）是一种涉及两种或多种药物的ADR。报告表明，从1999年至2003年，美国食品药品监督管理局（FDA）在美国撤回的药物中有50％与大量DDI有关。在FDA批准药物时，给定药物的ADR概况很少完成。因此，在普通人群使用药物（种族，性别，年龄，生活方式的多样性）之后，通常会发现以前未知的DDI。为了使事情复杂化，某些患者（例如精神病患者）通常会同时接受多种药物治疗。多种药物之间的潜在相互作用既没有充分理解也不完全表征。自愿报告，FDA用来监测新药的基本机制，遭受了报道低估，报告的延迟，报告质量不足，甚至缺乏有关稀有DDIS的报道。此背景，该协作项目旨在探索一种新型计算方法的可行性，以解决药物 - 毒品交互作用的新计算方法。它旨在开发新方法，以预测从在线来源和数字社交媒体收集的数据之间进行分子水平相互作用。该项目旨在检验以下假设：这种在线数据与传统药物相关数据库的数据结合使用，可用于可靠地预测潜在的DDI，而不是使用当前方法来预测潜在的DDI。通过验证针对未来报告的预测来评估该方法的有效性。如果成功，该项目可能会导致有效的，积极主动的计算方法来进行药物互动监视，并为联邦，地方和公共卫生机构，制药公司，临床从业人员，患者和整个公众带来好处。早期发现不良DDI可能会改善患者护理，并大大降低涉及DDIS的医疗保健成本和诉讼。该项目为在计算和健康科学方面具有专业知识的研究人员之间提供了增强的机会。它还为西维吉纳大学和弗吉尼亚大学的学生提供了基于研究的培训机会。研究结果将被自由传播到更广泛的学术和研究界。