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 Reports suggest that 50% percent of the drugs withdrawn in the U.S. by the Food and Drug Administration (FDA) from 1999 to 2003 were linked with significant DDIs.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.

药物不良反应 (ADR)（药物不良反应或过度反应）与显着的发病率和死亡率相关，占所有入院人数的 5% 之多。药物相互作用 (DDI) 是涉及两种或多种药物的 ADR 类型。报告显示，从 1999 年到 2003 年，美国食品和药物管理局 (FDA) 撤回的药物中有 50% 与重要的 DDI 有关。特定药物的 ADR 概况在 FDA 批准该药物时很少是完整的。因此，在普通人群（种族、性别、年龄、生活方式存在显着差异）使用某种药物后，通常会发现以前未识别的 DDI。使事情变得复杂的是，某些患者群体，例如精神病患者，经常同时接受多种药物治疗。多种药物之间的潜在相互作用既未得到充分理解，也未完全表征。自愿报告作为FDA监测新药的基本机制，存在漏报、迟报、报告质量参差不齐，甚至罕见DDI报告缺失等问题。在此背景下，本合作项目旨在探讨自愿报告的可行性。药物相互作用监测问题的新颖计算方法。它寻求开发新方法，根据从在线资源和数字社交媒体收集的数据来预测药物之间的分子水平相互作用。该项目旨在测试这样的假设：这些在线数据与传统药物相关数据库的数据相结合，可以比使用现有方法更快地可靠地预测潜在的 DDI。该方法的有效性是通过根据未来报告验证预测来评估的。如果成功，该项目可能会带来有效、主动的药物相互作用监测计算方法，从而使联邦、地方和公共卫生机构、制药公司、临床医生、患者和广大公众受益。及早发现不良 DDI 可以改善患者护理，并显着减少医疗费用和涉及 DDI 的诉讼。该项目为具有计算和健康科学专业知识的研究人员之间的合作提供了更多的机会。它还为西弗吉尼亚大学和弗吉尼亚大学的学生提供基于研究的培训机会。研究结果将免费传播给更广泛的学术和研究界。