EAGER: Collaborative Research: Computational Public Drug Surveillance

EAGER：合作研究：计算公共药物监测

• 批准号: 1236970
• 负责人: Ahmed Abbasi
• 金额: $ 5.01万
• 依托单位: University of Virginia Main Campus
• 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=1236970&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 Reports suggest that 50% percent of the drugs withdrawn in the U.S. by the Food and Drug Administration (FDA) from 1999 to 2003 were linked with significant DDIs.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）与显著的发病率和死亡率有关，占所有入院人数的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%与重大DDI有关。在FDA批准药物时，给定药物的ADR概况很少是完整的。因此，在一般人群（种族、性别、年龄、生活方式具有显著差异）使用药物后，通常会发现先前未识别的DDI。使问题复杂化的是，某些患者群体，例如，精神病患者通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没有得到很好的理解，也没有完全表征。自愿报告是FDA用于监测新药的基本机制，存在漏报、延迟报告、报告质量参差不齐、甚至缺乏罕见DDI报告等问题，在此背景下，本合作项目旨在探索一种新的计算方法来解决药物相互作用监测问题的可行性。它旨在开发新的方法，从在线来源和数字社交媒体收集的数据中预测药物之间的分子水平相互作用。该项目旨在测试这样一种假设，即这些在线数据与传统药物相关数据库的数据相结合，可以用于可靠地预测潜在的DDI，比使用现有方法更快。该方法的有效性是通过对未来报告的预测进行验证来评估的。如果成功的话，该项目可能会导致有效的，积极的计算方法来监测药物相互作用，有利于联邦，地方和公共卫生机构，制药公司，临床医生，患者和广大公众。早期发现不良DDI可以改善患者护理，并显著降低医疗成本和涉及DDI的诉讼。该项目为具有计算和健康科学专业知识的调查人员提供了更多的合作机会。它还为西卡罗来纳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。