An Informatics Framework for Discovery and Ascertainment of Drug-Supplement Interactions

用于发现和确定药物补充剂相互作用的信息学框架

• 批准号: 9894759
• 负责人: RUI ZHANG
• 金额: $ 26.95万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9894759
• 关键词: Address; Adult; Adverse event; Biological; Cancer Patient; Clinical; Clinical Decision Support Systems; Clinical Research; Complement; Data; Data Element; Databases; Development; Drug Targeting; Education; Effectiveness; Electronic Health Record; Failure; Food; Ginkgo biloba; Goals; Health; Healthcare; Herbal supplement; Individual; Informatics; Information Retrieval; Investigation; Knowledge; Label; Link; Literature; MEDLINE; Medicine; Methods; Mining; Minnesota; Natural Language Processing; Natural Products; Outcome; Pathway interactions; Patient risk; Pharmaceutical Preparations; Pharmacoepidemiology; Postoperative Hemorrhage; Probability; Research; Resources; Risk; Safety; Semantics; Signal Transduction; Standardization; Surveys; System; Targeted Research; Terminology; Therapeutic; Therapeutic Intervention; Translating; Treatment Efficacy; United States Food and Drug Administration; Universities; Validation; Warfarin; Work; base; clinical decision support; clinical practice; clinically significant; colon cancer patients; data modeling; design; dietary supplements; drug testing; evidence base; improved; individual patient; innovation; machine learning method; novel; nutrition; online resource; open source; patient population; patient safety; post-market; screening; structured data; tool; unstructured data

Most U.S. adults (68%) take dietary supplements (DS) and there is increasing evidence of drug-supplement interactions (DSIs); our ability to readily identify interactions between DS with prescription medications is currently very limited. To optimize the safe use of DS, there remains a critical and unmet need for informatics methods to detect DSIs. Our rationale is that an innovative informatics framework to discover potential DSIs from the large scale of biomedical literature will enable a new line of research for targeted DSI validation and will also significantly narrow the range of DSIs that must be further explored. Our long-term goal is to use informatics approaches to enhance DSI clinical research and translate its findings to clinical practice ultimately via clinical decision support systems. The objective of this application is to develop an informatics framework to enable the discovery of DSIs by creating a DS terminology and mining scientific evidence from the biomedical literature. Towards these objectives, we propose the following specific aims: (1) Compile a comprehensive DS terminology using online resources; and (2) Discover potential DSIs from the biomedical literature. The successful accomplishment of this project will deliver a novel informatics paradigm and resources for identifying most clinically significant DSI signals and their biological mechanisms. This information is critical to subsequent efforts aimed at improving patient safety and efficacy of therapeutic interventions. The results from this study are imperative in order to achieve the ultimate goal of reducing an individual’s risk of potential DSIs.

大多数美国成年人（68%）服用膳食补充剂（DS），并且有越来越多的证据表明药物补充剂相互作用（DSIs）；目前，我们识别退行性痴呆与处方药之间相互作用的能力非常有限。为了优化DS的安全使用，对检测dsi的信息学方法仍有一个关键和未满足的需求。我们的理由是，一个创新的信息学框架，从大规模的生物医学文献中发现潜在的DSI，将为靶向DSI验证提供一条新的研究路线，也将显著缩小必须进一步探索的DSI范围。我们的长期目标是使用信息学方法来加强DSI临床研究，并最终通过临床决策支持系统将其发现转化为临床实践。本应用程序的目标是开发一个信息学框架，通过创建DS术语和从生物医学文献中挖掘科学证据来发现DSIs。针对这些目标，我们提出以下具体目标：(1)利用网上资源编制一个全面的DS术语；(2)从生物医学文献中发现潜在的dsi。该项目的成功完成将为识别临床上最重要的DSI信号及其生物学机制提供一种新的信息学范式和资源。这一信息对于后续旨在提高患者安全性和治疗干预效果的努力至关重要。这项研究的结果是必要的，以实现最终目标，降低个人潜在的dsi的风险。