Using Biomedical Knowledge to Identify Plausible Signals for Pharmacovigilance

利用生物医学知识识别药物警戒的合理信号

• 批准号: 8914098
• 负责人: Trevor Cohen
• 金额: $ 16万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8914098
• 关键词: Accounting; Adverse Drug Experience Report; Adverse drug effect; Adverse effects; Adverse event; Antibiotics; Azithromycin; Biological; Biological Models; Biometry; Clinical; Clinical Data; Computational Linguistics; Coronary Arteriosclerosis; Data; Databases; Detection; Disease; Drug usage; Early Diagnosis; Early identification; Electronic Health Record; Ensure; Environment; Evaluation; Event; Healthcare; Human; Institutes; Knowledge; Label; Licensing; Literature; Marketing; Methodology; Methods; Modeling; Monitor; Morbidity - disease rate; Natural Language Processing; Output; Package Insert; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacology; Policies; Positioning Attribute; Procedures; Process; Records; Reporting; Research; Research Infrastructure; Resources; Rofecoxib; Secure; Signal Transduction; Statistical Methods; Structure; System; Testing; Text; United States Food and Drug Administration; Work; base; biomedical informatics; improved; inhibitor/antagonist; mortality; natural language; novel; post-market; statistics; tool

DESCRIPTION (provided by applicant): The need to monitor unintended effects of approved drugs has been highlighted by several recent high-profile events in which fatal side effects of drugs were detected after their release to market. Notoriously, the Cox-2 inhibitor Rofecoxib (Vioxx) was withdrawn from market on account of evidence suggesting that treatment with the drug increased the rate of coronary artery disease, and recently new evidence has emerged suggesting the commonly used antibiotic Azithromycin (Zithromax) may cause fatal arrythmias. In an effort to mitigate the morbidity and mortality resulting from such undetected side effects, regulatory bodies such as the Food and Drug Administration (FDA) have instituted spontaneous reporting systems to systematize post-marketing surveillance. However there is evidence that under-reporting of adverse drug events (ADEs) is widespread. Automated monitoring of events documented in the Electronic Health Record (EHR) as free text or structured data has been proposed as a path toward earlier identification of meaningfully correlated drug-event pairs. As these pairs must ultimately be reviewed by domain experts to assess their implications, there is a pressing need to develop methods to selectively identify plausible drug-event pairs within the large pool of correlations to be found in clinical data. In the proposed research, we will develop and evaluate models of biological plausibility, based on knowledge extracted from the biomedical literature and using methods of hyperdimensional computing for efficient search and inference across multiple concepts and relations simultaneously. These methods will be used to selectively identify plausible drug-event pairs found in structured clinical data, and extracted from unstructured data using natural language extraction. The developed methods will be evaluated formatively, for their ability to rediscover known side effects from the biomedical literature, and summatively for their ability to improve the precision of effects attributed to a st of known drugs using statistical methods alone. In addition we will evaluate their ability to predict recent FDA warnings, using historical data and knowledge. If successful, the proposed research will provide the means to identify automatically plausible drug-event pairs for regulatory purposes, mitigating consequent morbidity and mortality. In addition, the methods will provide a generalizable approach that can be used to apply knowledge derived from the biomedical literature to interpret clinical data.

说明(由申请人提供)：最近几个备受瞩目的事件突显了监测已批准药物的意外影响的必要性，在这些事件中，药物在上市后被检测到致命的副作用。众所周知，COX-2抑制剂罗非昔布(Vioxx)被从市场上召回，因为有证据表明，使用该药物治疗会增加冠状动脉疾病的发生率，最近出现的新证据表明，常用抗生素阿奇霉素(Zithromax)可能会导致致命性心律失常。为了减少这种未被发现的副作用导致的发病率和死亡率，食品和药物管理局(FDA)等监管机构建立了自发报告系统，以使上市后监测系统化。然而，有证据表明，对药物不良事件(ADE)的漏报现象十分普遍。对电子健康记录(EHR)中记录的自由文本或结构化数据中记录的事件进行自动监测，已被提议作为更早识别有意义的相关药物-事件对的途径。由于这些配对最终必须由领域专家审查以评估其影响，因此迫切需要开发方法，在临床数据中发现的大量相关性中选择性地识别可信的药物-事件配对。在拟议的研究中，我们将基于从生物医学文献中提取的知识，并使用超维计算方法来同时对多个概念和关系进行高效搜索和推理，来开发和评估生物似然模型。这些方法将被用来选择性地识别在结构化临床数据中发现的可信的药物-事件对，并使用自然语言提取从非结构化数据中提取。将对开发的方法进行形成性评估，以评估它们从生物医学文献中重新发现已知副作用的能力，并最终评估它们仅使用统计方法提高归因于一系列已知药物的效果的精确度的能力。此外，我们将使用历史数据和知识评估他们预测FDA最近警告的能力。如果成功，拟议的研究将提供自动识别可信的药物-事件对以用于监管目的的手段，从而减少随后的发病率和死亡率。此外，这些方法将提供一种通用的方法，可以用于应用从生物医学文献中获得的知识来解释临床数据。