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 Notoriously, the Cox-2 inhibitor Rofecoxib (Vioxx) was withdrawn from market on account of evidence suggesting that treatment with the drug increased the rate of coronary artery disease, and recently new evidence has emerged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.

描述（由申请人提供）：最近几起备受瞩目的事件突出了监测获批药物非预期效应的必要性，在这些事件中，药物在上市后检测到致命的副作用。众所周知，考克斯-2抑制剂罗非考昔（万络）因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而退出市场，最近出现的新证据表明常用抗生素阿奇霉素（齐舒美）可能导致致命的心律失常。为了减轻这种未检测到的副作用导致的发病率和死亡率，监管机构如食品和药物管理局（FDA）已经建立了自发报告系统，以系统化上市后监测。然而，有证据表明，药物不良事件（ADE）报告不足的情况很普遍。已提出自动监测电子健康记录（EHR）中记录为自由文本或结构化数据的事件，作为早期识别有意义的相关药物-事件对的途径。由于这些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来选择性地识别在临床数据中发现的大量相关性中的合理药物-事件对。在拟议的研究中，我们将开发和评估生物相容性模型，基于从生物医学文献中提取的知识，并使用多维计算方法同时在多个概念和关系中进行有效的搜索和推理。这些方法将用于选择性地识别在结构化临床数据中发现的合理药物-事件对，并使用自然语言提取从非结构化数据中提取。开发的方法将进行评估形成，他们的能力，重新发现已知的副作用，从生物医学文献，并总结他们的能力，以提高精度的影响归因于一个ST的已知药物单独使用统计方法。此外，我们还将利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合理的药物-事件对的方法，以用于监管目的，减轻随之而来的发病率和死亡率。此外，这些方法将提供一种可推广的方法，可用于应用来自生物医学文献的知识来解释临床数据。