Drug Effect Discovery Through Data Mining and Integrative Chemical Biology

通过数据挖掘和综合化学生物学发现药物作用

• 批准号: 9282587
• 负责人: Nicholas P Tatonetti
• 金额: $ 48.46万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9282587
• 关键词: Acute; Address; Adverse drug effect; Adverse drug event; Adverse effects; Adverse event; Algorithms; Bees; Binding; Biological Models; Biology; Chemicals; Clinical; Clinical Trials; Combined Modality Therapy; Data; Data Analyses; Data Element; Data Reporting; Databases; Detection; Development; Drug Combinations; Drug Interactions; Drug Modelings; Drug Monitoring; Drug Targeting; Electronic Health Record; Etiology; Federal Aid; Health; Hyperglycemia; Hypertension; Institution; Insurance; Knowledge; Link; Medical; Metabolism; Methodology; Methods; Mining; Modernization; Molecular; Monitor; Outcome; Pathology Report; Pathway Analysis; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacotherapy; Pharmacy facility; Phenotype; Physiological; Pilot Projects; Population; Proteins; Records; Research; Research Personnel; Resources; Safety; Sampling; Sentinel; Signal Transduction; Surveillance Methods; System; Systems Biology; United States Food and Drug Administration; Validation; base; clinical Diagnosis; clinical care; clinical data warehouse; cohort; combinatorial; data management; data mining; demographics; design; drug development; drug withdrawal; human disease; innovation; novel; novel therapeutics; phase 3 study; phase III trial; predictive modeling; predictive tools; prospective; public health relevance; public-private partnership; response; secondary analysis; small molecule; surveillance strategy; tool

DESCRIPTION (provided by applicant): Small molecule drugs are the cornerstone of modern medical practice. However, their use is plagued by the onset of unexpected side effects, often seen only in late-stage clinical trials or after release to the market. As a result, there have bee a number of high profile drug withdrawals and a dearth of new drug development. Characterizing the combinatorial effects of drug treatment is of particular concern. It is very difficult to empirically study these interactions before drugs enter the market because of the small samples of co- prescribed drugs in most late stage clinical drug (Phase III) studies. Some interactions can be predicted based on knowledge of shared pathways of metabolism, but many are idiosyncratic and difficult to predict. Thus, we must create surveillance methods to detect unexpected drug effects and interactions that leverage the power of large-scale clinical databases such as the electronic health records. Mining of electronic health record data for the purpose of identifying adverse drug effects is an increasingly important research challenge. For example, in response to a congressional mandate the Food and Drug Administration (FDA) established the mini-sentinel initiative in 2009 -- a pilot study that links claims and administratve data from over 31 institutions for the purpose of monitoring drug safety surveillance. In addition, public-private partnerships (e.g. the Observational Medical Outcomes Partnership) have sprouted to establish data management and analysis standards for safety surveillance. However, the potential of the EHR for drug surveillance is paralleled by an equal number of challenges. Many of these challenges are in the quality (or rather lack thereof) of data when used for secondary analyses. Data stored in the EHR are often dirty, noisy, and missing. In addition to issues regarding data capture, these data also suffer from bias which confounds analysis and makes data mining results difficult to interpret. These issues become especially acute in the context of combination therapies where the exposed patient cohorts are often small and suffer from unknown (i.e. unstudied) biases. In this proposal we present a drug safety surveillance strategy which integrates state-of-the-art signal detection algorithms with chemical systems biology data for the purpose of identifying unexpected effects of combination therapies. We present an integrative methodology which combines quantitative signal detection and chemical systems biology to mine drug effects from a large clinical database. This will require innovations in observational statistical data mining, network analysis, and integrative chemical systems biology. The result will be a set of tools for discovering drug effects and linking them to molecular interaction networks. These resources will aid federal regulators to better monitor the safety of drugs at the population level, pharmacologists who wish to understand the effects of drugs at the physiological level, and drug development researchers to explore new treatments of human disease.

描述（申请人提供）：小分子药物是现代医学实践的基石。然而，它们的使用受到意外副作用的困扰，通常仅在后期临床试验或上市后才能看到。因此，出现了一些引人注目的药物撤回和缺乏新药开发。药物治疗的组合效应的特征是特别关注的。在药物进入市场之前，很难对这些相互作用进行经验性研究，因为在大多数晚期临床药物（III期）研究中，联合处方药物的样本量很小。一些相互作用可以根据代谢的共同途径的知识来预测，但许多是特异质的，难以预测。因此，我们必须创建监测方法来检测意想不到的药物作用和相互作用，这些方法可以利用大规模临床数据库（如电子健康记录）的力量。挖掘电子健康记录数据以识别药物不良反应是一个越来越重要的研究挑战。例如，为了响应国会的授权，美国食品和药物管理局（FDA）于2009年建立了迷你哨兵倡议-一项试点研究，将来自31个以上机构的索赔和管理数据联系起来，以监测药物安全监督。此外，本发明还提供了一种方法， 公私伙伴关系（如观察性医学成果伙伴关系）已经萌芽，以建立安全监测的数据管理和分析标准。然而，EHR在药物监测方面的潜力也面临着同样多的挑战。其中许多挑战是用于二次分析的数据的质量（或者更确切地说是缺乏质量）。存储在EHR中的数据通常是脏的、嘈杂的和丢失的。除了有关数据捕获的问题外，这些数据还存在偏差，这会混淆分析并使数据挖掘结果难以解释。在联合治疗的背景下，这些问题变得尤其严重，因为接触的患者队列通常很小，并且存在未知（即未经研究）的偏差。在该提案中，我们提出了一种药物安全性监测策略，该策略将最先进的信号检测算法与化学系统生物学数据相结合，以识别联合治疗的意外效果。我们提出了一个综合的方法，结合定量信号检测和化学系统生物学挖掘药物的影响，从一个大型的临床数据库。这将需要在观测统计数据挖掘、网络分析和综合化学系统生物学方面进行创新。其结果将是一套发现药物作用并将其与 分子相互作用网络这些资源将帮助联邦监管机构更好地监测药物在人群水平上的安全性，帮助药理学家了解药物在生理水平上的作用，帮助药物开发研究人员探索人类疾病的新疗法。