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（由申请人提供）：小分子药物是现代医学实践的基石。但是，它们的使用受到意外副作用的开始困扰，通常仅在后期临床试验中或发布到市场后看到。结果，蜜蜂有许多知名的药物提取和新药物开发的匮乏。表征药物治疗的组合作用特别关注。由于在大多数晚期临床药物（III阶段）研究中，在药物进入市场之前，在药物进入市场之前的经验相互作用非常困难。可以根据对代谢的共同途径的了解来预测某些相互作用，但是许多相互作用是特质且难以预测的。因此，我们必须创建监视方法，以检测出意外的药物影响和相互作用，以利用大规模临床数据库（例如电子健康记录）的功能。为了识别不良药物影响，电子健康记录数据的挖掘是越来越重要的研究挑战。例如，为了应对国会授权，食品药品监督管理局（FDA）于2009年建立了迷你赛计划 - 一项试点研究，将31多家机构的主张和管理数据联系起来，目的是监测药物安全监视。此外， 公私合作伙伴关系（例如，观察性医疗成果伙伴关系）已发芽，以建立安全监视的数据管理和分析标准。但是，EHR对药物监测的潜力与同等数量的挑战相似。这些挑战中的许多挑战在用于二次分析时质量（或更确切地说是缺乏数据）。 EHR中存储的数据通常很脏，嘈杂且缺少。除了有关数据捕获的问题外，这些数据还遭受了混淆分析并使数据挖掘结果难以解释的偏见。这些问题在联合疗法的背景下变得尤为严重，因为裸露的患者队列通常很小并且患有未知（即未研究）的偏见。在此提案中，我们提出了一种药物安全监视策略，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，目的是确定联合疗法的意外影响。我们提出了一种综合方法，该方法将定量信号检测和化学系统生物学结合起来，从大型临床数据库中挖掘药物效应。这将需要在观察性统计数据挖掘，网络分析和集成化学系统生物学中进行创新。结果将是一组发现药物效应并将其联系到的工具 分子相互作用网络。这些资源将有助于联邦监管机构更好地监测人口一级的药物安全，希望了解药物在生理水平上的影响的药理学家，以及药物开发研究人员探索人类疾病的新治疗方法。