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: 8696226
• 负责人: Nicholas P Tatonetti
• 金额: $ 59.75万
• 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/8696226
• 关键词: Accounting; Acute; Address; Adverse drug effect; 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 Monitoring; Drug Prescriptions; Drug Targeting; Electronic Health Record; Etiology; Event; Federal Aid; Health; Hyperglycemia; Hypertension; Institution; Insurance; Knowledge; Link; Marketing; Medical; Metabolism; Methodology; Methods; Mining; Molecular; Monitor; Outcome; Pathology Report; Pathway Analysis; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacy facility; Phase III Clinical Trials; Phenotype; Physiological; Pilot Projects; Plague; Population; Proteins; Records; Research; Research Personnel; Resources; Safety; Sampling; Sentinel; Signal Transduction; Staging; 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; phase 3 study; predictive modeling; prospective; public health relevance; public-private partnership; response; 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。然而，它们的使用受到意想不到的副作用的困扰，通常只在后期临床试验或投放市场后才会出现。因此，出现了许多引人注目的药物停药和新药开发的缺乏。描述药物治疗的联合效应是一个特别值得关注的问题。在药物进入市场之前对这些相互作用进行实证研究是非常困难的，因为在大多数晚期临床药物（III期）研究中，共处方药物的样本很小。一些相互作用可以根据共享代谢途径的知识来预测，但许多是特殊的，难以预测。因此，我们必须利用电子健康记录等大规模临床数据库的力量，创造监测方法来检测意外的药物效应和相互作用。为识别药物不良反应而挖掘电子健康记录数据是一项日益重要的研究挑战。例如，为了响应国会的要求，美国食品和药物管理局（FDA）在2009年建立了小型哨兵倡议——一项将来自31家机构的索赔和行政数据联系起来的试点研究，目的是监测药物安全监测。此外,