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Evidence-based Drug-Interaction Discovery: In-Vivo, In-Vitro and Clinical

基于证据的药物相互作用发现：体内、体外和临床

基本信息

批准号：
9336353
负责人：
Lang Li
金额：
$ 39.43万
依托单位：
INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-20 至 2020-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9336353
关键词：
Address Adverse effects Area Basic Science Binding Cells Characteristics Clinical Clinical Research Conflict (Psychology)Development Discipline Drug Exposure Drug Interactions Drug Kinetics Emergency department visit Enzymes Evaluation FDA approved Future Health Hospitalization Human In Vitro Incidence Journals Knowledge Label Lead Level of Evidence Link Literature Medical Metabolic Methods Modeling Molecular Numerical value Ontology Pharmaceutical Preparations Pharmacoepidemiology Pharmacology Pharmacotherapy Polypharmacy PubMed Public Health Publications Reaction Reporting Research Research Design Retrieval Sampling Source Terminology Testing Text Translational Research Transportation United States Work base clinical effect clinically significant design drug development drug discovery drug efficacy evidence base experimental study follow-up human subject in vivo novel therapeutics prevent response statistics text searching tool

项目摘要

DESCRIPTION (provided by applicant): The proposed research aims to provide effective, large-scale means for obtaining reliable information about drug-drug interactions (DDIs), by focusing on and utilizing the multiple distinct types of evidence used in reporting DDIs. DDIs are a significant cause of adverse drug reactions, leading to emergency room visits and hospitalizations. DDI research aims to link between molecular mechanisms that underlie interactions and their actual clinical consequences, through several types of evidence. We distinguish three types of DDI evidence that are often provided in the literature: in vitro, in viv, and clinical. In vitro studies investigate molecular mechanisms of interaction; In vivo studies evaluate whether these interactions impact drug exposure in human subjects; Clinical studies test whether drug interactions change the actual response to drugs (e.g. drug-efficacy or adverse drug reactions). As such studies span several disciplines, typically the three types of evidence are not simultaneously available or reported. Missing evidence along any of the three types, creates a knowledge gap that can hinder translational research. For instance, if adverse interaction effects are clinically observed, but the molecular underpinnings are not yet reported, it is difficult to identify a safe, alternative drug treatment. In this project we propose to develop and use large-scale text-mining methods and tools to mine drug- interaction information from PubMed abstracts and from FDA drug labels. These tools will be designed to explicitly identify gaps across the three levels of DDI evidence, and to help close such gaps. While automated discovery of DDI mentions in text is an active research area, no other text-based work is concerned with identifying explicit evidence for DDI, while separately taking into consideration the distinct types of interaction evidence. As a follow-up step, we also propose to conduct selective molecular pharmacology experiments to close the identified knowledge-gaps at the in vitro evidence level. Specifically: In Aim 1, we shall construct the needed lexica and new text corpora pertaining to in vitro, in vivo, and clinical DDI evidence; In Aim 2, a suite of text mining tools to separately identify the three types of DDI evidence will be developed, utilizing the corpora created in Aim 1; In Aim 3, clinically significant DDIs that have no sufficient in vitro evidence will be selected using the tools developed in Aim 2, and experiments will be conducted to evaluate in vitro metabolic enzyme- based DDI mechanisms. To the best of our knowledge we are the first group that sets out to distinguish among - and make use of - the different types of text-based DDI evidence in a systematic way. Following the text- based discovery with a selective molecular pharmacology experimental evaluation, is another unique interdisciplinary characteristic that adds to the significance of the proposed work. The successful completion of the proposed project will provide methods and tools for large-scale extraction of DDIs from the literature, along with their supporting evidence at the three distinct levels. Moreover, DDIs that will be reliably supported by one type of evidence but not another will be identified as strong candidates for future pharmacology research.

描述（由申请人提供）：拟议的研究旨在通过关注和利用报告 DDI 时使用的多种不同类型的证据，提供有效、大规模的手段来获取有关药物相互作用 (DDI) 的可靠信息。 DDI 是药物不良反应的一个重要原因，导致急诊室就诊和住院治疗。 DDI 研究旨在通过多种类型的证据，将相互作用的分子机制与其实际的临床后果联系起来。我们区分了文献中经常提供的三种类型的 DDI 证据：体外、体内和临床。体外研究调查相互作用的分子机制；体内研究评估这些相互作用是否影响人类受试者的药物暴露；临床研究测试药物相互作用是否改变对药物的实际反应（例如药物功效或药物不良反应）。由于此类研究跨越多个学科，通常不会同时获得或报告这三种类型的证据。缺少这三种类型中任何一种的证据都会造成知识差距，从而阻碍转化研究。例如，如果临床上观察到不良相互作用，但分子基础尚未报告，则很难确定安全的替代药物治疗。在这个项目中，我们建议开发和使用大规模文本挖掘方法和工具，从 PubMed 摘要和 FDA 药物标签中挖掘药物相互作用信息。这些工具旨在明确识别三个级别的 DDI 证据之间的差距，并帮助缩小这些差距。虽然自动发现文本中提及的 DDI 是一个活跃的研究领域，但没有其他基于文本的工作涉及识别 DDI 的明确证据，同时单独考虑不同类型的交互证据。作为后续步骤，我们还建议进行选择性分子药理学实验，以弥补体外证据水平上已确定的知识差距。具体来说：在目标1中，我们将构建与体外、体内和临床DDI证据相关的所需词汇和新文本语料库；在目标 2 中，将利用目标 1 中创建的语料库开发一套文本挖掘工具，以分别识别三种类型的 DDI 证据；在目标3中，将使用目标2中开发的工具选择没有足够体外证据的具有临床意义的DDI，并进行实验来评估基于体外代谢酶的DDI机制。据我们所知，我们是第一个开始以系统的方式区分并利用不同类型的基于文本的 DDI 证据的团队。继基于文本的发现和选择性分子药理学实验评估之后，另一个独特的跨学科特征增加了拟议工作的重要性。拟议项目的成功完成将为从文献中大规模提取 DDI 提供方法和工具，以及三个不同层面的支持证据。此外，将得到一种类型证据而不是另一种证据可靠支持的 DDI 将被确定为未来药理学研究的有力候选者。