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

描述（由申请人提供）：拟定研究旨在通过关注和利用报告药物相互作用（DDI）中使用的多种不同类型的证据，提供有效的大规模方法，以获得有关药物相互作用（DDI）的可靠信息。DDI是药物不良反应的重要原因，导致急诊室就诊和住院。DDI研究旨在通过几种类型的证据将相互作用的分子机制与其实际临床后果联系起来。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（例如药物疗效或药物不良反应）。由于这些研究跨越多个学科，通常这三种类型的证据不能同时获得或报告。三种类型中的任何一种缺失证据沿着，都会造成知识鸿沟，从而阻碍转化研究。例如，如果在临床上观察到不良相互作用，但尚未报告分子基础，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的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。这些工具将被设计为明确识别DDI证据三个级别之间的差距，并帮助缩小这些差距。虽然自动发现文本中提到的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的分子药理学实验，以弥补体外证据水平上的知识差距。具体而言：在目标1中，我们将构建体外、体内和临床DDI证据所需的词汇和新的文本语料库;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，并将进行实验以评估体外代谢酶为基础的DDI机制。据我们所知，我们是第一个以系统的方式区分并利用不同类型的基于文本的DDI证据的小组。继基于文本的发现与选择性分子药理学实验评价，是另一个独特的跨学科的特点，增加了拟议的工作的意义。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，沿着三个不同层次的支持证据。此外，将由一种类型的证据而不是另一种类型的证据可靠地支持的DDI将被确定为未来药理学研究的强有力的候选者。

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.