Mechanism-guided drug repurposing for host-directed therapy of infectious diseases using interpretable and integrative ML

摘要 我们治疗传染病的能力受到两个主要问题的阻碍。一是快速增长的 抗生素耐药性，另一个是发现新药所需的高昂成本和时间。一 克服这些问题的潜在方法是专注于将现有药物重新用于宿主定向 疗法这是一个新兴的应用领域。虽然有几项研究使用了这种广泛的方法， 寻找特定病毒和细菌感染的候选药物，缺乏系统的计算能力。 这些框架可用于重新使用药物治疗任何传染病，特别是那些专注于药物治疗的框架。 和疾病机制，而不是单个药物和靶标特性。同样缺少的是框架， 可以利用非传染性疾病的大量数据和知识， 传染病治疗。在这个项目中，我们将开发一个综合框架，使用机制- 引导，可解释的机器学习（ML）模型，以重新使用药物来增强宿主对感染的反应。 我们的框架利用了大量的转录组数据收集和基因组规模的人类基因相互作用 网络;这是关于与此相关的分子机制的两个互补信息来源。 重新利用努力。它还使用了数百种非传染性疾病和数千种 小分子药物（包括FDA批准的药物），以创造大量的再利用机会。要求 只有主机转录组数据响应感染，我们的通用ML框架将适用于 新的、正在出现的和未充分研究的传染病。该项目还将产生高置信度药物 候选人的几种传染病沿着与机制的见解，新的途径，为主机导向 治疗学

ABSTRACT Our ability to treat infectious diseases is impeded by two major problems. One is the rapid increase of antimicrobial resistance, and the other is the prohibitive cost and time required for discovering new drugs. A potential approach to overcome these problems is to focus on repurposing existing drugs for host-directed therapy. However, this is an emerging application area. While several studies have used this broad approach to find drug candidates for specific viruses and bacterial infections, there is a dearth of systematic computational frameworks that can be used to repurpose drugs for any infectious disease, especially ones that focus on drug and disease mechanisms rather than individual drug and target properties. Also missing are frameworks that can leverage the massive amounts of data and knowledge available for non-infectious diseases to tackle infectious disease treatment. In this project, we will develop an integrative framework that uses mechanism- guided, interpretable machine learning (ML) models to repurpose drugs to bolster host response to infection. Our framework leverages massive transcriptome data collections and genome-scale human gene interaction networks; these are two complementary sources of information about molecular mechanisms relevant for this repurposing effort. It also uses data and knowledge about hundreds of non-infectious diseases and thousands of small molecules (including FDA-approved drugs) to create numerous repurposing opportunities. Requiring only host transcriptome data in response to infection, our general-purpose ML framework will be applicable to new, emerging, and understudied infectious diseases. This project will also result in high-confidence drug candidates for several infectious diseases along with mechanistic insights into new avenues for host-directed therapeutics.