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

使用可解释和集成的机器学习机制引导的药物再利用，用于针对宿主的传染病治疗

• 批准号: 10738676
• 负责人: Arjun Krishnan
• 金额: $ 22.17万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-09 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10738676
• 关键词: Mechanism; guided; drug; repurposing; host

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.

抽象的 两个主要问题阻碍了我们治疗传染病的能力。一个是快速增加 抗菌素耐药性，另一种是发现新药所需的成本和时间。一个 克服这些问题的潜在方法是专注于重新利用现有药物进行宿主定向 治疗。但是，这是一个新兴应用领域。虽然几项研究已使用这种广泛的方法 找到特定病毒和细菌感染的候选药物，缺乏系统计算 可用于为任何传染病重新利用药物的框架，尤其是专注于药物 以及疾病机制，而不是单独的药物和靶向特性。还缺少的是框架 可以利用可用于解决的大量数据和知识来解决 传染病治疗。在这个项目中，我们将开发一个使用机制的综合框架 - 指导的，可解释的机器学习（ML）模型以重新利用药物来增强宿主对感染的反应。 我们的框架利用大量转录组数据收集和基因组规模的人类基因相互作用 网络；这是有关与此相关的分子机制的两个补充信息来源 重新利用努力。它还使用有关数百种非感染疾病和数千种的数据和知识 小分子（包括FDA批准的药物）创造了许多重新利用的机会。需要 仅对感染的宿主转录组数据，我们的通用ML框架将适用于 新，新兴和研究的传染病。该项目还将导致高信任药物 几种传染病的候选人以及对宿主指导的新途径的机械见解 疗法。