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: 10619589
• 负责人: Arjun Krishnan
• 金额: $ 18.18万
• 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 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10619589
• 关键词: Address; Amino Acid Sequence; Anthrax disease; Anti-Infective Agents; Antimicrobial Resistance; Area; Bacterial Infections; Biology; Chemicals; Classification; Collaborations; Communicable Diseases; Computing Methodologies; Coronavirus; Data; Data Collection; Databases; Dengue; Diabetes Mellitus; Disease; Drug Targeting; FDA approved; Gene Expression Profile; Gene Targeting; Genes; Heart Diseases; Human; Immune response; Individual; Infection; Influenza; Knowledge; Libraries; Link; Machine Learning; Malignant Neoplasms; MeSH Thesaurus; Molecular; Network-based; Pathogenicity; Pathway interactions; Pattern; Pharmaceutical Preparations; Property; Proteins; Resistance; Severity of illness; Source; Structure; Therapeutic; Time; Tissues; Treatment outcome; Tuberculosis; Virus Diseases; Work; candidate identification; computer framework; cost; data exchange; differential expression; drug candidate; drug repurposing; fighting; gene interaction; gene network; genome-wide; improved; infectious disease treatment; insight; interest; machine learning framework; machine learning model; microorganism; molecular scale; novel; novel therapeutics; pathogen; predictive tools; protein structure; response; small molecule; stem; supervised learning; transcriptome

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 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 抗菌素耐药性，另一个是发现新药所需的高昂成本和时间。一个 克服这些问题的潜在方法是重点将现有药物重新用于宿主导向 治疗。然而，这是一个新兴的应用领域。虽然一些研究使用了这种广泛的方法来 寻找针对特定病毒和细菌感染的候选药物，缺乏系统计算 可用于重新利用药物治疗任何传染病的框架，尤其是专注于药物的框架 和疾病机制，而不是个体药物和靶点特性。还缺少的框架 可以利用可用于非传染性疾病的大量数据和知识来应对 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- 指导性、可解释的机器学习 (ML) 模型可重新调整药物的用途，以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；这是与此相关的分子机制的两个互补信息来源 重新调整努力的目的。它还使用有关数百种非传染性疾病和数千种疾病的数据和知识。 小分子（包括 FDA 批准的药物）创造了大量的再利用机会。要求 仅响应感染的宿主转录组数据，我们的通用机器学习框架将适用于 新的、正在出现的和未被充分研究的传染病。该项目还将产生高可信度的药物 几种传染病的候选者以及对宿主导向新途径的机制见解 疗法。