A Knowledge Map to Find Alzheimer's Disease Drugs

寻找阿尔茨海默病药物的知识图谱

• 批准号: 9928609
• 负责人: OLIVIER LICHTARGE
• 金额: $ 22.8万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9928609
• 关键词: Acute; Address; Affect; Algorithms; Alzheimer' s Disease; Calculi; Candidate Disease Gene; Cell model; Clinical; Combined Modality Therapy; Complex; DNA; Data; Disease; Drug Targeting; Evolution; Gene Targeting; Genes; Genome; Genotype; In Vitro; Incidence; Knowledge; Literature; Machine Learning; Maps; Mathematics; Methods; Modeling; Molecular Evolution; Mutation; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Resolution; Rest; System; Testing; Text; Training; Variant; Work; aging population; base; database structure; experimental study; falls; high dimensionality; in vivo; interest; mouse model; novel; stem; text searching

To stem the rising incidence of Alzheimer's disease (AD) in our aging population, new methods to repurpose and combine drugs against Alzheimer's disease (AD) are acutely required. This is a challenge, however, because the complex polygenic basis of AD remains opaque, and rational methods to repurpose drugs are in early years, even for well-defined gene targets. To address these problems, we propose new algorithms to integrate data on a very large scale so as to combine evolutionary information and high-throughput experimental observations with the knowledge conveyed by text in the literature. First, to detect disease-relevant genome variations in AD patients, Aim 1 will combine a novel mathematical calculus of mutational landscapes with machine learning, in so doing suggesting primary candidate genes for drug targeting based on signs of mutational selection in cases or controls. Next, to repurpose and combine drugs targeting these genes, Aim 2 will map a large fraction of all that is known about genes, phenotypes, and drugs into a single high-dimensional network that represents their interactions as described in various databases (structured data) and in the literature (unstructured data). The topology of this network will determine the optimal choice of single drug or combination therapy in an approach that can be personalized. Finally, to validate efficacy experimentally, Aim 3 will test both our candidate genes and drugs with state-of-the-art in vitro and in vivo screens. Feasibility rests with prior studies on evolution, networks, systems, and text-mining that demonstrate accurate predictions of deleterious mutations and their clinical sequelae and the discovery of drivers of diseases. Broadly, this work will yield proof of principle for a novel quantitative model that integrates fundamental concepts from mathematics and molecular evolution, and for a low resolution but large-scale map of biomedical knowledge in which network notions of distance computed by machine learning identify relevant functional hypothesis that would otherwise be easily overlooked. The result will be a new experimental ability to unravel the genotype-phenotype relationship in Alzheimer's Disease so as to guide drug therapy.

为了遏制老年人口中阿尔茨海默病（AD）发病率的上升，我们需要新的方法来重新定位