A knowledge map to find Alzheimer's disease drugs

一张知识图谱寻找阿尔茨海默病药物

• 批准号: 10456711
• 负责人: OLIVIER LICHTARGE
• 金额: $ 79.25万
• 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 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10456711
• 关键词: Acute; Address; Affect; Algorithms; Alleles; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease patient; Alzheimer' s disease risk; Amyloid beta-Protein; Amyloid beta-Protein Precursor; Animal Model; Bacterial Drug Resistance; Behavior; Calculi; Candidate Disease Gene; Cell model; Clinical; Combined Modality Therapy; Communities; Complex; Cultured Cells; DNA; Data; Databases; Dementia; Detection; Diffusion; Disease; Drug Combinations; Drug Targeting; Equilibrium; Evolution; Functional disorder; Gene Targeting; Genes; Genome; Genotype; Human; Hybrids; In Vitro; Incidence; Individual; Inflammation; Inflammatory; Knowledge; Language; Light; Literature; Machine Learning; Malignant Neoplasms; Maps; Mathematics; Measures; Metabolism; Methods; Modeling; Molecular; Molecular Evolution; Mutation; Pathogenesis; Pathology; Pathway interactions; Patients; Peptides; Pharmaceutical Preparations; Pharmacology; Pharmacotherapy; Phenotype; Process; Production; PubMed; Resolution; Rest; Risk Factors; Source; Structure; Symptoms; System; Testing; Text; Therapeutic; Time; Training; Validation; Variant; Work; aging population; autism spectrum disorder; base; clinically relevant; cohort; database structure; disorder risk; drug repurposing; drug testing; efficacy validation; exome; experimental study; falls; gene function; gene interaction; genetic information; genetic variant; genome wide association study; heterogenous data; high dimensionality; improved; in vivo; infancy; innovation; interest; molecular modeling; mouse model; neuroinflammation; novel; novel strategies; precision drugs; protective factors; screening; stem; structured data; success; synergism; tau Proteins; text searching; unstructured data; virtual

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.

为了遏制老龄化人口中阿尔茨海默病 (AD) 发病率的上升，新方法重新利用 迫切需要针对阿尔茨海默病（AD）的联合药物。然而，这是一个挑战 因为 AD 复杂的多基因基础仍然不透明，并且重新利用药物的合理方法正在研究中 早年，即使对于明确的基因目标也是如此。为了解决这些问题，我们提出了新的算法 大规模整合数据，将进化信息与高通量实验结合起来 利用文献中文本所传达的知识进行观察。一、检测疾病相关基因组 AD 患者的变异，目标 1 将结合突变景观的新颖数学计算与 机器学习，这样做可以根据以下迹象提出药物靶向的主要候选基因 病例或对照中的突变选择。接下来，为了重新利用和组合针对这些基因的药物，目标 2 将把关于基因、表型和药物的大部分已知信息映射到一个高维数据中 代表各种数据库（结构化数据）和文献中描述的相互作用的网络 （非结构化数据）。该网络的拓扑结构将决定单一药物或组合的最佳选择 采用可个性化的方法进行治疗。最后，为了通过实验验证功效，Aim 3 将测试两者 我们的候选基因和药物经过最先进的体外和体内筛选。可行性取决于先前的研究 关于进化、网络、系统和文本挖掘的研究，证明了对有害突变的准确预测 及其临床后遗症和疾病驱动因素的发现。总的来说，这项工作将产生原理证明 一个新的定量模型，整合了数学和分子进化的基本概念， 以及低分辨率但大规模的生物医学知识地图，其中距离的网络概念 通过机器学习计算确定相关的功能假设，否则很容易被忽视。 结果将是揭示阿尔茨海默病基因型与表型关系的新实验能力 从而指导疾病的药物治疗。