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.

为了遏制阿尔茨海默病(AD)在我们老龄化人口中发病率的上升，重新调整用途的新方法 以及联合治疗阿尔茨海默病(AD)的药物是迫切需要的。然而，这是一个挑战， 因为阿尔茨海默病复杂的多基因基础仍然不清楚，重新调整药物用途的合理方法正在 早些年，即使是明确定义的基因靶点。为了解决这些问题，我们提出了新的算法来 大规模集成数据，以便将进化信息和高通量实验相结合 用文献中的文字所传达的知识进行观察。第一，检测与疾病相关的基因组 AD患者的变异，目标1将把一种新的突变景观的数学演算与 机器学习，在这样做的基础上建议药物靶向的主要候选基因的迹象 病例或对照中的突变选择。下一步，重新调整用途并结合针对这些基因的药物，目标2 将把所有已知的基因、表型和药物的很大一部分映射到单一的高维 表示各种数据库(结构化数据)和文献中描述的它们的交互的网络 (非结构化数据)。这个网络的拓扑结构将决定单一药物或联合药物的最佳选择 以一种可以个性化的方法进行治疗。最后，为了从实验上验证有效性，Aim 3将测试这两种测试 我们的候选基因和药物具有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，它们展示了对有害突变的准确预测 以及他们的临床后遗症和疾病驱动因素的发现。总的来说，这项工作将产生原则性的证明 对于一个集成了数学和分子进化的基本概念的新的量化模型， 对于低分辨率但大规模的生物医学知识地图，其中网络距离的概念 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。 其结果将是一种新的实验能力，可以解开阿尔茨海默氏症患者的基因-表型关系 从而指导药物治疗。