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.

为了阻止老年痴呆症（AD）在我们老龄化人口中发病率的上升， 迫切需要抗阿尔茨海默病（AD）的联合收割机药物。然而，这是一个挑战， 因为AD复杂的多基因基础仍然不透明，合理的方法来重新使用药物， 早期，即使是明确的基因靶点。为了解决这些问题，我们提出了新的算法， 以非常大规模整合数据，从而将进化信息和高通量实验联合收割机结合 观察与文献中的文本所传达的知识。第一，检测疾病相关基因组 AD患者的变异，目标1将联合收割机结合一种新的突变景观数学演算， 机器学习，在这样做的基础上，建议主要候选基因的药物靶向的迹象， 在病例或对照中进行突变选择。接下来，为了重新利用和联合收割机针对这些基因的药物，目标2 将把所有已知的基因、表型和药物的大部分映射到一个单一的高维空间中。 一个网络，代表了各种数据库（结构化数据）和文献中描述的相互作用 （非结构化数据）。该网络的拓扑结构将决定单药或联合用药的最优选择 可以个性化的治疗方法。最后，为了验证实验的有效性，目标3将测试两者 我们的候选基因和药物与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，证明了有害突变的准确预测 及其临床后遗症和发现疾病的驱动因素。从广义上讲，这项工作将产生原理证明， 对于整合数学和分子进化基本概念的新型定量模型， 而对于低分辨率但大规模的生物医学知识地图， 通过机器学习计算，识别相关的功能假设，否则很容易被忽视。 结果将是一个新的实验能力，以解开阿尔茨海默氏症的基因型-表型关系 从而指导药物治疗。