TargetAD: A systems multi-omics approach to drug repositioning in Alzheimer's disease

TargetAD：一种用于阿尔茨海默病药物重新定位的系统多组学方法

基本信息

批准号：
10652504
负责人：
Matthias Arnold
金额：
$ 71.8万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10652504
关键词：
Acceleration Address Affect Age Aging Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease model Animal Model Atlases Attention Attenuated Autopsy Behavior assessment Behavioral Binding Biological Markers Biomedical Research Brain Clinical Clinical Trials Cognitive Collection Complement Complex Data Data Set Databases Development Disease Drug Combinations Drug Exposure Drug Screening Drug Targeting Drug usage Electronic Health Record Electrophysiology (science)Exhibits Future Gene Expression Regulation Genetic Complementation Test Graph Immunohistochemistry Individual Intake Intervention Investments Late Onset Alzheimer Disease Link Longitudinal Studies Machine Learning Measures Medicine Memory Metabolic Pathway Methods Mining Molecular Molecular Profiling Mus Network-based Neurodegenerative Disorders Outcome Pathology Pathway Analysis Pathway interactions Pharmaceutical Preparations Pharmacogenomics Pharmacologic Substance Pharmacotherapy Phase Population Study Positioning Attribute Probability Process Proteins Publishing Quantitative Trait Loci Reaction Research Resources Retrospective Studies Route Scoring Method Screening Result Synapses System Testing Tissue-Specific Gene Expression Tissues United States National Institutes of Health Validation Visit aged aging population alternative treatment amyloid pathology biobank biological systems candidate identification candidate selection data mining drug candidate drug development endophenotype experimental study genetic association genome wide association study graph database in silico machine learning method metabolomics mouse model multidisciplinary multiple omics neuroimaging neuropathology novel population based pre-clinical protein expression protein protein interaction religious order study standardize measure synaptic function tau Proteins treatment strategy validation studies

项目摘要

Project Summary Late-onset Alzheimer's Disease (AD) is a slowly progressing, untreatable neurodegenerative disorder that affects a substantial fraction of the aging population today. Hundreds of clinical trials and massive investments into drug development efforts have so far not resulted in a single disease-modifying therapy that showed a significant beneficial effect on the disease. Drug repositioning, the application of approved drugs in a novel disease context, has gained increasing attention as a promising alternative to identify treatment options for AD. For successful pharmaceutical intervention in AD, a drug or drug combination needs to target the complex molecular changes observed in AD in a specific manner. To identify drugs exerting these desired effects a detailed understanding of the molecular networks across regulatory layers that underly the biological system is required. However, these networks are not readily available and are scattered across hundreds of studies and complex databases. To address this challenge, we propose TargetAD, a network-based framework that builds this molecular network from genetic associations, co-expression/correlation networks, metabolic pathways, gene regulation data, protein-protein interactions, and tissue-specific gene and protein expression data augmented with AD multi-omics associations, as well as drug-drug target data and molecular drug signatures. We will achieve this by leveraging the power of large-scale, multi-omics association results generated within NIH's large “Accelerating Medicines Partnership - Alzheimer's Disease” initiative and other large-scale population-based studies. The collective evidence will be stored in a publicly accessible graph database, which we then use for the identification of candidate drugs or drug combinations (“candidates”). Through the development of a novel network-based machine-learning method, we will rank candidates in the database by their probability to affect AD networks in a beneficial way. High-ranking candidates will be subjected to a comprehensive prioritization pipeline. To this end, we will retrospectively investigate whether longitudinal AD-related biomarker profiles of individuals who took a repositioning candidate show evidence for healthier aging in large studies of AD. These analyses will be complemented by examining whether the post- mortem neuropathological burden supports a beneficial effect of the candidate. To increase power and coverage of candidates, we will further analyze electronic health records from the UK Biobank for additional evidence. The three most promising candidates will be selected in discussion with a panel of experts. These will be evaluated by preclinical validation studies in animal models of AD. In summary, the unique combination of multidisciplinary expertise, access to high-profile datasets and advanced computational integration pipelines will allow us to identify molecular pathways disturbed in AD that are targetable by drug repositioning candidates, which thus are prime candidates for testing in clinical trials.

项目概要晚发性阿尔茨海默病 (AD) 是一种进展缓慢且无法治愈的神经退行性疾病，影响着当今老龄化人口的很大一部分。数百项临床试验和大规模投资。迄今为止，对药物开发工作的投入尚未产生任何一种能够显示出有效效果的疾病缓解疗法。对疾病的药物重新定位具有显着的有益作用，已批准的药物在新颖中的应用。作为确定 AD 治疗方案的有前途的替代方案，它已受到越来越多的关注。为了成功地对 AD 进行药物干预，药物或药物组合需要针对复合物以特定方式观察 AD 中的分子变化，以确定发挥这些所需作用的药物。对生物系统下跨监管层的分子网络的详细了解然而，这些网络并不容易获得，并且分散在数百项研究和研究中。为了应对这一挑战，我们提出了 TargetAD，这是一个构建基于网络的框架。这个分子网络来自遗传关联、共表达/相关网络、代谢途径、基因调控数据、蛋白质-蛋白质相互作用以及组织特异性基因和蛋白质表达数据增强了 AD 多组学关联，以及药物-药物靶点数据和分子药物特征。我们将通过利用大规模、多组学关联结果的力量来实现这一目标 NIH 的大型“加速药物合作伙伴关系 - 阿尔茨海默病”计划和其他大规模基于人群的研究将存储在可公开访问的图形数据库中。然后我们用于识别候选药物或药物组合（“候选”）。通过开发一种新颖的基于网络的机器学习方法，我们将对候选人进行排名数据库中的高级候选人将通过其以有益的方式影响 AD 网络的概率。为此，我们将回顾性地调查是否需要进行全面的优先排序。接受重新定位候选者的纵向 AD 相关生物标志物概况显示证据大型 AD 研究中的健康老龄化将通过检查后是否会得到补充。尸检神经病理学负担支持候选人的有益效果，以增加力量和能力。候选人的覆盖范围，我们将进一步分析英国生物银行的电子健康记录，以获取更多信息证据。将与专家小组讨论选出三名最有希望的候选人。将通过 AD 动物模型的临床前验证研究进行评估。总之，多学科专业知识、访问知名数据集和先进的计算集成管道将使我们能够识别 AD 中受到干扰的分子途径是药物重新定位候选药物的目标，因此是临床试验中测试的主要候选药物。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Differences and commonalities in the genetic architecture of protein quantitative trait loci in European and Arab populations.

欧洲和阿拉伯人群蛋白质数量性状基因座遗传结构的差异和共性。

DOI：
发表时间：
2023-03-06
期刊：
影响因子：
3.5
作者：
Thareja, Gaurav;Belkadi, Aziz;Arnold, Matthias;Albagha, Omar M E;Graumann, Johannes;Schmidt, Frank;Grallert, Harald;Peters, Annette;Gieger, Christian;Consortium, The Qatar Genome Program Research;Suhre, Karsten
通讯作者：
Suhre, Karsten

Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS.

基于尿液的 COVID-19 和细菌败血症引起的 ARDS 的多组学比较分析。

DOI：
发表时间：
2023-01-26
期刊：
影响因子：
0
作者：
Batra, Richa;Uni, Rie;Akchurin, Oleh M;Alvarez;Gómez;Patino, Edwin;Hoffman, Katherine L;Simmons, Will;Whalen, William;Chetnik, Kelsey;Buyukozkan, Mustafa;Benedetti, Elisa;Suhre, Karsten;Schenck, Edward;Cho, Soo
通讯作者：
Cho, Soo

SGI: automatic clinical subgroup identification in omics datasets.

SGI：组学数据集中的自动临床亚组识别。

DOI：
发表时间：
2022-01-03
期刊：
影响因子：
0
作者：
Buyukozkan, Mustafa;Suhre, Karsten;Krumsiek, Jan
通讯作者：
Krumsiek, Jan

Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS.

基于尿液的 COVID-19 和细菌败血症引起的 ARDS 的多组学比较分析。

DOI：
发表时间：
2022-08-10
期刊：
影响因子：
0
作者：
Batra, Richa;Uni, Rie;Akchurin, Oleh M;Alvarez;Gómez;Patino, Edwin;Hoffman, Katherine L;Simmons, Will;Chetnik, Kelsey;Buyukozkan, Mustafa;Benedetti, Elisa;Suhre, Karsten;Schenck, Edward;Cho, Soo Jung;Choi, Augu
通讯作者：
Choi, Augu

piTracer - Automatic reconstruction of molecular cascades for the identification of synergistic drug targets.

piTracer - 自动重建分子级联，用于识别协同药物靶标。