Harnessing Diverse Bioinformatic Approaches To Repurpose Drugs For Alzheimers Disease And Related Dementias

利用多种生物信息学方法重新利用治疗阿尔茨海默病和相关痴呆症的药物

• 批准号: 10744875
• 负责人: MARK W ALBERS
• 金额: $ 105.47万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 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 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10744875
• 关键词: Accelerated Phase; Acceleration; Affinity; Alzheimer' s Disease; Alzheimer' s Disease Pathway; Alzheimer' s disease brain; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Award; Awareness; Big Data; Bioinformatics; Biological Markers; Biology; Brain; Brain region; Cells; Chemicals; Clinical; Clinical Investigator; Clinical Research; Clinical Trials; Clinical Trials Design; Collaborations; Communities; Compensation; Computer Systems; Computer software; Data; Data Set; Databases; Dementia; Diagnosis; Disease; Disease Progression; Drug Prescriptions; Drug Targeting; Drug usage; Economics; Electronic Health Record; Evaluation; Event; Exons; FDA approved; Gene Expression; Gene Expression Profile; Genetic Complementation Test; Genome; Human; Individual; Industry; Inflammatory; Informatics; Infrastructure; Israel; Kidney Failure; Knowledge; Laboratories; Lead; Literature; Machine Learning; Medicine; Mendelian randomization; Methodology; Methods; Modeling; National Health Services; Neurofibrillary Tangles; Neuroglia; Neurons; New Agents; Onset of illness; Outcome; Pathologic; Pathology; Pathway Analysis; Pathway interactions; Patients; Pattern; Peripheral; Pharmaceutical Preparations; Pharmacology; Phase II Clinical Trials; Phenotype; Placebo Control; Prevention; Production; Proteome; Proteomics; Proxy; Public Domains; Randomized; Records; Reproducibility; Risk; Running; Signal Transduction; Single Nucleotide Polymorphism; Site; Statistical Data Interpretation; Symptoms; Synapses; System; Target Populations; Testing; Therapeutic; Therapeutic Clinical Trial; Update; Work; biobank; candidate identification; cell type; cheminformatics; clinical care; clinically relevant; computer science; computerized tools; cost; dementia care; drug action; drug candidate; drug repurposing; federated learning; gene discovery; imaging study; improved; inhibitor; interoperability; kinase inhibitor; large datasets; meetings; member; multidisciplinary; novel; open data; open source; patient population; phase III trial; predictive marker; preference; prevent; programs; prospective; protein TDP-43; protein expression; response; tool; transcriptome; transcriptome sequencing; translational study

Abstract The exploration of genomes, transcriptomes, and proteomes derived from brains with Alzheimer's disease (AD) by powerful computational tools has developed new knowledge, including the identification of pathways and targets that may be involved in the initiation and/or progression of the disease. The challenge is to find drugs that impact those pathways and then validate the importance of those pathways – distinguishing primary disease drivers from secondary events. Repurposing FDA-approved drugs is one approach to probe potential pathways in proof of concept, and ultimately therapeutic, clinical trials. In this renewal application, we propose to discover and validate hypotheses for Drug Repurposing In AD (DRIAD) through three integrated, complementary informatics approaches. Specifically, we will extend our systems pharmacology (DRIAD-SP) tool of classical and network aware (prior-loaded) machine learning approaches to identify pathways and targets altered in AD brains at different stages of disease progression using data from Accelerating Medicines Partnership-AD available through Synapse (Aim 1); we will use chemical biology and systems pharmacology approaches to discover the target selectivity of lead kinase inhibitors within human neuronal and glial cell types using unbiased RNA-seq, proteomic and imaging studies followed by pathway analysis (Aim 2). We will implement additional causal inferential strategies to emulate clinical trials in electronic health records (DRIAD- EHR) data (Aim 3), with “prospective” outcomes using three big data sets: the UK-TRE with 20 year of longitudinal records of 50M National Health Service patients, and the RPDR Database (based at Mass General Brigham),and the Clalit database in Israel – each with 6M individuals followed for over 20 years. Each Aim has two approaches: data-driven, hypothesis-generating analyses to discern disease-relevant drug signals; and hypothesis-testing in which positive findings from one approach are evaluated using the other approaches to assess rigor and reproducibility. This coordinated program compensates for the limitations of each individual informatics approach to promote discovery and critical evaluation of “lead compounds” for known and novel AD pathways. To execute this strategy, we have assembled a multi-site, multi-disciplinary team with expertise ranging from clinical care to computer science and systems pharmacology. Some of the team members are AD experts and others bring an outsider's perspective. Finally, as a deliverable, we will continue to produce open- source data packages to release all the supporting evidence, software, and data with provenance in accordance with FAIR (findable, accessible, interoperable and reproducible) standards through Synapse. These data packages have lead to one clinical trial and will help to prioritize follow on clinical and translational studies including collaborations with industry or community members at large involved in new clinical trials.

摘要

项目成果

AI-assisted prediction of differential response to antidepressant classes using electronic health records.

• DOI: 10.1038/s41746-023-00817-8
• 发表时间: 2023-04-26
• 期刊: NPJ DIGITAL MEDICINE
• 影响因子: 15.2
• 作者: Sheu, Yi-han;Magdamo, Colin;Miller, Matthew;Das, Sudeshna;Blacker, Deborah;Smoller, Jordan W. W.
• 通讯作者: Smoller, Jordan W. W.