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Finding combinatorial drug repositioning therapy for Alzheimer's disease and related dementias

寻找治疗阿尔茨海默病和相关痴呆症的组合药物重新定位疗法

基本信息

批准号：
10377455
负责人：
Xiaoqian Jiang
金额：
$ 77.16万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10377455
关键词：
Adoption Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease related dementia Alzheimer&apos s disease therapy Big Data Biological Biological Markers Biology Cell model Cells Client Clinical Combination Drug Therapy Complex Computer Models Data Databases Dementia Development Disease Drug Combinations Drug Modelings Drug Targeting Drug resistance Electronic Health Record Equilibrium Fingerprint Genes Healthcare Informatics Infrastructure Investments Joints Knowledge Literature Methods Modeling Molecular Mus Neurodegenerative Disorders Ontology Outcome Pathway interactions Patients Pharmaceutical Preparations Pharmacogenomics Pharmacology Pharmacotherapy Phenotype Population Prevention Process Protective Agents Proteins Research Resource Sharing Risk Side Signal Pathway Source Speed Testing Toxic effect Validation big biomedical data combinatorial drug candidate drug discovery effective therapy efficacy validation experimental study gene interaction graph neural network in silico in vitro Model in vivo innovation knowledgebase multimodality novel novel drug combination novel therapeutics open source screening side effect simulation software development tool user-friendly

项目摘要

PROJECT SUMMARY AD/ADRD are highly complex diseases characterized by distinct molecular pathways and neuropathological phenotypes. Unfortunately, the treatment remains at best modestly effective and no new drugs have been approved since 2003. Combinatorial drug therapy for AD/ADRD treatment has not been intensively studied but it is highly promising. We hypothesize that finding repositioned drug combinations through innovative exploration of big data may uncover effective AD/ADRD treatments, with implicit advantages in overcoming drug resistance and targeting multiple biomarkers. We will combine big biomedical data from complementary sources, novel and advanced informatics models, clinical domain expertise, as well as biology knowledge and validation into a coherent framework to tackle AD/ADRD with potential combinatorial drug therapies. In an exponentially larger and more challenging space of combinatorial drug therapy, opportunities are also exponentially larger when compared with traditional single-drug models but many computational challenges need to be carefully handled. We will develop multiple computational models under two philosophical umbrellas, with focuses on quantifiable screening and biological understanding. Our findings will be validated with biological experiments from cell to mouse. If successful, we will significantly advance AD/ADRD research and benefit patients with safe and effective treatment.

项目总结 AD/ADRD是高度复杂的疾病，具有不同的分子途径和神经病理特征表型。不幸的是，这种治疗充其量仍然是适度有效的，而且还没有新药出现。自2003年以来获得批准。AD/ADRD的联合药物治疗尚未得到深入研究，但这是非常有希望的。我们假设，通过创新发现重新定位的药物组合探索大数据可能会发现有效的AD/ADRD治疗方法，在克服这些问题方面具有隐性优势耐药性和靶向多个生物标记物。我们将结合来自互补性的大型生物医学数据来源、新的和先进的信息学模型、临床领域的专门知识以及生物学知识和验证为一个连贯的框架，以潜在的组合药物疗法来应对AD/ADRD。在一个指数级的更大和更具挑战性的组合药物治疗空间，机会也与传统的单一药物模型相比，指数级更大，但存在许多计算挑战需要小心处理。我们将在两种哲学下开发多个计算模型雨伞，重点是可量化的筛选和生物学理解。我们的发现将得到验证从细胞到老鼠的生物实验。如果成功，我们将大大推进AD/ADRD研究并为患者提供安全有效的治疗。