Combine Genomics and Symptoms Data Driven Models to Discover Synergistic Combinatory Therapies for Alzheimer's Disease
结合基因组学和症状数据驱动模型来发现阿尔茨海默病的协同组合疗法
基本信息
- 批准号:10254376
- 负责人:
- 金额:$ 48.61万
- 依托单位:
- 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:2020-09-15 至 2024-05-31
- 项目状态:已结题
- 来源:
- 关键词:AcuteAcute Brain InjuriesAdverse effectsAlzheimer&aposs DiseaseAlzheimer&aposs disease modelAlzheimer&aposs disease patientAlzheimer&aposs disease therapyAmyloid beta-ProteinBrain DiseasesBrain InjuriesClinical TrialsClustered Regularly Interspaced Short Palindromic RepeatsCombined Modality TherapyCommunity HospitalsComplexDataData SetDementiaDiseaseDoseDrug CombinationsDrug TargetingElectronic Health RecordEpidemiologyFDA approvedFailureGenerationsGenesGenomicsGenotypeGoalsHealth Care CostsHealthcare SystemsHospitalsIllinoisInvestigationKnowledgeMedical RecordsMiningMissouriModelingMolecularMultiomic DataNerve DegenerationNetwork-basedNeuronsNeuroprotective AgentsPathogenesisPathologicPatientsPharmaceutical PreparationsPharmacogenomicsPharmacy facilityPolypharmacyProtective AgentsReportingResearchSafetySamplingSignal PathwaySignal TransductionSymptomsSystemTestingTimeTraumatic Brain InjuryUniversitiesWashingtonapolipoprotein E-4curative treatmentsdeep learningdesigndisorder subtypeeffective therapyexperiencefunctional genomicsgenomic locusinduced pluripotent stem cellinterestlong short term memorymedical schoolsmedication safetymultidisciplinarynetwork modelsneurite growthnovelnovel therapeuticspredictive modelingresponsescreeningsynergismtargeted treatmentvector
项目摘要
Project Summary
In 2018, an estimated 5.7 million people have Alzheimer's Disease (AD) or a related dementia in the U.S., with
related healthcare costs of ~$277 billion1. However, there is no cure yet for AD. One major challenge is that the
complicated pathogenesis of AD remains unclear, though >42 genes/loci have been associated with AD2,3.
These genes are not actionable or druggable yet for AD management2. Over 240 drugs were tested in clinical
trials but no new drugs have been approved for AD since 20031,4. The failure of these drugs is likely, in part,
due to the limited efficacy of single agents to treat AD that is a genetically complex, multifactorial disease2, i.e.,
robust molecular signaling crosstalks among multi-pathways2,5,6,7,8,9, as well as complicated niche factors, e.g.,
oxidative stress10,11,12,13, and inflammation14,15,16, leading to neuron de-generation. Therefore, combination
therapies eliminating these niche factors, and disrupting the dysfunctional signaling pathways and cross-talks,
can be more effective than single agents for in AD patients.
The goal of this study is to fill the gap of accelerating repositioning of combination therapies for AD using
following novel genomics and symptoms data-driven models seamlessly integrating well designed iPSC Aβ AD
models. The Washington University Charles F. and Joanne Knight Alzheimer's Disease Research Center
(Knight-ADRC), has generated comprehensive omics data for a large group of AD samples. We propose to (in
Aim 1) uncover core signaling pathways and crosstalks of ApoE4 genotype-specific AD subtypes via a novel
signaling convergence network model, and consequently to discover synergistic Signaling Network Disruption
drug combinations (SNDdc) via novel drug prediction models integrating heterogenous pharmacogenomics
datasets. On the other hand, we propose to (in Aim 2) discover potential Neuron Protective drug combinations
(NPdc) using electronic health records (EHR), available in BJC HealthCare system (includes 14 academic and
community hospitals in Missouri and Illinois), of patients with brain injury diseases, especially Traumatic Brain
Injury (TBI), via a novel high-order poly-pharmacy efficacy and safety model. We hypothesize that acute brain
damage in TBI will share the aforementioned key AD-related niche factors. Also because TBI patients often
require multiple drugs daily (high-order poly-pharmacy use), we propose that TBI provides an appropriate
model to study synergy and interactions of combination therapies that can ameliorate acute brain injury, and
thus suggest potentially neuron protective combinations. Combinations in SNDdc and NPdc provide
candidates for novel and effective AD treatment. To filter the false positives, we will (in Aim 3) utilize pooled
CRISPR functional genomics and iPSC neurodegeneration model to identify key signaling genes, and validate
combination therapies with ApoE4 genotype-specific iPSC Aβ models. Our new models represent a
potential breakthrough in AD combination therapies discovery.
项目总结
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data.
- DOI:10.1371/journal.pcbi.1011785
- 发表时间:2024-01
- 期刊:
- 影响因子:4.3
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Fuhai Li其他文献
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{{ truncateString('Fuhai Li', 18)}}的其他基金
AI models of multi-omic data integration for ming longevity core signaling pathways
长寿核心信号通路多组学数据整合的人工智能模型
- 批准号:
10745189 - 财政年份:2023
- 资助金额:
$ 48.61万 - 项目类别:
Modeling and targeting tumor-immune signaling interactions in tumor microenvironment
肿瘤微环境中肿瘤免疫信号相互作用的建模和靶向
- 批准号:
10659993 - 财政年份:2023
- 资助金额:
$ 48.61万 - 项目类别:
Combine Genomics and Symptoms Data Driven Models to Discover Synergistic Combinatory Therapies for Alzheimer's Disease
结合基因组学和症状数据驱动模型来发现阿尔茨海默病的协同组合疗法
- 批准号:
10228346 - 财政年份:2020
- 资助金额:
$ 48.61万 - 项目类别: