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:10474389
- 负责人:
- 金额:$ 56.69万
- 依托单位:
- 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:
- 关键词:AddressAffectAgeAgingAlgorithmsAlzheimer&aposs DiseaseAlzheimer&aposs disease modelAnimal ModelAtlasesAttentionAttenuatedAutopsyBehavior assessmentBehavioralBindingBiological MarkersBiomedical ResearchBrainClinicalClinical TrialsCognitiveCollectionComplementComplexDataData SetDatabasesDevelopmentDiseaseDrug CombinationsDrug ExposureDrug ScreeningDrug TargetingDrug usageElectronic Health RecordElectrophysiology (science)ExhibitsFutureGene Expression RegulationGraphImmunohistochemistryIndividualIntakeInterventionInvestmentsLate Onset Alzheimer DiseaseLinkLongitudinal StudiesMachine LearningMeasuresMedicineMemoryMetabolic PathwayMethodsMiningMolecularMolecular ProfilingMusNetwork-basedNeurodegenerative DisordersOutcomePathologyPathway AnalysisPathway interactionsPharmaceutical PreparationsPharmacogenomicsPharmacologic SubstancePharmacotherapyPhasePopulation StudyProbabilityProcessProteinsPublishingQuantitative Trait LociReactionResearchResourcesRetrospective StudiesRouteScoring MethodScreening ResultSynapsesSystemTestingTissue-Specific Gene ExpressionTissuesUnited States National Institutes of HealthValidationVisitagedaging populationalternative treatmentamyloid pathologybasebiobankbiological systemscandidate identificationclinical biomarkersdata miningdrug candidatedrug developmentendophenotypeexperimental studygenetic associationgenome wide association studyin silicomachine learning methodmetabolomicsmouse modelmultidisciplinarymultiple omicsneuroimagingneuropathologynovelpopulation basedpre-clinicalprotein expressionprotein protein interactionreligious order studystandardize measurestudy populationsynaptic functiontau Proteinstreatment strategyvalidation 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.
项目总结
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Matthias Arnold其他文献
Matthias Arnold的其他文献
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{{ truncateString('Matthias Arnold', 18)}}的其他基金
Metabolic age to define influences of the lipidome on brain aging in Alzheimer's disease
代谢年龄确定脂质组对阿尔茨海默氏病大脑衰老的影响
- 批准号:
10643738 - 财政年份:2023
- 资助金额:
$ 56.69万 - 项目类别:
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
- 批准号:
10299231 - 财政年份:2021
- 资助金额:
$ 56.69万 - 项目类别:
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
- 批准号:
10652504 - 财政年份:2021
- 资助金额:
$ 56.69万 - 项目类别:
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