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Collaborative Research: Personalized Modeling of Alzheimer’s Disease

合作研究：阿尔茨海默病的个性化建模

基本信息

批准号：
2052685
负责人：
Wenrui Hao
金额：
$ 22万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2052685&HistoricalAwards=false
关键词：
Collaborative Research Personalized Modeling Alzheimer

项目摘要

Alzheimer's disease (AD) affects more than 5 million people in the US. Although the FDA recently granted approval for a disease-modifying therapy for AD, questions remain regarding the efficacy of removing amyloid plaques for delaying cognitive decline. The conflicting results of clinical trials of anti-amyloid agents as well as the 99% failure rate of trials of other classes of AD treatments are rooted in an incomplete understanding of the complex mechanisms resulting in AD, and how the response to treatment may vary in the individual. Personalized optimization of treatment of AD will likely play a central role in future management and counseling of patients. Such treatment will be facilitated by the growing availability of electronic AD brain data. Although to date there are no clinical markers that can easily distinguish AD patients, nor predict AD risk, mathematical modeling and computational techniques could be helpful for constructing a personalized brain environment virtually to predict AD risk and therapeutic response. This project aims to provide an AD personalized prediction via validating a mathematical model on a multi-institutional dataset of AD biomarkers. Personalized therapeutic simulation studies for AD patients will also be performed via the validated mathematical model. Through their involvement with this project, graduate and undergraduate students will receive interdisciplinary training in both mathematical biology and AD research.The long-term goal of this project is to develop an operational understanding of AD using an integrated mathematical modeling approach, based on clinical, omics, imaging, and other biomarker data to accelerate drug discovery. The project will use publicly available data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to develop a personalized multifactorial mathematical model of AD progression that identifies patient-specific triggers, predicts disease trajectory, and simulates therapeutic response. The investigators plan to accomplish the objective through three specific aims: 1) Validate a newly developed mathematical model of AD using patient data from the ADNI database; 2) Expand the AD model by integrating spatial information available from multidimensional imaging biomarkers; 3) Develop personalized therapeutic plans for AD patients via mathematical modeling. At the completion of the project, the investigators expect to have a personalizable, data-driven mathematical model that will yield future personal biomarker trajectory predictions as well as model-optimized single or combination therapeutic strategies. The resulting model should enhance the ability to predict AD trajectory at an individual level and thereby accelerate personalized treatment.This award is being co-funded by the Division of Mathematical Sciences (DMS) Mathematical Biology and Computational Mathematics programs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

阿尔茨海默病(AD)在美国影响着500多万人。尽管FDA最近批准了AD的疾病修正疗法，但关于移除淀粉样斑块延缓认知功能衰退的有效性仍存在疑问。抗淀粉样蛋白药物临床试验相互矛盾的结果，以及其他类别AD治疗试验99%的失败率，根源于对导致AD的复杂机制以及个体对治疗的反应可能如何变化的不完全理解。AD的个性化优化治疗可能会在未来对患者的管理和咨询中发挥核心作用。随着电子AD脑数据的日益普及，这种治疗将变得更加便利。虽然到目前为止还没有临床标记物可以很容易地区分AD患者，也没有预测AD风险的指标，但数学建模和计算技术可能有助于构建一个个性化的脑环境，以预测AD风险和治疗反应。该项目旨在通过在AD生物标志物的多机构数据集上验证数学模型来提供AD个性化预测。AD患者的个性化治疗模拟研究也将通过经过验证的数学模型进行。通过参与该项目，研究生和本科生将接受数学生物学和AD研究的跨学科培训。该项目的长期目标是使用基于临床、组学、成像和其他生物标记物数据的综合数学建模方法来发展对AD的可操作性理解，以加速药物发现。该项目将使用阿尔茨海默病神经成像倡议(ADNI)数据库中的公开数据来开发AD进展的个性化多因素数学模型，该模型识别患者特定的触发因素，预测疾病轨迹，并模拟治疗反应。研究人员计划通过三个具体目标来实现这一目标：1)使用ADNI数据库中的患者数据验证新开发的AD数学模型；2)通过整合从多维成像生物标记物获得的空间信息来扩展AD模型；3)通过数学建模为AD患者制定个性化的治疗计划。在该项目完成后，研究人员希望拥有一个可个性化的、数据驱动的数学模型，该模型将产生未来个人生物标记物轨迹预测以及模型优化的单一或联合治疗策略。由此产生的模型应该会增强在个人层面预测AD轨迹的能力，从而加快个性化治疗。该奖项由数学科学部(DMS)数学生物学和计算数学项目共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。