SmartAD for Intelligent Alzheimer’s Disease(AD) Personalized Combination Therapy

SmartAD 智能阿尔茨海默病 (AD) 个性化联合治疗

基本信息

批准号：
10670481
负责人：
Xiang-Qun Xie
金额：
$ 39.54万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10670481
关键词：
Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease patient Alzheimer&apos s disease therapy Antihypertensive Agents Artificial Intelligence Bayesian Modeling Bayesian Network Big Data Biological Assay Biological Models Cardiovascular Diseases Caregivers Characteristics Clinic Clinical Clinical Data Clinical Decision Support Systems Clinical Trials Cognitive Combination Drug Therapy Combination Medication Combined Modality Therapy Complex Cox Proportional Hazards Models Data Data Analyses Data Analytics Data Collection Data Set Decision Theory Dementia Diabetes Mellitus Disease Disease Pathway Disease Progression Drug Combinations Drug Targeting Evaluation Failure Goals Healthcare Heterogeneity Hypertension Impaired cognition In Vitro Individual Institutional Review Boards Intelligence International Classification of Disease Codes Machine Learning Medical Medical History Memory Loss Mental Depression Methodology Methods Mobile Health Application Modeling Molecular Monitor Neurodegenerative Disorders Outcome Pathologic Pathway interactions Patient-Focused Outcomes Patients Pharmaceutical Preparations Pharmacology Pharmacology Study Protocols documentation Reporting Research Risk Running Scientist Symptoms System Technology Testing Therapeutic Effect Time Training Universities Update Validation base causal model clinical decision support clinical decision-making cognitive benefits cognitive function cohort comorbidity data mining design disease prognosis driving force drug action encryption genetic signature in vitro Bioassay individual patient individualized medicine induced pluripotent stem cell inter-individual variation knowledgebase mental state mobile application mobile computing neuroimaging next generation novel novel drug combination novel therapeutics personalized medicine prevent programs research clinical testing statistics treatment planning

项目摘要

Alzheimer’s Disease (AD) is a complex neurodegenerative disease that causes progressive memory loss and cognitive impairment. While current treatments have shown some amelioration of symptoms, the effects have been transient and limited to a small percentage of AD patients. Moreover, disease-modifying drugs based on current understanding of disease mechanisms have all shown negative results in clinical trials. Part of the failure is due to the heterogeneity in the disease mechanism, of which we do not yet have a clear understanding. Increasing evidence has indicated that medical comorbidities share common disease pathways with AD, and the medications used for these diseases can also alter the cognitive functions of AD patients. However, limited studies have assessed combinations of these medications as treatments for AD with common comorbidities. Thus, the goal of this proposal is to develop artificial intelligence (AI) analytics models and a SmartAD app to facilitate cognitive function evaluation and personalized treatment plans for AD patients with the most common comorbidities, such as cardiovascular diseases (CVD)/hypertension (HTN), diabetes mellitus (DM), and depression (DPN). To achieve our goal, we will carry out retrospective analysis of observational clinical data collected by the University of Pittsburgh Alzheimer’s Disease Research Center (ADRC). First, we will statistically investigate the effects of different comorbidity medications when used in combination with anti-AD medications on the trajectory of cognitive decline (Aim1). By identifying specific drug combination(s) that have a synergistic effect against cognitive decline, we will then study the underlying mechanisms using molecular systems pharmacology methods and validate the findings using in vitro iPSC and other bioassays as needed (Aim2). Subsequently, we will build a clinical decision support system, SmartAD, that will facilitate cognitive function evaluation and individualized treatment for AD patients with these common comorbidities. We will build a Bayesian Network model that can predict patient-tailored disease progression and treatment information provided by ADRC at the University of Pittsburgh (Aims 3 & 4). This model will be intelligently machine-learned and trained on the ADRC dataset using causal machine-learning approaches. Methodologies of decision theory will then be applied to search for a treatment combination that leads to the optimal outcome for that patient. Finally, we will use external medical data from AD Neuroimaging Initiative (ADNI) and National Alzheimer’s Coordinating Center (NACC) for model systems test validation (Aims 3 and 4). Taken all together, these studies will contribute to the discovery of novel drug combinations for AD patients with comorbidities and develop SmartAD as an intelligent clinical decision support system that can facilitate paperless cognitive function evaluation, progression prediction, as well as assist optimal personalized medication for Alzheimer’s patients.

阿尔茨海默病 (AD) 是一种复杂的神经退行性疾病，会导致进行性记忆丧失和虽然目前的治疗已显示出一定程度的症状改善，但效果却不佳。而且，基于疾病缓解的药物是短暂的，并且仅限于一小部分 AD 患者。目前对疾病机制的了解都在临床试验中显示出负面结果。失败是由于疾病机制的异质性，我们还没有明确的认识越来越多的证据表明，医学合并症具有共同的疾病途径。 AD 患者的认知功能也会发生改变，而治疗这些疾病的药物也会改变 AD 患者的认知功能。然而，有限的研究评估了这些药物的组合作为治疗 AD 的常见方法。因此，该提案的目标是开发人工智能（AI）分析模型和 SmartAD 应用程序可促进 AD 患者的认知功能评估和个性化治疗计划最常见的合并症，如心血管疾病 (CVD)/高血压 (HTN)、糖尿病为了实现我们的目标，我们将对糖尿病（DM）和抑郁症（DPN）进行回顾性分析。匹兹堡大学阿尔茨海默病研究中心收集的观察性临床数据（ADRC），我们将研究不同合并症药物在治疗中使用时的效果。与抗 AD 药物联合治疗可降低认知能力（目标 1）。组合对认知能力下降具有协同作用，然后我们将研究潜在的使用分子系统药理学方法研究机制，并使用体外 iPSC 和验证结果随后，我们将建立一个临床决策支持系统SmartAD，这将有助于对患有这些常见症状的 AD 患者进行认知功能评估和个体化治疗我们将建立一个贝叶斯网络模型，可以预测患者定制的疾病进展。匹兹堡大学 ADRC 提供的治疗信息（目标 3 和 4）。使用因果机器学习方法对 ADRC 数据集进行智能机器学习和训练。然后将应用决策理论的方法来寻找导致以下结果的治疗组合：最后，我们将使用 AD Neuroimaging Initiative 的外部医疗数据。 (ADNI) 和国家阿尔茨海默病协调中心 (NACC) 进行模型系统测试验证（目标 3 和 4) 总而言之，这些研究将有助于发现针对 AD 患者的新型药物组合。并开发 SmartAD 作为智能临床决策支持系统，可以促进无纸化认知功能评估、进展预测，以及协助优化个性化阿尔茨海默病患者的药物。