权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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，可导致进行性记忆丧失和记忆力减退