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

阿尔茨海默病（AD）是一种复杂的神经退行性疾病，其导致进行性记忆丧失， 认知障碍虽然目前的治疗已经显示出症状的一些改善，但效果仍然是不稳定的。 是短暂的，仅限于一小部分AD患者。此外，基于 目前对疾病机制的理解在临床试验中均显示出负面结果。的一部分 失败是由于疾病机制的异质性，对此我们还不清楚。 认识越来越多的证据表明，医学合并症有共同的疾病途径 与AD，和用于这些疾病的药物也可以改变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AD的治疗， 合并症。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，以促进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 糖尿病（DM）和抑郁症（DPN）。为了实现我们的目标，我们将对 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）。首先，我们将对不同合并用药的效果进行统计学研究， 与抗AD药物联合治疗认知功能下降（Aim 1）。通过识别特定药物 联合用药对认知能力下降有协同作用，我们将研究其潜在的 使用分子系统药理学方法研究其机制，并使用体外iPSC和 根据需要进行其他生物测定（Aim 2）。随后，我们将建立一个临床决策支持系统，SmartAD， 这将有助于认知功能评估和个性化治疗的AD患者，这些常见的 合并症。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 和匹兹堡大学ADRC提供的治疗信息（目标3和4）。这一模式将 使用因果机器学习方法在ADRC数据集上进行智能机器学习和训练。 然后将应用决策理论的方法论来寻找导致以下结果的治疗组合： 最佳的治疗效果最后，我们将使用来自AD Neuroimaging Initiative的外部医学数据， （ADNI）和国家阿尔茨海默氏症协调中心（NACC）进行模型系统测试验证（目标3和 4）。总之，这些研究将有助于发现新的药物组合治疗AD患者 并开发SmartAD作为智能临床决策支持系统， 无纸化认知功能评估，进展预测，以及协助最佳个性化 治疗阿尔茨海默病的药物

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.