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，会导致进行性记忆丧失和 认知障碍。虽然目前的治疗方法已经显示出一些症状的改善，但效果是 是短暂的，仅限于一小部分阿尔茨海默病患者。此外，基于以下因素的疾病修饰药物 目前对疾病机制的理解在临床试验中都显示出负面结果。部分内容 失败是由于发病机制的异质性，对此我们还没有一个明确的认识 理解。越来越多的证据表明，医学合并症具有共同的疾病途径。 与阿尔茨海默病有关，治疗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治疗AD的常见疾病 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 APP方便AD患者认知功能评估和个性化治疗计划 最常见的并发症，如心血管疾病(CVD)/高血压(HTN)、糖尿病 糖尿病(DM)和抑郁症(DPN)。为了达到我们的目标，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，我们将统计调查不同的共病药物在 联合抗AD药物对认知功能下降的影响(Aim1)。通过识别特定的药物 联合(S)认为对认知功能衰退有协同作用，我们将随后研究其潜在的 使用分子系统药理学方法的机制，并使用体外IPSC和 根据需要进行其他生物检测(AIM2)。随后，我们将构建一个临床决策支持系统SmartAD， 这将有助于AD患者认知功能的评估和个体化治疗 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 和匹兹堡大学红十字与发展中心提供的治疗信息(目标3和4)。这款车型将是 使用因果机器学习方法对ADRC数据集进行智能机器学习和培训。 然后将应用决策理论的方法论来寻找导致 对那个病人来说是最好的结果。最后，我们将使用AD神经成像计划的外部医疗数据 (ADNI)和国家阿尔茨海默病协调中心(NACC)，用于模型系统测试验证(AIMS 3和 4)。综上所述，这些研究将有助于发现治疗AD患者的新药物组合 并将SmartAD开发为智能临床决策支持系统，可以促进 无纸化认知功能评估，进步预测，以及辅助最优个性化 阿尔茨海默氏症患者的药物。

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.