Predictive Networks-based in-silico approach for Precision Medicine-repurposing for Alzheimer's Disease

项目摘要 阿尔茨海默病是最常见的痴呆症，据估计全世界有3600万人受到影响。 除非开发出有效的治疗方法，否则到2050年，这一数字预计将上升到1.15亿。最近， NIA通过AMP-/M2OVE-AD联盟组织了大规模的努力，产生了最丰富的基因， 基因组和临床数据，这使得一个前所未有的机会来探索巨大的复杂性 AD发病机制。另一方面，通过所有失败的临床试验，我们了解到一种有效的治疗方法 需要针对疾病的多个方面，并针对几个致病过程 广告。此外，不同性别和危险因素的患者对相同治疗的反应也会不同，因为 不同的病理机制，因此，开发针对患者的治疗方法变得至关重要。 针对每个患者亚组的目标和精准医学。然而，尽管有巨大的兴趣， 在推进AD的治疗和药物开发方面，缺乏先进的生物信息学方法 可用来指导有效和高效地开发药物，并在这些昂贵的投资中去风险 治疗方法。我们响应PAR(PAR-17-032)的目标1)应用新的计算 系统生物学方法，即自上而下和自下而上的预测网络)，来分析现有的 AMP-AD中丰富的遗传学、基因组学、蛋白质组学、代谢组学和临床数据集以及AD中的其他数据集 2)建立网络模型，预测单细胞型和多细胞串扰的治疗靶点 AD病理发生和发展的途径；3)将患者分成不同的亚组 根据性别、载脂蛋白E和疾病分期(只要有临床数据)并预测治疗 AD患者各亚组精准医学(药物再利用)的目标；4)在AD中使用新的药物。 电子预测流水线，以确定治疗目标的优先顺序；5)重新调整FDA批准的、研究和 实验药物通过靶点(已知)和/或(预测)与优先治疗靶点结合 对接)脱离目标；6)在电子计算机上评价再利用的药物：疗效、毒性、机制、可转移性 通过血脑屏障；7)使用体外和体内AD模型评估优先用药/组合。

Project Summary Alzheimer's disease is the most common form of Dementia estimated to affect 36 million people worldwide. This number is expected to rise to 115 million by 2050 unless an effective therapeutic is developed. Recently, NIA organized large-scale efforts, through AMP-/M2OVE-AD consortia, has generated the richest genotype, genomic and clinical data, which enabled an unprecedented opportunity to explore the enormous complexity of AD pathogenesis. On the other hand, through all failed clinic trials, we learned that an efficacious treatment would need to target multiple aspects of the disease and be directed towards several pathogenic processes in AD. Moreover, patients with different sex and risk factor will respond differently to the same treatment due to distinct pathological mechanisms, therefore, it became extremely critical to develop patient-specific therapeutic targets and precision medicine for each patient sub-group. However, despite tremendous interests in advancing therapy and drug development for AD, there is a paucity of advanced bioinformatics approaches available to guide the effective and efficient development of drugs and de-risk investment in these expensive therapeutic approaches. We respond to the PAR (PAR-17-032) with the goals 1) to apply novel computational systems biology approach, i.e. top-down and bottom-up predictive network for short), to analyze the existing rich genetics, genomics, proteomics, metabolomics, and clinical datasets in AMP-AD and other datasets in AD and 2) to build network models and to predict therapeutic targets of single-cell type and multi-cell cross-talk pathways contributing to the onset and progression of AD pathology; 3) to stratify patients into sub-groups according to Sex, APOE and disease-stage (whenever clinical data available) and to predict therapeutic targets for each sub-group of patients towards precision medicine (drug repurposing) in AD; 4) to use novel in- silico prediction pipeline to prioritize therapeutic targets; 5) to repurpose FDA-approved, investigational, and experimental drugs binding to prioritized therapeutic targets through (known) on-targets and/or (predicted by docking) off-targets; 6) to in-silico evaluate repurposed drugs: efficacy, toxicity, mechanism, transability through BBB; 7) to evaluate prioritized drug/combination using in-vitro and in-vivo AD models.