Delaying Alzheimer's Disease Progression Through Intranasally Administered Nano-Antioxidants

阿尔茨海默病(AD)是一种进行性神经退行性疾病，其特征是 淀粉样β蛋白斑块和神经原纤维缠结的神经病理堆积 过度磷酸化的tau。来自多个小组的最新数据支持，如果有足够的时间，Ab和tau的扩散 在整个大脑中以一种明确的方式沿着大脑网络传播，非常类似于普里恩蛋白质的传播方式 贯穿整个大脑。值得注意的是，据报道，嗅觉系统是首批受到影响的系统之一。 广告。我们过去的工作集中在使用锰增强磁共振成像(MEMRI)来评估轴突运输。 阿尔茨海默病小鼠模型的嗅觉系统。事实上，我们已经报道了嗅觉中的轴突运输缺陷 在学习和记忆缺陷发展之前和斑块形成之前很久就可以检测到系统。 这些数据与AD病理在整个大脑中传播的想法是一致的，从 嗅觉系统。此外，我们之前的工作也集中在减少氧化应激的影响上。 阿尔茨海默病小鼠模型。当我们通过过度表达超氧化物歧化酶2(SOD-2)来减少氧化应激时 在AD的小鼠模型中，我们观察到学习和记忆障碍的完全恢复，完全恢复 嗅觉系统中的轴突运输缺陷以及抗体斑块的形成减少了50%以上。 尽管氧化应激已被认为是阿尔茨海默病发病的重要因素，但减少 使用抗氧化剂进行氧化应激治疗的效果有限。这其中的一些原因被认为是 包括无法将足够数量的受控抗氧化剂靶向 大脑。此外，临床上可用的抗氧化剂溶解性差，不容易进入细胞。因此， 我们已经转向纳米技术，并一直在研究更有效的纳米抗氧化剂 比临床上可用的抗氧化剂，是无毒的，很容易进入细胞。因此，我们假设 用鼻内注射纳米抗氧化剂(聚乙二醇-羟基喜树碱)保护嗅觉和邻近结构 延缓阿尔茨海默病的进展通过行为分析、MEMRI、静息状态功能磁共振成像(Rs-fMRI)评估为 以及31P测量和组织学检查。我们还将结合机器学习，具体地说，概率学习 图形模型，以确定AD和AD小鼠模型中AIMS 1和AIMS 2中读数的相互作用 使用和不使用聚乙二醇组的对照。我们还建议将机器学习结合到预测中 1)应将超氧化物水平降低到何种程度以改善AD病理；以及2)AD的哪些阶段 (例如，前斑块与后斑块)无法挽救。这一极具创新性的项目的完成将具有重大意义 对未来AD治疗策略的影响。

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the neuropathological accumulation of amyloid beta (Ab) plaques and neurofibrillary tangles comprised of hyperphosphorylated tau. Recent data from multiple groups support that given enough time, Ab and tau spreads throughout the brain in a well-defined manner along brain networks, very similar to the way a prion protein travels throughout the brain. Notably, the olfactory system has been reported to be one of the first systems affected in AD. Our past work has focused on using Manganese Enhanced MRI (MEMRI) to assess axonal transport in the olfactory system in mouse models of AD. Indeed, we have reported that axonal transport deficits in the olfactory system are detectable prior to the development of learning and memory deficits and well before plaque formation. These data are consistent with the idea that AD pathology spreads throughout the brain, beginning with the olfactory system. Additionally, our prior work has also focused on the effects of reducing oxidative stress in mouse models of AD. When we reduced oxidative stress by overexpressing superoxide dismutase 2 (SOD-2) in mouse models of AD, we observed a complete recovery in learning and memory deficits, a complete recovery in axonal transport deficits in the olfactory system as well as an over 50% reduction in Ab plaque formation. Although oxidative stress has been identified as a significant player in the development of AD, efforts to reduce oxidative stress with antioxidants has met with limited success. Some of the reasons for this are thought to include the inability to target sufficient quantities of administered antioxidants to the appropriate regions within the brain. Additionally, clinically available antioxidants have poor solubility and do not readily enter cells. Thus, we have turned to nanotechnology and have been working with nano-antioxidants that are much more potent than clinically available antioxidants, are non-toxic and readily enter cells. We therefore hypothesize that protecting olfactory and adjoining structures with intranasally administered nano-antioxidants (PEG-HCCs) will slow down the progression of AD as assessed with behavioral assays, MEMRI, resting state fMRI (rs-fMRI) as well as 31P measurements and histology. We will also incorporate machine learning, specifically, a probabilistic graphical model, to determine the interactions of the readouts in Aims 1 and 2 in mouse models of AD and controls with and without treatment with PEG-HCCs. We also propose to incorporate machine learning to predict 1) the degree to which superoxide levels should be reduced to improve AD pathology and 2) which stages of AD (e.g. pre vs post plaque) are beyond rescue. Completion of this highly innovative project will have significant impact towards future AD therapeutic strategies.