Drug Discovery for Alzheimer’s Disease Enabled by Multi-Omics and Artificial Intelligence

通过多组学和人工智能实现阿尔茨海默病药物发现

• 批准号: 10473842
• 负责人: Jesse Meyer
• 金额: --
• 依托单位: MEDICAL COLLEGE OF WISCONSIN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2022-05-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10473842
• 关键词: Aging; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease pathology; Alzheimer' s disease therapeutic; American; Amyloid beta-Protein; Area; Artificial Intelligence; Biological Assay; Biological Models; Biology; Brain; Brain region; Cells; Chemical Structure; Chemicals; Data; Disease Progression; Drug Screening; Glutamates; Goals; Health; Human; In Vitro; Induced pluripotent stem cell derived neurons; Knowledge; Learning; Libraries; Literature; Maps; Mission; Molecular; N-Methylaspartate; Nerve Degeneration; Neurodegenerative Disorders; Neuronal Dysfunction; Neurons; Outcome; Partner in relationship; Pathway interactions; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Physiological; Prevention approach; Process; Proteome; Proteomics; Public Health; Research; Research Personnel; Resistance; Resources; Stress; Synapses; System; Testing; Therapeutic; Therapeutic Intervention; Toxic effect; Training; United States National Institutes of Health; Work; abeta accumulation; abeta oligomer; abeta toxicity; base; biological research; cost; drug candidate; drug discovery; drug structure; effective therapy; human data; human disease; human stem cells; improved; in silico; in vitro Model; in vivo; induced pluripotent stem cell; innovation; metabolomics; multiple omics; neuron loss; neuropathology; neurotoxicity; novel therapeutics; palliate; palliative; prevent; preventive intervention; programs; relating to nervous system; resilience; therapy development

PROJECT SUMMARY/ABSTRACT There is a fundamental gap in our understanding of how amyloid beta oligomers (AβO) induce neurotoxicity and neuron death in Alzheimer’s disease (AD), as evidenced by a dearth of therapies to prevent or halt AD progression. Continued existence of this knowledge gap represents a major issue for public health and the mission of the NIH because, until it is filled, development of treatments for neurodegeneration in AD will remain largely intractable. The long-term goal of this work is to discover pathways that enable resistance to AβO- induced neurotoxicity thereby allowing discovery of new AD therapeutics. The overall objective here, which is the next step in pursuit of this goal, is to build AI that accurately predicts the ability of drug candidates to cure or prevent toxicity of AβO in human stem cell-derived cortical glutamatergic neurons. To train this AI, a library of proteomic and metabolomic (hereafter referred to as multi-omic) phenotypes will be generated from neurons that are: 1) healthy, 2) AβO-treated (AD-like), or 3) drug library+AβO-treated. The central hypothesis is that some drugs at least partially palliate AβO-induced neurotoxicity, which is observable as a shift in multi-omic state toward the healthy state, and that AI can learn to predict this curative potential from drug structures. This hypothesis is based on preliminary data generated by the applicant and literature. The rationale for the proposed research is that mapping the difference in multi-omic phenotypes of healthy and AβO-stressed neurons, and mapping how chemical structures induce changes between those states, will allow AI to learn to make accurate predictions of whether additional, unmeasured molecules can improve neuron health. This will result in new and innovative approaches for prevention and treatment of AD. Guided by preliminary data and literature, this hypothesis will be tested by pursuing two specific aims: 1) validate the multi-omic phenotype landscape of healthy and AD-like neurons; and 2) build AI to discover new drugs that prevent AβO-induced neuron death in AD. The first aim will validate the human disease relevance of our model system using cell- based assays and by comparing omic profiles from our system to those observed in human AD brains. The second aim will build a map of how drugs candidates alter neural multi-omic states to use for training predictive AI. Completion of these aims will contribute (1) an in vitro system that mimics physiological milieu, and also (2) molecular ‘omics’ signatures of those healthy and AD-like human iPSC-derived neural cells, which are two areas of high program relevance defined in NOT-AG-19-007. This approach is innovative, in the applicant’s opinion, because it departs from the status quo by using highly translatable human iPSC-derived neurons for unbiased discovery of palliative drug candidates using a unique combination of multi-omics and AI. This contribution will be significant because it is expected to vertically advance our understanding of basic neuron stress resistance, as well as result in the first drugs that prevent AβO neural toxicity. Ultimately, such knowledge will be useful for other neurodegenerative disorders of aging.

项目总结/摘要 我们对β淀粉样蛋白寡聚体（AβO）如何诱导神经毒性的理解存在根本性的空白 阿尔茨海默病（AD）中的神经元死亡，缺乏预防或阻止AD的疗法就证明了这一点 进展这种知识差距的持续存在是公共卫生和环境保护的一个主要问题。 NIH的使命，因为在完成该使命之前，AD神经退行性疾病治疗方法的开发将继续下去 很难处理这项工作的长期目标是发现能够抵抗AβO的途径。 诱导的神经毒性，从而允许发现新的AD治疗剂。这里的总体目标是 追求这一目标的下一步是建立能够准确预测候选药物治愈能力的人工智能 或阻止AβO对人干细胞衍生的皮质神经元的毒性。为了训练这个人工智能， 蛋白质组学和代谢组学（下文称为多组学）表型的组合将从神经元产生 即：1）健康，2）Aβ O治疗（AD样），或3）药物库+Aβ O治疗。核心假设是， 一些药物至少部分减轻了Aβ O诱导的神经毒性，这可以观察到多组学改变， 我们相信，人工智能可以学会从药物结构中预测这种治疗潜力。这 假设基于申请人和文献生成的初步数据。的理由 拟议的研究是绘制健康和Aβ O应激的多组学表型的差异， 神经元，并绘制化学结构如何诱导这些状态之间的变化，将使人工智能学会 准确预测额外的、未测量的分子是否可以改善神经元的健康。这将 为预防和治疗AD提供新的创新方法。根据初步数据， 文献中，这一假设将通过追求两个具体目标进行测试：1）验证多组学表型 健康和AD样神经元的景观; 2）建立AI以发现预防Aβ O诱导的新药 AD时神经元死亡。第一个目标将验证我们的模型系统使用细胞的人类疾病相关性， 通过比较我们系统中的组学特征与在人类AD大脑中观察到的组学特征。的 第二个目标是构建候选药物如何改变神经多组学状态的地图，以用于预测训练 艾.这些目标的完成将有助于（1）模拟生理环境的体外系统，以及（2） 这些健康和AD样人类iPSC衍生神经细胞的分子“组学”特征， NOT-AG-19-007中定义的高度项目相关性领域。这种方法是创新的，在申请人的 因为它通过使用高度可翻译的人类iPSC衍生的神经元来 使用多组学和AI的独特组合，公正地发现姑息候选药物。这 这一贡献将是重大的，因为它有望纵向推进我们对基本神经元的理解。 抗应激，以及导致第一个药物，防止AβO神经毒性。最终，这样的 这些知识将对其他衰老神经退行性疾病有用。