Victor O.K. Li, Yang Han, Tushar Kaistha, Qi Zhang, Jocelyn Downey, Illana Gozes, Jacqueline C.K. Lam
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引用次数: 0
Abstract
Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firstly, it incorporates expert knowledge to extend candidate targets to include long genes, immunological and aging pathways, and somatic mutation markers that are associated with AD. Secondly, it incorporates a signed directed heterogeneous biomedical graph encompassing a rich set of nodes and edges, and node/edge weighting to capture crucial pathways associated with AD. Thirdly, it encodes the weighted biomedical graph through a Graph Neural Network into a new embedding space to capture the granular relationships across different nodes. Fourthly, it systematically selects the high-order drug combinations via diminishing return-based thresholds. A five-drug lead combination, consisting of Tofacitinib, Niraparib, Baricitinib, Empagliflozin, and Doxercalciferol, has been selected from the top drug candidates based on DeepDrug scores to achieve the maximum synergistic effect. These five drugs target neuroinflammation, mitochondrial dysfunction, and glucose metabolism, which are all related to AD pathology. DeepDrug offers a novel AI-and-big-data, expert-guided mechanism for new drug combination discovery and drug-repurposing across AD and other neuro-degenerative diseases, with immediate clinical applications.
阿尔茨海默病(AD)严重损害人类尊严和生活质量。虽然新批准的淀粉样蛋白免疫疗法已有报道,但有效的阿兹海默病药物仍有待确定。在此,我们提出了一种新型人工智能驱动的药物再利用方法--DeepDrug,以确定治疗AD患者的已获批准药物的先导组合。DeepDrug 从四个方面推进了药物再利用方法。首先,它结合了专家知识,将候选靶点扩展到包括长基因、免疫和衰老途径以及与AD相关的体细胞突变标记。其次,它纳入了一个有符号的有向异质生物医学图谱,其中包含丰富的节点和边,并对节点/边进行加权,以捕捉与艾滋病相关的关键通路。第三,它通过图神经网络将加权生物医学图编码到一个新的嵌入空间,以捕捉不同节点之间的细微关系。第四,它通过基于收益递减的阈值系统地选择高阶药物组合。根据DeepDrug的评分,从顶级候选药物中选出了由托法替尼、尼拉帕利、巴瑞替尼、恩帕格列嗪和多西卡西醇组成的五种药物先导组合,以实现最大的协同效应。这五种药物针对的是神经炎症、线粒体功能障碍和糖代谢,而这些都与注意力缺失症的病理相关。DeepDrug 提供了一种新颖的人工智能和大数据、专家指导机制,用于发现新的药物组合,并对 AD 和其他神经退行性疾病进行药物再利用,可立即应用于临床。