An unsupervised computational pipeline identifies potential repurposable drugs to treat Huntington's disease and multiple sclerosis

Luca Menestrina, Maurizio Recanatini
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Abstract

Drug repurposing consists in identifying additional uses for known drugs and, since these new findings are built on previous knowledge, it reduces both the length and the costs of the drug development. In this work, we assembled an automated computational pipeline for drug repurposing, integrating also a network-based analysis for screening the possible drug combinations. The selection of drugs relies both on their proximity to the disease on the protein-protein interactome and on their influence on the expression of disease-related genes. Combined therapies are then prioritized on the basis of the drugs’ separation on the human interactome and the known drug-drug interactions. We eventually collected a number of molecules, and their plausible combinations, that could be proposed for the treatment of Huntington's disease and multiple sclerosis. Finally, this pipeline could potentially provide new suggestions also for other complex disorders.

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一个无监督的计算管道识别潜在的可重复利用的药物治疗亨廷顿氏病和多发性硬化症
药物再利用包括确定已知药物的额外用途,由于这些新发现是建立在以前的知识基础上的,它减少了药物开发的时间和成本。在这项工作中,我们组装了一个用于药物再利用的自动计算管道,并集成了一个基于网络的分析来筛选可能的药物组合。药物的选择既取决于它们与疾病的接近程度,也取决于蛋白质-蛋白质相互作用组,以及它们对疾病相关基因表达的影响。然后根据药物在人体相互作用组上的分离和已知的药物-药物相互作用来优先考虑联合治疗。我们最终收集了一些分子,以及它们的合理组合,这些分子可以用于治疗亨廷顿舞蹈症和多发性硬化症。最后,这个管道也可能为其他复杂疾病提供新的建议。
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来源期刊
Artificial intelligence in the life sciences
Artificial intelligence in the life sciences Pharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)
CiteScore
5.00
自引率
0.00%
发文量
0
审稿时长
15 days
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