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将药物用作分子探针:神经退行性疾病中的计算化学生物学方法

Using Drugs as Molecular Probes: A Computational Chemical Biology Approach in Neurodegenerative Diseases.

作者信息

Emon Mohammad Asif Emran Khan, Kodamullil Alpha Tom, Karki Reagon, Younesi Erfan, Hofmann-Apitius Martin

机构信息

Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.

Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany.

出版信息

J Alzheimers Dis. 2017;56(2):677-686. doi: 10.3233/JAD-160222.

Abstract

Neurodegenerative diseases including Alzheimer's disease are complex to tackle because of the complexity of the brain, both in structure and function. Such complexity is reflected by the involvement of various brain regions and multiple pathways in the etiology of neurodegenerative diseases that render single drug target approaches ineffective. Particularly in the area of neurodegeneration, attention has been drawn to repurposing existing drugs with proven efficacy and safety profiles. However, there is a lack of systematic analysis of the brain chemical space to predict the feasibility of repurposing strategies. Using a mechanism-based, drug-target interaction modeling approach, we have identified promising drug candidates for repositioning. Mechanistic cause-and-effect models consolidate relevant prior knowledge on drugs, targets, and pathways from the scientific literature and integrate insights derived from experimental data. We demonstrate the power of this approach by predicting two repositioning candidates for Alzheimer's disease and one for amyotrophic lateral sclerosis.

摘要

包括阿尔茨海默病在内的神经退行性疾病难以攻克,因为大脑在结构和功能上都很复杂。这种复杂性体现在神经退行性疾病的病因涉及多个脑区和多种途径,这使得单一药物靶点方法无效。特别是在神经退行性变领域,人们已将注意力转向重新利用具有已证实疗效和安全性的现有药物。然而,缺乏对脑化学空间的系统分析来预测重新利用策略的可行性。我们使用基于机制的药物 - 靶点相互作用建模方法,确定了有前景的重新定位药物候选物。因果关系机制模型整合了科学文献中关于药物、靶点和途径的相关先验知识,并整合了从实验数据中获得的见解。我们通过预测两种用于阿尔茨海默病的重新定位候选物和一种用于肌萎缩侧索硬化症的重新定位候选物来证明这种方法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ce/5271458/cc77d26085ab/jad-56-jad160222-g001.jpg

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