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计算机辅助的阿尔茨海默病急症多靶点管理

Computer-Aided Multi-Target Management of Emergent Alzheimer's Disease.

作者信息

Kim Hyunjo, Han Hyunwook

机构信息

Department of Medical Informatics, Ajou Medical University Hospital, Suwon, Kyeounggido province, South Korea.

Department of Informatics, School of Medicine, CHA University, Seongnam, South Korea.

出版信息

Bioinformation. 2018 May 5;14(4):167-180. doi: 10.6026/97320630014167. eCollection 2018.

Abstract

Alzheimer's disease (AD) represents an enormous global health burden in terms of human suffering and economic cost. AD management requires a shift from the prevailing paradigm targeting pathogenesis to design and develop effective drugs with adequate success in clinical trials. Therefore, it is of interest to report a review on amyloid beta (Aβ) effects and other multi-targets including cholinesterase, NFTs, tau protein and TNF associated with brain cell death to be neuro-protective from AD. It should be noted that these molecules have been generated either by target-based or phenotypic methods. Hence, the use of recent advancements in nanomedicine and other natural compounds screening tools as a feasible alternative for circumventing specific liabilities is realized. We review recent developments in the design and identification of neuro-degenerative compounds against AD generated using current advancements in computational multi-target modeling algorithms reflected by theragnosis (combination of diagnostic tests and therapy) concern.

摘要

阿尔茨海默病(AD)在人类痛苦和经济成本方面构成了巨大的全球健康负担。AD的管理需要从当前针对发病机制的范式转变,以设计和开发在临床试验中取得足够成功的有效药物。因此,报告一篇关于淀粉样β(Aβ)效应以及其他与脑细胞死亡相关的多靶点(包括胆碱酯酶、神经纤维缠结、tau蛋白和肿瘤坏死因子)的综述,这些靶点具有神经保护作用以预防AD,是很有意义的。应当指出,这些分子是通过基于靶点或表型的方法产生的。因此,人们认识到利用纳米医学的最新进展和其他天然化合物筛选工具作为规避特定缺陷的可行替代方案。我们综述了利用当前计算多靶点建模算法的进展所产生的针对AD的神经退行性化合物的设计和鉴定方面的最新进展,这反映了诊疗一体化(诊断测试和治疗的结合)的关注点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b973/6016757/c4ea4e6d99c8/97320630014167F1.jpg

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