DOI: 10.1515/mr-2021-0017
Converging multi-modality datasets to build efficient drug repositioning pipelines against Alzheimer's disease and related dementias
Zheng Yin1
Stephen T.C.Wong2
1.Department of Systems Medicine and Bioengineering,Houston Methodist Cancer Center and Ting Tsung & Wei Fong Chao Center for BRAIN,Houston Methodist Research Institute,Weill Cornell Medicine,Houston,TX,USA2.Department of Systems Medicine and Bioengineering,Houston Methodist Cancer Center and Ting Tsung & Wei Fong Chao Center for BRAIN,Houston Methodist Research Institute,Weill Cornell Medicine,Houston 77030,TX,USA
摘要：Alzheimer's disease and related dementias(AD/ADRD)affects more than 50 million people worldwide but there is no clear therapeutic option affordable for the general patient population.Recently,drug repositioning studies featuring collaborations between academic in-stitutes,medical centers,and hospitals are generating novel therapeutics candidates against these devastating diseases and filling in an important area for healthcare that is poorly represented by pharmaceutical companies.Such drug repositioning studies converge expertise from bioinformat-ics,chemical informatics,medical informatics,artificial in-telligence,high throughput and high-content screening and systems biology.They also take advantage of multi-scale,multi-modality datasets,ranging from transcriptomic and proteomic data,electronical medical records,and medical imaging to social media information of patient behaviors and emotions and epidemiology profiles of disease pop-ulations,in order to gain comprehensive understanding of disease mechanisms and drug effects.We proposed a recursive drug repositioning paradigm involving the itera-tion of three processing steps of modeling,prediction,and validation to identify known drugs and bioactive com-pounds for AD/ADRD.This recursive paradigm has the po-tential of quickly obtaining a panel of robust novel drug candidates for AD/ADRD and gaining in-depth under-standing of disease mechanisms from those repositioned drug candidates,subsequently improving the success rate of predicting novel hits.
机标关键词：alzheimerbuildagainstconvergingdatasetsdementiasdiseasedrug
论文发表日期：2022-02-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：4( 110-113 )
英文信息