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Drug Repositioning for Alzheimer's Disease Based on Systematic 'omics' Data Mining.
PLoS One 2016; 11(12):e0168812Plos

Abstract

Traditional drug development for Alzheimer's disease (AD) is costly, time consuming and burdened by a very low success rate. An alternative strategy is drug repositioning, redirecting existing drugs for another disease. The large amount of biological data accumulated to date warrants a comprehensive investigation to better understand AD pathogenesis and facilitate the process of anti-AD drug repositioning. Hence, we generated a list of anti-AD protein targets by analyzing the most recent publically available 'omics' data, including genomics, epigenomics, proteomics and metabolomics data. The information related to AD pathogenesis was obtained from the OMIM and PubMed databases. Drug-target data was extracted from the DrugBank and Therapeutic Target Database. We generated a list of 524 AD-related proteins, 18 of which are targets for 75 existing drugs-novel candidates for repurposing as anti-AD treatments. We developed a ranking algorithm to prioritize the anti-AD targets, which revealed CD33 and MIF as the strongest candidates with seven existing drugs. We also found 7 drugs inhibiting a known anti-AD target (acetylcholinesterase) that may be repurposed for treating the cognitive symptoms of AD. The CAD protein and 8 proteins implicated by two 'omics' approaches (ABCA7, APOE, BIN1, PICALM, CELF1, INPP5D, SPON1, and SOD3) might also be promising targets for anti-AD drug development. Our systematic 'omics' mining suggested drugs with novel anti-AD indications, including drugs modulating the immune system or reducing neuroinflammation that are particularly promising for AD intervention. Furthermore, the list of 524 AD-related proteins could be useful not only as potential anti-AD targets but also considered for AD biomarker development.

Authors+Show Affiliations

Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada.Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada.Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada.Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada.Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada.Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada.Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada. Department of Medicine, Division of Neurology, University of Toronto, Toronto, Ontario, Canada. Cambridge Institute for Medical Research, and the Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada. Department of Medicine, Division of Neurology, University of Toronto, Toronto, Ontario, Canada.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28005991

Citation

Zhang, Ming, et al. "Drug Repositioning for Alzheimer's Disease Based On Systematic 'omics' Data Mining." PloS One, vol. 11, no. 12, 2016, pp. e0168812.
Zhang M, Schmitt-Ulms G, Sato C, et al. Drug Repositioning for Alzheimer's Disease Based on Systematic 'omics' Data Mining. PLoS ONE. 2016;11(12):e0168812.
Zhang, M., Schmitt-Ulms, G., Sato, C., Xi, Z., Zhang, Y., Zhou, Y., ... Rogaeva, E. (2016). Drug Repositioning for Alzheimer's Disease Based on Systematic 'omics' Data Mining. PloS One, 11(12), pp. e0168812. doi:10.1371/journal.pone.0168812.
Zhang M, et al. Drug Repositioning for Alzheimer's Disease Based On Systematic 'omics' Data Mining. PLoS ONE. 2016;11(12):e0168812. PubMed PMID: 28005991.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Drug Repositioning for Alzheimer's Disease Based on Systematic 'omics' Data Mining. AU - Zhang,Ming, AU - Schmitt-Ulms,Gerold, AU - Sato,Christine, AU - Xi,Zhengrui, AU - Zhang,Yalun, AU - Zhou,Ye, AU - St George-Hyslop,Peter, AU - Rogaeva,Ekaterina, Y1 - 2016/12/22/ PY - 2016/08/31/received PY - 2016/12/06/accepted PY - 2016/12/23/entrez PY - 2016/12/23/pubmed PY - 2017/7/19/medline SP - e0168812 EP - e0168812 JF - PloS one JO - PLoS ONE VL - 11 IS - 12 N2 - Traditional drug development for Alzheimer's disease (AD) is costly, time consuming and burdened by a very low success rate. An alternative strategy is drug repositioning, redirecting existing drugs for another disease. The large amount of biological data accumulated to date warrants a comprehensive investigation to better understand AD pathogenesis and facilitate the process of anti-AD drug repositioning. Hence, we generated a list of anti-AD protein targets by analyzing the most recent publically available 'omics' data, including genomics, epigenomics, proteomics and metabolomics data. The information related to AD pathogenesis was obtained from the OMIM and PubMed databases. Drug-target data was extracted from the DrugBank and Therapeutic Target Database. We generated a list of 524 AD-related proteins, 18 of which are targets for 75 existing drugs-novel candidates for repurposing as anti-AD treatments. We developed a ranking algorithm to prioritize the anti-AD targets, which revealed CD33 and MIF as the strongest candidates with seven existing drugs. We also found 7 drugs inhibiting a known anti-AD target (acetylcholinesterase) that may be repurposed for treating the cognitive symptoms of AD. The CAD protein and 8 proteins implicated by two 'omics' approaches (ABCA7, APOE, BIN1, PICALM, CELF1, INPP5D, SPON1, and SOD3) might also be promising targets for anti-AD drug development. Our systematic 'omics' mining suggested drugs with novel anti-AD indications, including drugs modulating the immune system or reducing neuroinflammation that are particularly promising for AD intervention. Furthermore, the list of 524 AD-related proteins could be useful not only as potential anti-AD targets but also considered for AD biomarker development. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/28005991/Drug_Repositioning_for_Alzheimer's_Disease_Based_on_Systematic_'omics'_Data_Mining_ L2 - http://dx.plos.org/10.1371/journal.pone.0168812 DB - PRIME DP - Unbound Medicine ER -