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Trends Pharmacol Sci. 2019 Aug;40(8):565-576. doi: 10.1016/j.tips.2019.06.003. Epub 2019 Jul 17.

Insights into Computational Drug Repurposing for Neurodegenerative Disease.

Author information

1
Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94158, USA. Electronic address: manish.paranjpe@ucsf.edu.
2
Gladstone Institutes, San Francisco, CA 94158, USA.
3
Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94158, USA; Gladstone Institutes, San Francisco, CA 94158, USA. Electronic address: marina.sirota@ucsf.edu.

Abstract

Computational drug repurposing has the ability to remarkably reduce drug development time and cost in an era where these factors are prohibitively high. Several examples of successful repurposed drugs exist in fields such as oncology, diabetes, leprosy, inflammatory bowel disease, among others, however computational drug repurposing in neurodegenerative disease has presented several unique challenges stemming from the lack of validation methods and difficulty in studying heterogenous diseases of aging. Here, we examine existing approaches to computational drug repurposing, including molecular, clinical, and biophysical methods, and propose data sources and methods to advance computational drug repurposing in neurodegenerative disease using Alzheimer's disease as an example.

KEYWORDS:

Alzheimer’s disease; EHR; EMR; artificial intelligence; machine learning; transcriptomic analysis

PMID:
31326236
PMCID:
PMC6771436
[Available on 2020-08-01]
DOI:
10.1016/j.tips.2019.06.003

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