Insights into Computational Drug Repurposing for Neurodegenerative Disease

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.

Figure 1:

Major limitations and future solutions to computational drug repurposing in neurodegenerative disease. The current major limitations to computational drug repurposing in neurodegenerative disease are listed on the left panel. To address these limitations, we propose several solutions that are presented in the right panel.