A review of network-based approaches to drug repositioning

• Published in: Briefings in bioinformatics Vol. 19; no. 5; pp. 878 - 892
• Main Authors: Lotfi Shahreza, Maryam, Ghadiri, Nasser, Mousavi, Sayed Rasoul, Varshosaz, Jaleh, Green, James R
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 28.09.2018; Oxford Publishing Limited (England)
• Subjects: Computational Biology - methods; Cost analysis; data collection; Databases, Pharmaceutical - statistics & numerical data; Datasets; Drug development; Drug Interactions; Drug Repositioning - classification; Drug Repositioning - methods; Drug Repositioning - statistics & numerical data; Drug-Related Side Effects and Adverse Reactions; drugs; Gene Regulatory Networks; Humans; Machine Learning; Metabolic Networks and Pathways; Molecular Docking Simulation - statistics & numerical data; Network analysis; Performance measurement; prediction; Protein Interaction Maps; risk; drug-target interaction; network analysis; drug repurposing; machine learning; biological networks
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbx017

Abstract Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics. We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.