A review of network-based approaches to drug repositioning

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.

ProphTools: general prioritization tools for heterogeneous biological networks

“…From protein-protein interaction prediction [3] to the identification of candidate disease genes to drug repositioning [4] or very recent applications on microbiology [5], it seems to be clear that inference on graph or network data structures can be effective for the purpose of finding relations between entities that interact in such ways [1]. These in silico predictions allow researchers to reduce the search space to focus on a small set of entities that are more likely to be related to the entities of interest.…”

Uncovering new drug properties in target-based drug-drug similarity networks

“…One of the most effective alternative strategies is drug repositioning (or drug 8 repurposing) [6,7], namely finding new pharmaceutical functions for already used drugs. 9 The extensive medical and pharmaceutical experience reveals a surprising propensity 10 towards multiple indications for many drugs [8], and the examples of successful drug 11 repositioning are steadily accumulating. Out of the 90 newly approved drugs in 2016 (a 12 10% decrease from 2015), 25% are repositionings in terms of formulations, combinations, 13 and indications [4].…”

“…Some approaches 31 build drug-drug similarity networks, where the similarity is defined based on 32 transcriptional responses [22,23]. These repositioning approaches analyze the network 33 parameters and the node centrality distributions in either drug-drug or drug-target 34 networks, using statistical analysis [10,11,24,25] and machine learning (i.e., graph 35 convolutional networks) [26][27][28][29] to link certain drugs to new pharmacological properties. 36 However, conventional statistics can be misleading when used to predict extreme 37 centrality values, such as degree and betweenness (which particularly indicate 38 nodes/drugs with a high potential for repositioning) [30].…”

“…March 7, 2020 2/22 volumes of useful multi-omics data (genomics, proteomics, metabolomics, and 20 others) [10,11].…”

“…• The recent developments in physics, computer science, and computer engineering 25 have created efficient methods and technologies for data exploration and mining, 26 such as complex network analysis, machine learning, or deep learning [11,[14][15][16][17][18]. 27…”