Topological network measures for drug repositioning

Abstract

Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning.

Keywords: computational methods; drug repositioning; networks; topological network measures; topology.

Figure 1

Some of the commonly used measures: degree (top, left)—describes the number of connections of a node, closeness centrality (top, right) highlights nodes in the network closest to other nodes (and can be easily reached), betweenness centrality (bottom, left)—nodes which channel communication in the network, and eigenvector centrality (bottom, right)—which is based on connections with highly connected neighbors.

Figure 2

Effect of data selection on network topology: seen here are the networks of the protein insulin with different criteria for selection of its interactions, built using the STRING [96] database. While one can chose interactions with lower confidence to allow scope for exploration of possible links, it leads to a denser structure. As more stringent criteria is applied, while the quality of interactions is higher, possibilities of discovering potentially novel interactions decreases. The effect on topology is prominent, and would directly affect analysis and prediction based on these different networks.