Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing

Abstract

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications.

Figure 1. The drug-drug interactome (DDI) analysis procedure for clustering drugs according to relevant pharmacological properties.

Our processing procedure is based on modularity classes and energy model topological clustering (Force Atlas 2).

Figure 2. Community-based drug-drug interaction network (CBDDIN) generated in Gephi with interaction data from DrugBank 4.1, containing 1141 nodes (representing drugs) and 11688 links (representing drug-drug interactions).

Topological clusters and functional communities are highlighted by using the Force Atlas 2 layout and color labeling of modularity classes.

Figure 3

Centrality metrics for the community-based drug-drug interaction network (CBDDIN): (A) degree distribution (B) betweenness distribution (C) closeness distribution, and (D) eigenvector distribution. The power law parameters, slope α and cutoff point X_min, are provided for degree, betwenness and eigenvector distributions; for the closeness distribution, the best fit is Gaussian function as indicated in panel C).

Figure 4. Zoomed details of our DDI indicating.

(A) Zafirlukast placement within topological community II (B) Thalidomide placement within topological community I.