Predicting new molecular targets for rhein using network pharmacology

Abstract

Background: Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.

Methods: In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.

Results: Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.

Conclusions: Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.

Figure 1

Chemical structure of rhein. ChemSpider ID: 9762; Systematic name: 4,5-Dihydroxy-9,10-dioxo-9,10-dihydro-2-anthracenecarboxylic acid; Molecular Formula: C15H8O6; Mass: 284.032074 Da.

Figure 2

Solid map on huge interactome of rhein-targets networks, built and visualized with Cytoscape. Edges: interactions. Nodes: specific proteins or genes. Central nodes (Yellow) of the interaction network were used to illustrate the gene expression obtained and represents significant change in expression. The connections between molecules show molecular interactions identified in the interactome. Gene expression illustrated in colored nodes was selected with a p < 0.05 value.

Figure 3

Illustration and visualization of the interactome network (Circle Layout). Drugs and proteins are linked as per the known drug-target network. Discrete network map with a customized visual Cytoscape Web style. Yellow nodes refer to the significant ontologies of the target. This subnetwork represents a coregulated unit containing 99 nodes and 153 edges.

Figure 4

Identification of novel molecular targets using network analysis. A: MMP2; B: MMP9; C: TNF.

Figure 5

Regulatory sub-network of differentially expressed TNF of rhein through network pharmacology.