Predictions of drug metabolism pathways through CYP 3A4 enzyme by analysing drug-target interactions network graph

Abstract

The available data of drugs and their targets has increased widely in recent years. Far from the traditional way of studying the drug-target interactions, we propose a network-based computational method to identify new targets for known drugs. In this study, the Stanford Biomedical Network Dataset Collection (BIOSNAP Datasets) is used. A network graph is constructed and analyzed to study the relationship between the drugs and their targets. Different centrality and similarity measures analyses are applied and predict new potential metabolism pathways for five drugs, namely (Wortmannin, Voacamine, Vancomycin, Dactinomycin and Arundic acid) through Cytochrome P450 3A4 enzyme in the liver. The application of network theory to the analysis of this dataset reveals a new significant approach. Finally the molecular docking is performed to confirm the results. Also, the importance of the presented method in drug discovery is highlighted/pointed out.