Network-based target ranking for polypharmacological therapies

Highlights

• We present a bioinformatics method for planning a polypharmacology approach.

• We built a protein–protein interaction network by integrating different data sources.

• The network analysis allowed to discover new target candidates.

• The multi-target selection is computed on the basis of a synergistic score.

• We illustrate the potential of the method through a study on Type 2 Diabetes Mellitus.

Abstract

With the growing understanding of complex diseases, the focus of drug discovery has shifted from the well-accepted “one target, one drug” model, to a new “multi-target, multi-drug” model, aimed at systemically modulating multiple targets. In this context, polypharmacology has emerged as a new paradigm to overcome the recent decline in productivity of pharmaceutical research. However, finding methods to evaluate multicomponent therapeutics and ranking synergistic agent combinations is still a demanding task.

At the same time, the data gathered on complex diseases has been progressively collected in public data and knowledge repositories, such as protein–protein interaction (PPI) databases. The PPI networks are increasingly used as universal platforms for data integration and analysis. A novel computational network-based approach for feasible and efficient identification of multicomponent synergistic agents is proposed in this paper. Given a complex disease, the method exploits the topological features of the related PPI network to identify possible combinations of hit targets. The best ranked combinations are subsequently computed on the basis of a synergistic score. We illustrate the potential of the method through a study on Type 2 Diabetes Mellitus. The results highlight its ability to retrieve novel target candidates, which role is also confirmed by the analysis of the related literature.