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Network-based target ranking for polypharmacological therapies.

With the growing understanding of complex diseases, the focus of drug discovery has shifted from the well-accepted "one target, one drug" model designed towards a single target, to a new "multi-target, multidrug" model, aimed at systemically modulating multiple targets. In this context polypharmacology has emerged as a new paradigm to overcome the recent decline in pharmaceutical research and productivity. Likewise the networks are increasingly used as universal platforms to integrate the knowledge of a complex disease. A novel computational network-based approach for the identification of multicomponent synergy is hereafter proposed. Given a complex disease, the method exploits the topological features of the related network to identify possible combinations of hit targets. The best ranked combinations are subsequently selected based on a synergistic score. The results obtained on Type 2 Diabetes Mellitus highlight the ability of the method to retrieve novel target candidates related to the considered disease.