title: Network-based target ranking for polypharmacological therapies.
abstract: 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.
authors.affiliation: Dipartimento di Ingegneria Industriale e dell'Informazione, Università di Pavia, Italia.
authors.forename: Francesca
authors.initials: F
authors.lastname: Vitali
authors.forename: Francesca
authors.initials: F
authors.lastname: Mulas
authors.forename: Pietro
authors.initials: P
authors.lastname: Marini
authors.forename: Riccardo
authors.initials: R
authors.lastname: Bellazzi