Network-based target ranking for polypharmacological therapies

Francesca Vitali, Francesca Mulas, Pietro Marini, Riccardo Bellazzi

Research output: Contribution to journalArticle

23 Citations (Scopus)

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.
Original languageEnglish
Article number168
JournalAMIA Joint Summits on Translational Science proceedings AMIA Summit on Translational Science
Volume2013
Publication statusPublished - 2013

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Polypharmacology
Drug Discovery
Therapeutics
Type 2 Diabetes Mellitus
Pharmaceutical Preparations
Pharmaceutical Research

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Network-based target ranking for polypharmacological therapies. / Vitali, Francesca; Mulas, Francesca; Marini, Pietro; Bellazzi, Riccardo.

In: AMIA Joint Summits on Translational Science proceedings AMIA Summit on Translational Science, Vol. 2013, 168, 2013.

Research output: Contribution to journalArticle

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