Knowledge-based identification of multicomponent therapies

In recent years, several approaches have been proposed to improve the capacity of pharmaceutical research to support personalized care. An approach that takes advantages of the large amount of biological knowledge continuously collected in different repositories could improve the drug discovery process. In this context, networks are increasingly used as universal platforms to integrate the knowledge available on a complex disease. The objective of this work is to provide a knowledge-based strategy to support polypharmacology, a new promising approach for drug discovery. Given a specific disease, the proposed method is able to identify the possible targets by analysing the topological features of the related network. The network-based analysis defines a score aimed at ranking the targets and selecting their best combinations. The results obtained on Type 2 Diabetes Mellitus highlight the ability of the method to retrieve novel target candidates related to the considered disease.