Host centric drug repurposing for viral diseases

Suzana de Siqueira Santos, Haixuan Yang, Aldo Galeano, Alberto Paccanaro

Abstract: Author summary Drug repurposing is the re-use of de-risked compounds in humans for new therapeutic indications. Computational approaches can help in this process by providing a ranking of compounds with potential effect against a given disease. For viral diseases, computational methods have mainly focused on a small number of antivirals that directly target pathogens (virus centric therapies). Another type of antiviral drugs is aimed at disrupting host cellular processes required for the viral infection, either directly or indirectly (host centric therapies). In this work, we propose a novel computational approach focused on host centric therapies that can make predictions on a large set of drugs. It relies on the human protein-protein interaction network for identifying drugs that may influence host cellular processes required for the viral infection, together with machine learning techniques to enhance prediction accuracy by integrating information across different viruses and drugs. We obtained predictions of drug efficacy for 143 viruses. We show that our method successfully identifies drugs with known evidence against viruses among the top-ranked predictions. Furthermore, our model captures meaningful biological information, providing insights into the underlying biology of viral infections.