Host Centric Drug Repurposing for Viral Diseases
Suzana de Siqueira Santos, Haixuan Yang, Aldo Galeano, Alberto Paccanaro.
Abstract
Computational approaches for drug repurposing for viral diseases have mainly focused on a small number of antivirals that directly target pathogens (virus centric therapies). In this work, we combine ideas from collaborative filtering and network medicine for making predictions on a much larger set of drugs that could be repurposed for host centric therapies, that are aimed at interfering with host cell factors required by a pathogen.
Introduction
Viral infections cause public health crises and numerous deaths across the world. COVID-19 alone is thought to have been responsible for more than million deaths worldwide between December 2019 and September 2023. Developing new drugs against viruses is a challenging, expensive and time-consuming task. Drug repurposing, the re-use of de-risked compounds in humans for new therapeutic indications, may result in shorter development times and lower costs.
Materials and Methods:
Drug-target associations.
We obtained FDA-approved drugs and their drug targets from DrugBank and Gysi et al. Our set of drugs consisted of FDA-approved drugs. Our set of drug target associations consisted of pairs of drug and targets.
Drug-virus associations.
We downloaded drug-virus associations from DrugVirus.info database. To select host-centric antivirals, we filtered drugs that have human targets on the “Potential targets” field of DrugVirus.info database. We found associations between viruses that are available from HDVIDB and have associations with host-centric antivirals with known targets in the interactome. We refer to this set as “assessment set” and it is available in S2 File.
Host proteins.
We downloaded host protein data from HDVIDB. We found host proteins for viruses that cause human diseases with entries on the DrugVirus.info database. For each virus, we considered the union of host proteins across different strains. Our final set of host proteins contains only those that have connections in the interactome. For mapping viruses between HDVIDB and DrugVirus.info databases, we used both virus names and abbreviations.
Discussion
Machine learning methods for drug repurposing rely on large datasets of drug-disease associations. Many of these methods are agnostic of the biology of the problem, which is framed simply as the problem of predicting known associations. For viruses, there are only a few approved antivirals, most of which are virus centric, directly targeting the pathogens.
To obtain predictions for a much larger set of drugs that could be repurposed for host centric therapies, we propose a method that exploits the biology of the problem.
Citation: de Siqueira Santos S, Yang H, Galeano A, Paccanaro A (2025) Host centric drug repurposing for viral diseases. PLoS Comput Biol 21(4): e1012876. https://doi.org/10.1371/journal.pcbi.1012876
Editor: Jessica M. Conway, Pennsylvania State University, UNITED STATES OF AMERICA
Received: November 2, 2024; Accepted: February 14, 2025; Published: April 2, 2025.
Copyright: © 2025 de Siqueira Santos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data and code that we used in our experiments are available at https://github.com/paccanarolab/VirusDrugRepo.
Funding: AP was supported by Biotechnology and Biological Sciences Research Council ( https://bbsrc.ukri.org/ ) grant numbers BB/K004131/1, BB/F00964X/1, and BB/M025047/1; Medical Research Council ( https://mrc.ukri.org ) grant number MR/ T001070/1; Consejo Nacional de Ciencia y Tecnología Paraguay ( https://www.conacyt.gov.py/ ) grant numbers PINV01-719, PINV01-108; National Science Foundation Advances in Bio Informatics ( https://www.nsf.gov/ ) grant number 1660648; Rio de Janeiro State Research Support Foundation ( https://www.faperj.br ) grant number E-26/201.079/2021 (260380) and E-26/204.352/2024; National Council for Scientific and Technological Development ( https://www.cnpq.br ) grant number 311181/2022-8; and Getulio Vargas Foundation. SS, and AG were supported in part by Rio de Janeiro State Research Support Foundation ( https://www.faperj.br ) grant number E-26/201.079/2021 (260380); National Council for Scientific and Technological Development ( https://www.cnpq.br ) grant number 311181/2022-8; and Getulio Vargas Foundation. AG was supported by the Coordination for the Improvement of Higher Education Personnel (CAPES). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors declare that they have no conflict of interest.
