Despite advances in modern medicine that led to improvements in cardiovascular outcomes, cardiovascular disease (CVD) remains the leading cause of mortality and morbidity globally [1-2]. Thus, there is an urgent need for new approaches to improve CVD drug treatments. As the development time and cost of drug discovery to clinical application are excessive, alternate strategies for drug development are warranted. Among these are included computational approaches based on omics data for drug repositioning, which have attracted increasing attention [3-5]. We developed an adjusted similarity measure implemented by the algorithm SAveRUNNER [6-7] to reposition drugs for cardiovascular diseases while, at the same time, considering the side effects of drug candidates [8]. We analyzed nine cardiovascular disorders and two side effects. We formulated both disease disorders and side effects as network modules in the human interactome, and considered those drug candidates that are proximal to disease modules but far from side-effects modules as ideal. Our method provides a list of drug candidates for cardiovascular diseases that are unlikely to produce common, adverse side-effects [8]. This approach incorporating side effects is applicable to other diseases, as well.
Figtree Gemma A, Broadfoot Keith, Casadei Barbara, Califf Robert, Crea Filippo, Drummond Grant R, Freedman Jane E, Guzik Tomasz J, Harrison David, Hausenloy Derek J, Hill Joseph A, Januzzi James L, Kingwell Bronwyn A, Lam Carolyn S P, MacRae Calum A, Misselwitz Frank, Miura Tetsuji, Ritchie Rebecca H, Tomaszewski Maciej, Wu Joseph C, Xiao Junjie, Zannad Faiez. A call to action for new global approaches to cardiovascular disease drug solutions. European Heart Journal. Vol. 42(15):1464–1475. 2021. Oxford University Press (OUP). [Cross Ref]
McClellan Mark, Brown Nancy, Califf Robert M., Warner John J.. Call to Action: Urgent Challenges in Cardiovascular Disease: A Presidential Advisory From the American Heart Association. Circulation. Vol. 139(9)2019. Ovid Technologies (Wolters Kluwer Health). [Cross Ref]
Ashburn Ted T., Thor Karl B.. Drug repositioning: identifying and developing new uses for existing drugs. Nature Reviews Drug Discovery. Vol. 3(8):673–683. 2004. Springer Science and Business Media LLC. [Cross Ref]
Crunkhorn Sarah. Deep learning framework for repurposing drugs. Nature Reviews Drug Discovery. Vol. 20(2)2021. Springer Science and Business Media LLC. [Cross Ref]
Park Kyungsoo. A review of computational drug repurposing. Translational and Clinical Pharmacology. Vol. 27(2)2019. Korean Society for Clinical Pharmacology and Therapeutics. [Cross Ref]
Fiscon Giulia, Paci Paola. SAveRUNNER: an R-based tool for drug repurposing. BMC Bioinformatics. Vol. 22(1)2021. Springer Science and Business Media LLC. [Cross Ref]
Fiscon Giulia, Conte Federica, Farina Lorenzo, Paci Paola. SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19. PLOS Computational Biology. Vol. 17(2)2021. Public Library of Science (PLoS). [Cross Ref]
Paci Paola, Fiscon Giulia, Conte Federica, Wang Rui-Sheng, Handy Diane E., Farina Lorenzo, Loscalzo Joseph. Comprehensive network medicine-based drug repositioning via integration of therapeutic efficacy and side effects. npj Systems Biology and Applications. Vol. 8(1)2022. Springer Science and Business Media LLC. [Cross Ref]