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Article

A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data Analysis-Based Framework

1
ESI International [email protected], Universidad Cardenal Herrera-CEU, CEU Universities, San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain
2
Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera-CEU, CEU Universities, San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain
3
Biomedical Research Networking Center of Mental Health (CIBERSAM), 28029 Madrid, Spain
4
ESI Group, 3bis rue Saarinen, 94528 Rungis, France
5
Departamento de Producción y Sanidad Animal, Salud Pública Veterinaria y Ciencia y Tecnología de los Alimentos, Universidad Cardenal Herrera-CEU, CEU Universities, C/Tirant lo Blanc 7, Alfara del Patriarca, 46115 Valencia, Spain
*
Authors to whom correspondence should be addressed.
These authors have contributed equally to this work.
Academic Editors: Lucreția Udrescu, Ludovic Kurunczi, Paul Bogdan and Mihai Udrescu
Pharmaceutics 2021, 13(4), 488; https://doi.org/10.3390/pharmaceutics13040488
Received: 21 February 2021 / Revised: 29 March 2021 / Accepted: 31 March 2021 / Published: 2 April 2021
(This article belongs to the Special Issue In Silico Strategies for Prospective Drug Repositionings)
Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 116 million cases and 2.5 million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We applied a novel computational pipeline aimed to accelerate the process of identifying drug repurposing candidates which allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib. This is the first time that a topological data analysis (TDA)-based strategy has been used to compare a massive number of protein structures with the final objective of performing drug repurposing to treat SARS-CoV-2 infection. View Full-Text
Keywords: COVID-19; drug repurposing; topological data analysis; persistent Betti function COVID-19; drug repurposing; topological data analysis; persistent Betti function
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MDPI and ACS Style

Pérez-Moraga, R.; Forés-Martos, J.; Suay-García, B.; Duval, J.-L.; Falcó, A.; Climent, J. A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data Analysis-Based Framework. Pharmaceutics 2021, 13, 488. https://doi.org/10.3390/pharmaceutics13040488

AMA Style

Pérez-Moraga R, Forés-Martos J, Suay-García B, Duval J-L, Falcó A, Climent J. A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data Analysis-Based Framework. Pharmaceutics. 2021; 13(4):488. https://doi.org/10.3390/pharmaceutics13040488

Chicago/Turabian Style

Pérez-Moraga, Raul, Jaume Forés-Martos, Beatriz Suay-García, Jean-Louis Duval, Antonio Falcó, and Joan Climent. 2021. "A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data Analysis-Based Framework" Pharmaceutics 13, no. 4: 488. https://doi.org/10.3390/pharmaceutics13040488

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