Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10637/15898

Computer-aided drug repurposing to tackle antibiotic resistance based on topological data analysis


Vista previa

Ver/Abrir:
 Computer-aided_Tarin_CIBAM_2023.pdf
2,73 MB
Adobe PDF
Título : Computer-aided drug repurposing to tackle antibiotic resistance based on topological data analysis
Autor : Tarín Pelló, Antonio
Suay García, Beatriz
Forés Martos, Jaume
Falcó Montesinos, Antonio
Pérez Gracia, María Teresa.
Materias: MedicamentoDrugsTopologíaTopologyBacteriaAntibiotic resistance of bacteriaResistencia a los antibióticos de las bacteriasData analysisAnálisis de datos
Editorial : Elsevier
Citación : Tarín-Pelló, A., Suay-García, B., Forés-Martos, J., Falcó, A. & Pérez-Gracia, M.T. (2023). Computer-aided drug repurposing to tackle antibiotic resistance based on topological data analysis. Computers in Biology and Medicine, vol. 166 (nov.), art. 107496. DOI: https://doi.org/10.1016/j.compbiomed.2023.107496
Resumen : The progressive emergence of antimicrobial resistance has become a global health problem in need of rapid solution. Research into new antimicrobial drugs is imperative. Drug repositioning, together with computational mathematical prediction models, could be a fast and efficient method of searching for new antibiotics. The aim of this study was to identify compounds with potential antimicrobial capacity against Escherichia coli from US Food and Drug Administration-approved drugs, and the similarity between known drug targets and E. coli proteins using a topological structure-activity data analysis model. This model has been shown to identify molecules with known antibiotic capacity, such as carbapenems and cephalosporins, as well as new molecules that could act as antimicrobials. Topological similarities were also found between E. coli proteins and proteins from different bacterial species such as Mycobacterium tuberculosis, Pseudomonas aeruginosa and Salmonella Typhimurium, which could imply that the selected molecules have a broader spectrum than expected. These molecules include antitumor drugs, antihistamines, lipid-lowering agents, hypoglycemic agents, antidepressants, nucleotides, and nucleosides, among others. The results presented in this study prove the ability of computational mathematical prediction models to predict molecules with potential antimicrobial capacity and/or possible new pharmacological targets of interest in the design of new antibiotics and in the better understanding of antimicrobial resistance.
URI : http://hdl.handle.net/10637/15898
Derechos: http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
Open Access
ISSN : 0010-4825
1879-0534 (Electrónico)
Cubierto por: Acuerdo Transformativo – 2023
Fecha de publicación : nov-2023
Centro : Universidad Cardenal Herrera-CEU
Aparece en las colecciones: Dpto. Farmacia





Los ítems de DSpace están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.