Please use this identifier to cite or link to this item: http://hdl.handle.net/10637/15898
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dc.contributor.otherUCH. Departamento de Farmacia-
dc.contributor.otherUCH. Departamento de Matemáticas, Física y Ciencias Tecnológicas-
dc.contributor.otherUCH. Instituto de Ciencias Biomédicas (CEU-ICB)-
dc.contributor.otherUCH. ESI International Chair@CEU-UCH-
dc.contributor.otherProducción Científica UCH 2023-
dc.creatorTarín Pelló, Antonio-
dc.creatorSuay García, Beatriz-
dc.creatorForés Martos, Jaume-
dc.creatorFalcó Montesinos, Antonio-
dc.creatorPérez Gracia, María Teresa.-
dc.date.accessioned2024-06-04T13:03:16Z-
dc.date.available2024-06-04T13:03:16Z-
dc.date.issued2023-11-
dc.identifier.citationTarín-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.2023.107496es_ES
dc.identifier.issn0010-4825-
dc.identifier.issn1879-0534 (Electrónico)-
dc.identifier.urihttp://hdl.handle.net/10637/15898-
dc.description.abstractThe 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.es_ES
dc.description.sponsorshipAcuerdo Transformativo – 2023-
dc.language.isoenes_ES
dc.publisherElsevieres_ES
dc.relationEste artículo de investigación ha sido financiado por la Universidad CEU Cardenal Herrera (INDI18/34, INDI19/39, INDI20/38, INDI21/44, INDI22/15 e INDI22/42).-
dc.relationUCH. Financiación Universidad-
dc.relation.ispartofComputers in Biology and Medicine, vol. 166 (nov.)-
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es-
dc.rightsOpen Access-
dc.subjectMedicamentoes_ES
dc.subjectDrugses_ES
dc.subjectTopologíaes_ES
dc.subjectTopologyes_ES
dc.subjectBacteriaes_ES
dc.subjectAntibiotic resistance of bacteriaes_ES
dc.subjectResistencia a los antibióticos de las bacteriases_ES
dc.subjectData analysises_ES
dc.subjectAnálisis de datoses_ES
dc.titleComputer-aided drug repurposing to tackle antibiotic resistance based on topological data analysises_ES
dc.typeArtículoes_ES
dc.identifier.doihttps://doi.org/10.1016/j.compbiomed.2023.107496-
dc.relation.projectIDINDI18/34-
dc.relation.projectIDINDI19/39-
dc.relation.projectIDINDI20/38-
dc.relation.projectIDINDI21/44-
dc.relation.projectIDINDI22/15-
dc.relation.projectIDINDI22/42-
dc.centroUniversidad Cardenal Herrera-CEU-
Appears in Collections:Dpto. Farmacia




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