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dc.date.accessioned2022-05-11T15:49:48Z
dc.date.available2022-05-11T15:49:48Z
dc.date.created2020-10-01T12:51:59Z
dc.date.issued2022
dc.identifier.citationTran, Vi Ngoc-Nha Shams, Alireza Ascioglu, Sinan Martinecz, Antal Liang, Jingyi Clarelli, Fabrizio Mostowy, Rafal Cohen, Ted Abel zur Wiesch, Pia . vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding. BMC Bioinformatics. 2022, 23(1), 1-15
dc.identifier.urihttp://hdl.handle.net/10852/93952
dc.description.abstractBackground As antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allow non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria. Results In this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis bacteria using our vCOMBAT tool. Conclusions The vCOMBAT online tool is publicly available at https://combat-bacteria.org/.
dc.languageEN
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titlevCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding
dc.title.alternativeENEngelskEnglishvCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding
dc.typeJournal article
dc.creator.authorTran, Vi Ngoc-Nha
dc.creator.authorShams, Alireza
dc.creator.authorAscioglu, Sinan
dc.creator.authorMartinecz, Antal
dc.creator.authorLiang, Jingyi
dc.creator.authorClarelli, Fabrizio
dc.creator.authorMostowy, Rafal
dc.creator.authorCohen, Ted
dc.creator.authorAbel zur Wiesch, Pia
cristin.unitcode185,57,0,0
cristin.unitnameNorsk Senter for Molekylærmedisin
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.cristin1836155
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=BMC Bioinformatics&rft.volume=23&rft.spage=1&rft.date=2022
dc.identifier.jtitleBMC Bioinformatics
dc.identifier.volume23
dc.identifier.issue1
dc.identifier.doihttps://doi.org/10.1186/s12859-021-04536-3
dc.identifier.urnURN:NBN:no-96494
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn1471-2105
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/93952/1/article.pdf
dc.type.versionPublishedVersion
cristin.articleid22
dc.relation.projectNFR/262686
dc.relation.projectGATESFOUND/OPP1111658


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Attribution 4.0 International
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