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dc.contributor.authorAbel zur Wiesch, Pia
dc.contributor.authorClarelli, Fabrizio
dc.contributor.authorCohen, Ted
dc.date.accessioned2018-03-21T11:10:43Z
dc.date.available2018-03-21T11:10:43Z
dc.date.issued2017-01-06
dc.description.abstractIdentifying optimal dosing of antibiotics has proven challenging—some antibiotics are most effective when they are administered periodically at high doses, while others work best when minimizing concentration fluctuations. Mechanistic explanations for why antibiotics differ in their optimal dosing are lacking, limiting our ability to predict optimal therapy and leading to long and costly experiments. We use mathematical models that describe both bacterial growth and intracellular antibiotic-target binding to investigate the effects of fluctuating antibiotic concentrations on individual bacterial cells and bacterial populations. We show that physicochemical parameters, e.g. the rate of drug transmembrane diffusion and the antibiotic-target complex half-life are sufficient to explain which treatment strategy is most effective. If the drug-target complex dissociates rapidly, the antibiotic must be kept constantly at a concentration that prevents bacterial replication. If antibiotics cross bacterial cell envelopes slowly to reach their target, there is a delay in the onset of action that may be reduced by increasing initial antibiotic concentration. Finally, slow drug-target dissociation and slow diffusion out of cells act to prolong antibiotic effects, thereby allowing for less frequent dosing. Our model can be used as a tool in the rational design of treatment for bacterial infections. It is easily adaptable to other biological systems, e.g. HIV, malaria and cancer, where the effects of physiological fluctuations of drug concentration are also poorly understood.en_US
dc.description.sponsorshipBill & Melinda Gates foundation Swiss National Science Foundation Northern Norway Regional Health Authority (Helse Nord) UiT - The Arctic University of Norwayen_US
dc.descriptionSource at <a href=https://doi.org/10.1371/journal.pcbi.1005321> https://doi.org/10.1371/journal.pcbi.1005321 </a>.en_US
dc.identifier.citationAbel zur Wiesch, P., Clarelli, F. & Cohen, T. (2017). Using chemical reaction kinetics to predict optimal antibiotic treatment strategies. PloS Computational Biology, 13(1), 1-28.en_US
dc.identifier.cristinIDFRIDAID 1478679
dc.identifier.doi10.1371/journal.pcbi.1005321
dc.identifier.issn1553-734X
dc.identifier.issn1553-7358
dc.identifier.urihttps://hdl.handle.net/10037/12399
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.journalPloS Computational Biology
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Medisinske Fag: 700::Basale medisinske, odontologiske og veterinærmedisinske fag: 710::Biofarmasi: 736en_US
dc.subjectVDP::Medical disciplines: 700::Basic medical, dental and veterinary science disciplines: 710::Biopharmacy: 736en_US
dc.subjectVDP::Medical disciplines: 700::Basic medical, dental and veterinary science disciplines: 710::Medical molecular biology: 711en_US
dc.subjectVDP::Medisinske Fag: 700::Basale medisinske, odontologiske og veterinærmedisinske fag: 710::Medisinsk molekylærbiologi: 711en_US
dc.titleUsing chemical reaction kinetics to predict optimal antibiotic treatment strategiesen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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