Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24743
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Optimising Antibiotic Usage to Treat Bacterial Infections
Authors: Paterson, Iona K
Hoyle, Andrew
Ochoa, Gabriela
Baker-Austin, Craig
Taylor, Nicholas G H
Contact Email: ash@cs.stir.ac.uk
Keywords: Applied mathematics
Computational models
Issue Date: Nov-2016
Citation: Paterson IK, Hoyle A, Ochoa G, Baker-Austin C & Taylor NGH (2016) Optimising Antibiotic Usage to Treat Bacterial Infections, Scientific Reports, 6, Art. No.: 37853.
Abstract: The increase in antibiotic resistant bacteria poses a threat to the continued use of antibiotics to treat bacterial infections. The overuse and misuse of antibiotics has been identified as a significant driver in the emergence of resistance. Finding optimal treatment regimens is therefore critical in ensuring the prolonged effectiveness of these antibiotics. This study uses mathematical modelling to analyse the effect traditional treatment regimens have on the dynamics of a bacterial infection. Using a novel approach, a genetic algorithm, the study then identifies improved treatment regimens. Using a single antibiotic the genetic algorithm identifies regimens which minimise the amount of antibiotic used while maximising bacterial eradication. Although exact treatments are highly dependent on parameter values and initial bacterial load, a significant common trend is identified throughout the results. A treatment regimen consisting of a high initial dose followed by an extended tapering of doses is found to optimise the use of antibiotics. This consistently improves the success of eradicating infections, uses less antibiotic than traditional regimens and reduces the time to eradication. The use of genetic algorithms to optimise treatment regimens enables an extensive search of possible regimens, with previous regimens directing the search into regions of better performance.
DOI Link: http://dx.doi.org/10.1038/srep37853
Rights: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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