Evolutionary optimisation of antibiotic dosing regimens for bacteria with different levels of resistance.
Antimicrobial resistance is one of the biggest threats to global health, food security, and development. Antibiotic overuse and misuse are the main drivers for the emergence of resistance. It is crucial to optimise the use of existing antibiotics in order to improve medical outcomes, decrease toxici...
| Publicado en: | Artificial Intelligence in Medicine Vol. 133 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Nov2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159952903&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159952903 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Nov2022 vid: 133 pid: 1004 pub: Elsevier B.V. artinfo: ui: 159952903 159952903 NLM36328666 10.1016/j.artmed.2022.102405 NLM36328666 159952903 ppct: 1 formats: tig: atl: Evolutionary optimisation of antibiotic dosing regimens for bacteria with different levels of resistance. aug: au: Goranova, Mila Ochoa, Gabriela Maier, Patrick Hoyle, Andrew affil: Computing Science and Mathematics, University of Stirling, Scotland, UK sug: subj: Bacterial Infections Drug Therapy Antibiotics Therapeutic Use Models, Theoretical Bacteria Algorithms Scales Ways of Coping Questionnaire ab: Antimicrobial resistance is one of the biggest threats to global health, food security, and development. Antibiotic overuse and misuse are the main drivers for the emergence of resistance. It is crucial to optimise the use of existing antibiotics in order to improve medical outcomes, decrease toxicity and reduce the emergence of resistance. We formulate the design of antibiotic dosing regimens as an optimisation problem, and use an evolutionary algorithm suited to continuous optimisation (differential evolution) to solve it. Regimens are represented as vectors of real numbers encoding daily doses, which can vary across the treatment duration. A stochastic mathematical model of bacterial infections with tuneable resistance levels is used to evaluate the effectiveness of evolved regimens. The objective is to minimise the treatment failure rate, subject to a constraint on the maximum total antibiotic used. We consider simulations with different levels of bacterial resistance, two ways of administering the drug (orally and intravenously), as well as coinfections with two strains of bacteria. Our approach produced effective dosing regimens, with an average improvement in lowering the failure rate 30%, when compared with standard fixed-daily-dose regimens with the same total amount of antibiotic. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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