title: Optimising Antibiotic Usage to Treat Bacterial Infections.
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.
authors.affiliation: University of Stirling, Computing Science and Mathematics, Faculty of Natural Sciences, Stirling, FK9 4LA, United Kingdom.
authors.forename: Iona K
authors.initials: IK
authors.lastname: Paterson
authors.affiliation: University of Stirling, Computing Science and Mathematics, Faculty of Natural Sciences, Stirling, FK9 4LA, United Kingdom.
authors.forename: Andy
authors.initials: A
authors.lastname: Hoyle
authors.affiliation: University of Stirling, Computing Science and Mathematics, Faculty of Natural Sciences, Stirling, FK9 4LA, United Kingdom.
authors.forename: Gabriela
authors.initials: G
authors.lastname: Ochoa
authors.affiliation: Centre for Environment, Fisheries and Aquaculture Science (Cefas), Weymouth Laboratory, Weymouth, DT4 8UB, United Kingdom.
authors.forename: Craig
authors.initials: C
authors.lastname: Baker-Austin
authors.affiliation: Centre for Environment, Fisheries and Aquaculture Science (Cefas), Weymouth Laboratory, Weymouth, DT4 8UB, United Kingdom.
authors.forename: Nick G H
authors.initials: NG
authors.lastname: Taylor
mesh_terms: D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000008:administration & dosage* / Q000493:pharmacokinetics* / Q000627:therapeutic use; D001424:Bacterial Infections / Q000188:drug therapy*; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D013269:Stochastic Processes; D016896:Treatment Outcome
chemical_list: D000900:Anti-Bacterial Agents