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Title: The fight against antimicrobial resistance : optimising antibiotic usage to treat bacterial infections
Author: Paterson, Iona K.
ISNI:       0000 0004 8502 7164
Awarding Body: University of Stirling
Current Institution: University of Stirling
Date of Award: 2019
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Antibiotic resistance is one of the major health concerns of the 21st century. Antibiotics are essential for the health and well-being of both humans and animals. However, the increase in antibiotic resistant bacteria poses a threat to the continued use of antibiotics to successfully treat bacterial infections. Current research within hospital settings has focused on the use of multi-antibiotic approaches in a variety of treatment patterns. Yet there is limited knowledge on the optimal use of single antibiotic treatments. With the spread of resistance linked to the overuse and misuse of antibiotics, optimal treatment regimens aim to maximise the success of eradicating an infection while minimising the amount of antibiotic required. This thesis therefore aimed to combine mathematical modelling with a genetic algorithm approach to identify optimal dosage regimens for the use of a single antibiotic. A mathematical model was developed to predict the dynamics of bacterial populations within an infection. A susceptible only infection was initially considered before being extended to include a resistant population. These models were incorporated into a genetic algorithm and used to search for dosage regimens which maximise bacterial eradication and minimise antibiotic use. Taking a theoretical approach, it was found that administering an antibiotic with a high initial dose followed by lowering doses is the optimal treatment regimen. A case study of a Vibrio anguillarum infection within Galleria mellonella larvae was used to parameterise the one strain bacterial model to a biologically realistic system. The results are consistent with those from the theoretical parameter sets. A tapered treatment regimen maximises the success of eradicating the bacterial infection while minimising the amount of antibiotic required. Laboratory experiments were performed which provided credibility to the results found. Finally, the assumption of fixed time intervals between doses was relaxed and the genetic algorithm used to identify both the dose and time intervals of optimal treatment regimens. Varying either the doses or the time intervals separately produced no significant difference in the success of eradicating an infection. When combined, the results showed that significantly better regimens could be identified. These regimens further increased bacterial eradication while using less antibiotic to do so. More work is required to identify a general treatment pattern when both variables are optimised due to the high variability in solutions. However, a shift away from conventional constant dose treatment regimens is required to prolong the future effectiveness of antibiotics.
Supervisor: Hoyle, Andrew ; Ochoa, Gabriela Sponsor: University of Stirling
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Antimicrobial ; Resistance ; Mathematical Modelling ; Antibiotics ; Drug resistance, Bacterial ; Infectious diseases ; Epidemiology ; Mathematical modelling--theory and applications