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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. Studies in the medical sphere have indicated that shortened antibiotic treatments can be as effective as standard fixed-dose ones and have shown that an initial higher dose followed by a lower maintenance dose are more beneficial to patients with critical illnesses. 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 a continuous optimisation problem and use several evolutionary algorithms as the search technique. 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 main 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. The 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. A general pattern of an optimised treatment is found, where if 2x is the standard daily dose then the optimised treatment follows the 3x mg, followed by several 2x mg with a last dose of x mg. A noise handling technique is used to minimise the runtime of the experiments while maintaining the quality of treatments. The results of this work indicate that clinical studies confirming the effectiveness of this approach could be highly beneficial to future of antibiotic treatments.

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.