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Abstract

Multi-drug treatments are promising strategies in the fight against the rapid evolution of drug resistance in bacteria. Laboratory experiments and a few clinical examples show that administering multiple drugs in combination or alternation can impede bacterial adaptation and effectively clear an infection. Yet, we still miss a deep understanding of how these treatments affect the underlying evolutionary processes. Gaining these insights will substantially contribute to the development and improvement of multi-drug strategies for clinical use. In this thesis, I use mathematical models to systematically explore how different multi-drug strategies affect the evolution of drug resistance.

In the first chapter, I analyse how fast antibiotics should be cycled to effectively clear a bacterial infection with a sequential regimen. I further study differences in the treatment dynamics between a laboratory and a patient environment. Using a pharmacokinetic-pharmacodynamic model, I found that the most rapid cycling is always ideal in the laboratory environment. In the patient, it is sometimes advantageous to cycle drugs more slowly. In the second chapter, I focus on drug combinations and investigate how antibiotics should be combined to optimally limit resistance evolution. I use a stochastic modelling approach to explore the effect of multiple factors, such as the number of drugs in combination, on the success probability of the treatment. The observations from the mathematical model can serve as guiding principles for treatment design. The antibiotic crisis concerns everyone, and it is important to communicate the problem of antibiotic resistance evolution and the strategies undertaken against it to the general public. In the last chapter, I therefore share my experience with developing an outreach activity for school students. This chapter offers insights that can support other research in implementing an outreach activity.