Modelling for MRSA control: can we harness phage and antibiotics to halt resistance spread?

Antibiotic resistance (ABR) is a major global health problem currently responsible for around 700,000 deaths per year worldwide and is predicted to cause 10 million deaths per year by 2050 if no critical action is taken. ABR arises when strains of bacteria survive exposure to levels of antibiotics that would normally kill them and they are allowed to grow and spread, dominating other bacteria which die from the selective pressure. Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic bacterial pathogen responsible for 'superbugs' endemic to hospital settings around the world and has the second-highest incidence of infections caused by antibiotic-resistant bacteria in the EU and EEA. While no individual strains have acquired resistance to all antimicrobials - the threat of ABR presents the serious possibility of further resistance development, meaning infections can become more difficult, even impossible, to treat thereby causing more fatalities. Furthermore, resistance development threatens the safety of many other medical procedures such as organ transplants and chemotherapy, where immunosuppressed patients rely on antibiotics to fight infections the body would normally clear itself.

To combat this issue, countries are now trying to limit the unnecessary use of antibiotics to treat infections and investigate alternative courses of treatment such as phage therapy - where viruses called bacteriophages are used to infect and kill bacteria. However, little research has been conducted into the mechanisms underlying resistance gene transfer, including generalised transduction, the prominent mechanism of transfer in MRSA. Here, bacteriophages inadvertently package a resistance gene during replication inside a bacterium and consequently act as vectors for the transmission of resistance. Cross-disciplinary solutions to resistance spread, like those of infectious disease spread, are being sought, but few mathematical models have been proposed to investigate these mechanisms and quantify how combination therapies might be used to minimise resistance spread.

The aim of this project is to explore and better understand the mechanisms underpinning ABR transmission dynamics in MRSA. To do this, new mathematical models will be developed and informed by experimental data obtained from laboratory experiments. These models will study the interaction between bacteria, bacteriophage, and antibiotic concentrations, based on key mechanisms such as transduction. The associated parameters will be quantified by using high-performance cluster computing and statistical methods such as Markov chain Monte-Carlo methods to fit models to large sets of data obtained from my own laboratory experiments. Clinical data, if available, may also be used. Following the verification of these models with experimental outcomes, I will identify the leading processes and factors for resistance spread and encode different scenarios with the model to evaluate possible intervention strategies for eliminating ABR with antibiotics and phage. Worst-case scenarios that maximise resistance spread will also be investigated to identify the most dangerous practices. Finally, I will test these optimal and worst strategies in the laboratory to authenticate the model results. The study will advance understanding of ABR, informing further investigations in vivo and for other types of bacteria, as well as contributing to research that potentially informs real-world public health strategies and protects and saves lives.

The project will allow me to explore and contribute to cutting-edge research in ABR, one of the biggest challenges in global health. I will conduct cross-disciplinary research in collaboration with scientists across a wide range of disciplines, developing interdisciplinary skills while extending my mathematical knowledge and scientific communication.