Within-Host Individual-Based Model for Diabetic Tuberculosis Patients

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For several decades, Tuberculosis (TB) has been preventable and curable. However, someone still dies from tuberculosis across the world every 15-20 seconds, and 50 percent of those deaths are children. Interestingly, data from WHO confirms the outcome of TB infection is usually different for an immunocompromised host, and thus, this project will look at comorbidity with diabetes. This would allow us to gain an in-depth understanding of how the immune system responds in tackling more than one disease and how this impacts the treatment and outcome of tuberculosis disease. Exploring TB and diabetes is important and timely, as data shows that in the next 10 years, the population of individuals with diabetes will double. This amounts to over 600 million people in the world, thus posing a major threat, as patients with diabetes have a higher mortality rate when infected with TB and have an increased likelihood of TB relapse. To this effect, building on the mathematical framework of Bowness et al. (2018), this Ph.D. project aims to use an individual-based model (also known as agent-based model) to develop a within-host model of TB infection in diabetic patients. The goal would be to understand the impact of type 2 diabetes on the severity of pulmonary tuberculosis (i.e., the differences in immune responses in diabetic patients to TB). Whilst we do not intend to establish novel drugs, this will involve the simulation of novel treatment strategies that could serve as personalised treatment therapy for tuberculosis patients with type 2 diabetes. Thus, our understanding of the altered disease dynamics of the TB/diabetes model will help inform our decision on the inclusion of different drug doses and combinations that will help with favourable treatment outcomes. For example, if it is found that diabetic patients typically have more lipid-rich (slower-growing) TB bacteria, more effective administration of antibiotics that are particularly known for targeting slow-growing bacteria will be simulated. In addition, provided there is available data on immunotherapy treatment, this will also be simulated. We will work with experimentalists, using laboratory data to calibrate the model, and clinicians who have access to clinical data to validate model findings. Specifically, this project will benefit from a collaboration with Dr. Muge Cevik (University of St Andrews/NHS Lothian), who is an expert on treating diabetic TB patients and is currently involved in a clinical trial in this area of research. Bowness, R., Chaplain, M.A., Powathil, G.G. and Gillespie, S.H., 2018. Modelling the effects of bacterial cell state and spatial location on tuberculosis treatment: Insights from a hybrid multiscale cellular automaton model. Journal of theoretical biology, 446, pp.87-100.