In silico modelling of in-host tuberculosis dynamics : towards building the virtual patient

Abstract

Tuberculosis (TB) accounts for over 1 million deaths each year, despite effective treatment regimens being available. Improving the treatment of TB will require new regimens, each of which will need to be put through expensive and lengthy clinical trials, with no guarantee of success. The ability to predict which of many novel regimens to progress through the clinical trial stages would be an important tool to TB research. as current models are constrained in their ability to reflect the whole spectrum of pathophysiology, particularly as there remains uncertainty around the events that occur.

This thesis explores the use of computational techniques to model a pulmonary human TB infection. We introduce the first in silico model of TB occurring over the whole lung that incorporates both the environmental heterogeneity that is exhibited within different spatial regions of the organ, and the different bacterial dissemination routes, in order to understand how bacteria move during infection and why post-primary disease is typically localised towards the apices of the lung.

Our results show that including environmental heterogeneity within the lung can have profound effects on the results of an infection, by creating a region towards the apex which is preferential for bacterial proliferation. We also present a further iteration of the model, whereby the environment is made more granular to better understand the regions which are afflicted during infection, and show how sensitivity analysis can determine the factors that contribute most to disease outcomes.

We show that in order to simulate TB disease within a human lung with sufficient accuracy, better understanding of the dynamics is required. The model presented in this thesis is intended to provide insight into these complicated dynamics, and thus make progress towards an end goal of a virtual clinical trial, consisting of a heterogeneous population of synthetic virtual patients.