In silico modelling of novel therapies for Invasive Aspergillosis
File(s)
Author(s)
Hameed, Tara
Type
Thesis
Abstract
Invasive aspergillosis (IA) is a critical infection that is characterised by uncontrolled fungal growth in the lungs. Locating novel therapies for IA is of increasing importance due to rising antifungal resistance. The cytokine Interferon Gamma (IFNg) demonstrates promise in some clinical trials but its main modes of action, whether that be immunomodulatory or as a direct antifungal, remain unknown. In silico (computational) modelling has been shown to be a powerful tool that can aid understanding of complex disease dynamics, with no added animal mortality.
In this thesis, we used in silico modelling to investigate possible IFNg modes of action in IA using three strategies. First, we proposed a simple in silico mechanistic model of IA, which would be needed for more complex models involving IFNg. A large-scale simulation study indicated a low feasibility of fitting this model to current data using a standard mechanistic modelling framework. We next assessed IFNg's potential as an immunotherapy using non-mechanistic Bayesian analysis of data from an in vivo experiment on IA. Although IFNg was indicated to be associated with altered immune cell and fungal burden levels, we were unable to conclude if IFNg acted as an immunotherapy. Finally, using a Bayesian mechanistic model that could infer fungal growth rates from optical density (OD) data, we established that IFNg is unlikely to inhibit fungal growth. Our model inferred rates that were less biased by the OD measurement process compared to commonly used models in literature.
Overall, the thesis examined the viability of using in silico models to investigate IFNg therapy for IA. We demonstrated that in silico models could investigate novel antifungals and suggested steps to support in silico modelling for testing novel immunotherapies. The work thereby creates a foundation for the development of future in silico models of novel therapies for IA.
In this thesis, we used in silico modelling to investigate possible IFNg modes of action in IA using three strategies. First, we proposed a simple in silico mechanistic model of IA, which would be needed for more complex models involving IFNg. A large-scale simulation study indicated a low feasibility of fitting this model to current data using a standard mechanistic modelling framework. We next assessed IFNg's potential as an immunotherapy using non-mechanistic Bayesian analysis of data from an in vivo experiment on IA. Although IFNg was indicated to be associated with altered immune cell and fungal burden levels, we were unable to conclude if IFNg acted as an immunotherapy. Finally, using a Bayesian mechanistic model that could infer fungal growth rates from optical density (OD) data, we established that IFNg is unlikely to inhibit fungal growth. Our model inferred rates that were less biased by the OD measurement process compared to commonly used models in literature.
Overall, the thesis examined the viability of using in silico models to investigate IFNg therapy for IA. We demonstrated that in silico models could investigate novel antifungals and suggested steps to support in silico modelling for testing novel immunotherapies. The work thereby creates a foundation for the development of future in silico models of novel therapies for IA.
Version
Open Access
Date Issued
2023-04-03
Date Awarded
01/09/2023
License URL
Advisor
Tanaka, Reiko
Sponsor
The Wellcome Trust (London, England)
Grant Number
215358/Z/19/Z
Publisher Department
Bioengineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)