In pursuit of the antifungal basis of recombinant interferon gamma immunotherapy: a quantitative approach
File(s)
Author(s)
Motsi, Natasha
Type
Thesis
Abstract
Invasive aspergillosis, an acute respiratory disease caused by the fungus Aspergillus fumigatus, is a devastating infection resulting in high morbidity and mortality. Therapeutic options are limited due to adverse effects caused by the few available antifungal drugs and by recent increases in antifungal resistance.
This research aimed to develop an in silico toolkit for optimising adjunctive antifungal immunotherapy, using rIFNγ as a test case. A mechanistic mathematical model of host-mediated fungal clearance was created and parameterised through extensive literature review. Mathematical simulations revealed gaps in the available literature which limited model development. To rectify this, innovations were developed for the quantitative study of fungal growth rates and approximation of host interactions under in vitro and in vivo conditions, including methods for quantitation of fungal burden from histological sections, quantitative in vitro methods to measure growth of A. fumigatus spores and hyphae, and an ultra-high sensitivity qPCR methodology for analysis of fungal burden in low-dose models of pulmonary aspergillosis.
Multiple in vitro analyses of fungal growth were used to discern the directly microbicidal effect of rIFNγ upon A. fumigatus spores and hyphae. Refuting previous claims of an anti-hyphal mode of action, rIFNγ demonstrated anti-conidial activity, but no effect on growing hyphae. The implications of this finding in the context of early versus late stage interventions with rIFNγ were examined by studying of disease progression in two murine models of invasive aspergillosis.
Timing of rIFNγ administration significantly affected antifungal efficacy, as did the method of immunosuppression. Leukopenic mice receiving late rIFNγ therapy demonstrated significant reductions in fungal burden. Immunophenotyping revealed that rIFNγ-mediated fungal clearance is qualitative, likely involving immune cell receptor expression and the expansion of antigen-presenting cells.
This interdisciplinary approach highlights the potential for optimising antifungal immunotherapy through mathematical modelling and innovative quantitative methods, ultimately improving treatment outcomes for invasive aspergillosis.
This research aimed to develop an in silico toolkit for optimising adjunctive antifungal immunotherapy, using rIFNγ as a test case. A mechanistic mathematical model of host-mediated fungal clearance was created and parameterised through extensive literature review. Mathematical simulations revealed gaps in the available literature which limited model development. To rectify this, innovations were developed for the quantitative study of fungal growth rates and approximation of host interactions under in vitro and in vivo conditions, including methods for quantitation of fungal burden from histological sections, quantitative in vitro methods to measure growth of A. fumigatus spores and hyphae, and an ultra-high sensitivity qPCR methodology for analysis of fungal burden in low-dose models of pulmonary aspergillosis.
Multiple in vitro analyses of fungal growth were used to discern the directly microbicidal effect of rIFNγ upon A. fumigatus spores and hyphae. Refuting previous claims of an anti-hyphal mode of action, rIFNγ demonstrated anti-conidial activity, but no effect on growing hyphae. The implications of this finding in the context of early versus late stage interventions with rIFNγ were examined by studying of disease progression in two murine models of invasive aspergillosis.
Timing of rIFNγ administration significantly affected antifungal efficacy, as did the method of immunosuppression. Leukopenic mice receiving late rIFNγ therapy demonstrated significant reductions in fungal burden. Immunophenotyping revealed that rIFNγ-mediated fungal clearance is qualitative, likely involving immune cell receptor expression and the expansion of antigen-presenting cells.
This interdisciplinary approach highlights the potential for optimising antifungal immunotherapy through mathematical modelling and innovative quantitative methods, ultimately improving treatment outcomes for invasive aspergillosis.
Version
Open Access
Date Issued
2022-11
Date Awarded
2024-07
Copyright Statement
Creative Commons Attribution NoDerivatives Licence
License URL
Advisor
Tanaka, Reiko
Bignell, Elaine
Sponsor
National Centre for the Replacement, Refinement, and Reduction of Animals in Research (Great Britain)
Grant Number
NC/P00217X/1
Publisher Department
Bioengineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)