In silico design and optimisation of novel host-directed therapies

Project background

Host directed therapies augment the natural immune response to infection by supplementing host defence mechanisms or reducing excessive inflammation and can shorten the treatment period of anti-infective agents. These therapies can also provide an alternative where infections are unresponsive to anti-infective agents. Aspergillus fumigatus causes the most common lung fungal infection, Aspergillosis, and experimental data from murine models suggests pro-inflammatory cytokines play an important role in controlling this infection. A host directed therapy treating with the pro-inflammatory cytokine, IFNγ, has become an accepted treatment for patients unresponsive to anti-fungal drugs. For these therapies to be successful, a mechanistic understanding of pathogen activity and host immunity is required.

Why we funded it

This PhD Studentship aims to develop an in silico model of underlying mechanisms of IFNγ therapy for A. fumigatus infection as a replacement for mice used in these studies.

Dr Tanaka and Dr Bignell have previously developed a simulated dose-finding experiment, used to validate predictions and inform experiments. This replaced 2000 animals that would have been required reducing the overall animal requirements for the study by 98.5%. It is estimated a similar level of reduction can be achieved through the proposed computational approach regarding the studies of IFNγ therapy. This will replace the use of up to 400 animals per annum in the Bignell laboratory.

Research methods

Routine use of simulations in infectious disease research has the potential to replace initial experiments by quantitatively assessing the impact of infection and identifying parameters which most potently influence the infection outcome. Additionally, it can reduce numbers of animals in further experiments by calculating minimal sample sizes required for the validation of simulation data. The proposed in silico model will be used to determine the optimal treatment strategy for IFNγ therapy of Aspergillosis in patient-specific cases. To demonstrate the scientific utility of the computational approach, the predictions of the optimal treatment strategies will be validated in mice. The simulation programmes developed in this proposal will then be incorporated into a prototype graphical-user-interface simulator suitable for use in the fungal research community.

Eyerich K et al. (2019). Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council. J Allergy Clin Immunol 143(1):36-45. doi: 10.1016/j.jaci.2018.10.033

Tanaka G et al. (2018). Bifurcation analysis of a mathematical model of atopic dermatitis to determine patient-specific effects of treatments on dynamic phenotypes. Journal of Theoretical Biology 448:66-79. doi: 10.1016/j.jtbi.2018.04.002

Christodoulides P et al. (2017). Computational design of treatment strategies for proactive therapy on atopic dermatitis using optimal control theory. Phil. Trans. R. Soc. 375:20160285. doi: 10.1098/rsta.2016.0285

Domínguez-Hüttinger E et al. (2017). Mathematical Modeling of Streptococcus pneumoniae Colonization, Invasive Infection and Treatment. Front. Physiol. 8:115. doi: 10.3389/fphys.2017.00115

Domínguez-Hüttinger E et al. (2017).  Mathematical modeling of atopic dermatitis reveals “double-switch” mechanisms underlying 4 common disease phenotypes. Journal of Allergy and Clinical Immunology 139(6):1861-1872. doi: 10.1016/j.jaci.2016.10.026

Back to top
PhD Studentship




Imperial College London

Grant reference number


Award date

Oct 2017 - Mar 2021

Grant amount