Computer modelling studies designed for use in oncology have been used to determine drug combinations suitable for the treatment of COVID-19
The use of a computer modelling designed to understand cancer cells, has been adapted to determine suitable drug combination therapies for the management of COVID-19 according to research by a team from the UCL Cancer Institute, University College London, UK.
Although the introduction of vaccines for COVID-19 represented a major advance in the treatment of the disease, literally millions of doses will be required to vaccinate the world’s population and this will take a considerable amount of time.
At UCL, the researchers adopted an approach and which they had previously used in oncology, to create models which capture key differences in signalling that later enabled them to accurately predict the unique proteomic changes and phenotypic responses of each cell line.
With this technique, the models created can then be used to tailor combination therapies to individual cell lines and successfully validated their efficacy experimentally.
In the present study, the team adopted the same modelling technique to create a better understanding of the interaction between COVID-19 and host cells. They considered both early and later stages of infection with the virus and initially focused on producing a detailed network of the interaction between COVID-19 and lung epithelial cells.
Once the viral-epithelial cell interaction had been modelled, they screened thousands of drug combinations to identify drug therapies which could block important viral-host interactions relevant to viral replications or the dysregulation of the immune response.
The first computer modelling identified that the combination of the protease inhibitor Camostat and the PIKfyve inhibitor apilimod, were able to prevent viral replication and thus of potential value in the early stages of the disease.
Computer modelling and later stage COVID-19 infection
The later stage of infection with COVID-19 is characterised by an inappropriate inflammatory response hence the researchers looked for treatments that might be able to attenuate this response. They reported on how the combination of ruxolitinib, an inhibitor of the JAK1 and JAK2 protein kinases with the immunosuppressive agent rapamycin was predicted to be more effective against inflammation than either drug alone. However, a downside to the combination was that it was also predicted to increase viral entry to cells.
In summarising their findings from the computer modelling, the authors predicted that camostat and apilimod would be of value in suppressing viral entry and replication and therefore limit the range of target host cells which could be infected by COVID-19.
Once the infection had taken hold of the host and progressed to a more severe stage, ruxolitinib and rapamycin would be a suitable combination to reduce inflammation.
They concluded that computational modelling is of enormous potential value by enabling the rapid screening of thousands of compounds and pre-clinical evaluation of combinations tailored to different stages of disease progression.
Howell R et al. Executable network of SARS-CoV-2-host interaction predicts drug combination treatments NPJ Digit Med 2022