Computer modeling designed for use in oncology identifies drug treatments for COVID-19


Computer modeling studies designed for use in oncology have been used to determine drug combinations suitable for the treatment of COVID-19

The use of computer modeling designed to understand cancer cells has been adapted to determine appropriate combination drug therapies for the management of COVID-19 according to research by a team from UCL Cancer Institute, University College London, UK.

Although the introduction of vaccines against COVID-19 has represented a major advance in the treatment of the disease, it will take literally millions of doses to vaccinate the world’s population and it will take a considerable time. At UCL, the researchers adopted an approach they had already used in oncology, to create models that capture key signaling differences that then allowed 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 validate their efficacy experimentally.

In the current study, the team adopted the same modeling technique to create a better understanding of the interaction between COVID-19 and host cells. They considered the early and later stages of virus infection and initially focused on producing a detailed web of the interaction between COVID-19 and lung epithelial cells. Once the viral-epithelial cell interaction was modeled, they screened thousands of drug combinations to identify drug therapies that might block important virus-host interactions relevant to viral replications or dysregulation of the immune response.

The first computer modeling identified that the combination of Camostat protease inhibitor and the PIKfyve inhibitor apilimodwere able to prevent viral replication and therefore have potential value in the early stages of the disease.

Computational modeling and later-stage COVID-19 infection

The later stage of COVID-19 infection is characterized by an inappropriate inflammatory response, so researchers have been looking for treatments that might dampen this response. They reported how the combination of ruxolitinib, an inhibitor of JAK1 and JAK2 protein kinases with the Iimmunosuppressive agent rapamycin has been predicted to be more effective against inflammation than either drug alone. However, a downside of the combination was that it also had to increase viral entry into cells.

Summarizing their results from computer modeling, the authors predicted that camostat and apilimod would be useful in suppressing viral entry and replication and therefore limit the range of target host cells that could be infected with COVID-19. Once the infection has 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 modeling is of enormous potential value in enabling rapid screening of thousands of compounds and preclinical evaluation of combinations suitable for different stages of disease progression.

Howell R et al. Executable network of SARS-CoV-2-host interaction predicts drug combination treatments NPJ Med Figure 2022


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