Executable network of SARS-CoV-2-host interaction predicts drug combination treatments

Area of Science:

• Computational biology
• Virology
• Pharmacology

Background:

• The COVID-19 pandemic necessitates urgent development of effective and affordable treatments.
• Drug repurposing of approved medications offers a viable strategy for rapid therapeutic intervention.
• Identifying optimal drug combinations and their efficacy at different disease stages remains a challenge.

Purpose of the Study:

• To develop the first disease-stage executable signaling network model of SARS-CoV-2-host interactions.
• To predict effective repurposed drug combinations for early- and late-stage severe COVID-19.
• To identify novel therapeutic strategies for COVID-19 treatment.

Main Methods:

• Construction of a disease-stage executable signaling network model for SARS-CoV-2-host interactions.
• In silico screening of 9870 drug pairs targeting 140 potential host factors.
• Experimental validation of predicted drug combinations in human Caco-2 cells.

Main Results:

• Identification of nine novel repurposed drug combinations for COVID-19 treatment.
• Prediction of Camostat and Apilimod as a highly promising combination for early-stage severe disease.
• Experimental validation of Camostat and Apilimod in suppressing SARS-CoV-2 replication in vitro.

Conclusions:

• Executable mechanistic modeling is a powerful tool for rapid pre-clinical evaluation of combination therapies.
• The developed model provides a novel, expandable resource for ongoing COVID-19 research and pandemic response.
• Drug combinations tailored to disease progression can significantly enhance treatment efficacy.