A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness

Abstract

Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding differences in disease outcomes and optimizing therapeutic interventions. We present a multiscale model and simulation of an epithelial tissue infected by a virus, a simplified cellular immune response and viral and immune-induced tissue damage. The model exhibits basic patterns of infection dynamics: widespread infection, slowed infection, recurrence, containment and clearance. Inhibition of viral internalization and faster immune-cell recruitment promote containment of infection. Fast viral internalization and slower immune response lead to uncontrolled spread of infection. Because antiviral drugs can have side effects at high doses and show reduced clinical effectiveness when given later during the course of infection, we studied the effects on infection progression of both treatment potency (which combines drug effectiveness and dosage) and time-of-first treatment after infection. Simulation of a drug which reduces the replication rate of viral RNA shows that even a low potency therapy greatly decreases the total tissue damage and virus burden when given near the beginning of infection. However, even a high potency therapy rapidly loses effectiveness when given later near the time of peak viral load in the untreated case. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control of the virus (treatment success), while others show rapid infection of all epithelial cells in the simulated tissue subregion (treatment failure). This switch between a regime of consistent treatment success and failure occurs as the time of treatment increases. However, stochastic variations in viral spread mean that high potency treatments at late times are occasionally effective. The model is open-source and modular, allowing rapid development and extension of its components by groups working in parallel.