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 disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.

Advancing therapies for viral infections using mechanistic computational models of the dynamic interplay between the virus and host immune response

“…From left to right: (1) top: Schematic of a whole body, bottom: PBPK model of drug absorption, distribution, metabolism, and excretion, (2) top: Lung and Lymph node, bottom: model of flow, transport and response in a lymph node (adapted from Ref. [ 16 ]), (3) top: Infection and immune response in a lung epithelial tissue, bottom: multi-cellular simulation of virus, target cells and immune cells in a patch of lung epithelium [ 17 • ]. (4) top: Viral life cycle inside a host cell, bottom: multiscale model of influenza A virus infection (adapted from Ref.…”

“…In cellular automaton (CA) and agent-based models (ABM), cells are discrete and occupy explicit volumes in space, and chemicals are expressed as concentration fields. CA and ABMs can explore the effects of spatial heterogeneity on the progression of infection, immune response and therapy [ 17 • , 39 ], and improve estimates of parameters and their typical ranges of variation for the non-spatial models we described above.…”

“…Figure 1 shows a tissue-scale agent based multicellular simulation of COVID infection and immune response (‘tissue level’, bottom row). This simulation represents the dynamic evolution of a small (typically 1 mm × 1 mm) patch of host tissue, the extracellular viral and cytokine concentrations, and various immune-cell types and their functions [ 17 • , 39 ]. Individual host cells contain independent models of viral entry, replication, and release.…”

“…Models of this type can explore the effects of stochasticity at the subcellular, cell and patch level in determining local outcomes and the effects of viral spread, interferon, antiviral and immune modulators. These models are often coupled to pharmacodynamic (PD) and PBPK models of therapeutics and to models of lymph-node response to allow more detailed understanding of heterogeneity in patient response and treatment optimization [ 17 • , 36 • ]. They can also be coupled to larger-scale spatial models of transport within and between organs, as has been done for bacterial infections like tuberculosis [ 40 • ], or generated from calibrated non-spatial models, as has been done for influenza infections [ 41 , 42 ].…”

Generation of multicellular spatiotemporal models of population dynamics from ordinary differential equations, with applications in viral infection

“…The ability to derive cell-based, spatiotemporal models from ordinary differential equation (ODE) models would enhance the utility of both types of models. Cell-based, spatiotemporal models can explicitly describe cellular and spatial mechanisms neglected by ODE models that affect the emergent dynamics and properties of multicellular systems, such as the influence of dynamic aggregate shape on diffusion-limited growth dynamics [ 16 ] and individual infected cells on the progression of viral infection [ 17 ]. Likewise, ODE models can inform cell-based, spatiotemporal models with efficient parameter fitting to experimental data, and can appropriately describe dynamics at coarser scales and distant locales with respect to a particular multicellular domain of interest (e.g., the population dynamics of a lymph node when explicitly modeling local viral infection).…”