Hepatitis C virus Modelling In Vitro and In Vivo

Abstract

Experiments have shown that hepatitis C virus (HCV) infections in vitro disseminate both distally via the release and diffusion of cell-free virus through the medium, and locally via direct, cell-to-cell transmission. To determine the relative contribution of each mode of infection to HCV dissemination, we developed an agent-based model (ABM) that explicitly incorporates both distal and local modes of infection. The ABM tracks the concentration of extracellular infectious virus in the supernatant and the number of intracellular HCV RNA segments within each infected cell over the course of simulated in vitro HCV infections. By constraining our ABM parameters using experimental data, we found that direct, cell-to-cell infection accounts for 98% (85%–100%, 95% credible region) of infection events, making it the dominant mode of HCV dissemination in vitro. Yet, while HCV spread via cell-free virus contributes little to the total number of infection events in vitro, it plays a critical role in enhancing cell-to-cell HCV dissemination by providing access to distant, uninfected areas, away from the already established large infection foci. Mathematical modelling of HCV load decay under antiviral therapy has allowed for the determination of antiviral efficacy and other important parameters. Current mathematical models (MMs) of HCV infection are based on a set of ordinary differential equations (ODEs) and assume that infectious cell lifespans are exponentially distributed over time, meaning that every infected cell has an equal probability of dying at any time. Here, we introduce a new MM which: (1) allows for a realistic eclipse delay between the moment of cell infection and the release of new virus; and (2) considers both exponential and (log)normal distributed durations for infectious cell lifespan, wherein cells are assumed to continuously producing virus. Application of this MM to HCV load data for patients undergoing antiviral therapy leads to different conclusions when predicting parameter values.