Multiscale model of hepatitis C virus dynamics in plasma and liver following combination therapy

Abstract

This work explores the application of a physiologically structured population (PSP) framework in modeling hepatitis C virus (HCV) kinetics. To do so, a model was developed for the viral RNA load in plasma and liver as observed in 15 patients treated with a combination therapy of pegylated interferon, ribavirin, and telaprevir. By including both intracellular and extracellular processes of the HCV lifecycle, the model provided a description of the treatment effect on the intracellular HCV lifecycle. Combining PSP models with a nonlinear mixed effects approach in a single model permits a natural inclusion of the direct-acting antiviral effect on intracellular processes, which can then be integrated with the viral kinetics within the host while accounting for the interindividual variability between patients. This should allow an exploration of the treatment effect within the entire chronic HCV-infected population.

FIGURE 1

Individual patient hepatocyte vRNA distributions. The density histograms of observed vRNA concentration in a sample of hepatocytes for a patient with hepatitis C virus is overlaid with a curve representing the probability density curve for the hepatocyte vRNA distribution at steady state (Equation 17). The distribution parameters were obtained by the maximum likelihood fitting of Equation (17) to the vRNA data reported in de Roos

FIGURE 2

Log10 transformed plasma concentrations of hepatitis C vRNA in individual patients. Symbols represent the observed data, and the lines are model‐fitted profiles using microscopic Models (1) to (11). The bars indicate the observations below the limit of quantification. Data were obtained from Canini and Perelson

FIGURE 3

Log10 transformed liver concentrations of hepatitis C vRNA in individual patients. Symbols represent the observed data, and the lines are model‐fitted profiles using microscopic Models (1) to (11). The bars indicate the observations below the limit of quantification. Data were obtained from Canini and Perelson

FIGURE 4

Simulations assessing the impact of inhibition of vRNA secretion versus inhibition of vRNA replication on the viral load time course in plasma (upper) and liver (lower). The values of treatment inhibition parameters ${\epsilon }_{\alpha }$ and ${\epsilon }_p$ are shown in the legend. The remaining parameter values used for simulations are presented in Table 2

FIGURE 5

Simulations assessing the impact of inhibition of vRNA secretion vs. inhibition of vRNA replication on the distribution of vRNA in hepatocytes at various times from the beginning of treatment. The black lines correspond to ${\epsilon }_{\alpha }=1.0$ and ${\epsilon }_p=0.999$ (scenario A), red lines to ${\epsilon }_{\alpha }=0$ and ${\epsilon }_p=0.999$ (scenario B), and blue lines to to ${\epsilon }_{\alpha }=1.0$ and ${\epsilon }_p=0$ (scenario C). The dashed line represents the steady‐state distribution. The vertical lines indicate the mean vRNA value for the corresponding distribution. The remaining parameter values used for simulations are presented in Table 2. The steady‐state distribution overlaps with the black lines