Treatment of hepatitis C virus infection with interferon and small molecule direct antivirals: viral kinetics and modeling

Abstract

Hepatitis C virus (HCV) infection remains a threat to global public health. Treatment with pegylated interferon (IFN) plus ribavirin leads to a sustained virologic response in about 50% of patients. New therapies using direct antiviral agents have the potential to cure patients unresponsive to IFN-based therapies. Mathematical modeling has played an important role in studying HCV kinetics. Using models, one can evaluate the effectiveness of new treatment agents, estimate important parameters that govern virus-host interactions, explore possible mechanisms of drug action against HCV, investigate the development of drug resistance, and study quasispecies dynamics during therapy. Here we review our current knowledge of HCV kinetics under IFN-based therapy and newly developed antiviral agents specifically targeted to attack HCV, and show how mathematical models have helped to improve our understanding of HCV infection and treatment.

Figure 1

Biphasic HCV RNA decline after IFN treatment of chronic hepatitis C. Before initiation of therapy, the viral load is at the set-point level. The first phase decline lowers HCV RNA levels 1-2 logs during the first 1-2 days of treatment. It is followed by a slower second phase viral decline during the subsequent weeks of therapy.

Figure 2

Schematic representation of the basic viral dynamic model. There are three variables: target cells (T), productively infected cells (I), and free virus (V). s, r_T and d are the recruitment rate, maximum proliferation rate and death rate of target cells, respectively; β is the infection rate of target cells by virus; r_I is the maximum proliferation rate of infected cells; δ is the death rate of infected cells; p is the viral production rate; c is the viral clearance rate; ε is the drug efficacy in reducing viral production; η is the drug efficacy in blocking viral infection.

Figure 3

The effects of RBV on HCV RNA decline in combination therapy with IFN. When IFN efficacy is small (ε=0.5), RBV enhances the second phase decline in a dose-dependent manner (ρ is the drug efficacy of RBV). When IFN efficacy is large (ε=0.95), RBV has negligible influence on both the first and the second phases.

Figure 4

HCV life cycle. (1) After viral binding, entry and fusion, nucleocapsid enters into the cytoplasm of the host cell; (2) Nucleocapsid releases a positive-strand RNA genome (uncoating); (3) HCV translation for synthesis of a large polyprotein, which is then cleaved by enzymes for generation of viral proteins; (4) HCV RNA replication, catalyzed by the NS5B polymerase, a product of the polyprotein cleavage; (5) Packaging and assembly of progeny virions; (6) Viral release.

Figure 5

Drug resistance profiles after monotherapy with telaprevir. The four HCV genotype 1a patients experienced viral rebound during a two-week dosing period. The limit of detection for the sequencing assay is 100 IU/mL, and the limit of HCV RNA detection is 10 IU/mL. Note here day 0 is the time of initiation of telaprevir therapy, whereas in the original study, the first dose of telaprevir was administered at day 2.

Figure 6

Schematic representation of the two-strain model. In addition to target cells (T), there are four variables: drug sensitive virus (V_s), drug resistant virus (V_r), cells infected with drug sensitive virus (I_s), and cells infected with resistant virus (I_r). p_s and p_r are the viral production rates of the two strains; ε_s and ε_r are the drug efficacies of telaprevir in reducing viral production; μ is the mutation rate. The other parameters are the same as those in Fig. 2.