Rapid emergence of protease inhibitor resistance in hepatitis C virus

Abstract

About 170 million people worldwide are infected with hepatitis C virus (HCV). The current standard therapy leads to sustained viral elimination in only approximately 50% of the treated patients. Telaprevir, an HCV protease inhibitor, has substantial antiviral activity in patients with chronic HCV infection. However, in clinical trials, drug-resistant variants emerge at frequencies of 5 to 20% of the total virus population as early as the second day after the beginning of treatment. Here, using probabilistic and viral dynamic models, we show that such rapid emergence of drug resistance is expected. We calculate that all possible single- and double-mutant viruses preexist before treatment and that one additional mutation is expected to arise during therapy. Examining data from a clinical trial of telaprevir therapy for HCV infection in detail, we show that our model fits the observed dynamics of both drug-sensitive and drug-resistant viruses and argue that therapy with only direct antivirals will require drug combinations that have a genetic barrier of four or more mutations.

Figure 1

Schematic representation of the viral dynamic model. There are five variables: target cells (T), 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). s, ρ_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; δ is the death rate of infected cells; and 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 from the drug-sensitive to drug-resistant strain; and c is the viral clearance rate. The red crosses represent the effect of treatment in blocking viral production.

Figure 2

Model predictions of the mutant frequency and viral load decay profiles after drug administration. (A) Predicted frequency of the mutant virus (V36A/M) that induces ~3.5-fold increase in IC₅₀ from wild-type and has a relative fitness of r ≈ 0.98 (13). (B) The two-phase decrease of both drug-sensitive virus (green dashed) and resistant V36A/M (blue dashed) after drug dosing. t_s and t_r represent the time at which drug-sensitive and drug-resistant virions start the second-phase decline, respectively, and t_r is always smaller than t_s (14). (C) Predicted frequency of the mutant virus (A156V/T) that induces ~466-fold increase in IC₅₀ and has a relative fitness of r ≈ 0.45 (13). (D) As for B, except with the variant A156V/T. Parameters used are c = 6.2 day^-1 (16), δ = 0.14 day^-1 (16), μ = 10⁻⁴ per copied nucleotide (17), ε_s = 0.9997 (23), and the Hill coefficient h = 2 (64).

Figure 3

Comparison between model predictions and patient data during telaprevir monotherapy. We employed the pretreatment steady-state values (14) as the initial conditions of the model. We fitted V_s (green dashed) and V_r (blue dashed) in Eq. (1) to the drug-sensitive (green triangle) and drug-resistant (blue diamond) viral load data simultaneously, where V_r is the sum of viral loads of all drug-resistant strains. Since we ignored the drug-sensitive viral load data when they were below the detection limit of the sequencing assay (< 5%) (11), we included fitting V_s + V_r (red solid) to the total viral load data (red square). The best-fit parameter values for each patient are listed in Table 2. Note here day 0 is the time of initiation of telaprevir therapy, whereas in the original study telaprevir therapy was started at day 2 (11, 12).

Figure 4

Comparison between model predictions and patient data during combination therapy. We fitted the model Eq. (S1) in (14)] to the viral load data from patients receiving both PEG-IFN-α-2a and telaprevir for 14 days. This model generalizes Eq. (1) by incorporating an effect of IFN in partially blocking viral production. The fitting procedure and the symbols used are the same as those in Fig. 3. The best-fit parameter values for each patient are listed in Table 3. Note that the two fits of the drug-sensitive (green dashed) and total viral load (red solid) overlap in a few patients.