Naturally Occurring Resistance-Associated Variants of Hepatitis C Virus Protease Inhibitors in Poor Responders to Pegylated Interferon-Ribavirin

Abstract

The pretherapeutic presence of protease inhibitor (PI) resistance-associated variants (RAVs) has not been shown to be predictive of triple-therapy outcomes in treatment-naive patients. However, they may influence the outcome in patients with less effective pegylated interferon (pegIFN)-ribavirin (RBV) backbones. Using hepatitis C virus (HCV) population sequence analysis, we retrospectively investigated the prevalence of baseline nonstructural 3 (NS3) RAVs in a multicenter cohort of poor IFN-RBV responders (i.e., prior null responders or patients with a viral load decrease of <1 log IU/ml during the pegIFN-RBV lead-in phase). The impact of the presence of these RAVs on the outcome of triple therapy was studied. Among 282 patients, the prevalances (95% confidence intervals) of baseline RAVs ranged from 5.7% (3.3% to 9.0%) to 22.0% (17.3% to 27.3%), depending to the algorithm used. Among mutations conferring a >3-fold shift in 50% inhibitory concentration (IC50) for telaprevir or boceprevir, T54S was the most frequently detected mutation (3.9%), followed by A156T, R155K (0.7%), V36M, and V55A (0.35%). Mutations were more frequently found in patients infected with genotype 1a (7.5 to 23.6%) than 1b (3.3 to 19.8%) (P = 0.03). No other sociodemographic or viroclinical characteristic was significantly associated with a higher prevalence of RAVs. No obvious effect of baseline RAVs on viral load was observed. In this cohort of poor responders to IFN-RBV, no link was found with a sustained virological response to triple therapy, regardless of the algorithm used for the detection of mutations. Based on a cross-study comparison, baseline RAVs are not more frequent in poor IFN-RBV responders than in treatment-naive patients and, even in these difficult-to-treat patients, this study demonstrates no impact on treatment outcome, arguing against resistance analysis prior to treatment.

FIG 1

Phylogenetic tree comparing the 282 NS3 sequences from our study population with 10 reference strains. A 550-nt long fragment was analyzed (nt 3420 to 3970, according to H77 numbering). Phylogenetic analysis was conducted with MAFFT (http://mafft.cbrc.jp/alignment/server/) (22) using the neighbor-joining method, the substitution model of Jukes-Cantor, and a bootstrap resampling of 1,000. Branches are colored purple for subtype 1b and blue for 1a. Nodes are shown in green when at least one mutation was detected (according to algorithm 3) and the patient achieved an SVR, in yellow when a mutation was detected and the patient did not achieve an SVR, and in red when no mutation was detected and the patient did not achieve an SVR.

FIG 2

Prevalence of mutations at baseline according to the algorithm used for analysis, and the virological outcome (SVR or no SVR) of triple therapy using a protease inhibitor. For each algorithm, the open circles correspond to the prevalence of mutation(s) with the 95% confidence interval for each algorithm for patients achieving a sustained virological response to triple therapy. The closed circles correspond to the prevalence of mutation(s) at baseline for patients who responded to triple therapy.

FIG 3

Baseline viral loads in patients with and without NS3 RAVs, according to algorithm 2. The no-mutation group included 220 patients, and the mean ± standard deviation (SD) viral load was 6.03 ± 0.05 log IU/ml. The mutation group contains 62 patients, and the mean ± SD viral load was 5.96 ± 0.13 log IU/ml. The estimated P value (Mann-Whitney U test) shows no significant difference between the two groups (P = 0.9121).