METHODS: A cross-sectional study was conducted to assess the correlation between HCV Ag and HCV RNA and the cost implications of different diagnostic algorithms to diagnose active HCV infection using Anti-HCV, HCV Ag, and HCV RNA. Pre-dialysis blood was tested for both HCV Ag and HCV RNA. HCV Ag was tested with Abbott ARCHITECT HCV Ag test.

METHODS: Cytokines were measured using a commercial Bio-plex Pro Human Cytokine Grp I Panel 17-plex kit (BioRad, Hercules, CA, USA). Inflammation was assessed by measuring an array of plasma cytokines, and phenotypic alterations in CD4+ T cells including circulating Tfh cells, CD8+ T cells, and TCR iVα7.2+ MAIT cells in chronic HBV, HCV, and HIV-infected patients and healthy controls. The cells were characterized based on markers pertaining to immune activation (CD69, ICOS, and CD27) proliferation (Ki67), cytokine production (TNF-α, IFN-γ) and exhaustion (PD-1). The cytokine levels and T cell phenotypes together with cell markers were correlated with surrogate markers of disease progression.

RESULTS: The activation marker CD69 was significantly increased in CD4+hi T cells, while CD8+ MAIT cells producing IFN-γ were significantly increased in chronic HBV, HCV and HIV infections. Six cell phenotypes, viz., TNF-α+CD4+lo T cells, CD69+CD8+ T cells, CD69+CD4+ MAIT cells, PD-1+CD4+hi T cells, PD-1+CD8+ T cells, and Ki67+CD4+ MAIT cells, were independently associated with decelerating the plasma viral load (PVL). TNF-α levels showed a positive correlation with increase in cytokine levels and decrease in PVL.

METHODS: In this single-arm, open-label, phase 3 trial, we recruited patients from 38 sites across China, Thailand, Vietnam, Singapore, and Malaysia, who were chronically infected with HCV genotypes 1-6, and were HCV treatment-naive or treatment-experienced, either without cirrhosis or with compensated cirrhosis. Patients self-administered a combined sofosbuvir (400 mg) and velpatasvir (100 mg) tablet once daily for 12 weeks. The primary efficacy endpoint was sustained virological response, defined as HCV RNA less than 15 IU/mL at 12 weeks after completion of treatment (SVR12), assessed in all patients who received at least one dose of study drug. The primary safety endpoint was the proportion of adverse events leading to premature discontinuation of study drug. This trial is registered with ClinicalTrials.gov, number NCT02671500, and is completed.

FINDINGS: Between April 14, 2016, and June 30, 2017, 375 patients were enrolled in the study, of whom 374 completed the full treatment course and one discontinued treatment. Overall, 362 (97% [95% CI 94-98]) of 375 patients achieved SVR12. Among 42 patients with HCV genotype 3b, all of whom had baseline resistance-associated substitutions in NS5A, 25 (89% [95% CI 72-98]) of 28 patients without cirrhosis and seven (50% [23-77]) of 14 patients with cirrhosis achieved SVR12. The most common adverse events were upper respiratory tract infection (36 [10%] patients) and headache (18 [5%] patients). There were no discontinuations due to adverse events. Serious adverse events were reported in three (1%) patients, none of which was judged to be related to sofosbuvir-velpatasvir treatment.

INTERPRETATION: Consistent with data from other phase 3 studies, single-tablet sofosbuvir-velpatasvir for 12 weeks is an efficacious and safe treatment for Asian patients with chronic HCV infection, but might have lower efficacy in those infected with HCV genotype 3b and with cirrhosis.

METHODS: Blips were defined as detectable VL (≥ 50 copies/mL) preceded and followed by undetectable VL (<50 copies/mL). Virological failure (VF) was defined as two consecutive VL ≥50 copies/ml. Cox proportional hazard models of time to first VF after entry, were developed.

RESULTS: 5040 patients (AHOD n = 2597 and TAHOD n = 2521) were included; 910 (18%) of patients experienced blips. 744 (21%) and 166 (11%) of high- and middle/low-income participants, respectively, experienced blips ever. 711 (14%) experienced blips prior to virological failure. 559 (16%) and 152 (10%) of high- and middle/low-income participants, respectively, experienced blips prior to virological failure. VL testing occurred at a median frequency of 175 and 91 days in middle/low- and high-income sites, respectively. Longer time to VF occurred in middle/low income sites, compared with high-income sites (adjusted hazards ratio (AHR) 0.41; p<0.001), adjusted for year of first cART, Hepatitis C co-infection, cART regimen, and prior blips. Prior blips were not a significant predictor of VF in univariate analysis (AHR 0.97, p = 0.82). Differing magnitudes of blips were not significant in univariate analyses as predictors of virological failure (p = 0.360 for blip 50-≤1000, p = 0.309 for blip 50-≤400 and p = 0.300 for blip 50-≤200). 209 of 866 (24%) patients were switched to an alternate regimen in the setting of a blip.

METHODS: Patients testing HBs antigen (Ag) or HCV antibody (Ab) positive within enrollment into TAHOD were considered HBV or HCV co-infected. Factors associated with HBV and/or HCV co-infection were assessed by logistic regression models. Factors associated with post-ART HIV immunological response (CD4 change after six months) and virological response (HIV RNA <400 copies/ml after 12 months) were also determined. Survival was assessed by the Kaplan-Meier method and log rank test.

RESULTS: A total of 7,455 subjects were recruited by December 2012. Of patients tested, 591/5656 (10.4%) were HBsAg positive, 794/5215 (15.2%) were HCVAb positive, and 88/4966 (1.8%) were positive for both markers. In multivariate analysis, HCV co-infection, age, route of HIV infection, baseline CD4 count, baseline HIV RNA, and HIV-1 subtype were associated with immunological recovery. Age, route of HIV infection, baseline CD4 count, baseline HIV RNA, ART regimen, prior ART and HIV-1 subtype, but not HBV or HCV co-infection, affected HIV RNA suppression. Risk factors affecting mortality included HCV co-infection, age, CDC stage, baseline CD4 count, baseline HIV RNA and prior mono/dual ART. Shortest survival was seen in subjects who were both HBV- and HCV-positive.

METHODS: Five graph models were fit using data from 1574 people who inject drugs in Hartford, CT, USA. We used a degree-corrected stochastic block model, based on goodness-of-fit, to model networks of injection drug users. We simulated transmission of HCV and HIV through this network with varying levels of HCV treatment coverage (0%, 3%, 6%, 12%, or 24%) and varying baseline HCV prevalence in people who inject drugs (30%, 60%, 75%, or 85%). We compared the effectiveness of seven treatment-as-prevention strategies on reducing HCV prevalence over 10 years and 20 years versus no treatment. The strategies consisted of treatment assigned to either a randomly chosen individual who injects drugs or to an individual with the highest number of injection partners. Additional strategies explored the effects of treating either none, half, or all of the injection partners of the selected individual, as well as a strategy based on respondent-driven recruitment into treatment.

FINDINGS: Our model estimates show that at the highest baseline HCV prevalence in people who inject drugs (85%), expansion of treatment coverage does not substantially reduce HCV prevalence for any treatment-as-prevention strategy. However, when baseline HCV prevalence is 60% or lower, treating more than 120 (12%) individuals per 1000 people who inject drugs per year would probably eliminate HCV within 10 years. On average, assigning treatment randomly to individuals who inject drugs is better than targeting individuals with the most injection partners. Treatment-as-prevention strategies that treat additional network members are among the best performing strategies and can enhance less effective strategies that target the degree (ie, the highest number of injection partners) within the network.

INTERPRETATION: Successful HCV treatment as prevention should incorporate the baseline HCV prevalence and will achieve the greatest benefit when coverage is sufficiently expanded.