HCV1b genome evolution under selective pressure of the cyclophilin inhibitor alisporivir during the DEB-025-HCV-203 phase II clinical trial

Inhibition of Porcine Viruses by Different Cell-Targeted Antiviral Drugs

Antiviral compounds targeting cellular metabolism instead of virus components have become an interesting issue for preventing and controlling the spread of virus infection, either as sole treatment or as a complement of vaccination. Some of these compounds are involved in the control of lipid metabolism and/or membrane rearrangements. Here, we describe the effect of three of these cell-targeting antivirals: lauryl gallate (LG), valproic acid (VPA), and cerulenin (CRL) in the multiplication of viruses causing important porcine diseases. The results confirm the antiviral action in cultured cells of LG against African swine fever virus (ASFV), foot and mouth disease virus (FMDV), vesicular stomatitis virus (VSV), and swine vesicular disease virus (SVDV), as well as the inhibitory effect of VPA and CRL on ASFV infection. Other gallate esters have been also assayed for their inhibition of FMDV growth. The combined action of these antivirals has been also tested in ASFV infections, with some synergistic effects when LG and VPA were co-administered. Regarding the mode of action of the antivirals, experiments on the effect of the time of its addition in infected cell cultures indicated that the inhibition by VPA and CRL occurred at early times after ASFV infection, while LG inhibited a late step in FMDV infection. In all the cases, the presence of the antiviral reduced or abolished the induction of virus-specific proteins. Interestingly, LG also reduced mortality and FMDV load in a mouse model. The possible use of cell-targeted antivirals against porcine diseases is discussed.

Exploring resistance pathways for first-generation NS3/4A protease inhibitors boceprevir and telaprevir using Bayesian network learning

Resistance-associated variants (RAVs) have been shown to influence treatment response to direct-acting antivirals (DAAs) and first generation NS3/4A protease inhibitors (PIs) in particular. Interpretation of hepatitis C virus (HCV) genotypic drug resistance remains a challenge, especially in patients who previously failed DAA therapy and need to be retreated with a second DAA based regimen. Bayesian network (BN) learning on HCV sequence data from patients treated with DAAs could provide insight in resistance pathways against PIs for HCV subtypes 1a and 1b, in a similar way as applied before for HIV. The publicly available 'Rega-BN' tool chain was developed to study associative analyses for various pathogens. Our first analysis, comparing sequences from PI-naïve and PI-experienced patients, determined that NS3 substitutions R155K and V36M arise with PI-exposure in HCV1a infected patients, and were defined as major and minor resistance-associated variants respectively. NS3 variant 174H was newly identified as potentially related to PI resistance. In a second analysis, NS3 sequences from PI-naïve patients who cleared the virus during PI therapy and from PI-naïve patients who failed PI therapy were compared, showing that NS3 baseline variant 67S predisposes to treatment-failure and variant 72I to treatment success. This approach has the potential to better characterize the role of more RAVs, if sufficient therapy annotated sequence data becomes available in curated public databases. In addition, polymorphisms present in baseline sequences that predispose patients to therapy failure can be identified using this approach.