A New Stochastic Model for Subgenomic Hepatitis C Virus Replication Considers Drug Resistant Mutants

Abstract

As an RNA virus, hepatitis C virus (HCV) is able to rapidly acquire drug resistance, and for this reason the design of effective anti-HCV drugs is a real challenge. The HCV subgenomic replicon-containing cells are widely used for experimental studies of the HCV genome replication mechanisms, for drug testing in vitro and in studies of HCV drug resistance. The NS3/4A protease is essential for virus replication and, therefore, it is one of the most attractive targets for developing specific antiviral agents against HCV. We have developed a stochastic model of subgenomic HCV replicon replication, in which the emergence and selection of drug resistant mutant viral RNAs in replicon cells is taken into account. Incorporation into the model of key NS3 protease mutations leading to resistance to BILN-2061 (A156T, D168V, R155Q), VX-950 (A156S, A156T, T54A) and SCH 503034 (A156T, A156S, T54A) inhibitors allows us to describe the long term dynamics of the viral RNA suppression for various inhibitor concentrations. We theoretically showed that the observable difference between the viral RNA kinetics for different inhibitor concentrations can be explained by differences in the replication rate and inhibitor sensitivity of the mutant RNAs. The pre-existing mutants of the NS3 protease contribute more significantly to appearance of new resistant mutants during treatment with inhibitors than wild-type replicon. The model can be used to interpret the results of anti-HCV drug testing on replicon systems, as well as to estimate the efficacy of potential drugs and predict optimal schemes of their usage.

Life cycle process dependencies of positive-sense RNA viruses suggest strategies for inhibiting productive cellular infection

“…While goodness of fit based on cumulative AIC values for the three viruses (electronic supplementary material, table S4) demonstrates only a marginal advantage in favour of our main model, measured CM formation dynamics [33,34] support our choice of the model. Corroboration with independent experimental data like recovery of steady-state levels of replication intermediate [17,59,60] and steady-state positive/ negative RNA ratios [46,47] (electronic supplementary material, table S4) further supports our model. Due to the lack of molecular details for virus assembly, our model only qualitatively captures the virus assembly and release dynamics and we cannot discriminate between alternative sites (CMs versus cytoplasm) for virus assembly.…”

Differential Evolution approach to detect recent admixture

“…It is worth noting that the DEEP method was previously successfully applied to several systems biology problems [34,35,36]. The distinctive features of the DEEP method are the flexible selection rule for handling multiple objective functions and substitution strategy that takes into account the number of iterations between updates of each parameter vector.…”

Multiscale modeling of virus replication and spread

“…, their estimated half‐life of intracellular genotype 1b subgenomic replicon RNA was between 16–19 h. However, Ivanisenko et al . showed that the observed bi‐phasic RNA decline can also be explained by the accumulation of drug resistant mutants considering stochastic viral mutation and selection in the previous subgenomic model . Two extensive reviews about viral kinetic modeling in the context of treatment with a specific focus on HCV and influenza virus can be found in and .…”