Modelling How Refractoriness to Interferon Compromises Interferon-Free Treatment of Hepatitis C Virus Infection
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Hepatitis C virus (HCV) infection globally affects 130-150 million people. It causes both acute and chronic infections. Due to the severe side effects and low success rates of interferon based treatments, which formed the standard treatment for HCV, the treatment paradigm shifted to direct acting antivirals (DAAs). DAAs have revolutionized the treatment of hepatitis C virus infection. Clinical trials with combinations of DAAs have recorded >90% response with shorter treatment durations and fewer side effects than earlier treatments involving IFN. Outside the controlled setting of a clinical trial, however, response rates with DAA combinations are much lower (<70%). DAAs can fail if HCV accumulates mutations that confer drug resistance. Interestingly, the pre-existence of mutant frequency in the virus appears not to influence treatment outcome. A better predictor for DAA treatment outcome is yet to be unravelled. Surprisingly, individuals who respond poorly to IFN appear to be more likely to fail DAA treatment. IFN is a generic antiviral that improves immune responses and is expected not to have any bearing on DAA treatment outcomes. Why individuals with poor IFN sensitivity fail DAA treatment remains a mystery. In a recent study of the IFN signalling network, HCV has been shown to compromise IFN activity. It induces bistability in the network leading to distinct phenotypic responses of cells to IFN exposure. In particular, individuals who respond poorly to IFN tend to have a higher percentage of cells that are refractory to IFN; these cells allow viral persistence despite IFN exposure. We hypothesized here that in such individuals, greater ongoing replication would allow increased development of resistance and thus lead to the failure of DAAs. We constructed a model of viral dynamics that accounts for the distinct phenotypic responses of cells to IFN, viral replication and mutation, and the development of resistance to DAAs. Our model predicted that although the relative prevalence of pre- existing mutants is unaffected by IFN sensitivity, in agreement with observations, the growth of drug resistant mutants is accelerated in individuals with poor IFN sensitivity. Based on a distribution of IFN sensitivity across individuals, our model accurately described clinical observations of the response rates to different current treatment protocols. With this model, we predict that the common strategy of increasing the genetic barrier by adding more drugs to the combination was not necessary to avert the development of drug resistance. Instead, an optimised increase in DAA dosage alone or DAA+PR or PR dosage depending on the patient’s IFN sensitivity could help achieve success.