Collaborative Integration of HCV Molecular Virology and Mathematical Modeling

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DESCRIPTION (provided by applicant): Hepatitis C virus (HCV) is a hepatotropic virus that establishes chronicity in ~70% of those exposed. As a result, currently more than 170 million people worldwide are infected and at increased risk of developing liver steatosis, insulin resistance, chronic inflammation, fibrosis, cirrhosis, and hepatocellular carcinoma. While effective interferon (IFN)-free direct acting antiviral (DAA) therapeutic combinations have recently become available, the risk of viral escape has not been determined in less than ideal compliance populations. Equally problematic, the astronomical cost of these drugs makes them prohibitively expensive for the majority of the world's HCV-positive populations including the US where AASLD guidelines recommending treatment for only the sickest patients. Hence, there is an immediate need to optimize therapy (i.e. speed viral decline and prevent escape) while also reducing treatment duration of therapy (i.e. cost ). Mathematical modeling of HCV RNA levels in the serum of infected patients during therapy has increased our understanding of HCV infection dynamics, the effects of treatment with IFN, and led to methods for the quantitative evaluation of HCV treatment efficacy and duration of therapy to achieve cure. We have pioneered the development of multiscale models of DAA treatment response in vitro and in patients to reveal a dual mechanism of action of NS5a inhibitors that accounts for rapid viral decline and suggests how patients treated with NS5a inhibitors that are HCV RNA positive at the end of treatment can go on to achieve SVR. Additionally, we and others have accumulated evidence that viral entry/spread plays a much large role in the maintenance of steady state infection that previously assumed having broad implications related to the potential effectiveness of viral entry/spread as an antiviral drug target and impacts important antiviral therapy considerations such as drug efficacy, viral escape, and drug synergy. Importantly however, these predictions need to be validated/tested and the dynamics of these additional aspects of infection incorporated into models that can accurately predict the minimum duration of treatment needed to cure the infection. Towards this end, the objective of this cross disciplinary R01 is to increase our knowledge of HCV by formulating and testing mathematical models of HCV infection and treatment response at the molecular level. Specifically, we hypothesize that a more quantitative understanding HCV infection dynamics and treatment response will help optimize HCV DAA therapy (e.g. enhance drug synergy and increase the barrier to viral escape), predict the duration of therapy needed to achieve viral clearance (i.e. define cure boundaries), and ultimately allow for individualize therapy enabling a desperately needed reduction in cost. Accordingly, the specific aims are: 1) Understand and determine criteria for HCV Cure; 2) Define the HCV life cycle, DAA mode of action and hepatocyte role in HCV clearance; 3) Develop and utilize mathematical models of HCV cell-to-cell spread.