Collaborative Integration of HCV Molecular Virology and Mathematical Modeling

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DESCRIPTION (provided by applicant): Hepatitis C virus (HCV) infects more than 180 million people worldwide, causing acute and chronic hepatitis and hepatocellular carcinoma, however, no protective vaccine is available and only a subset of infected patients respond to the current treatment of interferon (IFN) plus ribavirin. Statistics predict that without improved therapeutics one million people in the US will suffer from HCV-related cirrhosis by 2020. Mathematical modeling of HCV RNA levels in the serum of chronically infected patients during interferon therapy has increased our understanding of HCV infection dynamics and treatment response kinetics, and is playing an important role in the analysis of clinical data. Nevertheless, the absence of infectious cell culture systems has impeded full understanding of HCV infection and the mechanistic basis of response to therapy. Fortunately, significant advances were made recently with the identification of a genotype 2a HCV consensus clone (JFH-1) that we and others have shown can replicate and produce infectious HCV in vitro. Hence, for the first time, we can efficiently propagate HCV and study the entire viral lifecycle and the effects of potential antiviral on the infection process. The ability to study HCV in vitro provides an unprecedented cross disciplinary opportunity to increase our knowledge of HCV by quantifying HCV infection kinetics and formulate mathematical models of HCV infection and treatment response at the molecular level. A more quantitative understanding of intracellular and extracellular HCV infection and treatment dynamics will help define rate limiting steps required for infection, identify effective antiviral targets and define the mechanism of action (MOA) of antivirals that are under development thus facilitating the design of improved therapeutics. Accordingly, the specific aims of this proposal are: 1) Quantify HCV infection kinetics in vitro and develop new mathematical models to elucidate processes that regulate HCV dynamics from initiation to steady state. 2) Validate and refine our understanding of HCV replication and infection by characterizing HCV inhibition during treatment with antiviral agents of known MOA and then determining if our mathematical models accurately predict those empirically measured inhibition dynamics. 3) Use HCV mathematical models to predict the MOA by which clinically relevant drugs inhibit HCV and empirically test those hypotheses.