Hepatitis C virus (HCV) infects more than 180 million people worldwide, causing chronic hepatitis and hepatocellular carcinoma. Although it is the most common chronic blood-borne infection in the U.S, no protective vaccine exists and only a subset of patients respond to the current treatment of interferon (IFN) plus ribavirin. Mathematical modeling of HCV RNA in the serum of infected patients during IFN therapy has increased our understanding of HCV infection dynamics and IFN response kinetics, but the absence of in vitro infection systems has restricted the exploitation of similar approaches to decipher the mechanistic basis of HCV infection and IFN inhibition. Now however, with the development of the first robust cell culture HCV infection system, we can begin characterizing all aspects of the viral lifecycle and the antiviral effects of IFN. Specifically, this breakthrough provides a cross disciplinary opportunity to increase our knowledge of HCV by formulating and testing mathematical models of HCV infection and treatment response at the molecular level. A quantitatively understanding HCV infection and treatment dynamics should help define rate limiting steps of infection thus identifying effective antiviral targets. As such, this application represents a collaborative effort between HCV virologists and viral dynamic modelers to develop and utilize data-driven mathematical concepts to elucidate the biological processes that regulate HCV infection and determine viral response to treatment. Accordingly, the specific aims of this proposal are: 1) Elucidate the rate limiting processes that regulate HCV infection from initiation to steady state. We propose to quantify kinetic processes throughout HCV infection in vitro and develop mathematical models describing infection dynamics from initiation to steady state in order to identify key rate limiting processes. 2) Validate our understanding of HCV replication and infection by analyzing system dynamics following perturbation of steady state. Mathematical models will be used to simulate the effect of antivirals with different modes of action to predict for each mode of action the kinetics of HCV inhibition. Using inhibitors with known mechanisms of action, we will then perform experiments to test if the models predict the behavior of the systems. The discrepancies found will be used to identify aspects of infection which might not be completely understood and drive further investigation and model refinement. 3) Determine the molecular mechanisms of HCV inhibition by interferon. While modeling itself will enhance our understanding of HCV infection, the models created can then serve as powerful tool for analyzing the mechanism of action of HCV inhibitors. Hence, we will use mathematical models to predict the mechanisms by which interferon inhibits HCV and then empirically test those hypotheses.

Public Health Relevance

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 current treatment options. To design more effective antivirals it is crucial to study the HCV lifecycle to understand the dynamics of infection and identify which steps of infection represent effective antiviral targets. As such, we propose a collaborative effort between virologists and mathematicians to develop and utilize mathematical models to elucidate the biological processes that regulate HCV infection and determine viral response to treatment.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
High Priority, Short Term Project Award (R56)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Koshy, Rajen
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University of Illinois at Chicago
Internal Medicine/Medicine
Schools of Medicine
United States
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