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.

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)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Koshy, Rajen
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Loyola University Chicago
Internal Medicine/Medicine
Schools of Medicine
United States
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Aid, Malika; Abbink, Peter; Larocca, Rafael A et al. (2017) Zika Virus Persistence in the Central Nervous System and Lymph Nodes of Rhesus Monkeys. Cell 169:610-620.e14
Boodram, Basmattee; Hotton, Anna L; Shekhtman, Louis et al. (2017) High-Risk Geographic Mobility Patterns among Young Urban and Suburban Persons who Inject Drugs and their Injection Network Members. J Urban Health :
Nguyen, Thi Huyen Tram; Guedj, Jérémie; Uprichard, Susan L et al. (2017) The paradox of highly effective sofosbuvir-based combination therapy despite slow viral decline: can we still rely on viral kinetics? Sci Rep 7:10233
Malespin, Miguel; Benyashvili, Tamara; Uprichard, Susan L et al. (2017) Prevalence of end of treatment RNA-positive/sustained viral response in HCV patients treated with sofosbuvir combination therapies. Therap Adv Gastroenterol 10:68-73
Ohashi, Hirofumi; Koizumi, Yoshiki; Fukano, Kento et al. (2017) Reply to Padmanabhan and Dixit: Hepatitis C virus entry inhibitors for optimally boosting direct-acting antiviral-based treatments. Proc Natl Acad Sci U S A 114:E4527-E4529
Shteyer, Eyal; Dahari, Harel; Gafanovich, Ina et al. (2017) End of treatment RNA-positive/sustained viral response in an individual with acute hepatitis C virus infection treated with direct-acting antivirals. Therap Adv Gastroenterol 10:429-430
Koizumi, Yoshiki; Ohashi, Hirofumi; Nakajima, Syo et al. (2017) Quantifying antiviral activity optimizes drug combinations against hepatitis C virus infection. Proc Natl Acad Sci U S A 114:1922-1927
Best, Katharine; Guedj, Jeremie; Madelain, Vincent et al. (2017) Zika plasma viral dynamics in nonhuman primates provides insights into early infection and antiviral strategies. Proc Natl Acad Sci U S A 114:8847-8852
Dahari, Harel; Canini, Laetitia; Graw, Frederik et al. (2016) HCV kinetic and modeling analyses indicate similar time to cure among sofosbuvir combination regimens with daclatasvir, simeprevir or ledipasvir. J Hepatol 64:1232-9
Graw, Frederik; Perelson, Alan S (2016) Modeling Viral Spread. Annu Rev Virol 3:555-572

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