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Modelling how ribavirin improves interferon response rates in hepatitis C virus infection

Author

Listed:
  • Narendra M. Dixit

    (Los Alamos National Laboratory)

  • Jennifer E. Layden-Almer

    (University of Illinois at Chicago)

  • Thomas J. Layden

    (University of Illinois at Chicago)

  • Alan S. Perelson

    (Los Alamos National Laboratory)

Abstract

Nearly 200 million individuals worldwide are currently infected with hepatitis C virus (HCV)1. Combination therapy with pegylated interferon and ribavirin, the latest treatment for HCV infection, elicits long-term responses in only about 50% of patients treated2,3,4. No effective alternative treatments exist for non-responders5. Consequently, significant efforts are continuing to maximize response to combination therapy6,7. However, rational therapy optimization is precluded by the poor understanding of the mechanism(s) of ribavirin action against HCV8. Ribavirin alone induces either a transient early decline or no decrease in HCV viral load9,10,11,12, but in combination with interferon it significantly improves long-term response rates2,3,4,13,14,15. Here we present a model of HCV dynamics in which, on the basis of growing evidence16,17,18,19,20,21, we assume that ribavirin decreases HCV infectivity in an infected individual in a dose-dependent manner. The model quantitatively predicts long-term response rates to interferon monotherapy and combination therapy, fits observed patterns of HCV RNA decline in patients undergoing therapy, reconciles conflicting observations of the influence of ribavirin on HCV RNA decline, provides key insights into the mechanism of ribavirin action against HCV, and establishes a framework for rational therapy optimization.

Suggested Citation

  • Narendra M. Dixit & Jennifer E. Layden-Almer & Thomas J. Layden & Alan S. Perelson, 2004. "Modelling how ribavirin improves interferon response rates in hepatitis C virus infection," Nature, Nature, vol. 432(7019), pages 922-924, December.
    • Handle: RePEc:nat:nature:v:432:y:2004:i:7019:d:10.1038_nature03153
    DOI: 10.1038/nature03153
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    Citations

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    Cited by:

    1. Luo, Yantao & Zhang, Long & Zheng, Tingting & Teng, Zhidong, 2019. "Analysis of a diffusive virus infection model with humoral immunity, cell-to-cell transmission and nonlinear incidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    2. Tao Lu & Yangxin Huang & Min Wang & Feng Qian, 2014. "A refined parameter estimating approach for HIV dynamic model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1645-1657, August.
    3. Sonjoy Pan & Siddhartha P. Chakrabarty, 2020. "Hopf Bifurcation and Stability Switches Induced by Humoral Immune Delay in Hepatitis C," Indian Journal of Pure and Applied Mathematics, Springer, vol. 51(4), pages 1673-1695, December.
    4. Mphatso Kalemera & Dilyana Mincheva & Joe Grove & Christopher J R Illingworth, 2019. "Building a mechanistic mathematical model of hepatitis C virus entry," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-26, March.
    5. Libin Rong & Jeremie Guedj & Harel Dahari & Daniel J Coffield Jr & Micha Levi & Patrick Smith & Alan S Perelson, 2013. "Analysis of Hepatitis C Virus Decline during Treatment with the Protease Inhibitor Danoprevir Using a Multiscale Model," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-12, March.
    6. Pranesh Padmanabhan & Narendra M Dixit, 2011. "Mathematical Model of Viral Kinetics In Vitro Estimates the Number of E2-CD81 Complexes Necessary for Hepatitis C Virus Entry," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-11, December.
    7. Pranesh Padmanabhan & Narendra M Dixit, 2012. "Viral Kinetics Suggests a Reconciliation of the Disparate Observations of the Modulation of Claudin-1 Expression on Cells Exposed to Hepatitis C Virus," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.
    8. Santosh Kumar Sharma & Amar Nath Chatterjee & Bashir Ahmad, 2023. "Effect of Antiviral Therapy for HCV Treatment in the Presence of Hepatocyte Growth Factor," Mathematics, MDPI, vol. 11(3), pages 1-20, February.
    9. Pan, Sonjoy & Chakrabarty, Siddhartha P., 2022. "Analysis of a reaction–diffusion HCV model with general cell-to-cell incidence function incorporating B cell activation and cure rate," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 431-450.

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