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Modelling hepatitis C therapy—predicting effects of treatment

Nature Reviews Gastroenterology & Hepatology volume 12, pages 437–445 (2015)Cite this article

Subjects

Key Points

  • After patients receive therapy for HCV infection, HCV RNA declines in a biphasic manner, the first phase reflects viral clearance, the second phase the loss of infected cells

  • The use of mathematical modelling reveals that high viral production enables the daily production of all single or double mutant variants resulting in drug resistance for therapies with low genetic barriers

  • Modelling HCV RNA kinetics has enabled researchers to estimate the effectiveness of therapy and optimal treatment duration to achieve a sustained virologic response (SVR)

  • Multiscale models that include intracellular viral replication and extracellular spread indicate that NS5A and protease inhibitors can inhibit both viral replication and viral assembly or release

  • Interferon-free combination therapies are available, have little resistance and can generate a SVR after treatment times as short as 6 weeks

  • HCV RNA has been detected after treatment with some direct-acting antiviral combinations in patients who develop a SVR, but viral kinetic theory cannot currently explain this phenomenon

Abstract

Mathematically modelling changes in HCV RNA levels measured in patients who receive antiviral therapy has yielded many insights into the pathogenesis and effects of treatment on the virus. By determining how rapidly HCV is cleared when viral replication is interrupted by a therapy, one can deduce how rapidly the virus is produced in patients before treatment. This knowledge, coupled with estimates of the HCV mutation rate, enables one to estimate the frequency with which drug resistant variants arise. Modelling HCV also permits the deduction of the effectiveness of an antiviral agent at blocking HCV replication from the magnitude of the initial viral decline. One can also estimate the lifespan of an HCV-infected cell from the slope of the subsequent viral decline and determine the duration of therapy needed to cure infection. The original understanding of HCV RNA decline under interferon-based therapies obtained by modelling needed to be revised in order to interpret the HCV RNA decline kinetics seen when using direct-acting antiviral agents (DAAs). There also exist unresolved issues involving understanding therapies with combinations of DAAs, such as the presence of detectable HCV RNA at the end of therapy in patients who nonetheless have a sustained virologic response.

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Figure 1: Viral load decay in a patient with genotype 1 HCV treated with 15 MIU daily of IFN-α.
Figure 2: Median viral load decays caused by agents belonging to different drug classes during short-term monotherapy or combination therapy.
Figure 3: Comparison of the standard viral dynamic model and a multiscale model.
Figure 4: Curing viral infection.

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References

  1. Gane, E. J. et al. Efficacy of nucleotide polymerase inhibitor sofosbuvir plus the NS5A inhibitor ledipasvir or the NS5B non-nucleoside inhibitor GS-9669 against HCV genotype 1 infection. Gastroenterology 146, 736–743 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Ho, D. D. et al. Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection. Nature 373, 123–126 (1995).

    Article  CAS  PubMed  Google Scholar 

  3. Perelson, A. S. et al. Decay characteristics of HIV-1-infected compartments during combination therapy. Nature 387, 188–191 (1997).

    Article  CAS  PubMed  Google Scholar 

  4. Perelson, A. S., Neumann, A. U., Markowitz, M., Leonard, J. M. & Ho, D. D. HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time. Science 271, 1582–1586 (1996).

    Article  CAS  PubMed  Google Scholar 

  5. Wei, X. et al. Viral dynamics in human immunodeficiency virus type 1 infection. Nature 373, 117–122 (1995).

    Article  CAS  PubMed  Google Scholar 

  6. Neumann, A. U. et al. Hepatitis C viral dynamics in vivo and the antiviral efficacy of interferon-alpha therapy. Science 282, 103–107 (1998).

    Article  CAS  PubMed  Google Scholar 

  7. Dahari, H., Sainz, B. Jr., Perelson, A. S. & Uprichard, S. L. Modeling subgenomic hepatitis C virus RNA kinetics during treatment with alpha interferon. J. Virol. 83, 6383–6390 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rong, L. & Perelson, A. S. Mathematical analysis of multiscale models for hepatitis C virus dynamics under therapy with direct-acting antiviral agents. Math. Biosci. 245, 22–30 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Powers, K. A. et al. Modeling viral and drug kinetics: hepatitis C virus treatment with pegylated interferon alfa-2b. Sem. Liver Dis. 23 (Suppl. 1), 13–18 (2003).

    Google Scholar 

  10. Herrmann, E., Lee, J.-H., Marinos, G., Modi, M. & Zeuzem, S. Effect of ribavirin on hepatitis C viral kinetics in patients treated with pegylated interferon. Hepatology 37, 1351–1358 (2003).

    Article  CAS  PubMed  Google Scholar 

  11. Dixit, N. M., Layden-Almer, J. E., Layden, T. J. & Perelson, A. S. Modelling how ribavirin improves interferon response rates in hepatitis C virus infection. Nature 432, 922–924 (2004).

    Article  CAS  PubMed  Google Scholar 

  12. Pawlotsky, J.-M. et al. Antiviral action of ribavirin in chronic hepatitis C. Gastroenterology 126, 703–714 (2004).

    Article  CAS  PubMed  Google Scholar 

  13. Talal, A. H. et al. Pharmacodynamics of PEG-IFN alpha differentiate HIV/HCV coinfected sustained virological responders from nonresponders. Hepatology 43, 943–953 (2006).

    Article  CAS  PubMed  Google Scholar 

  14. Herrmann, E. et al. Viral kinetics in patients with chronic hepatitis C treated with the serine protease inhibitor BILN 2061. Antivir. Ther. 11, 371–376 (2006).

    CAS  PubMed  Google Scholar 

  15. Dahari, H., Ribeiro, R. M. & Perelson, A. S. Triphasic decline of hepatitis C virus RNA during antiviral therapy. Hepatology 46, 16–21 (2007).

    Article  CAS  PubMed  Google Scholar 

  16. Dahari, H., Lo, A., Ribeiro, R. M. & Perelson, A. S. Modeling hepatitis C virus dynamics: liver regeneration and critical drug efficacy. J. Theor. Biol. 247, 371–381 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Reluga, T. C., Dahari, H. & Perelson, A. S. Analysis of hepatitis C virus infection models with hepatocyte homeostasis. SIAM J. Appl. Math. 69, 999–1023 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Dahari, H., Shudo, E., Cotler, S. J., Layden, T. J. & Perelson, A. S. Modelling hepatitis C virus kinetics: the relationship between the infected cell loss rate and the final slope of viral decay. Antivir. Ther. 14, 459–464 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Dahari, H. et al. Pharmacodynamics of PEG-IFN-alpha-2a in HIV/HCV co-infected patients: implications for treatment outcomes. J. Hepatol. 53, 460–467 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Dahari, H., Rong, L., Layden, T. J. & Cotler, S. J. Hepatocyte proliferation and hepatitis C virus kinetics during treatment. Clin. Pharmacol. Ther. 89, 353–354 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Saltzman, J., Nachbar, R., Panochorchan, P., Stone, J. & Khan, A. in 2009 SIAM Conference on Mathematics for Industry (eds Fields, D. A. & Peters, T. J.) 73–83 (Society for Industrial and Applied Mathematics, 2010).

    Google Scholar 

  22. Reddy, M. B. et al. Pharmacokinetic/pharmacodynamic predictors of clinical potency for hepatitis C virus nonnucleoside polymerase and protease inhibitors. Antimicrob. Agents Chemother. 56, 3144–3156 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Nguyen, T. H. T., Mentré, F., Yu, J., Levi, M. & Guedj, J. A pharmacokinetic—viral kinetic model describes the effect of alisporivir monotherapy or in combination with peg-IFN on hepatitis C virologic response. Clin. Pharm. Ther. 96, 599–608 (2014).

    Article  CAS  Google Scholar 

  24. Nguyen, T. H. T. & Guedj, J. HCV kinetic models and their implication in drug development. CPT Pharmacometrics Syst. Pharmacol. 4, 231–242 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Dixit, N. M. & Perelson, A. S. The metabolism, pharmacokinetics and mechanisms of antiviral activity of ribavirin against hepatitis C virus. Cell. Mol. Life Sci. 63, 832–842 (2006).

    Article  CAS  PubMed  Google Scholar 

  26. Feld, J. J. Is there a role for ribavirin in the era of hepatitis C virus direct-acting antivirals? Gastroenterology 142, 1356–1359 (2012).

    Article  PubMed  Google Scholar 

  27. Feld, J. J. et al. Ribavirin improves early responses to peginterferon through improved interferon signaling. Gastroenterology 139, 154–162 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Rotman, Y. et al. Effect of ribavirin on viral kinetics and liver gene expression in chronic hepatitis C. Gut 63, 161–169 (2014).

    Article  CAS  PubMed  Google Scholar 

  29. Thomas, E. et al. Ribavirin potentiates interferon action by augmenting interferon-stimulated gene induction in hepatitis C virus cell culture models. Hepatology 53, 32–41 (2011).

    Article  CAS  PubMed  Google Scholar 

  30. Mihm, U., Herrmann, E., Sarrazin, C. & Zeuzem, S. Review article: predicting response in hepatitis C virus therapy. Aliment. Pharmacol. Ther. 23, 1043–1054 (2006).

    Article  CAS  PubMed  Google Scholar 

  31. Canini, L. et al. A pharmacokinetic/viral kinetic model to evaluate the treatment effectiveness of danoprevir against chronic HCV. Antivir. Ther. http://dx.doi.org/10.3851/IMP2879.

  32. Shudo, E., Ribeiro, R. M., Talal, A. H. & Perelson, A. S. A hepatitis C viral kinetic model that allows for time-varying drug effectiveness. Antivir. Ther. 13, 919–926 (2008).

    PubMed  Google Scholar 

  33. Conway, J. M. & Perelson, A. S. A hepatitis C virus infection model with time-varying drug effectiveness: solution and analysis. PLoS Comp. Biol. 10, e1003769 (2014).

    Article  CAS  Google Scholar 

  34. Shudo, E., Ribeiro, R. M. & Perelson, A. S. Modeling hepatitis C virus kinetics under therapy using pharmacokinetic and pharmacodynamic information. Expert Opin. Drug Metab. Toxicol. 5, 321–332 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Guedj, J. & Perelson, A. S. Second-phase hepatitis C virus RNA decline during telaprevir-based therapy increases with drug effectiveness: implications for treatment duration. Hepatology 53, 1801–1808 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Guedj, J., Dahari, H., Shudo, E., Smith, P. & Perelson, A. S. Hepatitis C viral kinetics with the nucleoside polymerase inhibitor mericitabine (RG7128). Hepatology 55, 1030–1037 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Guedj, J. et al. Analysis of the hepatitis C viral kinetics during administration of two nucleotide analogues: sofosbuvir (GS-7977) and GS-0938. Antivir. Ther. 19, 211–220 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. Canini, L. et al. Severity of liver disease affects HCV kinetics in patients treated with intravenous silibinin monotherapy. Antivir. Ther. 20, 149–155 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Canini, L. & Perelson, A. S. Viral kinetic modeling: state of the art. J. Pharmacokinet. Pharmacodyn. 41, 431–433 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gao, M. et al. Chemical genetics strategy identifies an HCV NS5A inhibitor with a potent clinical effect. Nature 465, 96–100 (2010).

    Article  CAS  PubMed  Google Scholar 

  41. Adiwijaya, B. S. et al. Rapid decrease of wild-type hepatitis C virus on telaprevir treatment. Antivir. Ther. 14, 591–595 (2009).

    CAS  PubMed  Google Scholar 

  42. Guedj, J., Dahari, H., Shudo, E., Smith, P. & Perelson, A. S. Hepatitis C viral kinetics with the nucleoside polymerase inhibitor mericitabine (RG7128). Hepatology 55, 1030–1037 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Guedj, J. et al. Modeling shows that the NS5A inhibitor daclatasvir has two modes of action and yields a shorter estimate of the hepatitis C virus half-life. Proc. Natl Acad. Sci. USA 110, 3991–3996 (2013).

    Article  CAS  PubMed  Google Scholar 

  44. Neumann, A. U. et al. Differences in viral dynamics between genotypes 1 and 2 of hepatitis C virus. J. Infect. Dis. 182, 28–35 (2000).

    Article  CAS  PubMed  Google Scholar 

  45. Rong, L. et al. Analysis of hepatitis C virus decline during treatment with the protease inhibitor danoprevir using a multiscale model. PLoS Comp. Biol. 9, e1002959 (2013).

    Article  CAS  Google Scholar 

  46. McGivern, D. R. et al. Kinetic analyses reveal potent and early blockade of hepatitis C virus assembly by NS5A inhibitors. Gastroenterology 147, 453–462 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. McGivern, D. R. et al. Protease inhibitors block multiple functions of the NS3/4A protease-helicase during the hepatitis C virus life cycle. J. Virol. 89, 5362–5370 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Meredith, L. W., Farquhar, M. J., Tarr, A. W. & McKeating, J. A. Type I interferon rapidly restricts infectious hepatitis C virus particle genesis. Hepatology 60, 1891–1901 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Dahari, H., Ribeiro, R. M., Rice, C. M. & Perelson, A. S. Mathematical modeling of subgenomic hepatitis C virus replication in Huh-7 cells. J. Virol. 81, 750–760 (2007).

    Article  CAS  PubMed  Google Scholar 

  50. Binder, M. et al. Replication vesicles are load- and choke-points in the hepatitis C virus lifecycle. PLoS Pathog. 9, e1003561 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Snoeck, E. et al. A comprehensive hepatitis C viral kinetic model explaining cure. Clin. Pharmacol. Ther. 87, 706–713 (2010).

    Article  CAS  PubMed  Google Scholar 

  52. Reesink, H. W. et al. Rapid HCV-RNA decline with once daily TMC435: a phase I study in healthy volunteers and hepatitis C patients. Gastroenterology 138, 913–921 (2010).

    Article  CAS  PubMed  Google Scholar 

  53. Blight, K. J., McKeating, J. A. & Rice, C. M. Highly permissive cell lines for subgenomic and genomic hepatitis C virus RNA replication. J. Virol. 76, 13001–13014 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Robinson, M. et al. Novel hepatitis C virus reporter replicon cell lines enable efficient antiviral screening against genotype 1a. Antimicrob. Agents Chemother. 54, 3099–3106 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Farley, S. A double whammy for hep C. Nat. Rev. Drug Discov. 2, 419 (2003).

    Article  CAS  Google Scholar 

  56. Liang, Y. et al. Antiviral suppression vs restoration of RIG-I signaling by hepatitis C protease and polymerase inhibitors. Gastroenterology 135, 1710–1718 (2008).

    Article  CAS  PubMed  Google Scholar 

  57. Osinusi, A. et al. Sofosbuvir and ribavirin for hepatitis C genotype 1 in patients with unfavorable treatment characteristics: a randomized clinical trial. JAMA 310, 804–811 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Guedj, J. & Neumann, A. U. Understanding hepatitis C viral dynamics with direct-acting antiviral agents due to the interplay between intracellular replication and cellular infection dynamics. J. Theor. Biol. 267, 330–340 (2010).

    Article  CAS  PubMed  Google Scholar 

  59. Laouenan, C. et al. Using pharmacokinetic and viral kinetic modeling to estimate the antiviral effectiveness of telaprevir, boceprevir, and pegylated interferon during triple therapy in treatment-experienced hepatitis C virus-infected cirrhotic patients. Antimicrob. Agents Chemother. 58, 5332–5341 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Centro, V. et al. Kinetics of hepatitis C virus RNA decay, quasispecies evolution and risk of virological failure during telaprevir-based triple therapy in clinical practice. Digestive Liver Dis. 47, 233–241 (2015).

    Article  CAS  Google Scholar 

  61. Kohli, A. et al. Virologic response after 6 week triple-drug regimes for hepatitis C: a proof-of-concept phase 2A cohort study. Lancet 385, 1107–1113 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Sarrazin, C. et al. Importance of very early HCV RNA kinetics for prediction of treatment outcome of highly effective all oral direct-acting antiviral combination therapy. J. Virol. Methods 214, 29–32 (2015).

    Article  CAS  PubMed  Google Scholar 

  63. Gane, E. J. et al. Oral combination therapy with a nucleoside polymerase inhibitor (RG7128) and danoprevir for chronic hepatitis C genotype 1 infection (INFORM-1): a randomised, double-blind, placebo-controlled, dose-escalation trial. Lancet 376, 1467–1475 (2010).

    Article  CAS  PubMed  Google Scholar 

  64. Gane, E. J. et al. Mericitabine and ritonavir-boosted danoprevir with or without ribavirin in treatment-naive HCV genotype 1 patients: INFORM-SVR study. Liver Intl. 35, 79–89 (2015).

    Article  CAS  Google Scholar 

  65. Kowdley, K. V. et al. Ledipasvir and sofosbuvir for 8 or 12 weeks for chronic HCV without cirrhosis. N. Engl. J. Med. 370, 1879–1888 (2014).

    Article  CAS  PubMed  Google Scholar 

  66. Sulkowski, M. et al. Efficacy and safety of 8 weeks versus 12 weeks of treatment with grazoprevir (MK-5172) and elbasvir (MK-8742) with or without ribavirin in patients with hepatitis C virus genotype 1 mono-infection and HIV/hepatitis C virus co-infection (C-WORTHY): a randomised, open-label phase 2 trial. Lancet 385, 1087–1097 (2014).

    Article  CAS  PubMed  Google Scholar 

  67. Colombatto, P. et al. Early and accurate prediction of Peg-IFNs/ribavirin therapy outcome in the individual patient with chronic hepatitis C by modeling the dynamics of the infected cells. Clin. Pharmacol. Ther. 84, 212–215 (2008).

    Article  CAS  PubMed  Google Scholar 

  68. Adiwijaya, B. S. et al. A viral dynamic model for treatment regimens with direct-acting antivirals for chronic hepatitis C infection. PLoS Comp. Biol. 8, e1002339 (2012).

    Article  CAS  Google Scholar 

  69. Guedj, J. et al. Modeling viral kinetics and treatment outcome during alisporivir interferon-free treatment in HCV genotype 2/3 patients. Hepatology 59, 1706–1714 (2014).

    Article  CAS  PubMed  Google Scholar 

  70. Kieffer, T. L. et al. Telaprevir and pegylated interferon-alpha-2a inhibit wild-type and resistant genotype 1 hepatitis C virus replication in patients. Hepatology 46, 631–639 (2007).

    Article  CAS  PubMed  Google Scholar 

  71. Adiwijaya, B. S. et al. A multi-variant, viral dynamic model of genotype 1 HCV to assess the in vivo evolution of protease-inhibitor resistant variants. PLoS Comp. Biol. 6, e1000745 (2010).

    Article  CAS  Google Scholar 

  72. Ribeiro, R. M. et al. Quantifying the diversification of hepatitis C virus (HCV) during primary infection: estimates of the in vivo mutation rate. PLoS Pathog. 8, e1002881 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Cuevas, J. M., González-Candelas, F., Moya, A. & Sanjuán, R. Effect of ribavirin on the mutation rate and spectrum of hepatitis C virus in vivo. J. Virol. 83, 5760–5764 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Rong, L., Dahari, H., Ribeiro, R. M. & Perelson, A. S. Rapid emergence of protease inhibitor resistance in hepatitis C virus. Sci. Trans. Med. 2, 30ra32 (2010).

    Article  CAS  Google Scholar 

  75. Haseltine, E. L. et al. Modeling viral evolutionary dynamics after telaprevir-based treatment. PLoS Comp. Biol. 10, e1003772 (2014).

    Article  CAS  Google Scholar 

  76. Rong, L., Ribeiro, R. M. & Perelson, A. S. Modeling quasispecies and drug resistance in hepatitis C patients treated with a protease inhibitor. Bull. Math. Biol. 74, 1789–1817 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Schaller, T. et al. Analysis of hepatitis C virus superinfection exclusion by using novel fluorochrome gene-tagged viral genomes. J. Virol. 81, 4591–4603 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Tscherne, D. M. et al. Superinfection exclusion in cells infected with hepatitis C virus. J. Virol. 81, 3693–3703 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Webster, B., Ott, M. & Greene, W. C. Evasion of superinfection exclusion and elimination of primary viral RNA by an adapted strain of hepatitis C virus. J. Virol. 87, 13354–13369 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Hedskog, C. et al. Characterization of hepatitis C virus intergenotypic recombinant strains and associated virological response to sofosbuvir/ribavirin. Hepatology 61, 471–480 (2015).

    Article  CAS  PubMed  Google Scholar 

  81. Pawelek, K. A. et al. Modeling within-host dynamics of influenza virus infection including immune responses. PLoS Comp. Biol. 8, e1002588 (2012).

    Article  CAS  Google Scholar 

  82. Greco, W. R., Bravo, G. & Parsons, J. C. The search for synergy: a critical review from a response surface perspective. Pharmacol. Rev. 47, 331–385 (1995).

    CAS  PubMed  Google Scholar 

  83. Lee, J. J., Kong, M., Ayers, G. D. & Lotan, R. Interaction index and different methods for determining drug interaction in combination therapy. J. Biopharm. Stat. 17, 461–480 (2007).

    Article  CAS  PubMed  Google Scholar 

  84. Cheng, G. et al. Antiviral activity and resistance profile of the novel HCV NS5A inhibitor GS-5885. J. Hepatol. 56, S464 (2012).

    Article  Google Scholar 

  85. Sidharthan, S. et al. Utility of hepatitis C viral load monitoring on directly acting antiviral therapy. Clin. Infect. Dis. 60, 1743–1751 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Saez-Cirion, A. et al. Post-treatment HIV-1 controllers with a long-term virological remission after the interruption of early initiated antiretroviral therapy ANRS VISCONTI Study. PLoS Pathog. 9, e1003211 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Conway, J. M. & Perelson, A. S. Post-treatment control of HIV infection. Proc. Natl. Acad Sci. USA 112, 5467–5472 (2015).

    Article  CAS  PubMed  Google Scholar 

  88. Shimizu, Y. K., Purcell, R. H. & Yoshikura, H. Correlation between the infectivity of hepatitis C virus in vivo and its infectivity in vitro. Proc. Natl Acad. Sci. USA 90, 6037–6041 (1993).

    Article  CAS  PubMed  Google Scholar 

  89. Lawitz, E. et al. A phase 2a trial of 12-week interferon-free therapy with two direct-acting antivirals (ABT-450/r, ABT-072) and ribavirin in IL28B C/C patients with chronic hepatitis C genotype 1. J. Hepatol 59, 18–23 (2013).

    Article  CAS  PubMed  Google Scholar 

  90. Soriano, V. et al. Very late relapse after discontinuation of antiviral therapy for chronic hepatitis, C. Antiviral therapy 18, 1033–1035 (2013).

    Article  CAS  PubMed  Google Scholar 

  91. Barreiro, P. et al. Very late HCV relapse following triple therapy for hepatitis, C. Antivir. Ther. 19, 723–724 (2014).

    Article  CAS  PubMed  Google Scholar 

  92. Veerapu, N. S., Raghuraman, S., Liang, T. J., Heller, T. & Rehermann, B. Sporadic reappearance of minute amounts of hepatitis C virus RNA after successful therapy stimulates cellular immune responses. Gastroenterology 140, 676–685 (2011).

    Article  CAS  PubMed  Google Scholar 

  93. Lin, J. C. et al. Interferon γ-induced protein 10 kinetics in treatment-naive versus treatment-experienced patients receiving interferon-free therapy for hepatitis C virus infection: Implications for the innate immune response. J. Infect. Dis. 10, 1881–1885 (2014).

    Article  CAS  Google Scholar 

  94. Food and Drug Administration Center for Drug Evaluation Research. Guidance for industry chronic Hepatitis C virus infection: developing direct-acting antiviral drugs for treatment [online], (2013).

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Acknowledgements

A.S.P. is supported by the U.S. Department of Energy under contract DE-AC52-06NA25396, and supported by NIH grants R01-AI028433, R01-HL109334, R01-AI078881, and the National Center for Research Resources and the Office of Research Infrastructure Programs (ORIP) through grant R01-OD011095.

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  1. Theoretical Biology and Biophysics, MS-K710, Los Alamos National Laboratory, Los Alamos, 87545, NM, USA

    Alan S. Perelson

  2. INSERM, IAME, UMR 1137, 16 Rue Henri Huchard, F-75018, Paris, France

    Jeremie Guedj

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Correspondence to Alan S. Perelson or Jeremie Guedj.

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A.S.P and J.G. have consulted for Gilead Sciences. A.S.P has also consulted for Achillion Pharmaceuticals and Bristol-Myers Squibb.

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Perelson, A., Guedj, J. Modelling hepatitis C therapy—predicting effects of treatment. Nat Rev Gastroenterol Hepatol 12, 437–445 (2015). https://doi.org/10.1038/nrgastro.2015.97

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