Modelling how responsiveness to interferon improves interferon-free treatment of hepatitis C virus infection

Venugopal, Vishnu and Padmanabhan, Pranesh and Raja, Rubesh and Dixit, Narendra M (2018) Modelling how responsiveness to interferon improves interferon-free treatment of hepatitis C virus infection. In: PLOS COMPUTATIONAL BIOLOGY, 14 (7).

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Abstract

Direct-acting antiviral agents (DAAs) for hepatitis C treatment tend to fare better in individuals who are also likely to respond well to interferon-alpha (IFN), a surprising correlation given that DAAs target specific viral proteins whereas IFN triggers a generic antiviral immune response. Here, we posit a causal relationship between IFN-responsiveness and DAA treatment outcome. IFN-responsiveness restricts viral replication, which would prevent the growth of viral variants resistant to DAAs and improve treatment outcome. To test this hypothesis, we developed a multiscale mathematical model integrating IFN-responsiveness at the cellular level, viral kinetics and evolution leading to drug resistance at the individual level, and treatment outcome at the population level. Model predictions quantitatively captured data from over 50 clinical trials demonstrating poorer response to DAAs in previous non-responders to IFN than treatment-naive individuals, presenting strong evidence supporting the hypothesis. Model predictions additionally described several unexplained clinical observations, viz., the percentages of infected individuals who 1) spontaneously clear HCV, 2) get chronically infected but respond to IFN-based therapy, and 3) fail IFN-based therapy but respond to DAA-based therapy, resulting in a comprehensive understanding of HCV infection and treatment. An implication of the causal relationship is that failure of DAA-based treatments may be averted by adding IFN, a strategy of potential use in settings with limited access to DAAs. A second, wider implication is that individuals with greater IFN-responsiveness would require shorter DAA-based treatment durations, presenting a basis and a promising population for response-guided therapy.

Item Type:	Journal Article
Publication:	PLOS COMPUTATIONAL BIOLOGY
Publisher:	PUBLIC LIBRARY SCIENCE, 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
Additional Information:	Copy right for this article belong to PUBLIC LIBRARY SCIENCE, 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
Department/Centre:	Division of Mechanical Sciences > Chemical Engineering
Date Deposited:	23 Aug 2018 15:59
Last Modified:	12 Oct 2018 10:27
URI:	http://eprints.iisc.ac.in/id/eprint/60491

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