Rational design and adaptive management of combination therapies for Hepatitis C virus infection

Ke, Ruian; Loverdo, Claude; Qi, Hangfei; Sun, Ren; Lloyd-Smith, James O.; Kouyos, Roger Dimitri

doi:10.1371/journal.pcbi.1004040

Title: Rational design and adaptive management of combination therapies for Hepatitis C virus infection

Journal Article · Tue Jun 30 00:00:00 EDT 2015 · PLoS Computational Biology (Online)

DOI:https://doi.org/10.1371/journal.pcbi.1004040· OSTI ID:1212703

Ke, Ruian ^[1]; Loverdo, Claude ^[2]; Qi, Hangfei ^[3]; Sun, Ren ^[4]; Lloyd-Smith, James O. ^[5]; Kouyos, Roger Dimitri ^[6]

Univ. of California, Los Angeles, CA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Univ. of California, Los Angeles, CA (United States); Lab. Jean Perrin LJP, Paris (France)
Univ. of California, Los Angeles, CA (United States)
Univ. of California, Los Angeles, CA (United States); Novartis Institutes for BioMedical Research, Emeryville, CA (United States); Zhejiang Univ., Hangzhou (China)
Univ. of California, Los Angeles, CA (United States); National Institutes of Health, Bethesda, MD (United States)
Univ. of Zurich, Irchel (Switzerland)

Recent discoveries of direct acting antivirals against Hepatitis C virus (HCV) have raised hopes of effective treatment via combination therapies. Yet rapid evolution and high diversity of HCV populations, combined with the reality of suboptimal treatment adherence, make drug resistance a clinical and public health concern. We develop a general model incorporating viral dynamics and pharmacokinetics/ pharmacodynamics to assess how suboptimal adherence affects resistance development and clinical outcomes. We derive design principles and adaptive treatment strategies, identifying a high-risk period when missing doses is particularly risky for de novo resistance, and quantifying the number of additional doses needed to compensate when doses are missed. Using data from large-scale resistance assays, we demonstrate that the risk of resistance can be reduced substantially by applying these principles to a combination therapy of daclatasvir and asunaprevir. By providing a mechanistic framework to link patient characteristics to the risk of resistance, these findings show the potential of rational treatment design.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1212703

Journal Information:: PLoS Computational Biology (Online), Vol. 11, Issue 6; ISSN 1553-7358

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 15 works

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