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

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.
Authors:
 [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [6]
  1. Univ. of California, Los Angeles, CA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of California, Los Angeles, CA (United States); Lab. Jean Perrin LJP, Paris (France)
  3. Univ. of California, Los Angeles, CA (United States)
  4. Univ. of California, Los Angeles, CA (United States); Novartis Institutes for BioMedical Research, Emeryville, CA (United States); Zhejiang Univ., Hangzhou (China)
  5. Univ. of California, Los Angeles, CA (United States); National Institutes of Health, Bethesda, MD (United States)
  6. Univ. of Zurich, Irchel (Switzerland)
Publication Date:
OSTI Identifier:
1212703
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 11; Journal Issue: 6; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Research Org:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES dose prediction methods; number theory; drug therapy; simulation and modeling; Hepatitis C virus; human genetics; cell death; treatment guidelines