Rational Design and Adaptive Management of Combination Therapies for Hepatitis C Virus Infection

Ruian Ke (1) , Claude Loverdo (2, 1) , Hangfei Qi (1) , Ren Sun (3, 4, 1) , James O. Lloyd-Smith (5, 1)

1 UCLA - University of California [Los Angeles]
2 LJP - Laboratoire Jean Perrin
3 NIBR - Novartis Institutes for BioMedical Research
4 Zhejiang University
5 Fogarty International Center

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.

Domaines: Maladies infectieuses

Ruian Ke, Claude Loverdo, Hangfei Qi, Ren Sun, James O. Lloyd-Smith. Rational Design and Adaptive Management of Combination Therapies for Hepatitis C Virus Infection. PLoS Computational Biology, 2015, 11 (6), pp.e1004040. ⟨10.1371/journal.pcbi.1004040⟩. ⟨hal-01224118⟩