Shanshan Bi, Jenny Zheng, Liang Li, Hechuan Wang, Zheng Guan, Fengbo Xie, Gailing Li
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Objectives: To understand the underlying mechanisms of each class of direct-acting antiviral (DAAs) agents and/or their interplay when co-administered for the treatment of chronic HCV and also to predict long term efficacy from early clinical outcome, using Model-Based Meta-Analysis.
Methods: A mechanism-based PK/PD model was developed to model the dynamics of hepatitis C virus and the antiviral effect of selected DAAs. 3 DAAs (NS3/4A and NS5A inhibitor) were selected to build the dynamic model. The data sources are collected from extensive literature search based on PUBMED and http://www.natap.org from year 2007 to 2013. The final model described the viral kinetic in hepatitis C via a biphasic decline in viral load with a rapid first phase lasting 1-2 days followed by a slower phase [1]. We learned that the initial rate of viral decline did not depend on the DAAs dose from the published representative data. A linear model with a breakpoint for the beginning time of the HCV relapse was used. A parameter of inhibitions for the virus production was estimated to describe the DAAs effect for these 3 compounds, respectively. The comparison of the potency for the 3 DAAs was evaluated.
Results: The model was developed using summary levels data from 5 clinical trials, 17 unique arms, and assessed by internal evaluation techniques. The beginning time for the relapse after virological response were estimated to be 2.75 days (NS3/4A), 2 days (NS5A) and 2.31 days (NS5A) for these 3 DAAs, respectively. The drug effect for the rapid first phase were different from the slower phase. The coefficient for the inhibition of the virus production of the NS3A/4A inhibitor was estimated to be 0.234 and 0.32 for the biphasic decline, respectively. And the NS5A inhibitors have ~4/2-fold greater drug potency than the NS3A/4A inhibitor.
Conclusions: This study demonstrates the application of MBMA in the field of HCV treatment. The modeling and simulation results will be used to improve confidence in the selection of DAA combinations.
[1] Laetitia Canini • Alan S. Perelson, Viral kinetic modeling: state of the art. J Pharmacokinet Pharmacodyn. 2014 Oct;41(5):431-43.
Reference: PAGE 24 (2015) Abstr 3502 [www.page-meeting.org/?abstract=3502]
Poster: Drug/Disease modeling - Infection