2008 - Marseille - France

PAGE 2008: Methodology
Eric Snoeck

The challenge of modelling hepatitis C virus dynamics after long term treatment: Application of MONOLIX

Eric Snoeck (1), Marc Lavielle (2,3), Pascal Chanu (4), Karin Jorga (4) and Nicolas Frey (4)

(1) Exprimo NV, Mechelen, Belgium; (2) INRIA Futurs, Paris, France; (3) University Paris 5, Paris, France; (4) F. Hoffmann-La Roche, Basel, Switzerland

Background: Mathematical models for hepatitis C viral (HCV) RNA kinetics have provided important insights into the life cycle of HCV and have increased the understanding of the mechanism of action of the current standard treatment of care: i.e. combination therapy of pegylated interferon (PEG-IFN) and ribavirin [1]. However, these models are unable to explain all of the observed long-term HCV RNA profiles under treatment and after cessation of therapy [2].

Objectives: 1) To develop a HCV viral kinetic model describing the individual HCV RNA profiles in chronic hepatitis C (CHC) patients after a long-term treatment with PEG-IFN alfa 2a (Pegasys®) and ribavirin (Copegus®). 2) To undertake exploratory mechanistic simulations explaining phenomena such as break-through during therapy and relapse after discontinuation of therapy.

Methods: A total of 18937 HCV RNA concentration-time data were available from 1773 CHC patients who participated in clinical trials evaluating different 24 or 48-week dosing schemes of PEG-IFN alfa-2a as monotherapy or in combination with different doses of ribavirin. The original model of HCV infection and treatment based on the Lotka-Volterra principle [3], including three differential equations representing the populations of target cells (T), productively infected cells (I) and virus (V), was modified and extended (e.g. liver regeneration and viral extinction) to allow fitting long-term viral load data. The MATLAB® version of MONOLIX 2.3 was used in combination with user-defined functions in C++ solving the ODE's describing the kinetics of T, I and V. Finally, an extension of the SAEM algorithm was used to handle left censoring due to the lower limits of quantification of the HCV RNA levels [4].

Results: The individual long-term HCV RNA versus time profiles were well described by the extended HCV viral kinetic model, with estimated free virus clearance rates and infected cells death rates similar to those previously found in the literature. The estimated effect of PEG-IFN alfa-2a was confirmed to be higher in HCV genotype non‑1 patients as compared to patients infected with HCV genotype 1. The model provided a convincing picture of how ribavirin enhances the long-term outcome of interferon-based therapy. Analogous to HIV [5], exploratory mechanistic simulations revealed that the concept of the basic reproductive ratio (RR0) is playing a major role in predicting the individual outcome in CHC patients.

Conclusions and Perspectives: Hepatitis C virus dynamics after long-term treatment in 1773 CHC patients was successfully modelled using MONOLIX. Mechanistic simulations have provided additional insights into the understanding of the possible synergy between ribavirin and PEG-IFN and the factors explaining long-term individual outcomes in CHC patients which could assist in treatment decisions. The effect of other hepatitis C drugs with a new mechanism of action can be incorporated into the existing model allowing predictions for these other drugs or drug combinations to aid in optimizing the design of future clinical trials.

[1] Layden-Almer JE, Cotler SJ, Layden TJ. Viral kinetics in the treatment of chronic hepatitis C. Journal of Viral Hepatitis, 2006; 13: 499-504.
[2] Layden JE, Layden TJ. Viral kinetics of hepatitis C: new insights and remaining limitations. Hepatology, 2002; 35: 967-970.
[3] Neumann AU, LAM NP, Dahari H, Gretch DR, Wiley TE, Layden TJ, Perelson AS. Hepatitis C viral dynamics in vivo and the antiviral efficacy of interferon-alpha therapy. Science, 1998; 282: 103-107.
[4] Samson A, Lavielle M, Mentré F. Extension of the SAEM algorithm to left-censored data in non-linear mixed-effects model: application to HIV dynamics model. Computational Statistics and Data Analysis, 2006; 51: 1562-1574.
[5] Jacqmin P, McFadyen L, Wade JR. Basic PK/PD principles of proliferative and circular systems. PAGE, 2007; Abstr 1194.

Reference: PAGE 17 (2008) Abstr 1348 [www.page-meeting.org/?abstract=1348]
Oral Presentation: Methodology