Applications of Discrete-Event Dynamic Simulation in HCV Treatment Dynamics
Bambang Adiwijaya (1), Joshua Henshaw (1), Maria Rosario (1), Holly Kimko (2), Varun Garg (1)
(1) Vertex Pharmaceuticals, Incorporated, Cambridge, MA, USA; (2) Johnson and Johnson. Raritan, NJ, USA
Backgrounds: The treatment objective in patients chronically infected with Hepatitis C Virus (HCV) is viral eradication, which allows patients to achieve a sustained viral response (SVR). Mathematical models of HCV dynamics in interferon and ribavirin treatment have been useful in predicting the percentage of patients achieving SVR [1]. In treatment combinations with direct-acting antiviral(s) such as telaprevir, the HCV must be considered as a mixed population, consisting predominantly of wild-type (WT) and a small population of variants with varying levels of susceptibility to telaprevir [2,3]. The HCV population response to telaprevir treatment in monotherapy has been quantified previously with a multi-variant viral dynamic model [4].
Objectives: To develop a HCV RNA dynamic model that predicts viral eradication in HCV treatment with combination regimens utilizing specifically-targeted antiviral therapies for hepatitis C (STAT-C).
Methods: HCV RNA and drug exposure vs. time data from a total of 1162 patients, participated in clinical trials evaluating regimens including Peg-IFN-alfa-2a, ribavirin and telaprevir, were used to improve a model previously published [4]. Eradication of each viral variant was modeled as discrete events occurring at variable times during treatment, and solved using Jacobian® software (RES group, Inc.).
Results: The improved model was qualified by comparing the a priori predictions and the observed data from two subsequent studies. The model-predicted SVR rates were compared to observed SVR rates across different patient populations with various durations of treatment and two dose-schedule regimens. The discrete-event simulations yielded reduced rates of integration failures commonly observed in other dynamic simulation software not specifically tailored to solve discrete-event system such as NONMEM® or Matlab®.
Conclusions: A model of viral eradication that requires an algorithm to accommodate discrete-events accurately predicts treatment-driven viral eradications in clinical study setting. The modeling and simulation approach was useful to support decisions in clinical trials.
References:
1. Dixit NM, Layden-Almer JE, Layden TJ, Perelson AS (2004) Modelling how ribavirin improves interferon response rates in hepatitis C virus infection. Nature 432: 922-924.
2. Sarrazin C, Kieffer TL, Bartels D, Hanzelka B, Muh U, et al. (2007) Dynamic Hepatitis C Virus Genotypic and Phenotypic Changes in Patients Treated With the Protease Inhibitor Telaprevir. Gastroenterology 132: 1767-1777.
3. Kieffer TL, Sarrazin C, Miller JS, Welker MW, Forestier N, et al. (2007) Telaprevir and pegylated interferon-alpha-2a inhibit wild-type and resistant genotype 1 hepatitis C virus replication in patients. Hepatology 46: 631-639.
4. Adiwijaya BS, Herrmann E, Hare B, Kieffer TL, Lin C, et al. (2010) A multi-variant, viral dynamic model of genotype 1 HCV to assess the in vivo evolution of protease-inhibitor resistant variants. People Library of Science Computational Biology. In Press.
