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 . 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 .
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 . 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.
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