Modeling-Based Response-Guided DAA Therapy for Chronic Hepatitis C to Identify Individuals for Shortening Treatment Duration

Modeling-Based Response-Guided Hepatitis C Treatment During Pregnancy and Postpartum

Treating hepatitis C virus (HCV) in pregnancy would address HCV during prenatal care and potentially reduce the risk of vertical transmission. Response-guided therapy could provide a means to individualize and the reduce duration of HCV treatment during pregnancy. Data from a 27-year-old woman indicated that, pretreatment, HCV was stable and that it dropped in a biphasic manner during sofosbuvir/velpatasvir therapy, reaching target not detected at time of delivery-16 days post-initiation of therapy. Mathematical modeling of measured HCV at days 0, 7, and 14 predicted that cure could have been achieved after 7 weeks of sofosbuvir/velpatasvir, reducing the duration of therapy by 5 weeks.

Advances in Parameter Estimation and Learning from Data for Mathematical Models of Hepatitis C Viral Kinetics

Mathematical models, some of which incorporate both intracellular and extracellular hepatitis C viral kinetics, have been advanced in recent years for studying HCV–host dynamics, antivirals mode of action, and their efficacy. The standard ordinary differential equation (ODE) hepatitis C virus (HCV) kinetic model keeps track of uninfected cells, infected cells, and free virus. In multiscale models, a fourth partial differential equation (PDE) accounts for the intracellular viral RNA (vRNA) kinetics in an infected cell. The PDE multiscale model is substantially more difficult to solve compared to the standard ODE model, with governing differential equations that are stiff. In previous contributions, we developed and implemented stable and efficient numerical methods for the multiscale model for both the solution of the model equations and parameter estimation. In this contribution, we perform sensitivity analysis on model parameters to gain insight into important properties and to ensure our numerical methods can be safely used for HCV viral dynamic simulations. Furthermore, we generate in-silico patients using the multiscale models to perform machine learning from the data, which enables us to remove HCV measurements on certain days and still be able to estimate meaningful observations with a sufficiently small error.