A PDE multiscale model of hepatitis C virus infection can be transformed to a system of ODEs

Area of Science:

• Virology
• Mathematical Biology
• Pharmacometrics

Background:

• Direct-acting antivirals (DAAs) are effective treatments for hepatitis C virus (HCV) infection.
• Optimizing DAA treatment regimens, especially multi-drug combinations, requires accurate quantification of antiviral effects.
• Existing multiscale mathematical models using partial differential equations (PDEs) for HCV replication are computationally intensive and difficult to parameterize.

Purpose of the Study:

• To develop a user-friendly and computationally efficient method for analyzing DAA treatment efficacy.
• To transform a complex PDE-based multiscale model of HCV infection into an equivalent ordinary differential equation (ODE) model.
• To facilitate improved data analysis and optimization of multi-drug DAA combinations.

Main Methods:

• Mathematical model transformation from PDEs to ODEs without assumptions.
• Confirmation of numerical solution consistency between the original PDE model and the transformed ODE model.
• Leveraging established methods for ODE parameter estimation.

Main Results:

• A mathematically identical ODE model was derived from a standard PDE multiscale model of HCV infection.
• The transformed ODE model avoids the time-consuming computations associated with PDE models.
• The ODE model offers a more accessible approach for parameter estimation and data analysis.

Conclusions:

• The developed ODE model provides a computationally efficient and broadly available tool for analyzing HCV DAA treatments.
• This model aggregation simplifies complex biological systems, aiding in the optimization of antiviral drug combinations.
• The approach facilitates further data analysis and enhances the understanding of intracellular viral replication dynamics.