Mathematical Model for Measles Virus Production in Batch Bioreactors

Area of Science:

• Biotechnology
• Virology
• Bioprocess Engineering

Background:

• Measles virus (MeV) shows potential as a vector for vaccines, gene therapy, and oncolytic virotherapy.
• MeV production is challenging due to virus sensitivity to environmental factors in bioreactors.
• Precise harvest time is crucial for maximizing MeV yield given its short half-life at optimal cell growth temperatures.

Purpose of the Study:

• To develop a mathematical model for predicting the optimal harvest time (TOH) of recombinant MeV in Vero cell bioreactor cultures.
• To improve the consistency and yield of MeV production for therapeutic applications.

Main Methods:

• A mathematical model was developed to predict the TOH for recombinant MeV production.
• The model's predictions were validated against experimental data from five independent bioreactor runs.
• Parameter analysis was conducted to identify key factors influencing infection dynamics and yield.

Main Results:

• Model predictions for TOH closely matched experimental observations across multiple bioreactor runs.
• Virus attachment parameters and thermal degradation rate were identified as critical factors affecting infection dynamics.
• Seed virus quality, specifically defective interfering particle (DIP) content, significantly impacts production variability.

Conclusions:

• The developed mathematical model effectively predicts MeV harvest time, aiding in optimizing production yields.
• Accurate characterization of seed virus and understanding of infection dynamics are essential for minimizing variability in MeV bioproduction.
• This approach supports the advancement of MeV-based therapeutic applications through improved bioprocess control.