Model Predictive Control for Dissolved Oxygen and Temperature to Study Adeno-Associated Virus (AAV) Production in Bioreactor

Abstract

Gene therapy is advancing rapidly, with Recombinant Adeno-associated virus (rAAV) being investigated for potential use in treating cancer and neurological disorders. Plasmid DNA transfection and viral infection are standard methods for producing large-scale rAAV vectors. However, improving yield production requires careful monitoring and control of process state variables, which can be expensive and time-consuming. This thesis proposes a model predictive control (MPC) model that can efficiently monitor, predict, and optimize the final product by controlling state variables like DOT and temperature. The model relies on an unstructured mechanistic kinetic model designed explicitly based on rAAV upstream production. Monitoring viral vector production based on substrate or biomass concentration enhances bioprocess production efficiency. However, other state variables like dissolved oxygen (DO), pH, and temperature should also be considered. The objective of this thesis is to enhance cell growth in bioreactors by regulating dissolved oxygen and temperature levels using a Model Predictive Control (MPC) system. This model can be employed in different processes to enhance cell growth and examine the impact of control measures. The goal is to achieve a high cell density, increase productivity, and lower costs in a shorter duration. Simulink, a software tool developed by MATLAB, seamlessly integrates Ordinary Differential Equations (ODEs) to optimize bioprocesses in bioreactors. The Model Predictive Control (MPC) controller expertly regulates Dissolved Oxygen Tension (DOT) and temperature, thereby increasing cell growth concentrations. This sophisticated controller efficiently manages multiple variables simultaneously and exceeds the Proportional Integral Derivative (PID) controller. The model is straightforward to comprehend and promptly responds to anomaly data. To evaluate the suggested resolution, we conducted tests on both PID and MPC controllers by introducing measurement noise to the DOT. Our analysis indicated that MPC demonstrated superior performance based on the ISE (Integral of Squared Error), IAE (Integral of Absolute Error), and ITAE (Integral of Time-weighted Absolute Error), all of which were substantially higher for the PID controller. Regardless of changing conditions, MPC adeptly tracks the setpoint and optimizes the variable to enhance production efficiency.