Towards "tighter" titer: model predictive control in fed-batch bioreactors

Cell culture processes for the production of therapeutic monoclonal antibodies (mAbs) often suffer from high batch-to-batch variability in the final concentration of the mAb (titer) in the bioreactor. In this study, we investigated the adoption of model predictive control (MPC) to reduce this variability, even under process disturbances. Both Linear MPC (LMPC) and Nonlinear MPC (NMPC) were tested in a Design of Experiments (DoE) involving 24 bioreactors at a 250 mL scale using human-in-the-loop (HITL) tracking MPC. The LMPC used a linear state-space model, while the NMPC employed a polynomial-NARX model to describe the process dynamics. Different estimators were applied for each MPC scheme. The effect of two different disturbances was evaluated. Experimental results showed that MPC significantly reduced process variation, with titer tracking error decreasing from a range of 18 percentage points without MPC to approximately 5 percentage points for both LMPC and NMPC case studies. These findings suggest that MPC can effectively control titer in bioprocessing, offering potential for increased efficiency and productivity.