Integrating Data-Driven Strategies into Model Predictive Control for Enhanced Production of Human Interferon α2b in Glycoengineered <i>Pichia pastoris</i>

The biopharmaceutical industry is experiencing rapid growth, necessitating scalable optimization and control strategies to meet strict process objectives. Model predictive control (MPC) offers a robust framework for regulating complex bioprocesses; however, its performance critically depends on the availability of reliable process models. While mechanistic models are often preferred, practical limitations have accelerated the adoption of data-driven approaches. In this study, we evaluate the applicability of artificial neural networks (ANNs) and Gaussian process (GP) models in MPC for fed-batch cultivation of glycoengineered Pichia pastoris to produce human interferon α2b (huIFNα2b). Experiments were performed in a fermentation calorimeter with real-time monitoring of P. pastoris metabolism through metabolic heat rate, capacitance, and exhaust gas analysis. Comparative results demonstrate that GP-based MPC achieved superior process control, efficient substrate utilization, and a 1.1-fold increase in huIFNα2b productivity relative to ANN-based MPC. Furthermore, GP-based adaptation of feeding strategies reduced methanol consumption by 14% compared with ANN-based control. These findings highlight the potential of GP-driven MPC as a promising tool for enhancing productivity and sustainability in industrial bioprocesses.