Abstract
This work presents a hybrid-modeling approach to monoclonal antibody (mAb) production processes design using automated bioreactor equipment. Experimental data covering a reasonable yet broad range of cultivation conditions was collected by the equipment. Using the data, a model applicable to a wide range of cultivation conditions was developed. In the modeling, a data-driven model was applied to describe complicated/unknown phenomena that could not be captured by previously proposed mechanistic models. In the hybrid model, while maintaining the mass balance of the mechanistic model, coefficients of the equations were estimated with random forest regression. Overall, the model could describe the dynamic concentration profiles of product mAb and quality-relevant impurities depending on the media/glucose feeding conditions. The model was then applied to determine an optimal condition that maximized product mAb concentration and satisfied the impurity constraints. The work can further support model-based design of cell cultivation processes with considering multi-input & multi-output nature.