Data‐driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins

Recently, the advancement in process analytical technology and artificial intelligence (AI) has enabled the generation of enormous culture data sets from biomanufacturing processes that produce various recombinant therapeutic proteins (RTPs), such as monoclonal antibodies (mAbs). Thus, now it is very important to exploit them for the enhanced reliability, efficiency, and consistency of the RTP‐producing culture processes and for the reduced incipient or abrupt faults. It is achievable by AI‐based data‐driven models (DDMs), which allow us to correlate biological and process conditions and cell culture states. In this work, we provide practical guidelines for choosing the best combination of model elements to design and implement successful DDMs for given hypothetical in‐line data sets during mAb‐producing Chinese hamster ovary cell culture, as such enabling us to forecast dynamic behaviors of culture performance such as viable cell density, mAb titer as well as glucose, lactate and ammonia concentrations. To do so, we created DDMs that balance computational load with model accuracy and reliability by identifying the best combination of multistep ahead forecasting strategies, input features, and AI algorithms, which is potentially applicable to implementation of interactive DDM within bioprocess digital twins. We believe this systematic study can help bioprocess engineers start developing predictive DDMs with their own data sets and learn how their cell cultures behave in near future, thereby rendering proactive decision possible. Park et al. developed a systematic evaluation framework for real‐time data‐driven predictive models, which has potential applications for implementing interactive data‐driven models within bioprocess digital twins. The framework uses historical data and real‐time measurements to select the best prediction model structures, including forecasting strategies, model inputs, and machine/deep learning algorithms for predicting near‐future profiles or identifying fault patterns upon receiving new monitoring data.