Industrial batch monitoring for cell culture processes during process scale-ups: data challenges and solutions

Abstract

Cell culture process scale-ups are common in biologics manufacturing as the process moves from the early stages of process development to commercial manufacturing. Statistical batch process monitoring (BPM) is frequently deployed for efficient real-time monitoring and control of cell culture processes. A BPM platform relies on historical process data to train in-control model for real-time monitoring of future batches. Deploying a BPM platform during or immediately after process scale-up is nontrivial due to limited batch campaigns at the commercial scale. In the absence of available at-scale commercial data during scale-ups, we propose an approach to reuse data sets generated at bench or pilot-scale for training BPM models at the commercial scale. The proposed method uses a scaling model to learn complex data scaling relations between bench/pilot-commercial scales. The model is then used to synthesise data sets at a commercial scale using existing in-control bench/pilot data. Using synthesised at-scale commercial scale data, a BPM model can be trained and deployed soon after the scale-up, which would not have been possible otherwise. The efficacy of the proposed approach is illustrated in an industrial cell culture process.