Using a Statistical and Risk Based Approach to Improve Process Robustness and Control in a Late Stage Antibody Process

This presentation describes an approach for improving robustness and enhancing control of a late stage antibody process. Traditional approaches have usually focused on developing independent statistical models for each individual process step. This limits the understanding of overall process robustness and the inter-relationship between process steps. A more holistic approach was used in the development of this late stage antibody process. A risk based approach identified process parameters that may influence quality attributes, which were then used to construct statistical experimental designs. These designs were used to characterize and create predictive models for each step of the process from cell culture through final ultrafiltration/diafiltration. Most of the impurities generated in cell culture were independently removed by the various downstream purification steps with minimal impact from the load levels in the clarified cell culture fluid or eluates from a previous chromatography step. However, a single type of impurity was identified as the having the greatest potential impact on the robustness of the manufacturing process because the impurity levels varied considerably during cell culture. Process parameters affecting this impurity generation and removal were holistically evaluated through the entire process, critical parameters identified, and effective controls established to regulate the impurity levels. This overall approach resulted in a more complete understanding of the design space around each process step and the capability of the process as a whole to clear the impurity to an acceptable level.