A robust scale-down model development and process characterization for monoclonal antibody biomanufacturing using multivariate data analysis

Abstract

Quality by Design (QbD) principles are extensively applied in biopharmaceutical manufacturing processes to ensure the consistent production of high-quality biotherapeutic products through achieving a deeper understanding of critical process parameters (CPPs), critical quality attributes (CQAs), and their interrelationships as well as establishing appropriate process control strategies. To do so, herein, we involve utilizing advanced multivariate data analysis (MVDA) in the context of scale-down model (SDM) development and validation as an ingenious approach for enhancing process efficiency and achieving greater regulatory compliance in the biomanufacturing of biologics. First, MVDA was applied to develop and evaluate several SDMs under various production conditions, including changes in scale-dependent parameters. This allowed the establishment of a practical SDM that closely approximated the process performance of manufacturing-scale batches. Furthermore, this approach enabled the identification not only of potential CPPs but also specific performance attributes such as ammonia, that had a significant impact on the CQAs. Moreover, it was deduced that the N-1 seed culture represents a critical process step influencing both quality and performance attributes in the upstream process from these approaches. This deduction was subsequently confirmed through experimental validation. Our findings offer valuable insights into streamlining the development of upstream biologics, particularly in terms of process characterization, thereby suggesting strategies for time and cost savings.