Scalability of single-use biopharmaceutical manufacturing processes using process analytical technology (PAT) tools

Abstract

Monoclonal antibodies (mAbs) are key molecules in biopharmaceutical manufacturing with important therapeutic use such as anticancer drugs. Mammalian cells serve as production hosts for mAbs. However, mammalian cell culture processes are complex and development timelines for new processes are long. To overcome these challenges the industry is moving towards high-throughput, single-use bioreactors and intensified processes. The scalability in both directions (scale-down and scale-up) is a key step towards fast and economic process development. Moreover, novel Process Analytical Technology (PAT) tools aim at improving process understanding and establishing process control and automation resulting in high and consistent product quality. In the first part of this PhD thesis a method to transfer an existing Chinese Hamster Ovary (CHO) cell culture fed-batch process platform into a semi-perfusion process with threefold higher cumulative product titers was developed. Design of Experiment (DoE) as a powerful PAT tool to screen medium and feed compositions speeded up significantly the semi-perfusion process development. The process transfer to a small scale, single-use bioreactor enabled process control for important parameters such as pH and dissolved oxygen (DO) while keeping the experimental costs low. However, important process attributes (e.g. Viable Cell Concentrations (VCC)) were measured offline limiting the automation possibilities. The second part of this thesis demonstrated how the implementation of an advanced inline capacitance sensor can support online monitoring of important biomass related changes in cell culture. The sensor implementation was proven to be scale-independent in single-use bioreactors (50 L up to 2000 L). Additionally, the transferability of the method to different CHO fed-batch processes was demonstrated. The Wet Cell Weight (WCW) and Viable Cell Volume (VCV) were predicted for the complete cultivation duration within an acceptance criterion based on the offline reference method. The VCC, however, correlated with the permittivity signal only until the end of the exponential growth phase due to the single-frequency measurement dependency on cell diameter changes. The third part of this PhD thesis successfully tested the inline capacitance sensor in frequency scanning mode to predict VCCs over the complete culture time by establishing a robust Multivariate Data Analysis (MVDA) model. A small scale bioreactor system served as method development tool. Therefore, a fast and economic development of a robust MVDA model was demonstrated, highlighting the benefits of scale-down models in biopharmaceutical manufacturing.