The development of robust Chinese Hamster Ovary (CHO) cell lines expressing high titers of monoclonal antibodies (MAbs) is central to bioprocess development. Following transfection and pool generation, clone selection is critical, as individual clones often behave differently in stirred-tank bioreactors. We propose a multivariate data analysis (MVDA) approach for clone selection that integrates productivity, growth, expression stability, and metabolism, with adaptable weighting based on process priorities. This method was applied to in-house data from CHO clones producing omalizumab. From 24 candidates, eight stable, high-performing clones were advanced for evaluation in 0.75-1 L bioreactors. MVDA revealed that including stability and metabolic parameters alters the ranking of lead clones compared with conventional screening. To assess scalability, cultures were run with or without air overlay to modulate dissolved CO2. Cultures without overlay reached up to 25% pCO2 (190 mmHg) and unexpectedly showed improved performance: 1.69-fold higher titer, 1.43-fold greater cell-specific productivity, 1.11-fold higher peak cell density, extended viability, and sustained product accumulation over 17-21 days. By integrating statistical tools and a historical dataset, our MVDA method identified a robust lead clone performing consistently across CO2 conditions, supporting its application in early upstream bioprocess development.
Gaudreault et al. (2026) studied this question.