Pseudo perfusion of Chinese Hamster Ovary (CHO) cells as a reliable platform for data generation to model and guide continuous perfusion biomanufacturing

Chinese Hamster Ovary (CHO) cell monoclonal antibody (mAb) production in continuous perfusion has witnessed a renewed interest within the biopharmaceutical industry. Widespread implementation of perfusion biomanufacturing, however, remains hindered by long process development timelines and high costs. Use of predictive scale-down platforms to generate large informative metabolic datasets and guide process development decisions is critical to decreasing a molecules time to market. While scale-down platforms based on the pseudo perfusion concept have been previously reported, they have not been rigorously validated. They are often limited by oxygen transport or insufficient metabolic characterization, reducing their role to a preliminary screening tool. Here, we report the design and validation of a pseudo perfusion platform based on a phenotype-driven approach to ascertain that the process emulates continuous perfusion characteristics and is not oxygen limited. Beyond metabolic and cell size steady state, we show that our pseudo perfusion design enables cell cycle subpopulation and intracellular antibody expression steady state. We also demonstrate that pseudo perfusion robustly predicts amino acid demands in continuous perfusion bioreactors with exceptional linear correlation across a broad range of cell-specific perfusion rates (CSPRs). When coupling the pseudo perfusion platform developed here with a workflow for metabolic characterization, we significantly augment the dimensionality and reliability of data which can be generated at this scale to gain actionable insights towards perfusion process design, ultimately reducing process development timelines and the associated costs. HighlightsResidual lactate is a key proxy for oxygen transport in scale down platform design Novel flow cytometry workflow confirms cell cycle and intracellular steady state Pseudo perfusion robustly predicts metabolic phenotypes in continuous perfusion K-means clustering analysis of nutrient rates provides insight into media design