Heterogeneous conditions in dissolved oxygen affect N-glycosylation but not productivity of a monoclonal antibody in hybridoma cultures.

It is known that heterogeneous conditions exist in large-scale animal cell cultures. However, little is known about how heterogeneities affect cells, productivities, and product quality. To study the effect of non-constant dissolved oxygen tension (DOT), hybridomas were subjected to sinusoidal DOT oscillations in a one-compartment scale-down simulator. Oscillations were forced by manipulating the inlet oxygen partial pressure through a feedback control algorithm in a 220-mL bioreactor maintained at a constant agitation. Such temporal DOT oscillations simulate spatial DOT gradients that can occur in large scales. Different oscillation periods, in the range of 800 to 12,800 s (axis of 7% (air saturation) and amplitude of 7%), were tested and compared to constant DOT (10%) control cultures. Oscillating DOT decreased maximum cell concentrations, cell growth rates, and viability indexes. Cultures at oscillating DOT had an increased glycolytic metabolism that was evidenced by a decrease in yield of cells on glucose and an increase in lactate yield. DOT gradients, even several orders of magnitude higher than those expected under practical large-scale conditions, did not significantly affect the maximum concentration of an IgG(1) monoclonal antibody (MAb). The glycosylation profile of the MAb produced at a constant DOT of 10% was similar to that reported in the literature. However, MAb produced under oscillating culture conditions had a higher amount of triantennary and sialylated glycans, which can interfere with effector functions of the antibody. It was shown that transient excursions of hybridomas to limiting DOT, as occurs in deficiently mixed large-scale bioreactors, is important to culture performance as the oscillation period, and thus the time cells spent at low DOT, affected cell growth, metabolism, and the glycosylation pattern of MAb. Such results underline the importance of monitoring protein characteristics for the development of large-scale processes.