Traces matter: Targeted optimization of monoclonal antibody N-glycosylation based on/by implementing automated...

The use of high-throughput systems in cell culture process optimization offers various opportunities in biopharmaceutical process development. Here we describe the potential for acceleration and enhancement of product quality optimization and de novo bioprocess design regarding monoclonal antibody N-glycosylation by using an iterative statistical Design of Experiments (DoE) strategy based on our automated microtiter plate-based system for suspension cell culture. In our example, the combination of an initial screening of trace metal building blocks with a comprehensive DoE-based screening of 13 different trace elemental ions at three concentration levels in one run revealed most effective levers for N-glycan processing and biomass formation. Obtained results served to evaluate optimal concentration ranges and the right supplementation timing of relevant trace elements at shake flask and 2 L bioreactor scale. This setup identified manganese, copper, zinc, and iron as major factors. Manganese and copper acted as inverse key players in N-glycosylation, showing a positive effect of manganese and a negative effect of copper on glycan maturation in a zinc-dependent manner. Zinc and iron similarly improved cell growth and biomass formation. These findings allowed determining optimal concentration ranges for all four trace elements to establish control on desired product quality attributes regarding premature afucosylated and mature galactosylated glycan species. Our results demonstrates the power of combining robotics with DoE screening to enhance product quality optimization and to improve process understanding, thus, enabling targeted product quality control.