Bioprocess scale-up is a fundamental component of process development in the biotechnology industry. When scaling up a mammalian cell culture process, it is important to consider factors such as mixing time, oxygen transfer, and carbon dioxide removal. In this study, cell-free mixing studies were performed in production scale 5,000-L bioreactors to evaluate scale-up issues. Using the current bioreactor configuration, the 5,000-L bioreactor had a lower oxygen transfer coefficient, longer mixing time, and lower carbon dioxide removal rate than that was observed in bench scale 5- and 20-L bioreactors. The oxygen transfer threshold analysis indicates that the current 5,000-L configuration can only support a maximum viable cell density of 7 x 10(6) cells mL(-1). Moreover, experiments using a dual probe technique demonstrated that pH and dissolved oxygen gradients may exist in 5,000-L bioreactors using the current configuration. Empirical equations were developed to predict mixing time, oxygen transfer coefficient, and carbon dioxide removal rate under different mixing-related engineering parameters in the 5,000-L bioreactors. These equations indicate that increasing bottom air sparging rate is more efficient than increasing power input in improving oxygen transfer and carbon dioxide removal. Furthermore, as the liquid volume increases in a production bioreactor operated in fed-batch mode, bulk mixing becomes a challenge. The mixing studies suggest that the engineering parameters related to bulk mixing and carbon dioxide removal in the 5,000-L bioreactors may need optimizing to mitigate the risk of different performance upon process scale-up.

Markov chain Monte Carlo (MCMC) method was applied to model kinetics of a fed-batch Chinese hamster ovary cell culture process in 5,000-L bioreactors. The kinetic model consists of six differential equations, which describe dynamics of viable cell density and concentrations of glucose, glutamine, ammonia, lactate, and the antibody fusion protein B1 (B1). The kinetic model has 18 parameters, six of which were calculated from the cell culture data, whereas the other 12 were estimated from a training data set that comprised of seven cell culture runs using a MCMC method. The model was confirmed in two validation data sets that represented a perturbation of the cell culture condition. The agreement between the predicted and measured values of both validation data sets may indicate high reliability of the model estimates. The kinetic model uniquely incorporated the ammonia removal and the exponential function of B1 protein concentration. The model indicated that ammonia and lactate play critical roles in cell growth and that low concentrations of glucose (0.17 mM) and glutamine (0.09 mM) in the cell culture medium may help reduce ammonia and lactate production. The model demonstrated that 83% of the glucose consumed was used for cell maintenance during the late phase of the cell cultures, whereas the maintenance coefficient for glutamine was negligible. Finally, the kinetic model suggests that it is critical for B1 production to sustain a high number of viable cells. The MCMC methodology may be a useful tool for modeling kinetics of a fed-batch mammalian cell culture process.

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