A robust feeding strategy to maintain set-point glucose in mammalian fed-batch cultures when input parameters have a large error.

Industrial CHO cell cultures run under fed-batch conditions are required to be controlled in particular ranges of glucose, while glucose is constantly consumed and must be replenished by a feed. The most appropriate feeding rate is ideally stoichiometric and adaptive in nature to balance the dynamically changing rate of glucose consumption. However, high errors in biomass and glucose estimation as well as limited knowledge of the true metabolic state challenge the control strategy. In this contribution, we take these errors into account and simulate the output with uncertainty trajectories in silico in order to control glucose concentration. Other than many control strategies, which require parameter estimation, our assumptions are founded on two pillars: (i) first principles and (ii) prior knowledge about the variability of fed-batch CHO cell culture. The algorithm was exposed to an in-silico Design of Experiments (DoE), in which variations of parameters were changed simultaneously, such as clone-specific behavior, precision of equipment and desired control range used. The results demonstrate that our method achieved the target of holding the glucose concentration within an acceptable range. A robust and sufficient level of control could be demonstrated even with high errors for biomass or metabolic state estimation. In a time where blockbuster drugs are queuing up for time slots of their production, this transferable control strategy that is independent of tedious establishment runs may be a decisive advantage for rapid implementation during technology transfer and scale up and decrease in campaign change over time. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:317-336, 2017.