By means of a model predictive control strategy it was possible to ensure a high batch-to-batch reproducibility in animal cell (CHO-cell) suspensions cultured for a recombinant therapeutic protein (EPO) production. The general control objective was derived by identifying an optimal specific growth rate taking productivity, protein quality and process controllability into account. This goal was approached indirectly by controlling the oxygen mass consumed by the cells which is related to specific biomass growth rate and cell concentration profile by manipulating the glutamine feed rate. Process knowledge represented by a classical model was incorporated into the model predictive control algorithm. The controller was employed in several cultivation experiments. During these cultivations, the model parameters were adapted after each sampling event to cope with changes in the process' dynamics. The ability to predict the state variables, particularly for the oxygen consumption, led to only moderate changes in the desired optimal operational trajectories. Hence, nearly identical oxygen consumption profiles, cell and protein titers as well as sialylation patterns were obtained for all cultivation runs.

Batch-to-batch reproducibility of animal cell cultures can significantly be enhanced using process control procedures. Most informative signals for advanced process control can be derived from the volume fractions of oxygen and carbon dioxide in the vent line of the reactors. Here we employed simple low-cost sensors, previously not considered for off-gas analysis at a laboratory-scale cell cultures, and compared them with a simultaneously used quadrupole mass spectrometer, i.e., the standard equipment. A decisive advantage is that the sensors did not need any calibration and are easy to use. We show that monitoring and advanced control of cell cultures can significantly be simplified using the devices tested here and that the same batch-to-batch reproducibility can be obtained with much less effort than before.

No abstract

A simple well-performing adaptive control technique for pH control in fermentations of recombinant protein production processes is described and its design procedure is explained. First, the entire control algorithm was simulated and parameterized. Afterwards it was tested in real cultivation processes. The results show that this simple technique leads to significant reductions in the fluctuations of the pH values in microbial cultures at a minimum of expenditures. The signal-to-noise ratio and thus the information captured by the pH signal were increased by about an order of magnitude. This leads to a substantial improvement in the noise of many other process signals that are used to monitor and control the process. For instance, respiratory off-gas data of CO(2) and its derived carbon dioxide production rate signals from the cultures carry much less noise as compared to those values obtained with conventional pH control. Detailed process analysis revealed that even very small pH jumps of 0.03 values during the fermentation were shown to result in pronounced deflections in CO(2)-volume fraction of 8% (peak to peak). The proposed controller, maintaining the pH within the interval of 0.01 around the setpoint, reduces the noise considerably.