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The current state-of-the-art in control of cultivation processes for recombinant protein production is examined including the quantitative knowledge that can be activated for this purpose and the measurement techniques that can be employed for control at industrial manufacturing sites.

Online biomass estimation for bioprocess supervision and control purposes is addressed. As the biomass concentration cannot be measured online during the production to sufficient accuracy, indirect measurement techniques are required. Here we compare several possibilities for the concrete case of recombinant protein production with genetically modified Escherichia coli bacteria and perform a ranking. At normal process operation, the best estimates can be obtained with artificial neural networks (ANNs). When they cannot be employed, statistical correlation techniques can be used such as multivariate regression techniques. Simple model-based techniques, e.g., those based on the Luedeking/Piret-type are not as accurate as the ANN approach; however, they are very robust. Techniques based on principal component analysis can be used to recognize abnormal cultivation behavior. For the cases investigated, a complete ranking list of the methods is given in terms of the root-mean-square error of the estimates. All techniques examined are in line with the recommendations expressed in the process analytical technology (PAT)-initiative of the FDA.

The performance of bioreactors is not only determined by productivity but also by process quality, which is mainly determined by variances in the process variables. As fluctuations in these quantities directly affect the variability in the product properties, combatting distortions is the main task of practical quality assurance. The straightforward way of reducing this variability is keeping the product formation process tightly under control. Purpose of this keynote is to show that there is enough evidence in literature showing that the performance of the fermentation processes can significantly be improved by feedback control. Most of the currently used open loop control procedures can be replaced by relatively simple feedback techniques. It is shown by practical examples that such a retrofitting does not require significant changes in the well-established equipment. Feedback techniques are best in assuring high reproducibility of the industrial cultivation processes and thus in assuring the quality of their products. Many developments in supervising and controlling industrial fermentations can directly be taken over in manufacturing processes. Even simple feedback controllers can efficiently improve the product quality. It's the time now that manufacturers follow the developments in most other industries and improve process quality by automatic feedback control.

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.

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Hybrid models aim to describe different components of a process in different ways. This makes sense when the corresponding knowledge to be represented is different as well. In this way, the most efficient representations can be chosen and, thus, the model performance can be increased significantly. From the various possible variants of hybrid model, three are selected which were applied for important biotechnical processes, two of them from existing production processes. The examples show that hybrid models are powerful tools for process optimisation, monitoring and control.

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