(353e) A Combined Computational-Fluid-Dynamics Model and Advanced Control Strategies for Direct Perfusion Bioreactor Systems

Tissue engineering (TE) involves growing cells within supporting scaffolds to obtain structures for in vivo implantation with adequate functionality. For the cultivation of cells, bioreactor systems are mandatory to receive tissue-engineered grafts with uniform cell distribution, growth, and viability in a reproducible way. The application of bioreactor systems gives rise to improved tissue quality with respect to static cultivation by providing proper cultivation conditions to mimic an in vivo environment [1]. To allow for the investigation of proper cultivation conditions and the reproducible generation of tissue engineered grafts, a bioreactor system, which comprises the control of crucial cultivation parameters, i.e. flow rate and nutrient concentration, in bioreactors, is beneficial. Furthermore, the use of a bioreactor as an automated cell seeding tool enables even cell distributions on stable scaffolds. The use of a perfused bioreactor allows for an optimal supply with nutrients, while toxic metabolites are effectively removed from the cell culture.[2] One of the major challenges bioreactor technology is facing at present is to translate research-scale production models into clinically applicable manufacturing designs that are reproducible, clinically effective and economically acceptable, while complying with Good Manufacturing Practice (GMP) requirements [3].

The development of mathematically and computationally orientated research has failed to catch up with the recent developments in biology. This can be attributed to a lack of true integration between engineering and biological disciplines [4]. However, it is becoming more apparent that computational methods can be a powerful and cost-effective tool for the design, optimization and control of bioreactor systems. With all the new recent advances, TE is now at the stage where it can shift from research-based technology into large-scale and commercially successful products. Nevertheless, we are ultimately faced with the fact that even the most clinically successful products will need to demonstrate cost-effectiveness and cost-benefits over existing therapies, absolute safety for patients, manufacturers, and the environment, and compliance to the current regulations.

This work sets the foundations for the design of several control algorithms to facilitate manufacturing for any type of cell culture using a continuous perfusion bioreactor. The algorithms are also capable of dealing with any disturbances in the process. Different types of control strategies are designed, implemented and tested starting from classic PID controller as well as advance model predictive control strategies. These strategies are designed to be able to work with different manipulated and controlled variables depending on the needs of the process. As Computational Fluid Dynamics (CFD) has the capability of describing the interplay between the flow field in a perfusion bioreactor and the cell growth kinetics the latter will be used to develop a comprehensive high fidelity mathematical model. Usually the mathematical model for such process is too complex to be used directly for control studies and therefore, a simplified version is approximated using discrete time models in state space form via model order reduction techniques or system identification techniques. The reduced model is then used to facilitate the implementation of advanced control strategies. Finally, the performance of the control strategies is validated against the original high-fidelity CFD model.

To test the performance and limitations of the developed control strategy, the controllers are tested for varying operating targets, process disturbances as well as changes in the parameters and for different cell cultures. This work illustrates how using model-based control approaches greatly improves the time and resource utilization during bioreactor operation. It will reduce or even eliminate the need for Design of Experiments (DoE) to design new processes. Moreover, the developed mathematical model can be further used to gain in depth understanding of the process and as a testing platform for the designed controllers. By automating and standardizing tissue manufacture controlled closed systems, bioreactors could reduce production costs and time, thus facilitating a wider use of engineered tissues. Moreover it can assure consistency of product quality and of the time spent producing the product which will bring great benefits from a scheduling point of view.

1. Schmid, J., et al., A Perfusion Bioreactor System for Cell Seeding and Oxygen-Controlled Cultivation of Three-Dimensional Cell Cultures. Tissue Eng Part C Methods, 2018. 24(10): p. 585-595.
2. Coletti, F., S. Macchietto, and N. Nlvassore, Mathematical modelling of three-dimensional cell cultures in perfusion bioreactors. Part II, in Computer Aided Chemical Engineering, W. Marquardt and C. Pantelides, Editors. 2006, Elsevier. p. 1699-1704.
3. Martin, I., D. Wendt, and M. Heberer, The role of bioreactors in tissue engineering. Trends in Biotechnology, 2004. 22(2): p. 80-86.
4. Kiparissides, A., et al., ‘Closing the loop’ in biological systems modeling — From the in silico to the in vitro. Automatica, 2011. 47(6): p. 1147-1155.