The production of high-quality biologics requires the development of robust and well-understood production processes using mammalian cell cultures. The control of these processes in order to achieve robust product quality and productivity can be significantly improved by online process monitoring followed by corrective actions. Digital Twins are virtual representations of the production process which enable preemptive process control by using online data to predict the process outcome in advance. This enables unprecedented possibilities for timely and automated intervention to steer the process results at an early stage. Insilico Biotechnology AG and IFAT are collaborating to develop such a MPC system for the production of monoclonal antibodies in CHO cells by combining their modelling, process control and automation expertise.

Insilico Digital Twins are virtual representations of the actual bioprocess that include a genome-based metabolic network of the cell, a mechanistic model of the process as well as an artificial neural network. Fusing these three model components enables simulations of a virtually unlimited number of process scenarios and the advance prediction of outcomes due to process parameter changes. Model predictive control will be based on the Insilico Digital Twin and online process monitoring to establish open-loop decision support (OPL-DS) for process control. For this purpose the project partners will develop a softsensor, conceive a robust control strategy and implement a self-learning system for online process control to be combined with the Digital Twin. Klaus Mauch, CEO of Insilico Biotechnology, summarizes: "The jointly developed solution will for the first time enable true online-control of critical quality attributes such as the glycosylation profile. We are positive that partnering with Rolf Findeisen's leading research group is the key to achieving this goal."

Rolf Findeisen, professor at University Magdeburg adds "Predictive process monitoring and control provides highly valuable decision support to the process operator and enables preemptive process steering to ensure product and process specifications are met. Insilico's state-of-the-art Digital Twins for biomanufacturing processes play a pivotal role in developing this innovative solution." “Integrating Digital Twins for prediction, optimization based decision support and control, with machine learning approaches allows to handle process uncertainties and variability unavoidable in biotechnological production” comments Dr. Lisa Carius, junior research group leader in the field of smart automation of biotechnological processes at the Laboratory for Systems Theory and Automatic Control.
 

About Institute for Automation Engineering of the Otto von Guericke University Magdeburg

Prof. Findeisen’s research group "System theory and automatic control" at the Institute for Automation Engineering of the Otto von Guericke University Magdeburg has a research focus on the control, estimation and learning for autonomous systems with applications spanning from robotics, mechatronics, to life sciences and biotechnology. The developments are based on a solid theoretical foundation in the field of system and control theory for decision making under uncertainty. The group is one of the leading groups in the world in the field of predictive and optimization based control approaches. The developed methods and theory are aimed to be applicable to practical relevant problems. To achieve this, the method oriented developments run in parallel to projects that are pursued in collaboration with industrial partners