Insilico Biotechnology and the Otto von Guericke University Magdeburg in Germany have partnered on a joint project. The project surrounds model predictive control (MPC) for the production of monoclonal antibodies in mammalian cell culture processes using Insilico's Digital Twins.
The Laboratory of Systems Theory and Automatic Control at the Institute for Automation Engineering (IFAT) is the department of the university that will be handling the work.
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 and IFAT are collaborating to develop such an MPC system for the production of monoclonal antibodies in CHO cells by combining their modelling, process control and automation expertise.
Digital twins will be used to establish open-loop decision support (OPL-DS) for process control
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, summarised: "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 added: "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, optimisation-based decision support and control, with machine learning approaches allows handling of process uncertainties and variability unavoidable in biotechnological production” commented Dr Lisa Carius, junior research group leader in the field of smart automation of biotechnological processes at IFAT.
