“ICNAP”: Fraunhofer IME researches the networked, adapted production of recombinant proteins
Within the Fraunhofer High Performance Center “International Center for Networked, Adapted Production” (ICNAP), the three Aachen-based Fraunhofer Institutes for Production Technology IPT, Laster Technology ILT, and Molecular Biology and Applied Ecology IME along with RWTH Aachen University and industry partners jointly develop and validate production systems for Industrie 4.0. The Fraunhofer IME sets its focus on the manufacturing of recombinant proteins by means of big data analytics.
Biopharmaceutical production processes are complex because they are accompanied by a multitude of data containing important information, for example volumetric productivity. Therefore, reliable knowledge and process understanding based on this information are crucial in order to ensure an equally reliable final product and to fully meet its high quality requirements. At ICNAP, the Fraunhofer Institute for Molecular Biology and Applied Ecology IME focuses on the networked, adaptive production of recombinant proteins from plant cells and fully exploits the potential of big data analytics to obtain the required process understanding.
Proteins are a vital part of the human body: they form the basis of our cell structure, regulate the correct metabolic functions, they are important for our muscles, they transport essential nutrients such as iron and hemoglobin, and act as antibodies as part of our immune defense. It is no surprise then that proteins are a major product type in biopharmaceutical manufacturing.
Big data analyitics for plant cultivation
The digitization of industrial manufacturing processes opens up new possibilities for biotechnology with a view to executing and optimizing the underlying processes. When it comes to generating protein from plant cells and converting it into a biopharmaceutical product, big data analytics and machine learning are promising methods to aid this process. Why? Because they allow to create data-based models which can not only enable a batch-specific adjustment of the conditions of the plant cultivation to eliminate potential errors from the beginning, but facilitate an a priori, in silico approach to design new productions processes. The latter can help to speed up the development of novel drugs, including vaccines or therapeutics for emerging diseases and in pandemic situations. Also, large-scale data analysis aids to gain a holistic process understanding.
Tracing the history of the plant cells
Working with plant cultivation goes hand in hand with a vast array of factors like temperature, light or growth rate – all of which have a determining influence on critical quality attributes such as stability and effectiveness of the final product. Also, different plants grow in different ways and supply different amounts of active ingredients.
Therefore it is useful to trace the history of the plant cells so the growth conditions and the production of active ingredients can be analyzed precisely. “We can then determine under which conditions the plants and cells are particularly productive and continuously adapt the process accordingly,” says Johannes Buyel from Fraunhofer IME. “We’re carrying out extensive big data analyses to find and to monitor the right parameters that affect the production of active ingredients in plants.”
Dr. Johannes Buyel
Fraunhofer IME, Aachen, Germany
Link to website: www.vernetzte-adaptive-produktion.de