Digitalization and Bioprocessing: Promises and Challenges

The production of pharmaceuticals, industrial chemicals, and food ingredients from biotechnological processes is a vast and rapidly growing industry. While advances in synthetic biology and metabolic engineering have made it possible to produce thousands

  • PDF / 538,127 Bytes
  • 13 Pages / 439.37 x 666.142 pts Page_size
  • 91 Downloads / 302 Views

DOWNLOAD

REPORT


Digitalization and Bioprocessing: Promises and Challenges Thomas Scheper, Sascha Beutel, Nina McGuinness, Stefanie Heiden, Marco Oldiges, Frank Lammers, and Kenneth F. Reardon Contents 1 Current Limitations of Bioprocess Development 2 Digitalization Opportunities for Biotechnological Processes: Biocatalysis 3 Digitalization Opportunities for Biotechnological Processes: Fermentation and Cell Culture 4 Digitalization Strategies 5 Regulatory Considerations 6 Conclusions and Outlook References

Abstract The production of pharmaceuticals, industrial chemicals, and food ingredients from biotechnological processes is a vast and rapidly growing industry. While advances in synthetic biology and metabolic engineering have made it possible to

T. Scheper (*), S. Beutel, and N. McGuinness Institute of Technical Chemistry, Leibniz University Hannover, Hannover, Germany e-mail: [email protected] S. Heiden Institute for Research on Innovation, Technology Management and Entrepreneurship, Leibniz University Hannover, Hannover, Germany M. Oldiges Forschungszentrum Jülich GmbH, Institute of Bio- and Geoscience, IBG-1: Biotechnologys, Jülich, Germany RWTH Aachen University, Institute of Biotechnology, Aachen, Germany F. Lammers Sanofi-Aventis Deutschland GmbH, Industriepark Hoechst, Frankfurt am Main, Germany K. F. Reardon Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, USA

T. Scheper et al.

produce thousands of new molecules from cells, few of these molecules have reached the market. The traditional methods of strain and bioprocess development that transform laboratory results to industrial processes are slow and use computers and networks only for data acquisition and storage. Digitalization, machine learning (ML), and artificial intelligence (AI) methods are transforming many fields – how can they be applied to bioprocessing to overcome current bottlenecks? What are the challenges, especially for regulatory issues, in the production of biopharmaceuticals? This chapter begins with a discussion of the current challenges for strain and bioprocess development and then considers how digitalization can be used to approach these tasks in completely new ways. Finally, regulatory considerations are addressed, with the goal of incorporating these issues from the outset as new digitalization methods are created. Keywords Digitalization, Digital twins, FDA, QbD, Regulatory considerations

1 Current Limitations of Bioprocess Development Biotechnological processes are complex sequences of operations that combine (bio)chemical engineering processing and biological systems. While chemical processes are typically well defined, based on exact, consistent thermodynamic and kinetic properties of the reaction system and purification steps, biological systems are extremely complex, and their behaviour is much harder to predict [1]. The reasons for this complexity include: (a) The choice or design of the optimal biocatalyst with high specificity and stability is often limited. (b) Production organism