Bioprocessing in the Digital Age: The Role of Process Models.
- Narayanan H Institute for Chemical and Bioengineering, ETHZ, Zurich, Switzerland.
- Luna MF Institute for Chemical and Bioengineering, ETHZ, Zurich, Switzerland.
- von Stosch M Technical R&D, GSK Vaccines, 1330, Rixensart, Belgium.
- Cruz Bournazou MN Institute for Chemical and Bioengineering, ETHZ, Zurich, Switzerland.
- Polotti G DataHow AG, c/o ETH Zurich, HCI, F137, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland.
- Morbidelli M Institute for Chemical and Bioengineering, ETHZ, Zurich, Switzerland.
- Butté A Institute for Chemical and Bioengineering, ETHZ, Zurich, Switzerland.
- Sokolov M Institute for Chemical and Bioengineering, ETHZ, Zurich, Switzerland.
- 2019-09-06
Published in:
- Biotechnology journal. - 2020
bioprocesses
digitalization
industry 4.0
predictive models
process analytical technology
Automation, Laboratory
Biopharmaceutics
Biotechnology
Drug Industry
Humans
Models, Biological
Research Design
English
In this age of technology, the vision of manufacturing industries built of smart factories is not a farfetched future. As a prerequisite for Industry 4.0, industrial sectors are moving towards digitalization and automation. Despite its tremendous growth reaching a sales value of worth $188 billion in 2017, the biopharmaceutical sector distinctly lags in this transition. Currently, the challenges are innovative market disruptions such as personalized medicine as well as increasing commercial pressure for faster and cheaper product manufacturing. Improvements in digitalization and data analytics have been identified as key strategic activities for the next years to face these challenges. Alongside, there is an emphasis by the regulatory authorities on the use of advanced technologies, proclaimed through initiatives such as Quality by Design (QbD) and Process Analytical Technology (PAT). In the manufacturing sector, the biopharmaceutical domain features some of the most complex and least understood processes. Thereby, process models that can transform process data into more valuable information, guide decision-making, and support the creation of digital and automated technologies are key enablers. This review summarizes the current state of model-based methods in different bioprocess related applications and presents the corresponding future vision for the biopharmaceutical industry to achieve the goals of Industry 4.0 while meeting the regulatory requirements.
- Language
-
- English
- Open access status
- bronze
- Identifiers
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- DOI 10.1002/biot.201900172
- PMID 31486583
- Persistent URL
- https://sonar.ch/global/documents/220101
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