Bioprocessing in the Digital Age: The Role of Process Models.

bioprocesses digitalization industry 4.0 predictive models process analytical technology

Journal

Biotechnology journal
ISSN: 1860-7314
Titre abrégé: Biotechnol J
Pays: Germany
ID NLM: 101265833

Informations de publication

Date de publication:
Jan 2020
Historique:
received: 01 05 2019
revised: 15 07 2019
pubmed: 6 9 2019
medline: 30 5 2020
entrez: 6 9 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 31486583
doi: 10.1002/biot.201900172
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1900172

Informations de copyright

© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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Auteurs

Harini Narayanan (H)

Institute for Chemical and Bioengineering, ETHZ, Zurich, Switzerland.

Martin F Luna (MF)

Institute for Chemical and Bioengineering, ETHZ, Zurich, Switzerland.

Moritz von Stosch (M)

Technical R&D, GSK Vaccines, 1330, Rixensart, Belgium.

Mariano Nicolas Cruz Bournazou (MN)

Institute for Chemical and Bioengineering, ETHZ, Zurich, Switzerland.
DataHow AG, c/o ETH Zurich, HCI, F137, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland.

Gianmarco Polotti (G)

DataHow AG, c/o ETH Zurich, HCI, F137, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland.

Massimo Morbidelli (M)

Institute for Chemical and Bioengineering, ETHZ, Zurich, Switzerland.
DataHow AG, c/o ETH Zurich, HCI, F137, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland.

Alessandro Butté (A)

Institute for Chemical and Bioengineering, ETHZ, Zurich, Switzerland.
DataHow AG, c/o ETH Zurich, HCI, F137, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland.

Michael Sokolov (M)

Institute for Chemical and Bioengineering, ETHZ, Zurich, Switzerland.
DataHow AG, c/o ETH Zurich, HCI, F137, Vladimir-Prelog-Weg 1, 8093, Zurich, Switzerland.

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