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A new generation of predictive models: The added value of hybrid models for manufacturing processes of therapeutic proteins.

Authors
  • Narayanan, Harini1
  • Sokolov, Michael1, 2
  • Morbidelli, Massimo1, 2
  • Butté, Alessandro1, 2
  • 1 Department of Chemistry and Applied Biosciences, Institute of Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland. , (Switzerland)
  • 2 DataHow AG, Zurich, Switzerland. , (Switzerland)
Type
Published Article
Journal
Biotechnology and Bioengineering
Publisher
Wiley (John Wiley & Sons)
Publication Date
Oct 01, 2019
Volume
116
Issue
10
Pages
2540–2549
Identifiers
DOI: 10.1002/bit.27097
PMID: 31237678
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Due to the lack of complete understanding of metabolic networks and reaction pathways, establishing a universal mechanistic model for mammalian cell culture processes remains a challenge. Contrarily, data-driven approaches for modeling these processes lack extrapolation capabilities. Hybrid modeling is a technique that exploits the synergy between the two modeling methods. Although mammalian cell cultures are among the most relevant processes in biotechnology and indeed looks ideal for hybrid modeling, their application has only been proposed but never developed in the literature. This study provides a quantitative assessment of the improvement brought by hybrid models with respect to the state-of-the-art statistical predictive models in the context of therapeutic protein production. This is illustrated using a dataset obtained from a 3.5 L fed-batch experiment. With the goal to robustly define the process design space, hybrid models reveal a superior capability to predict the time evolution of different process variables using only the initial and process conditions in comparison to the statistical models. Hybrid models not only feature more accurate prediction results but also demonstrate better robustness and extrapolation capabilities. For the future application, this study highlights the added value of hybrid modeling for model-based process optimization and design of experiments. © 2019 Wiley Periodicals, Inc.

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