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Pirrung SM, van der Wielen LAM, van Beckhoven RFWC, et al. Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks. Biotechnol Prog. 2017;33(3):696-707doi: 10.1002/btpr.2435.
Pirrung, S. M., van der Wielen, L. A. M., van Beckhoven, R. F. W. C., van de Sandt, E. J. A. X., Eppink, M. H. M., & Ottens, M. (2017). Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks. Biotechnology progress, 33(3), 696-707. https://doi.org/10.1002/btpr.2435
Pirrung, Silvia M, et al. "Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks." Biotechnology progress vol. 33,3 (2017): 696-707. doi: https://doi.org/10.1002/btpr.2435
Pirrung SM, van der Wielen LAM, van Beckhoven RFWC, van de Sandt EJAX, Eppink MHM, Ottens M. Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks. Biotechnol Prog. 2017 May;33(3):696-707. doi: 10.1002/btpr.2435. Epub 2017 Feb 03. PMID: 28054462.
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Biotechnol Prog. 2017 May;33(3):696-707. doi: 10.1002/btpr.2435. Epub 2017 Feb 03.

Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks.

Biotechnology progress

Silvia M Pirrung, Luuk A M van der Wielen, Ruud F W C van Beckhoven, Emile J A X van de Sandt, Michel H M Eppink, Marcel Ottens

Affiliations

  1. Dept. of Biotechnology, Delft University of Technology, Van der Maasweg 9, 2629 HZ Delft, The Netherlands.
  2. DSM Biotechnology Center, Alexander Fleminglaan 1, 2613 AX Delft, The Netherlands.
  3. Synthon Biopharmaceuticals B.V, Microweg 22, 6503 GN Nijmegen, The Netherlands.

PMID: 28054462 DOI: 10.1002/btpr.2435

Abstract

Downstream process development is a major area of importance within the field of bioengineering. During the design of such a downstream process, important decisions have to be made regarding the type of unit operations as well as their sequence and their operating conditions. Current computational approaches addressing these issues either show a high level of simplification or struggle with computational speed. Therefore, this article presents a new approach that combines detailed mechanistic models and speed-enhancing artificial neural networks. This approach was able to simultaneously optimize a process with three different chromatographic columns toward yield with a minimum purity of 99.9%. The addition of artificial neural networks greatly accelerated this optimization. Due to high computational speed, the approach is easily extendable to include more unit operations. Therefore, it can be of great help in the acceleration of downstream process development. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:696-707, 2017.

© 2017 American Institute of Chemical Engineers.

Keywords: chromatography; downstream processing; model-based process development approach; purification process synthesis

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