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

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

Research output: Contribution to journalArticleScientificpeer-review

18 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)696-707
Number of pages12
JournalBiotechnology Progress
Volume33
Issue number3
DOIs
Publication statusPublished - 1 May 2017

Keywords

  • chromatography
  • downstream processing
  • model-based process development approach
  • purification process synthesis

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