Details
| Originalsprache | Englisch |
|---|---|
| Fachzeitschrift | Biotechnology and bioengineering |
| Frühes Online-Datum | 31 Jan. 2025 |
| Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 31 Jan. 2025 |
Abstract
Chinese Hamster Ovary (CHO) cells are the most widely used cell lines to produce recombinant therapeutic proteins such as monoclonal antibodies (mAbs). However, the optimization of the CHO cell culture process is very complex and influenced by various factors. This study investigates the use of machine learning (ML) algorithms to optimize an established industrial CHO cell cultivation process. A ML algorithm in the form of an artificial neural network (ANN) was used and trained on datasets from historical and newly generated CHO cell cultivation runs. The algorithm was then used to find better cultivation conditions and improve cell productivity. The selected artificial intelligence (AI) tool was able to suggest optimized cultivation settings and new condition combinations, which promised both increased cell growth and increased mAb titers. After performing the validation experiments, it was shown that the ML algorithm was able to successfully optimize the cultivation process and significantly improve the antibody production. The best results showed an increase in final mAb titer up to 48%, demonstrating that the use of ML algorithms is a promising approach to optimize the productivity of bioprocesses like CHO cell cultivation processes clearly.
ASJC Scopus Sachgebiete
- Biochemie, Genetik und Molekularbiologie (insg.)
- Biotechnologie
- Chemische Verfahrenstechnik (insg.)
- Bioengineering
- Immunologie und Mikrobiologie (insg.)
- Angewandte Mikrobiologie und Biotechnologie
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in: Biotechnology and bioengineering, 31.01.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Machine Learning-Powered Optimization of a CHO Cell Cultivation Process
AU - Richter, Jannik
AU - Wang, Qimin
AU - Lange, Ferdinand
AU - Thiel, Phil
AU - Yilmaz, Nina
AU - Solle, Dörte
AU - Zhuang, Xiaoying
AU - Beutel, Sascha
N1 - Publisher Copyright: © 2025 The Author(s). Biotechnology and Bioengineering published by Wiley Periodicals LLC.
PY - 2025/1/31
Y1 - 2025/1/31
N2 - Chinese Hamster Ovary (CHO) cells are the most widely used cell lines to produce recombinant therapeutic proteins such as monoclonal antibodies (mAbs). However, the optimization of the CHO cell culture process is very complex and influenced by various factors. This study investigates the use of machine learning (ML) algorithms to optimize an established industrial CHO cell cultivation process. A ML algorithm in the form of an artificial neural network (ANN) was used and trained on datasets from historical and newly generated CHO cell cultivation runs. The algorithm was then used to find better cultivation conditions and improve cell productivity. The selected artificial intelligence (AI) tool was able to suggest optimized cultivation settings and new condition combinations, which promised both increased cell growth and increased mAb titers. After performing the validation experiments, it was shown that the ML algorithm was able to successfully optimize the cultivation process and significantly improve the antibody production. The best results showed an increase in final mAb titer up to 48%, demonstrating that the use of ML algorithms is a promising approach to optimize the productivity of bioprocesses like CHO cell cultivation processes clearly.
AB - Chinese Hamster Ovary (CHO) cells are the most widely used cell lines to produce recombinant therapeutic proteins such as monoclonal antibodies (mAbs). However, the optimization of the CHO cell culture process is very complex and influenced by various factors. This study investigates the use of machine learning (ML) algorithms to optimize an established industrial CHO cell cultivation process. A ML algorithm in the form of an artificial neural network (ANN) was used and trained on datasets from historical and newly generated CHO cell cultivation runs. The algorithm was then used to find better cultivation conditions and improve cell productivity. The selected artificial intelligence (AI) tool was able to suggest optimized cultivation settings and new condition combinations, which promised both increased cell growth and increased mAb titers. After performing the validation experiments, it was shown that the ML algorithm was able to successfully optimize the cultivation process and significantly improve the antibody production. The best results showed an increase in final mAb titer up to 48%, demonstrating that the use of ML algorithms is a promising approach to optimize the productivity of bioprocesses like CHO cell cultivation processes clearly.
KW - antibody production
KW - artificial neural network
KW - bioprocess optimization
KW - CHO cells
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85216494375&partnerID=8YFLogxK
U2 - 10.1002/bit.28943
DO - 10.1002/bit.28943
M3 - Article
AN - SCOPUS:85216494375
JO - Biotechnology and bioengineering
JF - Biotechnology and bioengineering
SN - 0006-3592
ER -