Machine Learning-Powered Optimization of a CHO Cell Cultivation Process

authored by: Jannik Richter, Qimin Wang, Ferdinand Lange, Phil Thiel, Nina Yilmaz, Dörte Solle, Xiaoying Zhuang, Sascha Beutel
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.
Organisation(s): Institute of Technical Chemistry
Institute of Photonics
Type: Article
Journal: Biotechnology and bioengineering
ISSN: 0006-3592
Publication date: 31.01.2025
Publication status: E-pub ahead of print
Peer reviewed: Yes
ASJC Scopus subject areas: Biotechnology, Bioengineering, Applied Microbiology and Biotechnology
Electronic version(s): https://doi.org/10.1002/bit.28943 (Access: Open)

BibTeX

@article{a0c832500a87418693f5cd9c90af239e,
title = "Machine Learning-Powered Optimization of a CHO Cell Cultivation Process",
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.",
keywords = "antibody production, artificial neural network, bioprocess optimization, CHO cells, machine learning",
author = "Jannik Richter and Qimin Wang and Ferdinand Lange and Phil Thiel and Nina Yilmaz and D{\"o}rte Solle and Xiaoying Zhuang and Sascha Beutel",
note = "Publisher Copyright: {\textcopyright} 2025 The Author(s). Biotechnology and Bioengineering published by Wiley Periodicals LLC.",
year = "2025",
month = jan,
day = "31",
doi = "10.1002/bit.28943",
language = "English",
journal = "Biotechnology and bioengineering",
issn = "0006-3592",
publisher = "Wiley-VCH Verlag",
}