ArticleBiotechnology and bioengineering2025
Machine Learning-Powered Optimization of a CHO Cell Cultivation Process.
Article in Biotechnology and bioengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Data-driven optimization of salt shock for high-efficiency ectoine production inSynthetic and systems biotechnology · 2027Article
- Article
- Data-Driven Approach for Bioreactor Monitoring and Prediction in Monoclonal Antibody Production.ACS omega · 2026Article
- Advances in recombinant protein bioprocessing: integrating technological innovation and environmental sustainability.Biotechnology letters · 2026Review
- AI-nanotech synergies: recent advances in sustainable bio-manufacturing.World journal of microbiology & biotechnology · 2026Review
- The landscape of antibody production systems: recombinant expression for research, diagnostics and therapy.Frontiers in bioengineering and biotechnology · 2026Review
- Review
- Advancing IoT in the lab: Next generation Gateway-Module for laboratory device integration.HardwareX · 2025Article
- Advancements and challenges in next-generation mRNA vaccine manufacturing systems.Clinical and experimental vaccine research · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.