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Feasibility and performance of cross-clone Raman calibration models in CHO cultivation
Umeå University, Faculty of Science and Technology, Department of Chemistry. Sartorius Stedim Biotech GmbH, Göttingen, Germany.
Sartorius Stedim Biotech GmbH, Göttingen, Germany.
Sartorius Stedim Biotech GmbH, Göttingen, Germany.
Sartorius Stedim UK Ltd., Epsom, United Kingdom.
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2023 (English)In: Biotechnology Journal, ISSN 1860-6768, E-ISSN 1860-7314Article in journal (Refereed) Epub ahead of print
Abstract [en]

Raman spectroscopy is widely used in monitoring and controlling cell cultivations for biopharmaceutical drug manufacturing. However, its implementation for culture monitoring in the cell line development stage has received little attention. Therefore, the impact of clonal differences, such as productivity and growth, on the prediction accuracy and transferability of Raman calibration models is not yet well described. Raman OPLS models were developed for predicting titer, glucose and lactate using eleven CHO clones from a single cell line. These clones exhibited diverse productivity and growth rates. The calibration models were evaluated for clone-related biases using clone-wise linear regression analysis on cross validated predictions. The results revealed that clonal differences did not affect the prediction of glucose and lactate, but titer models showed a significant clone-related bias, which remained even after applying variable selection methods. The bias was associated with clonal productivity and lead to increased prediction errors when titer models were transferred to cultivations with productivity levels outside the range of their training data. The findings demonstrate the feasibility of Raman-based monitoring of glucose and lactate in cell line development with high accuracy. However, accurate titer prediction requires careful consideration of clonal characteristics during model development.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023.
Keywords [en]
bioprocess development, bioprocess engineering, bioprocess monitoring, CHO cells
National Category
Analytical Chemistry
Identifiers
URN: urn:nbn:se:umu:diva-218135DOI: 10.1002/biot.202300289PubMedID: 38015079Scopus ID: 2-s2.0-85178957570OAI: oai:DiVA.org:umu-218135DiVA, id: diva2:1820417
Available from: 2023-12-18 Created: 2023-12-18 Last updated: 2023-12-18

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Machleid, RafaelNilsson, DavidTrygg, Johan

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