This work aimed at developing a real-time cell culture monitoring method to optimize the culture conditions of CHO (Chinese hamster ovary cells) for the production of monoclonal antibodies; and to guarantee a constant quality of the main metabolic parameters and the IgG (immunoglobulins) yield for biopharmaceutical production.
Raman spectroscopy, combined with Machine Learning methods for spectral processing, has established itself as a robust process analytical technology (PAT) tool thanks to its real-time, in-situ, and non-invasive measurement capabilities in bioreactors. This study evaluates online Raman spectroscopy and chemometric and machine learning methods for in-process prediction, with a view to monitoring the following parameters of interest: nutrients, metabolites, antibody titers, and cell density.
Following the development of regression models using chemometric and machine learning methods by varying cell culture conditions, this study confirms the potential of Raman spectroscopy for in-situ and real-time monitoring of bioprocesses, particularly for CHO cell cultures and monoclonal antibody production, without manual sampling. Chemometric analysis improves the accuracy and robustness of the models and enables the monitoring and even automated control of bioreactors. Raman data could enable continuous regulation of critical nutrients such as glucose, thus ensuring control of Critical Process Parameters (CPPs) during biopharmaceutical production.
This work was conducted within the framework of the CLIMBIN collaborative R&D project, in collaboration with various researchers from the University of Tours (Dr. Morandise Rubini, Prof. Igor Chourpa), the LRGP laboratory in Nancy, the Servier Group (Anaïs Berger, Thomas Saillard, Sylvain Arnould, Muriel Vergès), the spectroscopy equipment manufacturer INDATECH (Julien Louet, Dr. Fabien Chauchard), and Ondalys (Julien Boyer, Jordane Poulain, Dr. Sylvie Roussel).
For this article, Julien Boyer, Jordane Poulain and Dr. Sylvie Roussel from Ondalys contributed their expertise in Chemometrics and Machine Learning. They brought their experience for data processing using online Raman spectroscopy measurements for the bioproduction of mAbs from CHO cells under industrial conditions, in Servier laboratories, alongside the University of Tours.
M. Rubini, J. Boyer, J. Poulain, A. Berger, T. Saillard, J. Louet, M. Soucé, S. Roussel, S. Arnould, M. Vergès, F. Chauchard-Rios & I. Chourpa. Monitoring of Nutrients, Metabolites, IgG Titer, and Cell Densities in 10 L Bioreactors Using Raman Spectroscopy and PLS Regression Models. Pharmaceutics 2025, Volume 17, Issue 4, 473, Avril 2025
