Feasibility and performance of cross-clone Raman calibration models in CHO cultivation.
CHO cells
bioprocess development
bioprocess engineering
bioprocess monitoring
Journal
Biotechnology journal
ISSN: 1860-7314
Titre abrégé: Biotechnol J
Pays: Germany
ID NLM: 101265833
Informations de publication
Date de publication:
27 Nov 2023
27 Nov 2023
Historique:
revised:
30
10
2023
received:
15
06
2023
accepted:
21
11
2023
pubmed:
28
11
2023
medline:
28
11
2023
entrez:
28
11
2023
Statut:
aheadofprint
Résumé
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.
Identifiants
pubmed: 38015079
doi: 10.1002/biot.202300289
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2300289Informations de copyright
© 2023 The Authors. Biotechnology Journal published by Wiley-VCH GmbH.
Références
Grand View Research. Monoclonal Antibodies Market Size & Share Report, 2030. (2021).
U.S. Department of Health and Human Services, Guidance for Industry PAT-A Framework for Innovative Pharmaceutical Development, manufacturing, and Quality Assurance (2004).
Santos, R. M., Kessler, J. M., Salou, P., Menezes, J. C., & Peinado, A. (2018). Monitoring mAb cultivations with in-situ raman spectroscopy: The influence of spectral selectivity on calibration models and industrial use as reliable PAT tool. Biotechnology Progress, 34, 659-670.
Whelan, J., Craven, S., & Glennon, B. (2012). In situ Raman spectroscopy for simultaneous monitoring of multiple process parameters in mammalian cell culture bioreactors. Biotechnology Progress, 28, 1355-1362.
Abu-Absi, N. R., Kenty, B. M., Cuellar, M. E., Borys, M. C., Sakhamuri, S., Strachan, D. J., Hausladen, M. C., & Li, Z. J. (2011). Real time monitoring of multiple parameters in mammalian cell culture bioreactors using an in-line Raman spectroscopy probe. Biotechnology and Bioengineering, 108, 1215-1221.
Matthews, T. E., Berry, B. N., Smelko, J., Moretto, J., Moore, B., & Wiltberger, K. (2016). Closed loop control of lactate concentration in mammalian cell culture by Raman spectroscopy leads to improved cell density, viability, and biopharmaceutical protein production. Biotechnology and Bioengineering, 113, 2416-2424.
Graf, A., Lemke, J., Schulze, M., Soeldner, R., Rebner, K., Hoehse, M., & Matuszczyk, J. (2022). A novel approach for non-invasive continuous in-line control of perfusion cell cultivations by raman spectroscopy. Frontiers in bioengineering and biotechnology, 10, 719614.
Li, M.-Y., Ebel, B., Paris, C., Chauchard, F., Guedon, E., & Marc, A. (2018). Real-time monitoring of antibody glycosylation site occupancy by in situ Raman spectroscopy during bioreactor CHO cell cultures. Biotechnology Progress, 34, 486-493.
Schwarz, H., Mäkinen, M. E., Castan, A., & Chotteau, V. (2022). Monitoring of amino acids and antibody N-glycosylation in high cell density perfusion culture based on Raman spectroscopy. Biochemical Engineering Journal, 182, 108426.
Sandner, V., Pybus, L. P., McCreath, G., & Glassey, J. (2019). Scale-down model development in Ambr systems: an industrial perspective. Biotechnology Journal, 14, e1700766.
Graf, A., Woodhams, A., Nelson, M., Richardson, D. D., Short, S. M., Brower, M., & Hoehse, M. (2022). Automated data generation for raman spectroscopy calibrations in multi-parallel mini bioreactors. Sensors, 22, 3397.
Berry, B., Moretto, J., Matthews, T., Smelko, J., & Wiltberger, K. (2015). Cross-scale predictive modeling of CHO cell culture growth and metabolites using R aman spectroscopy and multivariate analysis. Biotechnology Progress, 31, 566-577.
Rowland-Jones, R. C., Graf, A., Woodhams, A., Diaz-Fernandez, P., Warr, S., Soeldner, R., Finka, G., & Hoehse, M. (2021). Spectroscopy integration to miniature bioreactors and large scale production bioreactors-Increasing current capabilities and model transfer. Biotechnology Progress, 37, e3074.
Classen, J., Langer, M., Jockwer, A., & Traenkle, J. (2022). Raman spectrometric PAT models: Successful transfer from minibioreactors to larger-scale, stirred-tank bioreactors. Bioprocess International, 20, 34-38.
André, S., Lagresle, S., Da Sliva, A., Heimendinger, P., Hannas, Z., Calvosa, É., & Duponchel, L. (2017). Developing global regression models for metabolite concentration prediction regardless of cell line. Biotechnology and Bioengineering, 114, 2550-2559.
Webster, T. A., Hadley, B. C., Hilliard, W., Jaques, C., & Mason, C. (2018). Development of generic Raman models for a GS-KO TM CHO platform process. Biotechnology Progress, 34, 730-737.
Mehdizadeh, H., Lauri, D., Karry, K. M., Moshgbar, M., Procopio-Melino, R., & Drapeau, D. (2015). Generic R aman-based calibration models enabling real-time monitoring of cell culture bioreactors. Biotechnology Progress, 31, 1004-1013.
Santos, R. M., Kaiser, P., Menezes, J. C., & Peinado, A. (2019). Improving reliability of Raman spectroscopy for mAb production by upstream processes during bioprocess development stages. Talanta, 199, 396-406.
Eilers, P. H. C., & Boelens, H. F. M., Baseline correction with asymmetric least squares smoothing. Leiden University Medical Centre Report. (2005). 1.
Trygg, J., & Wold, S. (2002). Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics, 16, 119-128.
Wold, S., Martens, H., & Wold, H. Lecture Notes in Mathematics. (1982).
Johannson, E. A., & Mazarakis, K. (Sartorius Stedim Data Analytics AB), Sweden, EP4036919. (2021).
Wold, S., Johansson, E., & Cocchi, M. 3D QSAR in Drug Design; Theory, Methods and Applications, ESCOM. (1993). 523-550.
Galindo-Prieto, B., Eriksson, L., & Trygg, J. (2014). Variable influence on projection (VIP) for orthogonal projections to latent structures (OPLS). Journal of Chemometrics, 28, 623-632.
Farrés, M., Platikanov, S., Tsakovski, S., & Tauler, R. (2015). Comparison of the variable importance in projection (VIP) and of the selectivity ratio (SR) methods for variable selection and interpretation. Journal of Chemometrics, 29, 528-536.
Rajalahti, T., Arneberg, R., Berven, F. S., Myhr, K.-M., Ulvik, R. J., & Kvalheim, O. M. (2009). Biomarker discovery in mass spectral profiles by means of selectivity ratio plot. Chemometrics and Intelligent Laborary Systems, 95, 35-48.
Ratner, B. (2009). The correlation coefficient: Its values range between +1/-1, or do they? Journal of Targeting, Measurement and Analysis for Marketing, 17, 139-142.
Kengne-Momo, R. P., Daniel, P., Lagarde, F., Jeyachandran, Y. L., Pilard, J. F., Durand-Thouand, M. J., & Thouand, G. (2012). Protein interactions investigated by the raman spectroscopy for biosensor applications. International Journal of Spectroscopy, 2012, 1-7.
Ota, C., Noguchi, S., Nagatoishi, S., & Tsumoto, K. (2016). Assessment of the protein-protein interactions in a highly concentrated antibody solution by using raman spectroscopy. Pharmaceutical Research, 33, 956-969.
Iversen, J. A., Berg, R. W., & Ahring, B. K. (2014). Quantitative monitoring of yeast fermentation using Raman spectroscopy. Analytical and Bioanalytical Chemistry. Anal. Bioanal, 406, 4911-4919.
Voss, J. P., Mittelheuser, N. E., Lemke, R., & Luttmann, R. (2017). Advanced monitoring and control of pharmaceutical production processes with Pichia pastoris by using Raman spectroscopy and multivariate calibration methods. Engineering in Life Sciences, 17, 1281-1294.