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Zavala-Ortiz DA, Denner A, Aguilar-Uscanga MG, Marc A, Ebel B, Guedon E. Comparison of partial least square, artificial neural network, and support vector regressions for real-time monitoring of CHO cell culture processes using in situ near-infrared spectroscopy. Biotechnol Bioeng 2021; 119:535-549. [PMID: 34821379 DOI: 10.1002/bit.27997] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 10/05/2021] [Accepted: 11/13/2021] [Indexed: 11/08/2022]
Abstract
The biopharmaceutical industry must guarantee the efficiency and biosafety of biological medicines, which are quite sensitive to cell culture process variability. Real-time monitoring procedures based on vibrational spectroscopy such as near-infrared (NIR) spectroscopy, are then emerging to support innovative strategies for retro-control of key parameters as substrates and by-product concentration. Whereas monitoring models are mainly constructed using partial least squares regression (PLSR), spectroscopic models based on artificial neural networks (ANNR) and support vector regression (SVR) are emerging with promising results. Unfortunately, analysis of their performance in cell culture monitoring has been limited. This study was then focused to assess their performance and suitability for the cell culture process challenges. PLSR had inferior values of the determination coefficient (R2 ) for all the monitored parameters (i.e., 0.85, 0.93, and 0.98, respectively for the PLSR, SVR, and ANNR models for glucose). In general, PLSR had a limited performance while models based on ANNR and SVR have been shown superior due to better management of inter-batch heterogeneity and enhanced specificity. Overall, the use of SVR and ANNR for the generation of calibration models enhanced the potential of NIR spectroscopy as a monitoring tool.
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Affiliation(s)
- Daniel A Zavala-Ortiz
- Laboratoire Réactions et Génie des Procédés, Université de Lorraine, CNRS, LRGP, Vandœuvre-lès-Nancy, France.,Tecnológico Nacional de México/Instituto Tecnológico de Veracruz, Veracruz, Ver., México
| | - Aurélia Denner
- Laboratoire Réactions et Génie des Procédés, Université de Lorraine, CNRS, LRGP, Vandœuvre-lès-Nancy, France
| | | | - Annie Marc
- Laboratoire Réactions et Génie des Procédés, Université de Lorraine, CNRS, LRGP, Vandœuvre-lès-Nancy, France
| | - Bruno Ebel
- Laboratoire Réactions et Génie des Procédés, Université de Lorraine, CNRS, LRGP, Vandœuvre-lès-Nancy, France
| | - Emmanuel Guedon
- Laboratoire Réactions et Génie des Procédés, Université de Lorraine, CNRS, LRGP, Vandœuvre-lès-Nancy, France
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At‐line raman spectroscopy and design of experiments for robust monitoring and control of miniature bioreactor cultures. Biotechnol Prog 2018; 35:e2740. [DOI: 10.1002/btpr.2740] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 10/08/2018] [Accepted: 10/29/2018] [Indexed: 02/04/2023]
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André S, Lagresle S, Da Sliva A, Heimendinger P, Hannas Z, Calvosa É, Duponchel L. Developing global regression models for metabolite concentration prediction regardless of cell line. Biotechnol Bioeng 2017; 114:2550-2559. [PMID: 28667738 DOI: 10.1002/bit.26368] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Revised: 05/25/2017] [Accepted: 06/30/2017] [Indexed: 01/14/2023]
Abstract
Following the Process Analytical Technology (PAT) of the Food and Drug Administration (FDA), drug manufacturers are encouraged to develop innovative techniques in order to monitor and understand their processes in a better way. Within this framework, it has been demonstrated that Raman spectroscopy coupled with chemometric tools allow to predict critical parameters of mammalian cell cultures in-line and in real time. However, the development of robust and predictive regression models clearly requires many batches in order to take into account inter-batch variability and enhance models accuracy. Nevertheless, this heavy procedure has to be repeated for every new line of cell culture involving many resources. This is why we propose in this paper to develop global regression models taking into account different cell lines. Such models are finally transferred to any culture of the cells involved. This article first demonstrates the feasibility of developing regression models, not only for mammalian cell lines (CHO and HeLa cell cultures), but also for insect cell lines (Sf9 cell cultures). Then global regression models are generated, based on CHO cells, HeLa cells, and Sf9 cells. Finally, these models are evaluated considering a fourth cell line(HEK cells). In addition to suitable predictions of glucose and lactate concentration of HEK cell cultures, we expose that by adding a single HEK-cell culture to the calibration set, the predictive ability of the regression models are substantially increased. In this way, we demonstrate that using global models, it is not necessary to consider many cultures of a new cell line in order to obtain accurate models. Biotechnol. Bioeng. 2017;114: 2550-2559. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Silvère André
- LASIR CNRS UMR 8516, Université de Lille, Sciences et Technologies, 59655, Villeneuve d'Ascq Cedex, France
| | | | | | | | | | | | - Ludovic Duponchel
- LASIR CNRS UMR 8516, Université de Lille, Sciences et Technologies, 59655, Villeneuve d'Ascq Cedex, France
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Rowland-Jones RC, van den Berg F, Racher AJ, Martin EB, Jaques C. Comparison of spectroscopy technologies for improved monitoring of cell culture processes in miniature bioreactors. Biotechnol Prog 2017; 33:337-346. [PMID: 28271638 PMCID: PMC5413828 DOI: 10.1002/btpr.2459] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Revised: 02/06/2017] [Indexed: 11/24/2022]
Abstract
Cell culture process development requires the screening of large numbers of cell lines and process conditions. The development of miniature bioreactor systems has increased the throughput of such studies; however, there are limitations with their use. One important constraint is the limited number of offline samples that can be taken compared to those taken for monitoring cultures in large‐scale bioreactors. The small volume of miniature bioreactor cultures (15 mL) is incompatible with the large sample volume (600 µL) required for bioanalysers routinely used. Spectroscopy technologies may be used to resolve this limitation. The purpose of this study was to compare the use of NIR, Raman, and 2D‐fluorescence to measure multiple analytes simultaneously in volumes suitable for daily monitoring of a miniature bioreactor system. A novel design‐of‐experiment approach is described that utilizes previously analyzed cell culture supernatant to assess metabolite concentrations under various conditions while providing optimal coverage of the desired design space. Multivariate data analysis techniques were used to develop predictive models. Model performance was compared to determine which technology is more suitable for this application. 2D‐fluorescence could more accurately measure ammonium concentration (RMSECV 0.031 g L−1) than Raman and NIR. Raman spectroscopy, however, was more robust at measuring lactate and glucose concentrations (RMSECV 1.11 and 0.92 g L−1, respectively) than the other two techniques. The findings suggest that Raman spectroscopy is more suited for this application than NIR and 2D‐fluorescence. The implementation of Raman spectroscopy increases at‐line measuring capabilities, enabling daily monitoring of key cell culture components within miniature bioreactor cultures. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:337–346, 2017
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Affiliation(s)
- Ruth C Rowland-Jones
- BBTC, Newcastle University, Newcastle Upon Tyne, NE1 7RU, U.K.,Lonza Biologics plc, 228 Bath Road, Slough, SL1 4DX, U.K
| | - Frans van den Berg
- University of Copenhagen, Rolighedsvej 30, Frederiksberg, DK-1958, Denmark
| | | | - Elaine B Martin
- School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, U.K
| | - Colin Jaques
- Lonza Biologics plc, 228 Bath Road, Slough, SL1 4DX, U.K
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Pinto ASS, Pereira SC, Ribeiro MPA, Farinas CS. Monitoring of the cellulosic ethanol fermentation process by near-infrared spectroscopy. BIORESOURCE TECHNOLOGY 2016; 203:334-40. [PMID: 26748047 DOI: 10.1016/j.biortech.2015.12.069] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 12/21/2015] [Accepted: 12/22/2015] [Indexed: 05/22/2023]
Abstract
Rapid, efficient, and low-cost technologies for monitoring the fermentation process during second generation (2G) or cellulosic ethanol production are essential for the successful implementation of this process at the commercial scale. Here, the use of near-infrared (NIR) spectroscopy associated with partial least squares (PLS) regression was investigated as a tool for monitoring the production of 2G ethanol from lignocellulosic sugarcane residues including bagasse, straw, and tops. The spectral data was based on a set of 103 alcoholic fermentation samples. Models based on different pre-processing techniques were evaluated. The best root mean square error of prediction (RMSEP) values obtained in the external validation were around 3.02 g/L for ethanol and 6.60 g/L for glucose. The findings showed that the PLS-NIR methodology was efficient in accurately predicting the glucose and ethanol concentrations during the production of 2G ethanol, demonstrating potential for use in monitoring and control of large-scale industrial processes.
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Affiliation(s)
- Ariane S S Pinto
- Graduate Program of Chemical Engineering, Federal University of São Carlos, 13565-905, PO Box 676, São Carlos, SP, Brazil
| | - Sandra C Pereira
- Embrapa Instrumentation, Rua XV de Novembro 1452, 13560-970 São Carlos, SP, Brazil
| | - Marcelo P A Ribeiro
- Chemical Engineering Department, Federal University of São Carlos, 13565-905, PO Box 676, São Carlos, SP, Brazil
| | - Cristiane S Farinas
- Graduate Program of Chemical Engineering, Federal University of São Carlos, 13565-905, PO Box 676, São Carlos, SP, Brazil; Embrapa Instrumentation, Rua XV de Novembro 1452, 13560-970 São Carlos, SP, Brazil.
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Mercier SM, Rouel PM, Lebrun P, Diepenbroek B, Wijffels RH, Streefland M. Process analytical technology tools for perfusion cell culture. Eng Life Sci 2015. [DOI: 10.1002/elsc.201500035] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Affiliation(s)
- Sarah M. Mercier
- Vaccine Process and Analytical Development Janssen Leiden The Netherlands
| | - Perrine M. Rouel
- Vaccine Process and Analytical Development Janssen Leiden The Netherlands
| | | | - Bas Diepenbroek
- Vaccine Process and Analytical Development Janssen Leiden The Netherlands
| | - René H. Wijffels
- Bioprocess Engineering Wageningen University Wageningen The Netherlands
- Faculty of Biosciences and Aquaculture University of Nordland Bodø Norway
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Biechele P, Busse C, Solle D, Scheper T, Reardon K. Sensor systems for bioprocess monitoring. Eng Life Sci 2015. [DOI: 10.1002/elsc.201500014] [Citation(s) in RCA: 120] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Philipp Biechele
- Institute of Technical Chemistry; Leibniz University; Hannover Germany
| | - Christoph Busse
- Institute of Technical Chemistry; Leibniz University; Hannover Germany
| | - Dörte Solle
- Institute of Technical Chemistry; Leibniz University; Hannover Germany
| | - Thomas Scheper
- Institute of Technical Chemistry; Leibniz University; Hannover Germany
| | - Kenneth Reardon
- Department of Chemical and Biological Engineering; Colorado State University; Fort Collins CO USA
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Zhao L, Fu HY, Zhou W, Hu WS. Advances in process monitoring tools for cell culture bioprocesses. Eng Life Sci 2015. [DOI: 10.1002/elsc.201500006] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Liang Zhao
- Department of Chemical Engineering and Materials Science; University of Minnesota; Minneapolis MN USA
| | - Hsu-Yuan Fu
- Department of Chemical Engineering and Materials Science; University of Minnesota; Minneapolis MN USA
| | - Weichang Zhou
- Biologics Process Development; WuXi AppTec Co; Ltd; Shanghai China
| | - Wei-Shou Hu
- Department of Chemical Engineering and Materials Science; University of Minnesota; Minneapolis MN USA
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Enhancement of production of protein biopharmaceuticals by mammalian cell cultures: the metabolomics perspective. Curr Opin Biotechnol 2014; 30:73-9. [DOI: 10.1016/j.copbio.2014.06.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Revised: 05/26/2014] [Accepted: 06/08/2014] [Indexed: 01/01/2023]
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