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Multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors

Viable cell concentration (VCC) is one of the most important process attributes during mammalian cell cultivations. Current state-of-the-art measurements of VCC comprise offline methods which do not allow for continuous process data. According to the FDA’s process analytical technology initiative, p...

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Autores principales: Metze, Sabrina, Blioch, Stefanie, Matuszczyk, Jens, Greller, Gerhard, Grimm, Christian, Scholz, Jochen, Hoehse, Marek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8285309/
https://www.ncbi.nlm.nih.gov/pubmed/31608427
http://dx.doi.org/10.1007/s00216-019-02096-3
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author Metze, Sabrina
Blioch, Stefanie
Matuszczyk, Jens
Greller, Gerhard
Grimm, Christian
Scholz, Jochen
Hoehse, Marek
author_facet Metze, Sabrina
Blioch, Stefanie
Matuszczyk, Jens
Greller, Gerhard
Grimm, Christian
Scholz, Jochen
Hoehse, Marek
author_sort Metze, Sabrina
collection PubMed
description Viable cell concentration (VCC) is one of the most important process attributes during mammalian cell cultivations. Current state-of-the-art measurements of VCC comprise offline methods which do not allow for continuous process data. According to the FDA’s process analytical technology initiative, process monitoring and control should be applied to gain process understanding and to ensure high product quality. In this work, the use of an inline capacitance probe to monitor online VCCs of a mammalian CHO cell culture process in small-scale bioreactors (250 mL) was investigated. Capacitance sensors using single frequency are increasingly common for biomass monitoring. However, the single-frequency signal corresponds to the cell polarization that represents the viable cell volume. Therefore single-frequency measurements are dependent on cell diameter changes. Measuring the capacitance across various frequencies (frequency scanning) can provide information about the VCC and cope with changing cell diameter. Applying multivariate data analysis on the frequency scanning data successfully enabled direct online monitoring of VCCs in this study. The multivariate model was trained with data from 5 standard cultivations. The model provided a prediction of VCCs with relative errors from 5.5 to 11%, which is a good agreement with the acceptance criterion based on the offline reference method accuracy (approximately 10% relative error) and strongly improved compared with single-frequency results (16 to 23% relative error). Furthermore, robustness trials were conducted to demonstrate the model’s predictive ability under challenging conditions. The process deviations in regard to dilution steps and feed variations were detected immediately in the online prediction of the VCC with relative errors between 6.7 and 13.2%. Thus in summary, the presented method on capacitance frequency scanning demonstrates its suitability for process monitoring and control that can save batches, time, and cost. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00216-019-02096-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-82853092021-07-20 Multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors Metze, Sabrina Blioch, Stefanie Matuszczyk, Jens Greller, Gerhard Grimm, Christian Scholz, Jochen Hoehse, Marek Anal Bioanal Chem Research Paper Viable cell concentration (VCC) is one of the most important process attributes during mammalian cell cultivations. Current state-of-the-art measurements of VCC comprise offline methods which do not allow for continuous process data. According to the FDA’s process analytical technology initiative, process monitoring and control should be applied to gain process understanding and to ensure high product quality. In this work, the use of an inline capacitance probe to monitor online VCCs of a mammalian CHO cell culture process in small-scale bioreactors (250 mL) was investigated. Capacitance sensors using single frequency are increasingly common for biomass monitoring. However, the single-frequency signal corresponds to the cell polarization that represents the viable cell volume. Therefore single-frequency measurements are dependent on cell diameter changes. Measuring the capacitance across various frequencies (frequency scanning) can provide information about the VCC and cope with changing cell diameter. Applying multivariate data analysis on the frequency scanning data successfully enabled direct online monitoring of VCCs in this study. The multivariate model was trained with data from 5 standard cultivations. The model provided a prediction of VCCs with relative errors from 5.5 to 11%, which is a good agreement with the acceptance criterion based on the offline reference method accuracy (approximately 10% relative error) and strongly improved compared with single-frequency results (16 to 23% relative error). Furthermore, robustness trials were conducted to demonstrate the model’s predictive ability under challenging conditions. The process deviations in regard to dilution steps and feed variations were detected immediately in the online prediction of the VCC with relative errors between 6.7 and 13.2%. Thus in summary, the presented method on capacitance frequency scanning demonstrates its suitability for process monitoring and control that can save batches, time, and cost. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00216-019-02096-3) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2019-10-13 2020 /pmc/articles/PMC8285309/ /pubmed/31608427 http://dx.doi.org/10.1007/s00216-019-02096-3 Text en © The Author(s) 2019 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Paper
Metze, Sabrina
Blioch, Stefanie
Matuszczyk, Jens
Greller, Gerhard
Grimm, Christian
Scholz, Jochen
Hoehse, Marek
Multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors
title Multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors
title_full Multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors
title_fullStr Multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors
title_full_unstemmed Multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors
title_short Multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors
title_sort multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8285309/
https://www.ncbi.nlm.nih.gov/pubmed/31608427
http://dx.doi.org/10.1007/s00216-019-02096-3
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