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Automated Data Generation for Raman Spectroscopy Calibrations in Multi-Parallel Mini Bioreactors
Raman spectroscopy is an analytical technology for the simultaneous measurement of important process parameters, such as concentrations of nutrients, metabolites, and product titer in mammalian cell culture. The majority of published Raman studies have concentrated on using the technique for the mon...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099804/ https://www.ncbi.nlm.nih.gov/pubmed/35591088 http://dx.doi.org/10.3390/s22093397 |
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author | Graf, Alexander Woodhams, Angus Nelson, Michael Richardson, Douglas D. Short, Steven M. Brower, Mark Hoehse, Marek |
author_facet | Graf, Alexander Woodhams, Angus Nelson, Michael Richardson, Douglas D. Short, Steven M. Brower, Mark Hoehse, Marek |
author_sort | Graf, Alexander |
collection | PubMed |
description | Raman spectroscopy is an analytical technology for the simultaneous measurement of important process parameters, such as concentrations of nutrients, metabolites, and product titer in mammalian cell culture. The majority of published Raman studies have concentrated on using the technique for the monitoring and control of bioreactors at pilot and manufacturing scales. This research presents a novel approach to generating Raman models using a high-throughput 250 mL mini bioreactor system with the following two integrated analysis modules: a prototype flow cell enabling on-line Raman measurements and a bioanalyzer to generate reference measurements without a significant time-shift, compared to the corresponding Raman measurement. Therefore, spectral variations could directly be correlated with the actual analyte concentrations to build reliable models. Using a design of experiments (DoE) approach and additional spiked samples, the optimized workflow resulted in robust Raman models for glucose, lactate, glutamine, glutamate and titer in Chinese hamster ovary (CHO) cell cultures producing monoclonal antibodies (mAb). The setup presented in this paper enables the generation of reliable Raman models that can be deployed to predict analyte concentrations, thereby facilitating real-time monitoring and control of biologics manufacturing. |
format | Online Article Text |
id | pubmed-9099804 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90998042022-05-14 Automated Data Generation for Raman Spectroscopy Calibrations in Multi-Parallel Mini Bioreactors Graf, Alexander Woodhams, Angus Nelson, Michael Richardson, Douglas D. Short, Steven M. Brower, Mark Hoehse, Marek Sensors (Basel) Article Raman spectroscopy is an analytical technology for the simultaneous measurement of important process parameters, such as concentrations of nutrients, metabolites, and product titer in mammalian cell culture. The majority of published Raman studies have concentrated on using the technique for the monitoring and control of bioreactors at pilot and manufacturing scales. This research presents a novel approach to generating Raman models using a high-throughput 250 mL mini bioreactor system with the following two integrated analysis modules: a prototype flow cell enabling on-line Raman measurements and a bioanalyzer to generate reference measurements without a significant time-shift, compared to the corresponding Raman measurement. Therefore, spectral variations could directly be correlated with the actual analyte concentrations to build reliable models. Using a design of experiments (DoE) approach and additional spiked samples, the optimized workflow resulted in robust Raman models for glucose, lactate, glutamine, glutamate and titer in Chinese hamster ovary (CHO) cell cultures producing monoclonal antibodies (mAb). The setup presented in this paper enables the generation of reliable Raman models that can be deployed to predict analyte concentrations, thereby facilitating real-time monitoring and control of biologics manufacturing. MDPI 2022-04-28 /pmc/articles/PMC9099804/ /pubmed/35591088 http://dx.doi.org/10.3390/s22093397 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Graf, Alexander Woodhams, Angus Nelson, Michael Richardson, Douglas D. Short, Steven M. Brower, Mark Hoehse, Marek Automated Data Generation for Raman Spectroscopy Calibrations in Multi-Parallel Mini Bioreactors |
title | Automated Data Generation for Raman Spectroscopy Calibrations in Multi-Parallel Mini Bioreactors |
title_full | Automated Data Generation for Raman Spectroscopy Calibrations in Multi-Parallel Mini Bioreactors |
title_fullStr | Automated Data Generation for Raman Spectroscopy Calibrations in Multi-Parallel Mini Bioreactors |
title_full_unstemmed | Automated Data Generation for Raman Spectroscopy Calibrations in Multi-Parallel Mini Bioreactors |
title_short | Automated Data Generation for Raman Spectroscopy Calibrations in Multi-Parallel Mini Bioreactors |
title_sort | automated data generation for raman spectroscopy calibrations in multi-parallel mini bioreactors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099804/ https://www.ncbi.nlm.nih.gov/pubmed/35591088 http://dx.doi.org/10.3390/s22093397 |
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