Monitoring of Nutrients, Metabolites, IgG Titer, and Cell Densities in 10 L Bioreactors Using Raman Spectroscopy and PLS Regression Models
<b>Background:</b> Chinese hamster ovary (CHO) cell metabolism is complex, influenced by nutrients like glucose and glutamine and metabolites such as lactate. Real-time monitoring is necessary for optimizing culture conditions and ensuring consistent product quality. Raman spectroscopy h...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Pharmaceutics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4923/17/4/473 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849714260752465920 |
|---|---|
| author | Morandise Rubini Julien Boyer Jordane Poulain Anaïs Berger Thomas Saillard Julien Louet Martin Soucé Sylvie Roussel Sylvain Arnould Murielle Vergès Fabien Chauchard-Rios Igor Chourpa |
| author_facet | Morandise Rubini Julien Boyer Jordane Poulain Anaïs Berger Thomas Saillard Julien Louet Martin Soucé Sylvie Roussel Sylvain Arnould Murielle Vergès Fabien Chauchard-Rios Igor Chourpa |
| author_sort | Morandise Rubini |
| collection | DOAJ |
| description | <b>Background:</b> Chinese hamster ovary (CHO) cell metabolism is complex, influenced by nutrients like glucose and glutamine and metabolites such as lactate. Real-time monitoring is necessary for optimizing culture conditions and ensuring consistent product quality. Raman spectroscopy has emerged as a robust process analytical technology (PAT) tool due to its non-invasive, in situ capabilities. This study evaluates Raman spectroscopy for monitoring key metabolic parameters and IgG titer in CHO cell cultures. <b>Methods:</b> Raman spectroscopy was applied to five 10 L-scale CHO cell cultures. Partial least squares (PLS) regression models were developed from four batches, including one with induced cell death, to enhance robustness. The models were validated against blind test sets. <b>Results:</b> PLS models exhibited high predictive accuracy (R<sup>2</sup> > 0.9). Glucose and IgG titer predictions were reliable (RMSEP = 0.51 g/L and 0.12 g/L, respectively), while glutamine and lactate had higher RMSEP due to lower concentrations. Specific Raman bands contributed to the specificity of glucose, lactate, and IgG models. Predictions for viable (VCD) and total cell density (TCD) were less accurate due to the absence of direct Raman signals. <b>Conclusions:</b> This study confirms Raman spectroscopy’s potential for real-time, in situ bioprocess monitoring without manual sampling. Chemometric analysis enhances model robustness, supporting automated control systems. Raman data could enable continuous feedback regulation of critical nutrients like glucose, ensuring consistent critical quality attributes (CQAs) in biopharmaceutical production. |
| format | Article |
| id | doaj-art-a074e1e2423343cc80dc325285aa3500 |
| institution | DOAJ |
| issn | 1999-4923 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceutics |
| spelling | doaj-art-a074e1e2423343cc80dc325285aa35002025-08-20T03:13:45ZengMDPI AGPharmaceutics1999-49232025-04-0117447310.3390/pharmaceutics17040473Monitoring of Nutrients, Metabolites, IgG Titer, and Cell Densities in 10 L Bioreactors Using Raman Spectroscopy and PLS Regression ModelsMorandise Rubini0Julien Boyer1Jordane Poulain2Anaïs Berger3Thomas Saillard4Julien Louet5Martin Soucé6Sylvie Roussel7Sylvain Arnould8Murielle Vergès9Fabien Chauchard-Rios10Igor Chourpa11Centre de Biophysique Moléculaire (UPR CNRS 4301), Département Nanomédicaments et Nanosondes, UFR de Pharmacie Philippe Maupas, Université de Tours, 31 avenue Monge, 37 000 Tours, FranceOndalys, 4 Rue Georges Besse, 34 830 Clapiers, FranceOndalys, 4 Rue Georges Besse, 34 830 Clapiers, FranceBioengineering, Bio-S, Technologie Servier, 905 Route de Saran, 45 520 Gidy, FranceBioengineering, Bio-S, Technologie Servier, 905 Route de Saran, 45 520 Gidy, FranceIndatech—Chauvin Arnoux Group, 4 Rue George Besse, 34 830 Clapiers, FranceCentre de Biophysique Moléculaire (UPR CNRS 4301), Département Nanomédicaments et Nanosondes, UFR de Pharmacie Philippe Maupas, Université de Tours, 31 avenue Monge, 37 000 Tours, FranceOndalys, 4 Rue Georges Besse, 34 830 Clapiers, FranceBioengineering, Bio-S, Technologie Servier, 905 Route de Saran, 45 520 Gidy, FranceBioengineering, Bio-S, Technologie Servier, 905 Route de Saran, 45 520 Gidy, FranceIndatech—Chauvin Arnoux Group, 4 Rue George Besse, 34 830 Clapiers, FranceCentre de Biophysique Moléculaire (UPR CNRS 4301), Département Nanomédicaments et Nanosondes, UFR de Pharmacie Philippe Maupas, Université de Tours, 31 avenue Monge, 37 000 Tours, France<b>Background:</b> Chinese hamster ovary (CHO) cell metabolism is complex, influenced by nutrients like glucose and glutamine and metabolites such as lactate. Real-time monitoring is necessary for optimizing culture conditions and ensuring consistent product quality. Raman spectroscopy has emerged as a robust process analytical technology (PAT) tool due to its non-invasive, in situ capabilities. This study evaluates Raman spectroscopy for monitoring key metabolic parameters and IgG titer in CHO cell cultures. <b>Methods:</b> Raman spectroscopy was applied to five 10 L-scale CHO cell cultures. Partial least squares (PLS) regression models were developed from four batches, including one with induced cell death, to enhance robustness. The models were validated against blind test sets. <b>Results:</b> PLS models exhibited high predictive accuracy (R<sup>2</sup> > 0.9). Glucose and IgG titer predictions were reliable (RMSEP = 0.51 g/L and 0.12 g/L, respectively), while glutamine and lactate had higher RMSEP due to lower concentrations. Specific Raman bands contributed to the specificity of glucose, lactate, and IgG models. Predictions for viable (VCD) and total cell density (TCD) were less accurate due to the absence of direct Raman signals. <b>Conclusions:</b> This study confirms Raman spectroscopy’s potential for real-time, in situ bioprocess monitoring without manual sampling. Chemometric analysis enhances model robustness, supporting automated control systems. Raman data could enable continuous feedback regulation of critical nutrients like glucose, ensuring consistent critical quality attributes (CQAs) in biopharmaceutical production.https://www.mdpi.com/1999-4923/17/4/473Raman spectroscopyprocess analytical technology (PAT)CHO cell culturebioprocess monitoringchemometric analysisIgG titer prediction |
| spellingShingle | Morandise Rubini Julien Boyer Jordane Poulain Anaïs Berger Thomas Saillard Julien Louet Martin Soucé Sylvie Roussel Sylvain Arnould Murielle Vergès Fabien Chauchard-Rios Igor Chourpa Monitoring of Nutrients, Metabolites, IgG Titer, and Cell Densities in 10 L Bioreactors Using Raman Spectroscopy and PLS Regression Models Pharmaceutics Raman spectroscopy process analytical technology (PAT) CHO cell culture bioprocess monitoring chemometric analysis IgG titer prediction |
| title | Monitoring of Nutrients, Metabolites, IgG Titer, and Cell Densities in 10 L Bioreactors Using Raman Spectroscopy and PLS Regression Models |
| title_full | Monitoring of Nutrients, Metabolites, IgG Titer, and Cell Densities in 10 L Bioreactors Using Raman Spectroscopy and PLS Regression Models |
| title_fullStr | Monitoring of Nutrients, Metabolites, IgG Titer, and Cell Densities in 10 L Bioreactors Using Raman Spectroscopy and PLS Regression Models |
| title_full_unstemmed | Monitoring of Nutrients, Metabolites, IgG Titer, and Cell Densities in 10 L Bioreactors Using Raman Spectroscopy and PLS Regression Models |
| title_short | Monitoring of Nutrients, Metabolites, IgG Titer, and Cell Densities in 10 L Bioreactors Using Raman Spectroscopy and PLS Regression Models |
| title_sort | monitoring of nutrients metabolites igg titer and cell densities in 10 l bioreactors using raman spectroscopy and pls regression models |
| topic | Raman spectroscopy process analytical technology (PAT) CHO cell culture bioprocess monitoring chemometric analysis IgG titer prediction |
| url | https://www.mdpi.com/1999-4923/17/4/473 |
| work_keys_str_mv | AT morandiserubini monitoringofnutrientsmetabolitesiggtiterandcelldensitiesin10lbioreactorsusingramanspectroscopyandplsregressionmodels AT julienboyer monitoringofnutrientsmetabolitesiggtiterandcelldensitiesin10lbioreactorsusingramanspectroscopyandplsregressionmodels AT jordanepoulain monitoringofnutrientsmetabolitesiggtiterandcelldensitiesin10lbioreactorsusingramanspectroscopyandplsregressionmodels AT anaisberger monitoringofnutrientsmetabolitesiggtiterandcelldensitiesin10lbioreactorsusingramanspectroscopyandplsregressionmodels AT thomassaillard monitoringofnutrientsmetabolitesiggtiterandcelldensitiesin10lbioreactorsusingramanspectroscopyandplsregressionmodels AT julienlouet monitoringofnutrientsmetabolitesiggtiterandcelldensitiesin10lbioreactorsusingramanspectroscopyandplsregressionmodels AT martinsouce monitoringofnutrientsmetabolitesiggtiterandcelldensitiesin10lbioreactorsusingramanspectroscopyandplsregressionmodels AT sylvieroussel monitoringofnutrientsmetabolitesiggtiterandcelldensitiesin10lbioreactorsusingramanspectroscopyandplsregressionmodels AT sylvainarnould monitoringofnutrientsmetabolitesiggtiterandcelldensitiesin10lbioreactorsusingramanspectroscopyandplsregressionmodels AT murielleverges monitoringofnutrientsmetabolitesiggtiterandcelldensitiesin10lbioreactorsusingramanspectroscopyandplsregressionmodels AT fabienchauchardrios monitoringofnutrientsmetabolitesiggtiterandcelldensitiesin10lbioreactorsusingramanspectroscopyandplsregressionmodels AT igorchourpa monitoringofnutrientsmetabolitesiggtiterandcelldensitiesin10lbioreactorsusingramanspectroscopyandplsregressionmodels |