Monitoring of ultra- and diafiltration processes by Kalman-filtered Raman measurements.


Journal

Analytical and bioanalytical chemistry
ISSN: 1618-2650
Titre abrégé: Anal Bioanal Chem
Pays: Germany
ID NLM: 101134327

Informations de publication

Date de publication:
Feb 2023
Historique:
received: 21 07 2022
accepted: 07 12 2022
revised: 24 11 2022
pubmed: 19 1 2023
medline: 1 2 2023
entrez: 18 1 2023
Statut: ppublish

Résumé

Monitoring the protein concentration and buffer composition during the Ultrafiltration/Diafiltration (UF/DF) step enables the further automation of biopharmaceutical production and supports Real-time Release Testing (RTRT). Previously, in-line Ultraviolet (UV) and Infrared (IR) measurements have been used to successfully monitor the protein concentration over a large range. The progress of the diafiltration step has been monitored with density measurements and Infrared Spectroscopy (IR). Raman spectroscopy is capable of measuring both the protein and excipient concentration while being more robust and suitable for production measurements in comparison to Infrared Spectroscopy (IR). Regardless of the spectroscopic sensor used, the low concentration of excipients poses a challenge for the sensors. By combining sensor measurements with a semi-mechanistic model through an Extended Kalman Filter (EKF), the sensitivity to determine the progress of the diafiltration can be improved. In this study, Raman measurements are combined with an EKF for three case studies. The advantages of Kalman-filtered Raman measurements for excipient monitoring are shown in comparison to density measurements. Furthermore, Raman measurements showed a higher measurement speed in comparison to Variable Pathlength (VP) UV measurement at the trade-off of a slightly worse prediction accuracy for the protein concentration. However, the Raman-based protein concentration measurements relied mostly on an increase in the background signal during the process and not on proteinaceous features, which could pose a challenge due to the potential influence of batch variability on the background signal. Overall, the combination of Raman spectroscopy and EKF is a promising tool for monitoring the UF/DF step and enables process automation by using adaptive process control.

Identifiants

pubmed: 36651972
doi: 10.1007/s00216-022-04477-7
pii: 10.1007/s00216-022-04477-7
pmc: PMC9883314
doi:

Substances chimiques

Excipients 0
Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

841-854

Informations de copyright

© 2023. The Author(s).

Références

Biotechnol Bioeng. 2020 Nov;117(11):3591-3606
pubmed: 32687221
Proc R Soc Lond A Math Phys Sci. 1947 Aug 12;190(1022):341-56
pubmed: 20260844
Lasers Med Sci. 2020 Jul;35(5):1141-1151
pubmed: 31853808
QJM. 2004 Nov;97(11):705-16
pubmed: 15496527
Protein Sci. 2002 Sep;11(9):2067-79
pubmed: 12192063
Anal Bioanal Chem. 2020 Apr;412(9):2047-2064
pubmed: 32146498
Biotechnol Prog. 2009 Jul-Aug;25(4):964-72
pubmed: 19569193
Spectrochim Acta A Mol Biomol Spectrosc. 2014 Jul 15;128:300-11
pubmed: 24681316
Biotechnol Bioeng. 2021 Jun;118(6):2293-2300
pubmed: 33666234
Biotechnol Adv. 2006 Sep-Oct;24(5):482-92
pubmed: 16687233
J Pharm Sci. 2004 Sep;93(9):2332-42
pubmed: 15295793
Biotechnol Bioeng. 2021 Feb;118(2):905-917
pubmed: 33150957
Biotechnol Bioeng. 2020 Dec;117(12):3766-3774
pubmed: 32776504
Curr Opin Biotechnol. 2009 Dec;20(6):708-14
pubmed: 19880308
Int J Pharm. 2021 May 1;600:120456
pubmed: 33711473
Biotechnol Bioeng. 2019 Jun;116(6):1366-1379
pubmed: 30684365
Biotechnol Bioeng. 2021 Nov;118(11):4255-4268
pubmed: 34297358
MAbs. 2022 Jan-Dec;14(1):2007564
pubmed: 34965193
J Chem Phys. 2013 Jan 14;138(2):024901
pubmed: 23320715
J Phys Chem B. 2016 Jan 21;120(2):278-91
pubmed: 26707135
Biotechnol J. 2019 Jul;14(7):e1800517
pubmed: 30791230
Phys Rev Lett. 2015 Nov 27;115(22):228302
pubmed: 26650319
Anal Bioanal Chem. 2020 Apr;412(9):2123-2136
pubmed: 32072210

Auteurs

Laura Rolinger (L)

Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
Hoffmann-La Roche AG, Basel, Switzerland.

Jürgen Hubbuch (J)

Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.

Matthias Rüdt (M)

Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. matthias.rudt@hes-so.ch.
Haute Ecole d'Ingénierie (HEI), HES-SO Valais-Wallis, Rue de l'industrie 19, Sion, Switzerland. matthias.rudt@hes-so.ch.

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Classifications MeSH