Cargando…
A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept
Glycosylation is considered a critical quality attribute of therapeutic proteins as it affects their stability, bioactivity, and safety. Hence, the development of analytical methods able to characterize the composition and structure of glycoproteins is crucial. Existing methods are time consuming, e...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317070/ https://www.ncbi.nlm.nih.gov/pubmed/35889277 http://dx.doi.org/10.3390/molecules27144405 |
_version_ | 1784754967993647104 |
---|---|
author | Hamla, Sabrina Sacré, Pierre-Yves Derenne, Allison Cowper, Ben Goormaghtigh, Erik Hubert, Philippe Ziemons, Eric |
author_facet | Hamla, Sabrina Sacré, Pierre-Yves Derenne, Allison Cowper, Ben Goormaghtigh, Erik Hubert, Philippe Ziemons, Eric |
author_sort | Hamla, Sabrina |
collection | PubMed |
description | Glycosylation is considered a critical quality attribute of therapeutic proteins as it affects their stability, bioactivity, and safety. Hence, the development of analytical methods able to characterize the composition and structure of glycoproteins is crucial. Existing methods are time consuming, expensive, and require significant sample preparation, which can alter the robustness of the analyses. In this context, we developed a fast, direct, and simple drop-coating deposition Raman imaging (DCDR) method combined with multivariate curve resolution alternating least square (MCR-ALS) to analyze glycosylation in monoclonal antibodies (mAbs). A database of hyperspectral Raman imaging data of glycoproteins was built, and the glycoproteins were characterized by LC-FLR-MS as a reference method to determine the composition in glycans and monosaccharides. The DCDR method was used and allowed the separation of excipient and protein by forming a “coffee ring”. MCR-ALS analysis was performed to visualize the distribution of the compounds in the drop and to extract the pure spectral components. Further, the strategy of SVD-truncation was used to select the number of components to resolve by MCR-ALS. Raman spectra were processed by support vector regression (SVR). SVR models showed good predictive performance in terms of RMSECV, R(2)(CV). |
format | Online Article Text |
id | pubmed-9317070 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93170702022-07-27 A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept Hamla, Sabrina Sacré, Pierre-Yves Derenne, Allison Cowper, Ben Goormaghtigh, Erik Hubert, Philippe Ziemons, Eric Molecules Article Glycosylation is considered a critical quality attribute of therapeutic proteins as it affects their stability, bioactivity, and safety. Hence, the development of analytical methods able to characterize the composition and structure of glycoproteins is crucial. Existing methods are time consuming, expensive, and require significant sample preparation, which can alter the robustness of the analyses. In this context, we developed a fast, direct, and simple drop-coating deposition Raman imaging (DCDR) method combined with multivariate curve resolution alternating least square (MCR-ALS) to analyze glycosylation in monoclonal antibodies (mAbs). A database of hyperspectral Raman imaging data of glycoproteins was built, and the glycoproteins were characterized by LC-FLR-MS as a reference method to determine the composition in glycans and monosaccharides. The DCDR method was used and allowed the separation of excipient and protein by forming a “coffee ring”. MCR-ALS analysis was performed to visualize the distribution of the compounds in the drop and to extract the pure spectral components. Further, the strategy of SVD-truncation was used to select the number of components to resolve by MCR-ALS. Raman spectra were processed by support vector regression (SVR). SVR models showed good predictive performance in terms of RMSECV, R(2)(CV). MDPI 2022-07-09 /pmc/articles/PMC9317070/ /pubmed/35889277 http://dx.doi.org/10.3390/molecules27144405 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 Hamla, Sabrina Sacré, Pierre-Yves Derenne, Allison Cowper, Ben Goormaghtigh, Erik Hubert, Philippe Ziemons, Eric A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept |
title | A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept |
title_full | A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept |
title_fullStr | A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept |
title_full_unstemmed | A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept |
title_short | A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept |
title_sort | new alternative tool to analyse glycosylation in monoclonal antibodies based on drop-coating deposition raman imaging: a proof of concept |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317070/ https://www.ncbi.nlm.nih.gov/pubmed/35889277 http://dx.doi.org/10.3390/molecules27144405 |
work_keys_str_mv | AT hamlasabrina anewalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept AT sacrepierreyves anewalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept AT derenneallison anewalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept AT cowperben anewalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept AT goormaghtigherik anewalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept AT hubertphilippe anewalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept AT ziemonseric anewalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept AT hamlasabrina newalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept AT sacrepierreyves newalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept AT derenneallison newalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept AT cowperben newalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept AT goormaghtigherik newalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept AT hubertphilippe newalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept AT ziemonseric newalternativetooltoanalyseglycosylationinmonoclonalantibodiesbasedondropcoatingdepositionramanimagingaproofofconcept |