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Abstract

Many protein-based vaccines comprise viral surface proteins which are chosen for their ability to stimulate the immune system. These vaccine molecules are often heavily glycosylated, and glycosylation plays critical roles in the immunological and stability properties of vaccines. The structural characterization and product quality attribute monitoring of such complex vaccine therapeutics during process development and manufacturing is very challenging. High throughput monitoring of multiple molecular attributes, particularly glycosylation, of recombinant glycoprotein subunit vaccines are needed to support entire vaccine production processes. Multi-attribute monitoring (MAM) technology involves assessing multiple critical molecular attributes of molecules in one set of analyses in an automated fashion, for product quality attribute requirements. MAM is still in the early development stages and is currently applied to therapeutics with very low levels of glycosylation such as monoclonal antibodies. MAM on glycoproteins with a higher number of glycosylation sites with high glycan heterogeneity such as subunit vaccine molecules is challenging as each glycan site and glycan modification exponentially increases data processing complexity. We developed a MAM workflow to perform detailed structural characterization of subunit protein vaccines, monitoring critical parameters such as intact mass, sequence identity, protein clipping, glycosylation, other post-translational modifications, and host cell proteins (HCP). By using a combination of software tools and product process monitoring strategy, we performed data processing at multiple steps and identified key attributes for each vaccine candidate under the development pipeline. Further, a high-throughput critical attribute monitoring MAM workflow was developed to support the influenza and HIV vaccine development processes including cell line selection, cell clone selection, cell culture optimization, stability study evaluation and final vaccine product characterization.

Details

1009240
Business indexing term
Title
Multi-attribute monitoring (MAM) methodology for glycosylated subunit vaccines
Author
Shajahan, Asif 1 ; Jenkins, Lisa M. 2 ; Barefoot, Nathan 1 ; Maldonado, Darielys 1 ; Wolff, Jeremy J. 1 ; Yang, Yanhong 1 ; Kueltzo, Lisa A. 1 ; Ficca, Valerie 1 ; Scheideman, Elizabeth 1 ; Loukinov, Ivan 1 ; Carruthers, Carl 1 ; Benmohamed, Dorra 1 ; Gowetski, Daniel B. 1 ; Jiang, Rong 1 ; Yang, Sylvie R. 1 ; Carlton, Kevin 1 ; Gall, Jason G. 1 ; Lei, Q. Paula 1 

 Vaccine Production Program Laboratory, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Gaithersburg, MD, USA (ROR: https://ror.org/043z4tv69) (GRID: grid.419681.3) (ISNI: 0000 0001 2164 9667) 
 Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA (ROR: https://ror.org/040gcmg81) (GRID: grid.48336.3a) (ISNI: 0000 0004 1936 8075) 
Volume
15
Issue
1
Pages
41198
Number of pages
18
Publication year
2025
Publication date
2025
Section
Article
Publisher
Nature Publishing Group
Place of publication
London
Country of publication
United States
Publication subject
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-11-21
Milestone dates
2025-10-16 (Registration); 2025-07-17 (Received); 2025-10-16 (Accepted)
Publication history
 
 
   First posting date
21 Nov 2025
ProQuest document ID
3274342038
Document URL
https://www.proquest.com/scholarly-journals/multi-attribute-monitoring-mam-methodology/docview/3274342038/se-2?accountid=208611
Copyright
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-11-23
Database
2 databases
  • Coronavirus Research Database
  • ProQuest One Academic