Near-Infrared Spectroscopy to Determine Residual Moisture in Freeze-Dried Products: Model Generation by Statistical Design of Experiments.
factorial design
freeze-drying
lyophilization
moisture sorption
monoclonal antibody(s)
near-infrared spectroscopy
partial least squares
principal component analysis
protein formulation
quality by design
Journal
Journal of pharmaceutical sciences
ISSN: 1520-6017
Titre abrégé: J Pharm Sci
Pays: United States
ID NLM: 2985195R
Informations de publication
Date de publication:
01 2020
01 2020
Historique:
received:
28
02
2019
revised:
27
08
2019
accepted:
27
08
2019
pubmed:
10
9
2019
medline:
25
3
2021
entrez:
10
9
2019
Statut:
ppublish
Résumé
Moisture content (MC) is a critical quality attribute of lyophilized biopharmaceuticals and can be determined by near-infrared (NIR) spectroscopy as nondestructive alternative to Karl-Fischer titration. In this study, we create NIR models to determine MC in mAb lyophilisates by use of statistical design of experiments (DoE) and multivariate data analysis. We varied the composition of the formulation as well as lyophilization parameters covering a large range of representative conditions, which is commonly referred to as "robustness testing" according to quality-by-design concepts. We applied principles of chemometrics with partial least squares and principal component analysis. The NIR model excluded samples with complete collapse and MC > 6%. The 2 main components in the principal component analysis were MC (91%) and protein:sugar ratio (6%). The third component amounted to only 3% and remained unspecified but may include variations in process parameters and cake structure. In contrast to traditional approaches for NIR model creation, the DoE-based model can be used to monitor MC during drug product development work including scale-up, and transfer without the need to update the NIR model if protein:sugar ratio and MC stays within the tested limits and cake structure remains macroscopically intact. The use of the DoE approach and multivariate data analysis ensures product consistency and improves understanding of the manufacturing process.
Identifiants
pubmed: 31499067
pii: S0022-3549(19)30561-1
doi: 10.1016/j.xphs.2019.08.028
pii:
doi:
Substances chimiques
Antibodies, Monoclonal
0
Water
059QF0KO0R
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
719-729Informations de copyright
Copyright © 2020 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.