Literature DB >> 31499067

Near-Infrared Spectroscopy to Determine Residual Moisture in Freeze-Dried Products: Model Generation by Statistical Design of Experiments.

Matthieu Clavaud1, Carmen Lema-Martinez2, Yves Roggo3, Michael Bigalke2, Aurélie Guillemain4, Philippe Hubert5, Eric Ziemons5, Andrea Allmendinger6.   

Abstract

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.
Copyright © 2020 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  factorial design; freeze-drying; lyophilization; moisture sorption; monoclonal antibody(s); near-infrared spectroscopy; partial least squares; principal component analysis; protein formulation; quality by design

Year:  2019        PMID: 31499067     DOI: 10.1016/j.xphs.2019.08.028

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  2 in total

1.  Integration of Response Surface Methodology (RSM) and Principal Component Analysis (PCA) as an Optimization Tool for Polymer Inclusion Membrane Based-Optodes Designed for Hg(II), Cd(II), and Pb(II).

Authors:  Jeniffer García-Beleño; Eduardo Rodríguez de San Miguel
Journal:  Membranes (Basel)       Date:  2021-04-14

Review 2.  Process Analytical Technology Tools for Monitoring Pharmaceutical Unit Operations: A Control Strategy for Continuous Process Verification.

Authors:  Eun Ji Kim; Ji Hyeon Kim; Min-Soo Kim; Seong Hoon Jeong; Du Hyung Choi
Journal:  Pharmaceutics       Date:  2021-06-21       Impact factor: 6.321

  2 in total

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