Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale-Up Model Validation and Missing Data on a Micro-Bioreactor System.
cell culture
missing data
multivariate data analysis
scale-up/down
two one-sided test
Journal
Biotechnology journal
ISSN: 1860-7314
Titre abrégé: Biotechnol J
Pays: Germany
ID NLM: 101265833
Informations de publication
Date de publication:
Mar 2020
Mar 2020
Historique:
received:
13
11
2018
revised:
26
09
2019
pubmed:
17
10
2019
medline:
29
9
2020
entrez:
17
10
2019
Statut:
ppublish
Résumé
Multivariate data analysis (MVDA) is a highly valuable and significantly underutilized resource in biomanufacturing. It offers the opportunity to enhance understanding and leverage useful information from complex high-dimensional data sets, recorded throughout all stages of therapeutic drug manufacture. To help standardize the application and promote this resource within the biopharmaceutical industry, this paper outlines a novel MVDA methodology describing the necessary steps for efficient and effective data analysis. The MVDA methodology is followed to solve two case studies: a "small data" and a "big data" challenge. In the "small data" example, a large-scale data set is compared to data from a scale-down model. This methodology enables a new quantitative metric for equivalence to be established by combining a two one-sided test with principal component analysis. In the "big data" example, this methodology enables accurate predictions of critical missing data essential to a cloning study performed in the ambr15 system. These predictions are generated by exploiting the underlying relationship between the off-line missing values and the on-line measurements through the generation of a partial least squares model. In summary, the proposed MVDA methodology highlights the importance of data pre-processing, restructuring, and visualization during data analytics to solve complex biopharmaceutical challenges.
Identifiants
pubmed: 31617682
doi: 10.1002/biot.201800684
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1800684Subventions
Organisme : H2020 Marie Skłodowska-Curie Actions
ID : 643056
Organisme : Engineering and Physical Sciences Research Council
ID : EP/I033270/1
Informations de copyright
© 2019 The Authors. Biotechnology Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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