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Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale-Up Model Validation and Missing Data on a Mirco-Bioreactor System

Lookup NU author(s): Professor Jarka Glassey

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Multivariate data analysis (MVDA) is a highly valuable and significantly underutilised resource in biomanufacturing. It offers the opportunity to enhance our understanding and leverage useful information from complex high-dimensional data sets, recorded throughout all stages of therapeutic drug manufacture. To help standardise 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 was followed to solve two case studies: a ‘small data’ and a ‘big data’ challenge. In the ‘small data’ example, a large-scale data set was compared to data from a scale-down model. This methodology enabled a new quantitative metric for equivalence to be established by combining a Two One-Sided Test (TOST) with principal component analysis. In the ‘big data’ example, this methodology enabled accurate predictions of critical missing data essential to a cloning study performed in the ambr15 TM system. These predictions were 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 visualisation during data analytics to solve complex biopharmaceutical challenges.


Publication metadata

Author(s): Goldrick S, Sandner V, Cheeks M, Turner R, Farid SS, McCreath G, Glassey J

Publication type: Article

Publication status: Published

Journal: Biotechnology Journal

Year: 2019

Issue: ePub ahead of Print

Online publication date: 16/10/2019

Acceptance date: 06/09/2019

Date deposited: 16/09/2019

ISSN (print): 1860-6768

ISSN (electronic): 1860-7314

Publisher: Wiley -VCH Verlag GmbH & Co. KGaA

URL: https://doi.org/10.1002/biot.201800684

DOI: 10.1002/biot.201800684


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