Goldrick, S;
Sandner, V;
Cheeks, M;
Turner, R;
Farid, SS;
McCreath, G;
Glassey, J;
(2020)
Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale-up Model Validation and Missing Data on a Micro-Bioreactor System.
Biotechnology Journal
, 15
(3)
, Article e1800684. 10.1002/biot.201800684.
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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 ambr15TM 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. This article is protected by copyright. All rights reserved.
| Type: | Article |
|---|---|
| Title: | Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale-up Model Validation and Missing Data on a Micro-Bioreactor System |
| Location: | Germany |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/biot.201800684 |
| Publisher version: | https://doi.org/10.1002/biot.201800684 |
| Language: | English |
| Additional information: | Copyright © 2019 The Authors. Biotechnology Journal published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Cell Culture, Missing Data, Multivariate Data Analysis, Scale-up/down, TOST |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10083852 |
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