Goldrick, S;
Holmes, W;
Bond, N;
Lewis, G;
Kuiper, M;
Turner, R;
Farid, SS;
(2017)
Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody-peptide fusion.
Biotechnology and Bioengineering
, 144
(10)
pp. 2222-2234.
10.1002/bit.26339.
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Abstract
Product quality heterogeneities, such as a trisulfide bond (TSB) formation, can be influenced by multiple interacting process parameters. Identifying their root cause is a major challenge in biopharmaceutical production. To address this issue, this paper describes the novel application of advanced multivariate data analysis (MVDA) techniques to identify the process parameters influencing TSB formation in a novel recombinant antibody-peptide fusion expressed in mammalian cell culture. The screening dataset was generated with a high-throughput (HT) micro-bioreactor system (Ambr(TM) 15) using a design of experiments (DoE) approach. The complex dataset was firstly analyzed through the development of a multiple linear regression model focusing solely on the DoE inputs and identified the temperature, pH and initial nutrient feed day as important process parameters influencing this quality attribute. To further scrutinize the dataset, a partial least squares model was subsequently built incorporating both on-line and off-line process parameters and enabled accurate predictions of the TSB concentration at harvest. Process parameters identified by the models to promote and suppress TSB formation were implemented on five 7 L bioreactors and the resultant TSB concentrations were comparable to the model predictions. This study demonstrates the ability of MVDA to enable predictions of the key performance drivers influencing TSB formation that are valid also upon scale-up.
| Type: | Article |
|---|---|
| Title: | Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody-peptide fusion |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/bit.26339 |
| Publisher version: | http://doi.org/10.1002/bit.26339 |
| Language: | English |
| Additional information: | Copyright © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc. 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: | multivariate data analysis; mammalian cell culture; trisulfide bond; partial least squares modeling; multiple linear regression modeling; product-related variant |
| 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/1561480 |
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