Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody–peptide fusion. Issue 10 (5th June 2017)

• Record Type: Journal Article
• Authors: Goldrick, Stephen; Holmes, William; Bond, Nicholas J.; Lewis, Gareth; Kuiper, Marcel; Turner, Richard; Farid, Suzanne S.
• Is Part Of: Biotechnology and bioengineering. Volume 114:Issue 10(2017)
• Page Start: 2222
• Page End: 2234
• Publication Date: 2017-06-05
• Subjects: multivariate data analysis -- mammalian cell culture -- trisulfide bond -- partial least squares modeling -- multiple linear regression modeling -- product‐related variant; Biotechnology -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/bit.26339
• Languages: English
• ISSNs: 0006-3592

Abstract

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. Biotechnol. Bioeng. 2017;114: 2222–2234. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc. Abstract : This paper implements advanced multivariate data analysis to identify the key performance drivers influencing trisulfide bond (TSB) formation on a novel antibody‐peptide fusion. A partial least squares model (A) and multiple linear regression model (B) identified the temperature, pH and initial nutrient feed day as important process parameters influencing this quality attribute and were validated across multiple scales (C). The insights generated from this work enable the control limits of the key process parameters to be redefined to minimize TSB formation.