Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody-peptide fusion

作者：Goldrick Stephen; Holmes William; Bond Nicholas J; Lewis Gareth; Kuiper Marcel; Turner Richard; Farid Suzanne S^*

来源：Biotechnology and Bioengineering, 2017, 114(10): 2222-2234.

DOI：10.1002/bit.26339

• multivariate data analysis
• mammalian cell culture
• trisulfide bond
• partial least squares modeling
• multiple linear regression modeling
• product-related variant

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 7L 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.

• 出版日期2017-10