Reduced scale model qualification of 5‐L and 250‐ml bioreactors using multivariant visualization and Bayesian inferential methods. Issue 5 (30th January 2020)

• Record Type: Journal Article
• Authors: Banton, Dwaine; Canova, Christopher; Clark, Kevin; Naguib, Sara; Schaefer, Eugene
• Is Part Of: Biotechnology and bioengineering. Volume 117:Issue 5(2020)
• Page Start: 1337
• Page End: 1347
• Publication Date: 2020-01-30
• Subjects: Bayesian -- PLS‐DA -- reduced scale model -- statistical qualification; Biotechnology -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/bit.27282
• Languages: English
• ISSNs: 0006-3592

Abstract

Abstract: A novel method for the qualification of reduced scale models (RSMs) was illustrated using data from both a 250‐ml advanced microscale bioreactor (ambr) and a 5‐L bioreactor RSM for a 2, 000‐L manufacturing scale process using a CHO cell line to produce a recombinant monoclonal antibody. The example study showed how the method was used to identify process performance attributes and product quality attributes that capture important aspects of the RSM qualification process. The method uses two novel statistical approaches: multivariate dimension reduction and data visualization techniques, via partial least squares discriminant analysis (PLS‐DA), and Bayesian multivariate linear modeling for inferential analysis. Bayesian multivariate linear modeling allows for individual probability distributions of the differences of the mean of each attribute for each scale, as well as joint probability statements on the differences of the means for multiple attributes. Depending on the results of this inferential procedure, PLS‐DA is used to identify the process performance outputs at the different scales which have the greatest negative impact on the multivariate Bayesian joint probabilities. Experience with that particular process can then be leveraged to adjust operating conditions to minimize these differences, and then equivalence can be reassessed using the multivariate linear model. Abstract : A method for the qualification of reduced scale models (RSMs) is presented that combines two novel statistical approaches: multivariate dimension reduction and data visualization techniques, via partial least squares discriminant analysis (PLS‐DA), and Bayesian multivariate linear modeling for inferential analysis. This method is illustrated by comparing data from both a 250 ml advanced microscale bioreactor (ambr) and a 5 L bioreactor RSM to a 2000 L manufacturing scale process that uses a CHO cell line to produce a recombinant monoclonal antibody. The iterative multi‐step approach for reduced scale model qualification described in this paper can be applied to any stage of the bioprocess, and for any given compound.