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Reduced scale model qualification of 5‐L and 250‐ml bioreactors using multivariant visualization and Bayesian inferential methods
Author(s) -
Banton Dwaine,
Canova Christopher,
Clark Kevin,
Naguib Sara,
Schaefer Eugene
Publication year - 2020
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.27282
Subject(s) - multivariate statistics , partial least squares regression , visualization , bayesian probability , dimensionality reduction , linear discriminant analysis , computer science , scale (ratio) , microscale chemistry , statistics , artificial intelligence , mathematics , pattern recognition (psychology) , data mining , machine learning , physics , quantum mechanics , mathematics education
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.