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Prospective inference of bioprocess cell viability through chemometric modeling of fluorescence multiway data
Author(s) -
Chiappini Fabricio A.,
Azcarate Silvana,
Alcaraz Mirta R.,
Forno Ángela G.,
Goicoechea Hector C.
Publication year - 2021
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1002/btpr.3173
Subject(s) - bioprocess , biochemical engineering , context (archaeology) , process analytical technology , computer science , process (computing) , process engineering , critical quality attributes , microbiology and biotechnology , biological system , engineering , biology , downstream (manufacturing) , paleontology , chemical engineering , operating system , operations management
In this investigation, the fermentation step of a standard mammalian cell‐based industrial bioprocess for the production of a therapeutic protein was studied, with particular emphasis on the evolution of cell viability. This parameter constitutes one of the critical variables for bioprocess monitoring since it can affect downstream operations and the quality of the final product. In addition, when the cells experiment an unpredictable drop in viability, the assessment of this variable through classic off‐line methods may not provide information sufficiently in advance to take corrective actions. In this context, Process Analytical Technology (PAT) framework aims to develop novel strategies for more efficient monitoring of critical variables, in order to improve the bioprocess performance. Thus, in this work, a set of chemometric tools were integrated to establish a PAT strategy to monitor cell viability, based on fluorescence multiway data obtained from fermentation samples of a particular bioprocess, in two different scales of operation. The spectral information, together with data regarding process variables, was integrated through chemometric exploratory tools to characterize the bioprocess and stablish novel criteria for the monitoring of cell viability. These findings motivated the development of a multivariate classification model, aiming to obtain predictive tools for the monitoring of future lots of the same bioprocess. The model could be satisfactorily fitted, showing the non‐error rate of prediction of 100%.