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Online prediction of product titer and solubility of recombinant proteins in Escherichia coli fed‐batch cultivations
Author(s) -
Luchner Markus,
Striedner Gerald,
CserjanPuschmann Monika,
Strobl Florian,
Bayer Karl
Publication year - 2015
Publication title -
journal of chemical technology and biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.64
H-Index - 117
eISSN - 1097-4660
pISSN - 0268-2575
DOI - 10.1002/jctb.4463
Subject(s) - computer science , context (archaeology) , partial least squares regression , biopharmaceutical , process analytical technology , quality by design , bottleneck , process (computing) , quality (philosophy) , biochemical engineering , production (economics) , new product development , bioprocess , data mining , process engineering , machine learning , microbiology and biotechnology , engineering , biology , paleontology , philosophy , macroeconomics , epistemology , marketing , chemical engineering , business , embedded system , economics , operating system
Abstract BACKGROUND A goal in the production of biopharmaceuticals is to replace the cost‐intensive, empirical ‘quality by testing’ approach with rational, knowledge‐based ‘quality by design’ concepts. The major challenges in this context are the complexity of bioprocesses and the limited online access to process variables related to product quality. The implementation of advanced monitoring strategies combined with chemometric‐based approaches represents a strategy to overcome this bottleneck. RESULTS A series of recombinant E. coli fed‐batch production processes was conducted to provide an accurate data set for development of predictive statistical models. The applicability of partial least squares regression and radial basis function artificial neural network‐based models to predict gene dosage, product titer, and solubility of the target protein was evaluated. In addition to signals from standard online measurements, multi‐wavelength fluorescence signals turned out to provide essential information for prediction of product titer and quality. CONCLUSIONS The application of advanced process monitoring concepts comprising recently established analytical targets and statistical modeling allows for real‐time prediction of product related not directly accessible process variables. Availability of such process information facilitates design of advanced process‐control strategies and will strongly support the implementation of quality by design in biopharmaceutical production. © 2014 Society of Chemical Industry