Universal Capacitance Model for Real-Time Biomass in Cell Culture
Viktor Konakovsky,
Ali Civan Yagtu,
Christoph Clemens,
Markus Michael Müller,
Martina Berger,
Stefan Schlatter,
Christoph Herwig
Affiliations
Viktor Konakovsky
Institute of Chemical Engineering, Division of Biochemical Engineering, Vienna University of Technology, Gumpendorfer Strasse 1A 166-4, 1060 Vienna, Austria
Ali Civan Yagtu
Institute of Chemical Engineering, Division of Biochemical Engineering, Vienna University of Technology, Gumpendorfer Strasse 1A 166-4, 1060 Vienna, Austria
Christoph Clemens
Boehringer Ingelheim Pharma GmbH & Co. KG Department Bioprocess Development, 88400 Biberach, Germany
Markus Michael Müller
Boehringer Ingelheim Pharma GmbH & Co. KG Department Bioprocess Development, 88400 Biberach, Germany
Martina Berger
Boehringer Ingelheim Pharma GmbH & Co. KG Department Bioprocess Development, 88400 Biberach, Germany
Stefan Schlatter
Boehringer Ingelheim Pharma GmbH & Co. KG Department Bioprocess Development, 88400 Biberach, Germany
Christoph Herwig
Institute of Chemical Engineering, Division of Biochemical Engineering, Vienna University of Technology, Gumpendorfer Strasse 1A 166-4, 1060 Vienna, Austria
Capacitance probes have the potential to revolutionize bioprocess control due to their safe and robust use and ability to detect even the smallest capacitors in the form of biological cells. Several techniques have evolved to model biomass statistically, however, there are problems with model transfer between cell lines and process conditions. Errors of transferred models in the declining phase of the culture range for linear models around +100% or worse, causing unnecessary delays with test runs during bioprocess development. The goal of this work was to develop one single universal model which can be adapted by considering a potentially mechanistic factor to estimate biomass in yet untested clones and scales. The novelty of this work is a methodology to select sensitive frequencies to build a statistical model which can be shared among fermentations with an error between 9% and 38% (mean error around 20%) for the whole process, including the declining phase. A simple linear factor was found to be responsible for the transferability of biomass models between cell lines, indicating a link to their phenotype or physiology.
