Journal article
Development and validation of a hybrid model for prediction of viable cell density, titer and cumulative glucose consumption in a mammalian cell culture system
BS Yatipanthalawa, SE Wallace Fitzsimons, T Horning, YY Lee, SL Gras
Computers and Chemical Engineering | Elsevier | Published : 2024
Abstract
Among the many approaches to build predictive models for bioprocesses, hybrid models have gained increased attention due to their ability to combine data driven approaches with process knowledge. This study develops and compares hybrid models that predict the performance of a mammalian CHO cell culture producing a therapeutic product. Three machine learning algorithms, MLP, Random Forest and XGBoost regressors are compared to understand the effect of algorithm choice on the rates predicted and overall performance of the hybrid model. When combined in series with mechanistic equations, all three algorithms could predict next day Viable Cell Density, titer and the cumulative amount of glucose ..
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Awarded by Australian Research Council
Funding Acknowledgements
<B>Acknowledgements</B> This research was financially supported by the Victorian Govern-ment under the Higher Education State Investment Fund (VHESIF) program with co-funding from CSL Limited, Australia. Timothy Her-manto is also acknowledged for his assistance with data cleaning. The later stages of writing were were also supported under the Australian Research Council's Industrial Transformation Research Pro-gram (ITRP) funding scheme (project number IH210100051) . The ARC Digital Bioprocess Development Hub is a collaboration between The University of Melbourne, University of Technology Sydney, RMIT Uni-versity, CSL Innovation Pty Ltd, Cytiva (Global Life Science Solutions Australia Pty Ltd) and Patheon Biologics Australia Pty Ltd.