201. Development and validation of a hybrid model for prediction of viable cell density, titer and cumulative glucose consumption in a mammalian cell culture system.
- Author
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Yatipanthalawa, Bhagya S., Wallace Fitzsimons, Shauna E., Horning, Tizita, Lee, Yih Yean, and Gras, Sally L.
- Subjects
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PREDICTION models , *MACHINE learning , *TITERS , *CHO cell , *MODEL validation , *GLUCOSE , *CELL culture - Abstract
• Hybrid models successfully predicted key performance metrics for antibody production in mammalian cells. • Such models allow predictions under varying process conditions, without the need to re-estimate parameters. • Performance could be predicted for a different product under similar process conditions with reasonable accuracy. • Algorithm choice did not significantly impact model performance, but computational requirements differed. 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 consumed with low error, although decision tree-based algorithm required less computational time for training. The models performed well using a dataset for a new product, without further model training. Retraining the model on a subset of data from the new product improved the prediction accuracy of VCD and titer. The potential for application and further model developments are also outlined. [Display omitted] [ABSTRACT FROM AUTHOR]
- Published
- 2024
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