Machine Learning-based Prediction of the Long-term Stability of Chinese Hamster Ovary Cells due to Epigenetic Changes

Background: Chinese hamster ovary (CHO) cells are the main system for producing recombinant protein biopharmaceuticals, but are inherently unstable, affecting their long-term productivity. This cell instability reduces their productivity over time during perfusion operation, which increases the costs of the resulting biopharmaceutical. No models have been published for the prediction of long-term stability. Results: In this work, we create the first models for predicting the long-term stability of CHO cells due to changes in chromatin modification levels and methylation. Multilayer perceptrons are the best-performing models, reaching an F1 score of 59.1% and a Matthews correlation coefficient of 19.4%. The models are successful at identifying stable and highly productive CHO cells. Furthermore, Shapley values and interpretable models are used to investigate model coefficients, contributing biological insight to this problem and helping focus future data collection efforts. The models trained in this work are free and open source and available at github.com/PedroSeber/CHO_stability_prediction, allowing their use in the biopharmaceutical industry, reproduction of this work, and the retraining of models on other datasets. Conclusions: We show that it is possible to train accurate machine learning models to predict the long-term stability of CHO cells using only epigenetic data. The models have high performance and excel in industrially relevant contexts, and thus can improve the bioproduction of medications, especially recombinant proteins. By providing the first predictive models for this task, this work also serves as a foundation for future data collection and modeling efforts.