International Journal of Innovative Research in Engineering and Management
Year: 2024, Volume: 12, Issue: 6
First page : ( 18) Last page : ( 29)
Online ISSN : 2350-0557.
DOI: 10.55524/ijircst.2024.12.6.4 | DOI URL: https://doi.org/10.55524/ijircst.2024.12.6.4
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
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Wenxuan Zheng , Mingxuan Yang, Decheng Huang, Meizhizi Jin
This study presents a new deep learning method for optimizing monoclonal antibody (mAb) production processes using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture. The model was developed and validated using industry data from 50 products over 18 months. The proposed design outperforms statistical models, machine learning algorithms, and other deep learning models, achieving a root mean squared error of 0.412 g/L and R^ 2 value of 0.947 for mAb titer prediction. Feature importance analysis identified temperature, dissolved oxygen, and pH as the most critical parameters affecting mAb production. In silico optimization, experiments demonstrated a 28.1% increase in mAb titer and a 27.9% improvement in volumetric productivity. The model's robustness and generalizability were validated across cell lines and bioreactor scales (50L to 2000L). A novel Dynamic Trajectory Similarity (DTS) score was introduced to quantify the model's ability to capture process dynamics, yielding a score of 0.923. This approach offers significant potential for enhancing process understanding, optimizing production efficiency, and facilitating scale-up in industrial mAb manufacturing. The study also discusses limitations, including interpretability challenges and the need for uncertainty quantification in future work.
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Applied Math,University of California, Los Angeles, CA, USA
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