A Deep Learning Approach for Optimizing Monoclonal Antibody Production Process Parameters
Keywords:
Monoclonal antibody production, Deep learning, Process optimization, CNN-LSTMAbstract
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.
References
X. Zhang, Q. Qi, and W. Liu, “Combined Hybrid Neural Networks and Swarm Intelligence Optimization Algorithms for Photovoltaic Panel Segmentation from Remote Sensing Images,” IEEE Access, 2024. Available From: https://doi.org/10.1109/ACCESS.2024.3406551
Z. Sun, Y. Li, Q. He, H. Xu, W. Wang, and X. Liu, “Causality Enhanced Global-Local Graph Neural Network for Bioprocess Factor Forecasting,” IEEE Transactions on Industrial Informatics, 2024. Available From: https://doi.org/10.1109/TII.2024.3424266
A. Khrisna, H. H. Nuha, and S. Andi, “The Use of Convolutional Neural Networks for RNA Protein Prediction,” in 2023 3rd International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), 2023, pp. 39-43. Available From: https://doi.org/10.1109/ICICyTA60173.2023.10429034
S. Sarna, N. Patel, P. Mhaskar, B. Corbett, and C. McCready, “Data Driven Modeling and Model Predictive Control of Bioreactor for Production of Monoclonal Antibodies,” in 2022 American Control Conference (ACC), 2022, pp. 1347-1352. Available From: https://doi.org/10.23919/ACC53348.2022.9867419
K. Cai and Y. Zhu, “A Method for Identifying Essential Proteins Based on Deep Convolutional Neural Network Architecture with Particle Swarm Optimization,” in 2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE), 2022, pp. 7-12. Available From: https://doi.org/10.1109/ARACE56528.2022.00010
Y. Zhu, K. Yu, M. Wei, Y. Pu, and Z. Wang, “AI-Enhanced Administrative Prosecutorial Supervision in Financial Big Data: New Concepts and Functions for the Digital Era,” Social Science Journal for Advanced Research, vol. 4, no. 5, pp. 40-54, 2024. Available From: https://doi.org/10.5281/zenodo.13766965
F. Zhao, et al., “Application of Deep Reinforcement Learning for Cryptocurrency Market Trend Forecasting and Risk Management,” Journal of Industrial Engineering and Applied Science, vol. 2, no. 5, pp. 48-55, 2024. Available From: https://doi.org/10.20944/preprints202410.0682.v1
X. Ma, W. Zeyu, X. Ni, and G. Ping, “Artificial Intelligence-Based Inventory Management for Retail Supply Chain Optimization: A Case Study of Customer Retention and Revenue Growth,” Journal of Knowledge Learning and Science Technology, vol. 3, no. 4, pp. 260-273, 2024. Available From: https://doi.org/10.60087/jklst.v3.n4.p260
X. Ni, Y. Zhang, Y. Pu, M. Wei, and Q. Lou, “A Personalized Causal Inference Framework for Media Effectiveness Using Hierarchical Bayesian Market Mix Models,” Journal of Artificial Intelligence and Development, vol. 3, no. 1, 2024. Available From: https://edujavare.com/index.php/JAI/article/view/546
X. Zhan, Y. Xu, and Y. Liu, “Personalized UI Layout Generation Using Deep Learning: An Adaptive Interface Design Approach for Enhanced User Experience,” Journal of Artificial Intelligence and Development, vol. 3, no. 1, 2024.
S. Zhou, W. Zheng, Y. Xu, and Y. Liu, “Enhancing User Experience in VR Environments through AI-Driven Adaptive UI Design,” Journal of Artificial Intelligence General Science (JAIGS), vol. 6, no. 1, pp. 59-82, 2024. Available From: https://doi.org/10.60087/jaigs.v6i1.230
S. Wang, H. Zhang, S. Zhou, J. Sun, and Q. Shen, “Chip Floorplanning Optimization Using Deep Reinforcement Learning,” International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 5, pp. 100-109, 2024. Available From: https://doi.org/10.55524/ijircst.2024.12.5.14
M. Wei, Y. Pu, Q. Lou, Y. Zhu, and Z. Wang, “Machine Learning-Based Intelligent Risk Management and Arbitrage System for Fixed Income Markets: Integrating High-Frequency Trading Data and Natural Language Processing,” Journal of Industrial Engineering and Applied Science, vol. 2, no. 5, pp. 56-67, 2024. Available From: https://doi.org/10.5281/zenodo.13858262
B. Wang, H. Zheng, K. Qian, X. Zhan, and J. Wang, “Edge Computing and AI-Driven Intelligent Traffic Monitoring and Optimization,” Applied and Computational Engineering, vol. 77, pp. 225-230, 2024. Available From: https://doi.org/10.54254/2755-2721/77/2024MA0062
H. Li, S. X. Wang, F. Shang, K. Niu, and R. Song, “Applications of Large Language Models in Cloud Computing: An Empirical Study Using Real-World Data,” International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 4, pp. 59-69, 2024. Available From: https://doi.org/10.55524/ijircst.2024.12.4.10
S. Wang, K. Xu, and Z. Ling, “Deep Learning-Based Chip Power Prediction and Optimization: An Intelligent EDA Approach,” International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 4, pp. 77-87, 2024. Available From: https://doi.org/10.55524/ijircst.2024.12.4.13
K. Xu, H. Zhou, H. Zheng, M. Zhu, and Q. Xin, “Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning,” arXiv preprint arXiv:2403.19345, 2024. Available From: https://doi.org/10.48550/arXiv.2403.19345
K. Xu, H. Zheng, X. Zhan, S. Zhou, and K. Niu, “Evaluation and Optimization of Intelligent Recommendation System Performance with Cloud Resource Automation Compatibility,” 2024. Available From: https://www.preprints.org/manuscript/202407.2199
H. Zheng, K. Xu, H. Zhou, Y. Wang, and G. Su, “Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis,” Academic Journal of Science and Technology, vol. 10, no. 1, pp. 62-68, 2024. Available From: https://doi.org/10.54097/v160aa61
H. Zheng, J. Wu, R. Song, L. Guo, and Z. Xu, “Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis,” Applied and Computational Engineering, vol. 87, pp. 26-32, 2024. Available from: https://www.preprints.org/manuscript/202407.0895
B. Liu and Y. Zhang, “Implementation of Seamless Assistance with Google Assistant Leveraging Cloud Computing,” Journal of Cloud Computing, vol. 12, no. 4, pp. 1-15, 2023. Available From: https://shorturl.at/HiHok
M. Zhang, B. Yuan, H. Li, and K. Xu, “LLM-Cloud Complete: Leveraging Cloud Computing for Efficient Large Language Model-Based Code Completion,” Journal of Artificial Intelligence General Science (JAIGS), vol. 5, no. 1, pp. 295-326, 2024. Available From: https://doi.org/10.60087/jaigs.v5i1.200
P. Li, Y. Hua, Q. Cao, and M. Zhang, “Improving the Restore Performance via Physical-Locality Middleware for Backup Systems,” in Proceedings of the 21st International Middleware Conference, 2020, pp. 341-355. Available From: https://doi.org/10.1145/3423211.3425691
S. Zhou, B. Yuan, K. Xu, M. Zhang, and W. Zheng, “The Impact of Pricing Schemes on Cloud Computing and Distributed Systems,” Journal of Knowledge Learning and Science Technology, vol. 3, no. 3, pp. 193-205, 2024. Available From: https://doi.org/10.60087/jklst.v3.n3.p206-224
F. Shang, F. Zhao, M. Zhang, J. Sun, and J. Shi, “Personalized Recommendation Systems Powered By Large Language Models: Integrating Semantic Understanding and User Preferences,” International Journal of Innovative Research in Engineering and Management, vol. 11, no. 4, pp. 39-49, 2024. Available From: https://doi.org/10.55524/ijirem.2024.11.4.6
J. Sun, X. Wen, G. Ping, and M. Zhang, “Application of News Analysis Based on Large Language Models in Supply Chain Risk Prediction,” Journal of Computer Technology and Applied Mathematics, vol. 1, no. 3, pp. 55-65, 2024. Available From: https://n2t.net/ark:/40704/JCTAM.v1n3a08
F. Zhao, M. Zhang, S. Zhou, and Q. Lou, “Detection of Network Security Traffic Anomalies Based on Machine Learning KNN Method,” Journal of Artificial Intelligence General Science (JAIGS), vol. 1, no. 1, pp. 209-218, 2024. Available From: https://doi.org/10.60087/jaigs.v1i1.213
C. Ju and Y. Zhu, “Reinforcement Learning-Based Model for Enterprise Financial Asset Risk Assessment and Intelligent Decision Making,” 2024.
K. Yu, et al., “Loan Approval Prediction Improved by XGBoost Model Based on Four-Vector Optimization Algorithm,” 2024. Available From: https://doi.org/10.20944/preprints202410.0698.v1
S. Zhou, J. Sun, and K. Xu, “AI-Driven Data Processing and Decision Optimization in IoT through Edge Computing and Cloud Architecture,” 2024. Available From: https://doi.org/10.20944/preprints202410.0736.v1
J. Sun, S. Zhou, X. Zhan, and J. Wu, “Enhancing Supply Chain Efficiency with Time Series Analysis and Deep Learning Techniques,” 2024. Available From: https://shorturl.at/RgLPs
H. Zheng, K. Xu, M. Zhang, H. Tan, and H. Li, “Efficient Resource Allocation in Cloud Computing Environments Using AI-Driven Predictive Analytics,” Applied and Computational Engineering, vol. 82, pp. 6-12, 2024. Available From: https://doi.org/10.54254/2755-2721/82/2024GLG0055
S. Wang, H. Zheng, X. Wen, K. Xu, and H. Tan, “Enhancing Chip Design Verification through AI-Powered Bug Detection in RTL Code,” Applied and Computational Engineering, vol. 82, pp. 87-93, 2024 Available From: https://doi.org/10.54254/2755-2721/92/20241685
C. Che, Z. Huang, C. Li, H. Zheng, and X. Tian, “Integrating generative AI into financial market prediction for improved decision making,” arXiv preprint arXiv:2404.03523, 2024. Available From: https://doi.org/10.48550/arXiv.2404.03523
C. Che, H. Zheng, Z. Huang, W. Jiang, and B. Liu, “Intelligent robotic control system based on computer vision technology,” arXiv preprint arXiv:2404.01116, 2024. Available From: https://doi.org/10.48550/arXiv.2404.01116
Y. Jiang, Q. Tian, J. Li, M. Zhang, and L. Li, “The Application Value of Ultrasound in the Diagnosis of Ovarian Torsion,” International Journal of Biology and Life Sciences, vol. 7, no. 1, pp. 59-62, 2024. Available From: https://doi.org/10.54097/nnvdz490
L. Li, X. Li, H. Chen, M. Zhang, and L. Sun, “Application of AI-assisted Breast Ultrasound Technology in Breast Cancer Screening,” International Journal of Biology and Life Sciences, vol. 7, no. 1, pp. 1-4, 2024. Available From: https://doi.org/10.54097/1y59dx48
L. Lijie, P. Caiying, S. Liqian, Z. Miaomiao, and J. Yi, “The application of ultrasound automatic volume imaging in detecting breast tumors.” Available From: https://francis-press.com/papers/17047
S. Wang, H. Zheng, X. Wen, and S. Fu, “Distributed high-performance computing methods for accelerating deep learning training,” Journal of Knowledge Learning and Science Technology, vol. 3, no. 3, pp. 108-126, 2024. Available From: https://doi.org/10.60087/jklst.v3.n3.p108-126
B. Yuan, G. Cao, J. Sun, and S. Zhou, “Optimising AI Workload Distribution in Multi-Cloud Environments: A Dynamic Resource Allocation Approach,” Journal of Industrial Engineering and Applied Science, vol. 2, no. 5, pp. 68-79, 2024. Available From: https://doi.org/10.5281/zenodo.13863194
