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Mechanistic modeling for cell culture process of trastuzumab and intelligent algorithm comparison
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Wen-hui WANG1, Yu-xiang WAN1, 2, Jing-yu JIAO2, Dan GAO2, Dong GAO2, Hai-bin QU1, *
Acta Pharmaceutica Sinica | 2025, 60(9) : 2788 - 2797
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Acta Pharmaceutica Sinica | 2025, 60(9): 2788-2797
• Original Articles •
Mechanistic modeling for cell culture process of trastuzumab and intelligent algorithm comparison
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Wen-hui WANG1, Yu-xiang WAN1, 2, Jing-yu JIAO2, Dan GAO2, Dong GAO2, Hai-bin QU1, *
Affiliations
  • 1Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
  • 2BioRay Pharmaceutical Co., Ltd., Hangzhou 311404, China
Published: 2025-09-12 doi: 10.16438/j.0513-4870.2025-0071
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Mechanistic modeling, which describes cellular metabolic behavior through mathematical equations, is an important theoretical tool for optimizing and predicting biological processes. In this study, a cell culture mechanistic model for biosimilar trastuzumab was constructed based on the Monod growth metabolism model. To address the high complexity of differential equation parameter fitting and the tendency to fall into local optima, particle swarm optimization (PSO), grey wolf optimization (GWO), and differential evolution (DE) algorithms were introduced for model parameter fitting. The results show that GWO outperforms the others in global search capability and fitting accuracy, demonstrating higher stability and adaptability in handling multi-parameter fitting problems. Furthermore, improvements were made to the position and velocity update methods of the GWO algorithm. The improved grey wolf optimization (IGWO) significantly enhanced the model's predictive ability, enabling accurate predictions of cell density, metabolite concentration, and antibody titer. This study provides important theoretical support for improving the production efficiency of biopharmaceuticals, reducing development costs, and optimizing cell culture processes.

cell culture  /  monoclonal antibody  /  trastuzumab  /  mechanistic modeling  /  intelligent algorithm
王文慧, 万宇翔, 焦静雨, 高丹, 高栋, 瞿海斌. 曲妥珠单抗细胞培养过程模型建立与智能算法对比. 药学学报, 2025 , 60 (9) : 2788 -2797 . DOI: 10.16438/j.0513-4870.2025-0071
Wen-hui WANG, Yu-xiang WAN, Jing-yu JIAO, Dan GAO, Dong GAO, Hai-bin QU. Mechanistic modeling for cell culture process of trastuzumab and intelligent algorithm comparison[J]. Acta Pharmaceutica Sinica, 2025 , 60 (9) : 2788 -2797 . DOI: 10.16438/j.0513-4870.2025-0071
Year 2025 volume 60 Issue 9
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doi: 10.16438/j.0513-4870.2025-0071
  • Receive Date:2025-01-20
  • Online Date:2026-09-20
  • Published:2025-09-12
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  • Received:2025-01-20
  • Revised:2025-04-30
Affiliations
    1Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
    2BioRay Pharmaceutical Co., Ltd., Hangzhou 311404, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
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Percentage of
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属
Genus
种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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