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相关概念视频

Hybridoma Technology01:31

Hybridoma Technology

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Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
Hybridoma Selection
Commonly used fusion techniques — electroporation,...
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Pharmacokinetic Models: Overview01:20

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Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Purification and Analytics of a Monoclonal Antibody from Chinese Hamster Ovary Cells Using an Automated Microbioreactor System
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实时模型预测控制单克隆抗体捕获在连续制造使用物理信息的神经网络加速机械建模加速机械建模.

Si-Yuan Tang1, Yun-Hao Yuan1, Yan-Na Sun1

  • 1Manufacturing Science and Technology (MSAT), WuXi Biologics, Wuxi, Jiangsu, China.

Biotechnology and bioengineering
|December 29, 2025
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概括

蒸的物理信息神经网络 (PINNs) 加快了蛋白质A染色学优化,以实现连续的生物制造. 这种人工智能方法提高了单克隆抗体生产过程的控制和生产率.

关键词:
连续色谱学 连续色谱学连续制造 连续制造模型预测控制模型预测控制定期逆流色谱法 定期逆流色谱法基于物理学的神经网络.

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科学领域:

  • 生物技术是生物技术.
  • 化学工程是化学工程的重要组成部分.
  • 人工智能的人工智能

背景情况:

  • 使用蛋白A亲和色谱的持续生物处理为单克隆抗体 (mAb) 生产提供了显著的优势,包括提高生产率和降低成本.
  • 实时优化和控制多列周期反流色谱 (PCC) 存在挑战,因为机械模型的计算需求.

研究的目的:

  • 开发和验证蒸的物理信息神经网络 (PINNs),以加速突破曲线的拟合和四列PCC (4C-PCC) 的优化.
  • 提高连续生物制造过程优化过程的计算效率和准确性.

主要方法:

  • 基于一般速率模型 (GRM) 的蒸PINN的实施,以建模蛋白A染色学.
  • 将PINN性能与突破曲线拟合和4C-PCC优化的传统数值方法进行比较.
  • 整合PINN加速GRM与模型预测控制 (MPC) 进行实验室规模的连续制造过程.

主要成果:

  • 与数值方法相比,蒸PINNs实现了~10倍的优化速度,精度提高了~40%.
  • 一个较小的PINN模型提供了22倍的加速,将优化时间缩短到1.44秒.
  • 基于PINN的MPC证明了强大的控制,在动态条件下实现了35g/L树脂/h的生产率和90%的树脂容量利用率.

结论:

  • 蒸PINN提供了一个计算效率高,物理一致的框架,用于实时优化和控制连续生物过程.
  • 将机械模型与神经网络集成,提高了流程理解,稳定性,并支持持续生物制造的进步.
  • 这种人工智能驱动的方法对于优化治疗性蛋白质生产中的复杂染色学过程至关重要.