迈向先进的生物工艺优化:一种多尺度建模方法
Mariana Monteiro1, Sarah Fadda1, Cleo Kontoravdi1
1Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
Computational and structural biotechnology journal
|July 31, 2023
概括
这项研究介绍了一种新的实时养策略,用于使用模型预测控制 (MPC) 进行中国仓鼠卵巢 (CHO) 细胞培养. 这种可适应的方法通过响应当前的工艺状态来优化蛋白质的生产,提高生物制药制造的效率.
科学领域:
- 生物技术是生物技术.
- 生物工艺工程 生物工艺工程
- 细胞培养技术 细胞培养技术
背景情况:
- 哺乳动物细胞培养,特别是使用中国仓鼠卵巢 (CHO) 细胞,对于生产治疗性蛋白质至关重要,占商业供应的高达80%.
- 目前的料批处理过程依赖于预先确定的料策略,往往忽视实时工艺条件,这可能会限制最佳蛋白质产量和质量.
- 适应性和高效的生物制造战略的需求至关重要,以满足对生物制药品日益增长的需求.
研究的目的:
- 开发和验证一种模型预测控制 (MPC) 配方,以优化CHO细胞食批量培养中的养政策.
- 创建一种不依赖培养的控制策略,可以在不同的CHO细胞培养系统中转移.
- 为了证明与传统的开环方法相比,实时,状态意识的养控制的有效性.
主要方法:
- 开发一种混合动力-静态反应堆模型,以捕捉细胞生长和蛋白质生产动态.
- 实施模型预测控制 (MPC) 框架,利用开发的模型实时优化料速率.
- 使用数字双胞胎作为过程模拟器进行验证,将闭环MPC控制与开环模拟进行比较.
主要成果:
- 该MPC配方成功地在实时生成了最佳的养策略,适应细胞培养的动态状态.
- 与开放循环策略相比,模拟显示在闭环MPC控制下过程性能显著改善.
- 建议的控制策略被证明是对特定培养条件的不可知,这表明在CHO细胞制造中具有广泛的适用性.
结论:
- 模型预测控制提供了一种强大的实时优化料批量CHO细胞培养的方法.
- 开发的文化不可知MPC战略提高了生物制药生产的效率和稳定性.
- 这种先进的控制方法有望改善治疗蛋白质的制造.
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