用于动态灌流细胞培养过程硅优化的混合模型

IF 2.5 3区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Piyush Agarwal, Chris McCready, Say Kong Ng, Jake Chng Ng, Jeroen van de Laar, Maarten Pennings, Gerben Zijlstra
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引用次数: 0

摘要

生物制药行业严重依赖哺乳动物细胞来生产生物治疗蛋白。目前,实施这些工艺的复杂性和高昂的商品成本限制了患者更广泛地使用这些工艺。这推动了提高细胞培养生产率和降低成本的努力。在种子系和/或主生物反应器中使用灌流方法进行上游工艺强化(PI),已显示出提高生产率的巨大前景。然而,由于资源和时间的限制,为基于灌流的工艺开发最佳工艺条件仍具有挑战性。基于模型的优化提供了一种解决方案,即通过系统筛选温度、pH 值和培养基等工艺参数,找到最佳的硅学条件。据我们所知,这是首个经过实验验证的模型,用于解释不同操作条件和规模下的灌流动态,以实现工艺优化。该混合模型准确地描述了中国仓鼠卵巢(CHO)细胞培养的生长动态,神经网络模型则解释了 mAb 的生产,从而实现了培养基交换率的优化。在 Ambr® 250 中进行的六次灌流运行结果表明,该模型具有很高的准确性,证实了其实用性。此外,通过基于模型的优化确定的动态培养基交换率计划的实施使体积生产率提高了 50%。此外,两个 5 升规模的实验验证了该模型对大型生物反应器的可靠外推能力。这种方法可以减少培养过程优化所需的湿实验室实验数量,为提高生物制药生产的生产率和产品成本提供了一条很有前景的途径,进而改善了患者获得关键药物的机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid modeling for in silico optimization of a dynamic perfusion cell culture process
The bio‐pharmaceutical industry heavily relies on mammalian cells for the production of bio‐therapeutic proteins. The complexity of implementing and high cost‐of‐goods of these processes are currently limiting more widespread patient access. This is driving efforts to enhance cell culture productivity and cost reduction. Upstream process intensification (PI), using perfusion approaches in the seed train and/or the main bioreactor, has shown substantial promise to enhance productivity. However, developing optimal process conditions for perfusion‐based processes remain challenging due to resource and time constraints. Model‐based optimization offers a solution by systematically screening process parameters like temperature, pH, and culture media to find the optimum conditions in silico. To our knowledge, this is the first experimentally validated model to explain the perfusion dynamics under different operating conditions and scales for process optimization. The hybrid model accurately describes Chinese hamster ovary (CHO) cell culture growth dynamics and a neural network model explains the production of mAb, allowing for optimization of media exchange rates. Results from six perfusion runs in Ambr® 250 demonstrated high accuracy, confirming the model's utility. Further, the implementation of dynamic media exchange rate schedule determined through model‐based optimization resulted in 50% increase in volumetric productivity. Additionally, two 5 L‐scale experiments validated the model's reliable extrapolation capabilities to large bioreactors. This approach could reduce the number of wet lab experiments needed for culture process optimization, offering a promising avenue for improving productivity, cost‐of‐goods in bio‐pharmaceutical manufacturing, in turn improving patient access to pivotal medicine.
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来源期刊
Biotechnology Progress
Biotechnology Progress 工程技术-生物工程与应用微生物
CiteScore
6.50
自引率
3.40%
发文量
83
审稿时长
4 months
期刊介绍: Biotechnology Progress , an official, bimonthly publication of the American Institute of Chemical Engineers and its technological community, the Society for Biological Engineering, features peer-reviewed research articles, reviews, and descriptions of emerging techniques for the development and design of new processes, products, and devices for the biotechnology, biopharmaceutical and bioprocess industries. Widespread interest includes application of biological and engineering principles in fields such as applied cellular physiology and metabolic engineering, biocatalysis and bioreactor design, bioseparations and downstream processing, cell culture and tissue engineering, biosensors and process control, bioinformatics and systems biology, biomaterials and artificial organs, stem cell biology and genetics, and plant biology and food science. Manuscripts concerning the design of related processes, products, or devices are also encouraged. Four types of manuscripts are printed in the Journal: Research Papers, Topical or Review Papers, Letters to the Editor, and R & D Notes.
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