生物反应器
比例(比率)
过程(计算)
一致性(知识库)
放大
计算机科学
工艺工程
过程开发
生化工程
化学
工程类
人工智能
物理
有机化学
量子力学
经典力学
操作系统
作者
Jinxin Gao,Laurie B. Hazeltine,Neal Stroud,Ning Liu,Yao‐ming Huang
摘要
Scale-down model qualification is an important step for developing a large-scale cell culture process to enhance process understanding and support process characterization studies. Traditionally, only harvest data are used to show consistency between small-scale and large-scale bioreactor performance, allowing attributes that are dynamic over the cell culture period to be overlooked. A novel statistical method, orthogonal projections to latent structures (OPLS) analysis, can be utilized to compare time-course cell culture data across scales. Here we describe an example where OPLS is used to identify gaps between small-scale and large-scale bioreactor performances. In this case, differences in the partial pressure of carbon dioxide (pCO2) and lactate profiles were observed between small- and large-scale bioreactors, which were linked to differences in the product-quality attributes fragments and galactosylation. An improved small-scale model was developed, leading to improved consistency in the process performance and product qualities across scales and qualification of the scale-down model for regulatory submissions. This new statistical approach can provide valuable insights into process understanding and process scale-up.
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