生物过程
过程分析技术
过程(计算)
校准
计算机科学
生物系统
化学计量学
周转时间
集合(抽象数据类型)
效价
生化工程
工艺工程
机器学习
数学
工程类
统计
抗体
操作系统
生物
化学工程
程序设计语言
免疫学
作者
Yuxiang Zhao,Yawen Tang,Dhanuka P. Wasalathanthri,Jinying Xu,James Ding
摘要
Intensified and continuous processes require fast and robust methods and technologies to monitor product titer for faster analytical turnaround time, process monitoring, and process control. The current titer measurements are mostly offline chromatography-based methods which may take hours or even days to get the results back from the analytical labs. Thus, offline methods will not meet the requirement of real time titer measurements for continuous production and capture processes. FTIR and chemometric based multivariate modeling are promising tools for real time titer monitoring in clarified bulk (CB) harvests and perfusate lines. However, empirical models are known to be vulnerable to unseen variability, specifically a FTIR chemometric titer model trained on a given biological molecule and process conditions often fails to provide accurate predictions of titer in another molecule under different process conditions. In this study, we developed an adaptive modeling strategy: the model was initially built using a calibration set of available perfusate and CB samples and then updated by augmenting spiking samples of the new molecules to the calibration set to make the model robust against perfusate or CB harvest of the new molecule. This strategy substantially improved the model performance and significantly reduced the modeling effort for new molecules.
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