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在昆虫细胞生物工艺中启用 PAT:通过荧光光谱法对重组腺相关病毒生产进行原位监测。

Enabling PAT in insect cell bioprocesses: In situ monitoring of recombinant adeno-associated virus production by fluorescence spectroscopy.

机构信息

iBET, Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal.

Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal.

出版信息

Biotechnol Bioeng. 2019 Nov;116(11):2803-2814. doi: 10.1002/bit.27117. Epub 2019 Jul 26.

Abstract

The process analytical technology (PAT) initiative shifted the bioprocess development mindset towards real-time monitoring and control tools to measure relevant process variables online, and acting accordingly when undesirable deviations occur. Online monitoring is especially important in lytic production systems in which released proteases and changes in cell physiology are likely to affect product quality attributes, as is the case of the insect cell-baculovirus expression vector system (IC-BEVS), a well-established system for production of viral vectors and vaccines. Here, we applied fluorescence spectroscopy as a real-time monitoring tool for recombinant adeno-associated virus (rAAV) production in the IC-BEVS. Fluorescence spectroscopy is simple, yet sensitive and informative. To overcome the strong fluorescence background of the culture medium and improve predictive ability, we combined artificial neural network models with a genetic algorithm-based approach to optimize spectra preprocessing. We obtained predictive models for rAAV titer, cell viability and cell concentration with normalized root mean squared errors of 7%, 4%, and 7%, respectively, for leave-one-batch-out cross-validation. Our approach shows fluorescence spectroscopy allows real-time determination of the best time of harvest to maintain rAAV infectivity, an important quality attribute, and detection of deviations from the golden batch profile. This methodology can be applied to other biopharmaceuticals produced in the IC-BEVS, supporting the use of fluorescence spectroscopy as a versatile PAT tool.

摘要

过程分析技术(PAT)倡议将生物工艺开发思路转向实时监测和控制工具,以便在线测量相关过程变量,并在出现不良偏差时采取相应措施。在线监测在裂解生产系统中尤为重要,因为释放的蛋白酶和细胞生理变化很可能影响产品质量属性,昆虫细胞-杆状病毒表达载体系统(IC-BEVS)就是一个很好的例子,该系统是生产病毒载体和疫苗的成熟系统。在这里,我们将荧光光谱法应用于 IC-BEVS 中的重组腺相关病毒(rAAV)生产的实时监测工具。荧光光谱法简单、灵敏且信息量丰富。为了克服培养基的强荧光背景并提高预测能力,我们结合人工神经网络模型和基于遗传算法的方法来优化光谱预处理。我们通过留一法交叉验证获得了 rAAV 效价、细胞活力和细胞浓度的预测模型,归一化均方根误差分别为 7%、4%和 7%。我们的方法表明,荧光光谱法可实时确定最佳收获时间以保持 rAAV 感染力,这是一个重要的质量属性,并可检测到偏离黄金批次曲线的情况。该方法可应用于 IC-BEVS 生产的其他生物制药产品,支持将荧光光谱法用作多功能 PAT 工具。

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