Enhanced process understanding and multivariate prediction of the relationship between cell culture process and monoclonal antibody quality
Enhanced process understanding and multivariate prediction of the relationship between cell culture process and monoclonal antibody quality
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增强对细胞培养过程与单克隆抗体质量之间关系的过程理解和多变量预测
DOI：
10.1002/btpr.2502
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发表时间：
2017
影响因子：
2.9
通讯作者：
A. Butté
中科院分区：
文献类型：
--
作者：
M. Sokolov;Jonathan Ritscher;Nicola MacKinnon;J. Souquet;H. Broly;M. Morbidelli;A. Butté
这项工作研究了基于毫升 (ambr-15®) 规模进行的设计高通量细胞培养实验，从单克隆抗体产品质量的预测过程模型中推断出的见解和理解。研究的工艺条件包括各种培养基补充剂以及工艺过程中应用的 pH 值和温度变化。首先，使用主成分分析（PCA）来显示产品质量属性之间的强相关特征，包括聚集体、碎片、电荷变体和聚糖。然后，应用偏最小二乘回归（PLS1 和 PLS2）根据过程信息（逐一或同时）预测产品质量变量。这两种建模技术的比较表明，单个 (PLS2) 模型能够揭示过程特征与大量产品质量变量之间的相互关系。为了显示过程可预测性的动态演变，在不同时间点定义了单独的模型，表明一些产品质量属性主要由介质成分驱动，因此可以在过程的早期进行适当的预测，而其他属性则受到过程中过程参数变化的强烈影响。最后，通过首先将 PLS2 模型与遗传算法相结合，可以进一步提高模型性能，最重要的是，可以显着简化对大维过程-产品-相互关系的解释。本案例研究中介绍的普遍适用的工具集为整个流程开发过程中的决策制定和流程优化提供了坚实的基础。 © 2017 美国化学工程师生物技术研究所。编, 33:1368–1380, 2017
This work investigates the insights and understanding which can be deduced from predictive process models for the product quality of a monoclonal antibody based on designed high‐throughput cell culture experiments performed at milliliter (ambr‐15®) scale. The investigated process conditions include various media supplements as well as pH and temperature shifts applied during the process. First, principal component analysis (PCA) is used to show the strong correlation characteristics among the product quality attributes including aggregates, fragments, charge variants, and glycans. Then, partial least square regression (PLS1 and PLS2) is applied to predict the product quality variables based on process information (one by one or simultaneously). The comparison of those two modeling techniques shows that a single (PLS2) model is capable of revealing the interrelationship of the process characteristics to the large set product quality variables. In order to show the dynamic evolution of the process predictability separate models are defined at different time points showing that several product quality attributes are mainly driven by the media composition and, hence, can be decently predicted from early on in the process, while others are strongly affected by process parameter changes during the process. Finally, by coupling the PLS2 models with a genetic algorithm first the model performance can be further improved and, most importantly, the interpretation of the large‐dimensioned process–product‐interrelationship can be significantly simplified. The generally applicable toolset presented in this case study provides a solid basis for decision making and process optimization throughout process development. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1368–1380, 2017