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Enhanced Process Understanding and Multivariate Prediction of the Relationship Between Cell Culture Process and Monoclonal Antibody Quality

机译：增强的过程理解和多元预测细胞培养过程与单克隆抗体质量之间的关系

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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 (R)) 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. (C) 2017 American Institute of Chemical Engineers
机译：这项工作调查了基于在毫流（AMBR-15（R））规模的设计的高通量细胞培养实验的单克隆抗体产品质量的预测过程模型中可以推导出的见解和理解。研究的工艺条件包括各种介质补充剂以及在该过程中施加的pH和温度偏移。首先，主要成分分析（PCA）用于显示产品质量属性中的强相关特性，包括聚集体，片段，电荷变体和聚糖。然后，应用部分最小二乘回归（PLS1和PLS2）以基于处理信息（一个接一个地或同时）来预测产品质量变量。这两个建模技术的比较表明，单个（PLS2）模型能够揭示过程特征对大型产品质量变量的相互关系。为了示出工艺可预测性的动态演进，在不同的时间点定义了单独的模型，示出了几个产品质量属性主要由媒体组成驱动，因此可以从过程中从早期开始预测，而其他产品过程中受到进程过程中的影响受到强烈影响。最后，通过将PLS2模型与遗传算法耦合，首先可以进一步提高模型性能，最重要的是，可以显着简化对大尺寸的过程 - 产品 - 相互关系的解释。在本案例研究中提供的一般适用的工具集为整个过程开发的决策和过程优化提供了坚实的基础。 （c）2017美国化学工程师研究所

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