Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA.
J Biotechnol. 2012 Dec 31;162(2-3):210-23. doi: 10.1016/j.jbiotec.2012.08.021. Epub 2012 Sep 10.
Multivariate analysis of cell culture bioprocess data has the potential of unveiling hidden process characteristics and providing new insights into factors affecting process performance. This study investigated the time-series data of 134 process parameters acquired throughout the inoculum train and the production bioreactors of 243 runs at the Genentech's Vacaville manufacturing facility. Two multivariate methods, kernel-based support vector regression (SVR) and partial least square regression (PLSR), were used to predict the final antibody concentration and the final lactate concentration. Both product titer and the final lactate level were shown to be predicted accurately when data from the early stages of the production scale were employed. Using only process data from the inoculum train, the prediction accuracy of the final process outcome was lower; the results nevertheless suggested that the history of the culture may exert significant influence on the final process outcome. The parameters contributing most significantly to the prediction accuracy were related to lactate metabolism and cell viability in both the production scale and the inoculum train. Lactate consumption, which occurred rather independently of the residual glucose and lactate concentrations, was shown to be a prominent factor in determining the final outcome of production-scale cultures. The results suggest possible opportunities to intervene in metabolism, steering it towards the type with a strong propensity towards high productivity. Such intervention could occur in the inoculum stage or in the early stage of the production-scale reactors. Overall, this study presents pattern recognition as an important process analytical technology (PAT). Furthermore, the high correlation between lactate consumption and high productivity can provide a guide to apply quality by design (QbD) principles to enhance process robustness.
对细胞培养生物工艺数据进行多元分析具有揭示隐藏工艺特性和提供影响工艺性能因素新见解的潜力。本研究调查了在 Genentech 的 Vacaville 制造工厂的 243 个生产罐中，在接种物培养和生产罐中获得的 134 个过程参数的时间序列数据。两种多元方法，基于核的支持向量回归（SVR）和偏最小二乘回归（PLSR），用于预测最终抗体浓度和最终乳酸浓度。当使用生产规模早期的数据时，产品滴度和最终乳酸水平都被证明可以准确预测。当仅使用接种物培养的数据时，最终工艺结果的预测精度较低；但结果表明，培养物的历史可能对最终工艺结果产生重大影响。对预测精度贡献最大的参数与生产规模和接种物培养物中的乳酸代谢和细胞活力有关。乳酸消耗与残余葡萄糖和乳酸浓度的关系相对独立，它被证明是决定生产规模培养物最终结果的一个重要因素。结果表明，有可能在代谢方面进行干预，使其朝着具有高生产力倾向的类型发展。这种干预可以发生在接种物阶段或生产规模反应器的早期阶段。总的来说，本研究提出了模式识别作为一种重要的过程分析技术（PAT）。此外，乳酸消耗与高生产力之间的高度相关性可以为应用质量源于设计（QbD）原则提供指导，以增强工艺稳健性。
