Multivariate analysis of cell culture bioprocess data—Lactate consumption as process indicator

• Published in: Journal of biotechnology Vol. 162; no. 2-3; pp. 210 - 223
• Main Authors: Le, Huong, Kabbur, Santosh, Pollastrini, Luciano, Sun, Ziran, Mills, Keri, Johnson, Kevin, Karypis, George, Hu, Wei-Shou
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 31.12.2012
• Subjects: Animals; antibodies; Bioprocess data mining; bioprocessing; bioreactors; Biotechnology - methods; Cell Count; Cell culture; Cell Culture Techniques - methods; cell viability; Chinese hamster ovary (CHO) cells; CHO Cells; Computational Biology - methods; Cricetinae; Cricetulus; Culture Media; glucose; Glucose - metabolism; inoculum; Lactate consumption; Lactic Acid - metabolism; least squares; manufacturing; metabolism; Multivariate Analysis; Multivariate data analysis; Partial least square regression; prediction; Regression Analysis; Support Vector Machine; Support vector regression; Cell culture; Bioprocess data mining; Partial least square regression; Lactate consumption; Support vector regression; Chinese hamster ovary (CHO) cells; Multivariate data analysis
• ISSN: 0168-1656; 1873-4863
• DOI: 10.1016/j.jbiotec.2012.08.021

► Cell culture bioprocess data from 243 runs at a Genentech facility were analyzed. ► Process outcome were reliably predicted at early stages of the production scale. ► Inoculum data indicate a “memory” effect that persists throughout the run. ► Majority of pivotal parameters are related to cell growth and lactate metabolism. ► Results suggest possible intervention means to render a process more robust. 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.