초록
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.