Model-based identification of cell-cycle-dependent metabolism and putative autocrine effects in antibody producing CHO cell culture

The understanding of cell-cycle-dependent population heterogeneities in mammalian cell culture and their influence on production rates is still limited. Furthermore, metabolic regulations arising from self-expressed signaling factors (autocrine/autoinhibitory factors) have been postulated in the past, but no determination of such effects have been made so far for fast-growing production Chinese hamster ovary (CHO) cells in chemically defined media. In this study, a novel approach combining near-physiological treatment of cells (including synchronization), population resolved mechanistic modeling and statistical analysis was developed to identify population inhomogeneities. Cell-cycle-dependent population dynamics and metabolic regulations due to a putative autocrine factor were examined and their impact on the metabolic rates and antibody production of near-physiologically synchronized CHO DP-12 cell cultures was determined. To achieve this, a population resolved model was extended to describe putative autocrine-dependentt and cell-cycle-related metabolic rates for glucose, glutamine, lactate, ammonia, and antibody production. The model parameters were estimated based on data of two repeated batch cultivations (three batches each), with main substrates in excess and potentially inhibiting waste products (lactate and ammonium) controlled within narrow ranges. Significant changes, due to a putative autocrine factor, were identified for lactate and ammonia formation and antibody production. The cell growth and the uptake of glucose and glutamine were only partially affected by the putative autocrine under the given conditions. The results indicate the presence of a self-expressed autocrine factor and its strong impact on the metabolism of CHO DP-12 cells. Furthermore, glucose and glutamine uptake, as well as the formation of ammonium and antibody were found to be significantly cell-cycle-dependent. The combined approach has a strong potential to improve the understanding of the interplay of population heterogeneities and signal factors in mammalian cell culture.