Mixed substrate feeding strategies are frequently investigated to enhance the productivity of recombinant Pichia pastoris processes. For this purpose, numerous fed batch experiments or time-consuming continuous cultivations are required to optimize control parameters such as the substrate mixing ratio and the applied methanol concentration. In this study, we decoupled the feeding of methanol and glycerol in a mixed substrate fed batch environment to gain process understanding for a recombinant P. pastoris Muts strain producing the model enzyme horseradish peroxidase. Specific substrate uptake rates (qs) were controlled separately, and a stepwise increased qGly-control scheme was applied to investigate the effect of various substrate fluxes on the culture. The qs-controlled strategy allowed a parallel characterization of the metabolism and the recombinant protein expression in a fed batch environment. A critical-specific glycerol uptake rate was determined, where a decline of the specific productivity occurred, and a time-dependent acceleration of protein expression was characterized with the dynamic fed batch approach. Based on the observations on recombinant protein expression, propositions for an optimal feeding design to target maximal productivities were stated. Thus, the dynamic fed batch strategy was found to be a valuable tool for both process understanding and optimization of product formation for P. pastoris in a mixed substrate environment.

A growing body of knowledge is available on the cellular regulation of overflow metabolism in mammalian hosts of recombinant protein production. However, to develop strategies to control the regulation of overflow metabolism in cell culture processes, the effect of process parameters on metabolism has to be well understood. In this study, we investigated the effect of pH and temperature shift timing on lactate metabolism in a fed-batch Chinese hamster ovary (CHO) process by using a Design of Experiments (DoE) approach. The metabolic switch to lactate consumption was controlled in a broad range by the proper timing of pH and temperature shifts. To extract process knowledge from the large experimental dataset, we proposed a novel methodological concept and demonstrated its usefulness with the analysis of lactate metabolism. Time-resolved metabolic flux analysis and PLS-R VIP were combined to assess the correlation of lactate metabolism and the activity of the major intracellular pathways. Whereas the switch to lactate uptake was mainly triggered by the decrease in the glycolytic flux, lactate uptake was correlated to TCA activity in the last days of the cultivation. These metabolic interactions were visualized on simple mechanistic plots to facilitate the interpretation of the results. Taken together, the combination of knowledge-based mechanistic modeling and data-driven multivariate analysis delivered valuable insights into the metabolic control of lactate production and has proven to be a powerful tool for the analysis of large metabolic datasets.

Pichia pastoris is widely used for the production of recombinant proteins in industrial biotechnology. In general, industrial production processes describe fed-batch processes based on the specific growth rate. Recently, we introduced the specific substrate uptake rate (qs) as a novel parameter to design fed-batch strategies for P. pastoris. We showed that a dynamic feeding strategy where the feed was adjusted in steps to the maximum specific substrate uptake rate was superior to more traditional strategies in terms of specific productivity. In the present study, we compare three different dynamic feeding strategies based on qs for a recombinant P. pastoris strain with respect to cell physiology, methanol accumulation, productivity and product quality. By comparing (A) a feeding profile at constant high qs, (B) a periodically adjusted feeding profile for a stepwise qs ramp, and (C) a feeding profile at linear increasing qs, we evaluated potential effects of the mode of feeding. Although a dynamic feeding strategy with stepwise increases of qs to qs max resulted in the highest specific productivity, a feeding profile where the feeding rate was stepwise increased to a constant high qs value was superior in terms of the amount of active enzyme produced and in the amount of accumulated methanol. Furthermore, this feeding strategy could be run automatically by integrating an online calculator tool, thus rendering manual interventions by the operator unnecessary.

The application of dielectric spectroscopy was frequently investigated as an on-line cell culture monitoring tool; however, it still requires supportive data and experience in order to become a robust technique. In this study, dielectric spectroscopy was used to predict viable cell density (VCD) at industrially relevant high levels in concentrated fed-batch culture of Chinese hamster ovary cells producing a monoclonal antibody for pharmaceutical purposes. For on-line dielectric spectroscopy measurements, capacitance was scanned within a wide range of frequency values (100-19,490 kHz) in six parallel cell cultivation batches. Prior to detailed mathematical analysis of the collected data, principal component analysis (PCA) was applied to compare dielectric behavior of the cultivations. PCA analysis resulted in detecting measurement disturbances. By using the measured spectroscopic data, partial least squares regression (PLS), Cole-Cole, and linear modeling were applied and compared in order to predict VCD. The Cole-Cole and the PLS model provided reliable prediction over the entire cultivation including both the early and decline phases of cell growth, while the linear model failed to estimate VCD in the later, declining cultivation phase. In regards to the measurement error sensitivity, remarkable differences were shown among PLS, Cole-Cole, and linear modeling. VCD prediction accuracy could be improved in the runs with measurement disturbances by first derivative pre-treatment in PLS and by parameter optimization of the Cole-Cole modeling.

The shift from empirical to science-based process development is considered to be a key factor to increase bioprocess performance and to reduce time to market for biopharmaceutical products in the near future. In the last decade, expanding knowledge in systems biology and bioprocess technology has delivered the foundation of the scientific understanding of relationships between process input parameters and process output features. Based on this knowledge, advanced process development approaches can be applied to maximize process performance and to generate process understanding. This review focuses on tools which enable the integration of physiological knowledge into cell culture process development. As a structured approach, the availability and the proposed benefit of the application of these tools are discussed for the subsequent stages of process development. The ultimate aim is to deliver a comprehensive overview of the current role of physiological understanding during cell culture process development from clone selection to the scale-up of advanced control strategies for ensuring process robustness.