Development, characterization, and application of a 2-Compartment system to investigate the impact of pH inhomogeneities in large-scale CHO-based processes

Mimicking CHO large-scale effects in the single multicompartment bioreactor: A new approach to access scale-up behavior

During the scale-up of biopharmaceutical production processes, insufficiently predictable performance losses may occur alongside gradients and heterogeneities. To overcome such performance losses, tools are required to explain, predict, and ultimately prohibit inconsistencies between laboratory and commercial scale. In this work, we performed CHO fed-batch cultivations in the single multicompartment bioreactor (SMCB), a new scale-down reactor system that offers new access to study large-scale heterogeneities in mammalian cell cultures. At volumetric power inputs of 20.4-1.5 W m-3, large-scale characteristics like long mixing times and dissolved oxygen (DO) heterogeneities were mimicked in the SMCB. Compared to a reference bioreactor (REFB) set-up, the conditions in the SMCB provoked an increase in lactate accumulation of up to 87%, an increased glucose uptake, and reduced viable cell concentrations in the stationary phase. All are characteristic for large-scale performance. The unique possibility to distinguish between the effects of changing power inputs and observed heterogeneities provided new insights into the potential reasons for altered product quality attributes. Apparently, the degree of galactosylation in the evaluated glycan patterns changed primarily due to the different power inputs rather than the provoked heterogeneities. The SMCB system could serve as a potent tool to provide new insights into scale-up behavior and to predict cell line-specific drawbacks at an early stage of process development.

Scaling-down biopharmaceutical production processes via a single multi-compartment bioreactor (SMCB)

Biopharmaceutical production processes often use mammalian cells in bioreactors larger than 10,000 L, where gradients of shear stress, substrate, dissolved oxygen and carbon dioxide, and pH are likely to occur. As former tissue cells, producer cell lines such as Chinese hamster ovary (CHO) cells sensitively respond to these mixing heterogeneities, resulting in related scenarios being mimicked in scale-down reactors. However, commonly applied multi-compartment approaches comprising multiple reactors impose a biasing shear stress caused by pumping. The latter can be prevented using the single multi-compartment bioreactor (SMCB) presented here. The exchange area provided by a disc mounted between the upper and lower compartments in a stirred bioreactor was found to be an essential design parameter. Mimicking the mixing power input at a large scale on a small scale allowed the installation of similar mixing times in the SMCB. The particularities of the disc geometry may also be considered, finally leading to a converged decision tree. The work flow identifies a sharply contoured operational field comprising disc designs and power input to install the same mixing times on a large scale in the SMCB without the additional shear stress caused by pumping. The design principle holds true for both nongassed and gassed systems.

Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down

Typically, bioprocesses on an industrial scale are dynamic systems with a certain degree of variability, system inhomogeneities, and even population heterogeneities. Therefore, the scaling of such processes from laboratory to industrial scale and vice versa is not a trivial task. Traditional scale-down methodologies consider several technical parameters, so that systems on the laboratory scale tend to qualitatively reflect large-scale effects, but not the dynamic situation in an industrial bioreactor over the entire process, from the perspective of a cell. Supported by the enormous increase in computing power, the latest scientific focus is on the application of dynamic models, in combination with computational fluid dynamics to quantitatively describe cell behavior. These models allow the description of possible cellular lifelines which in turn can be used to derive a regime analysis for scale-down experiments. However, the approaches described so far, which were for a very few process examples, are very labor- and time-intensive and cannot be validated easily. In parallel, alternatives have been developed based on the description of the industrial process with hybrid process models, which describe a process mechanistically as far as possible in order to determine the essential process parameters with their respective variances. On-line analytical methods allow the characterization of population heterogeneity directly in the process. This detailed information from the industrial process can be used in laboratory screening systems to select relevant conditions in which the cell and process related parameters reflect the situation in the industrial scale. In our opinion, these technologies, which are available in research for modeling biological systems, in combination with process analytical techniques are so far developed that they can be implemented in industrial routines for faster development of new processes and optimization of existing ones.

Investigation of cell line specific responses to pH inhomogeneity and consequences for process design

With increasing bioreactor volumes, the mixing time of the reactor increases as well, which creates an inhomogeneous environment for the cells. This can result in impaired process performance in large-scale production reactors. Particularly the addition of base through the reactor headspace can be problematic, since it creates an area, where cells are repeatedly exposed to an increased pH. The aim of this study is to simulate this large-scale phenomenon at lab-scale and investigate its impact. Two different cell lines were exposed to pH amplitudes of a maximal magnitude of 0.05 units (pH of 6.95). Both cell lines showed similar responses, like decreased viable cell counts, but unaffected lactate levels. However, cell line B showed an initially increased specific productivity in response to the introduced amplitudes, whereas cell line A showed a consistently lower specific productivity. Furthermore, the time point at which base addition is started influences the impact, which pH amplitudes have on process performance. When pH control was started earlier in the process, maximal viable cell counts decreased and the lactate metabolic shift was less pronounced. These results show that the potential negative impact of pH amplitudes can be minimized by strategic process design.