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.

A model-based framework for parallel scale-down fed-batch cultivations in mini-bioreactors for accelerated phenotyping.

Concentration gradients that occur in large industrial-scale bioreactors due to mass transfer limitations have significant effects on process efficiency. Hence, it is desirable to investigate the response of strains to such heterogeneities to reduce the risk of failure during process scale-up. Although there are various scale-down techniques to study these effects, scale-down strategies are rarely applied in the early developmental phases of a bioprocess, as they have not yet been implemented on small-scale parallel cultivation devices. In this study, we combine mechanistic growth models with a parallel mini-bioreactor system to create a high-throughput platform for studying the response of Escherichia coli strains to concentration gradients. As a scaled-down approach, a model-based glucose pulse feeding scheme is applied and compared with a continuous feed profile to study the influence of glucose and dissolved oxygen gradients on both cell physiology and incorporation of noncanonical amino acids into recombinant proinsulin. The results show a significant increase in the incorporation of the noncanonical amino acid norvaline in the soluble intracellular extract and in the recombinant product in cultures with glucose/oxygen oscillations. Interestingly, the amount of norvaline depends on the pulse frequency and is negligible with continuous feeding, confirming observations from large-scale cultivations. Most importantly, the results also show that a larger number of the model parameters are significantly affected by the scale-down scheme, compared with the reference cultivations. In this example, it was possible to describe the effects of oscillations in a single parallel experiment. The platform offers the opportunity to combine strain screening with scale-down studies to select the most robust strains for bioprocess scale-up.

Integrated Robotic Mini Bioreactor Platform for Automated, Parallel Microbial Cultivation With Online Data Handling and Process Control.

During process development, the experimental search space is defined by the number of experiments that can be performed in specific time frames but also by its sophistication (e.g., inputs, sensors, sampling frequency, analytics). High-throughput liquid-handling stations can perform a large number of automated experiments in parallel. Nevertheless, the experimental data sets that are obtained are not always relevant for development of industrial bioprocesses, leading to a high rate of failure during scale-up. We present an automated mini bioreactor platform that enables parallel cultivations in the milliliter scale with online monitoring and control, well-controlled conditions, and advanced feeding strategies similar to industrial processes. The combination of two liquid handlers allows both automated mini bioreactor operation and at-line analysis in parallel. A central database enables end-to-end data exchange and fully integrated device and process control. A model-based operation algorithm allows for the accurate performance of complex cultivations for scale-down studies and strain characterization via optimal experimental redesign, significantly increasing the reliability and transferability of data throughout process development. The platform meets the tradeoff between experimental throughput and process control and monitoring comparable to laboratory-scale bioreactors.

Comparison of time-gated surface-enhanced raman spectroscopy (TG-SERS) and classical SERS based monitoring of Escherichia coli cultivation samples.

The application of Raman spectroscopy as a monitoring technique for bioprocesses is severely limited by a large background signal originating from fluorescing compounds in the culture media. Here, we compare time-gated Raman (TG-Raman)-, continuous wave NIR-process Raman (NIR-Raman), and continuous wave micro-Raman (micro-Raman) approaches in combination with surface enhanced Raman spectroscopy (SERS) for their potential to overcome this limit. For that purpose, we monitored metabolite concentrations of Escherichia coli bioreactor cultivations in cell-free supernatant samples. We investigated concentration transients of glucose, acetate, AMP, and cAMP at alternating substrate availability, from deficiency to excess. Raman and SERS signals were compared to off-line metabolite analysis of carbohydrates, carboxylic acids, and nucleotides. Results demonstrate that SERS, in almost all cases, led to a higher number of identifiable signals and better resolved spectra. Spectra derived from the TG-Raman were comparable to those of micro-Raman resulting in well-discernable Raman peaks, which allowed for the identification of a higher number of compounds. In contrast, NIR-Raman provided a superior performance for the quantitative evaluation of analytes, both with and without SERS nanoparticles when using multivariate data analysis. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1533-1542, 2018.

Importance of the cultivation history for the response of Escherichia coli to oscillations in scale-down experiments.

Large-scale bioreactors are inhomogeneous systems, in which the fluid phase expresses concentration gradients. They depend on the mass transfer and fluid dynamics in the reactor, the feeding strategy, the cell-specific substrate uptake parameters, and the cell density. As high cell densities are only obtained at low specific growth rates, it is necessary to investigate the cellular responses to oscillations in particular under such conditions, an issue which is mostly neglected. Instead, the feed oscillations are often started directly after the batch phase, when the specific growth rate is close to the maximum. We show here that the cultivation mode before oscillations are started has a tremendous effect on the metabolic responses. In difference to cells, which were pre-grown under batch conditions at a high growth rate, Escherichia coli cells that were pre-grown under glucose limitation at a low growth rate accumulate short-chain fatty acids (acetate, lactate, succinate) and glycolysis-related amino acids to a higher extent in a two-compartment scale-down bioreactor. Thus, cells which enter oscillations from a lower specific growth rate seem to react more sensitive to oscillations than cells that are subjected to oscillations directly after a batch phase. These results are interesting in designing reliable scale-down systems, which better reflect large-scale bioprocesses.