Efficient feeding profile optimization for recombinant protein production using physiological information

Self-Sufficient In Vitro Multi-Enzyme Cascade for Efficient Synthesis of Danshensu from l-DOPA

Danshensu (DSS), a traditional Chinese medicine, is widely used for the treatment of cardiovascular and cancer diseases. Here, a one-pot multi-enzyme cascade pathway was designed for DSS synthesis from l-DOPA using tyrosine aminotransferase from Escherichia coli (EcTyrB) and d-isomer-specific 2-hydroxyacid dehydrogenase from Lactobacillus frumenti (LfD2-HDH). Glutamate dehydrogenase from Clostridium difficile (CdgluD) was also introduced for a self-sufficient system of α-ketoglutaric acid and NADH. Under optimal conditions (35 °C, pH 7.0, EcTyrB:LfD2-HDH:CdgluD = 3:2:1, glutamate:NAD⁺ = 1:1), 98.3% yield (at 20 mM l-DOPA) and space-time yield of 6.61 g L^-1 h^-1 (at 40 mM l-DOPA) were achieved. Decreased yields of DSS at elevated l-DOPA concentrations (100 mM) could be attributed to an inhibited CdgluD activity caused by NH₄⁺ accumulation. This developed multi-enzyme cascade pathway (including EcTyrB, LfD2-HDH, and CdgluD) provides an efficient and sustainable approach for the production of DSS from l-DOPA.

Upstream development of Escherichia coli fermentation process with PhoA promoter using design of experiments (DoE)

In this work, a fed-batch fermentation development was performed with recombinant E. coli carrying the PhoA promoter system. The phosphate concentrations tested for this PhoA strain, 2.79 mM to 86.4 mM, were beyond the concentrations previously evaluated for cell growth and product titer. The results from the scouting work was used for design of experiments (DoE) where a range of phosphate levels from 27.1 mM to 86.4 mM was simultaneously evaluated with temperature, pH and DO set points. Definitive screening was used to evaluate these parameters simultaneously and the results indicate that fermentation temperature and phosphate content are the major contributors of product titer. The other factors tested such as pH had a minimal effect and DO had no impact on product titer.

Practical Solutions for Specific Growth Rate Control Systems in Industrial Bioreactors

This contribution discusses the main challenges related to successful application of automatic control systems used to control specific growth rate in industrial biotechnological processes. It is emphasized that, after the implementation of basic automatic control systems, primary attention shall be paid to the specific growth rate control systems because this process variable critically affects the physiological state of microbial cultures and the formation of the desired product. Therefore, control of the specific growth rate enables improvement of the quality and reproducibility of the biotechnological processes. The main requirements have been formulated that shall be met to successfully implement the specific growth rate control systems in industrial bioreactors. The relatively easy-to-implement schemes of specific growth rate control systems have been reviewed and discussed. The recommendations for selection of particular control systems for specific biotechnological processes have been provided.

Physiological capacities decline during induced bioprocesses leading to substrate accumulation

During the cultivation of E. coli for recombinant protein production, substrate accumulation is often observed in induction phase. Uncontrolled substrate accumulation leads to difficulties in transferring or scaling processes and even to failed batches. The phenomenon of metabolite/substrate accumulation occurs as a result of exceeding the physiological capacity to metabolize substrate (q_Scrit ). In contrast to the common understanding of q_Scrit as "static" value, we hypothesize that q_Scrit essentially has a dynamic nature. Following the state of the art approach of physio logical strain characterization, substrate pulse experiments were used to quantify q_Scrit in induction phase. The q_Scrit was found to be temperature and time dependent. Subsequently, q_Scrit was expressed through a linear equation, to serve as boundary for physiologically controlled experiments. Nevertheless, accumulation was observed within a physiologically controlled verification experiment, although the q_Scrit boundary was not exceeded. A second set of experiments was conducted, by oscillating the q_S set point between discrete plateaus during physiologically controlled experiments. From the results, we deduced a significant interrelation between the metabolic activity and the timely decline of qScrit. This finding highlights the necessity of a comprehensive but laborious physiological characterization for each strain or alternatively, to use physio logical feedback control to facilitate real time monitoring of q_Scrit , in order to effectively avoid substrate accumulation.

Bioprocess development workflow: Transferable physiological knowledge instead of technological correlations

Microbial bioprocesses need to be designed to be transferable from lab scale to production scale as well as between setups. Although substantial effort is invested to control technological parameters, usually the only true constant parameter is the actual producer of the product: the cell. Hence, instead of solely controlling technological process parameters, the focus should be increasingly laid on physiological parameters. This contribution aims at illustrating a workflow of data life cycle management with special focus on physiology. Information processing condenses the data into physiological variables, while information mining condenses the variables further into physiological descriptors. This basis facilitates data analysis for a physiological explanation for observed phenomena in productivity. Targeting transferability, we demonstrate this workflow using an industrially relevant Escherichia coli process for recombinant protein production and substantiate the following three points: (1) The postinduction phase is independent in terms of productivity and physiology from the preinduction variables specific growth rate and biomass at induction. (2) The specific substrate uptake rate during induction phase was found to significantly impact the maximum specific product titer. (3) The time point of maximum specific titer can be predicted by an easy accessible physiological variable: while the maximum specific titers were reached at different time points (19.8 ± 7.6 h), those maxima were reached all within a very narrow window of cumulatively consumed substrate dSn (3.1 ± 0.3 g/g). Concluding, this contribution provides a workflow on how to gain a physiological view on the process and illustrates potential benefits. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:261-270, 2017.

Propagation of measurement accuracy to biomass soft-sensor estimation and control quality

In biopharmaceutical process development and manufacturing, the online measurement of biomass and derived specific turnover rates is a central task to physiologically monitor and control the process. However, hard-type sensors such as dielectric spectroscopy, broth fluorescence, or permittivity measurement harbor various disadvantages. Therefore, soft-sensors, which use measurements of the off-gas stream and substrate feed to reconcile turnover rates and provide an online estimate of the biomass formation, are smart alternatives. For the reconciliation procedure, mass and energy balances are used together with accuracy estimations of measured conversion rates, which were so far arbitrarily chosen and static over the entire process. In this contribution, we present a novel strategy within the soft-sensor framework (named adaptive soft-sensor) to propagate uncertainties from measurements to conversion rates and demonstrate the benefits: For industrially relevant conditions, hereby the error of the resulting estimated biomass formation rate and specific substrate consumption rate could be decreased by 43 and 64 %, respectively, compared to traditional soft-sensor approaches. Moreover, we present a generic workflow to determine the required raw signal accuracy to obtain predefined accuracies of soft-sensor estimations. Thereby, appropriate measurement devices and maintenance intervals can be selected. Furthermore, using this workflow, we demonstrate that the estimation accuracy of the soft-sensor can be additionally and substantially increased.

A control strategy to investigate the relationship between specific productivity and high-mannose glycoforms in CHO cells

The integration of physiological knowledge into process control strategies is a cornerstone for the improvement of biopharmaceutical cell culture technologies. The present contribution investigates the applicability of specific productivity as a physiological control parameter in a cell culture process producing a monoclonal antibody (mAb) in CHO cells. In order to characterize cell physiology, the on-line oxygen uptake rate (OUR) was monitored and the time-resolved specific productivity was calculated as physiological parameters. This characterization enabled to identify the tight link between the deprivation of tyrosine and the decrease in cell respiration and in specific productivity. Subsequently, this link was used to control specific productivity by applying different feeding profiles. The maintenance of specific productivity at various levels enabled to identify a correlation between the rate of product formation and the relative abundance of high-mannose glycoforms. An increase in high mannose content was assumed to be the result of high specific productivity. Furthermore, the high mannose content as a function of cultivation pH and specific productivity was investigated in a design of experiment approach. This study demonstrated how physiological parameters could be used to understand interactions between process parameters, physiological parameters, and product quality attributes.

A dynamic method for the investigation of induced state metabolic capacities as a function of temperature

Background

Science-based recombinant bioprocess designs as well as the design of statistical experimental plans for process optimization (Design of Experiments, DoE) demand information on physiological bioprocess boundaries, such as the onset of acetate production, adaptation times, mixed feed metabolic capabilities or induced state maximum metabolic rates as at the desired cultivation temperature. Dynamic methods provide experimental alternatives to determine this information in a fast and efficient way. Information on maximum metabolic capabilities as a function of temperature is needed in case a reduced cultivation temperature is desirable (e.g. to avoid inclusion body formation) and an appropriate feeding profile is to be designed.

Results

Here, we present a novel dynamic method for the determination of the specific growth rate as a function of temperature for induced recombinant bacterial bioprocesses. The method is based on the control of the residual substrate concentration at non-limiting conditions with dynamic changes in cultivation temperature. The presented method was automated in respect to information extraction and closed loop control by means of in-line Fourier Transformation Infrared Spectroscopy (FTIR) residual substrate measurements and on-line first principle rate-based soft-sensors. Maximum induced state metabolic capabilities as a function of temperature were successfully extracted for a recombinant E. coli C41 fed-batch bioprocess without the need for sampling in a time frame of 20 hours.

Conclusions

The presented method was concluded to allow the fast and automated extraction of maximum metabolic capabilities (specific growth rate) as a function of temperature. This complements the dynamic toolset necessary for science-based recombinant bacterial bioprocess design and DoE design.

Integration of models and experimentation to optimise the production of potential biotherapeutics

Despite decades of clinical and commercial success, the current paradigm for drug discovery and development is still empirical and costly. The many hundreds of therapeutic proteins (TPs) in the development pipeline and the FDA-led quality-by-design initiative represent opportunities to address this issue. Advances in our understanding of cellular mechanisms as well as the physicochemical and biological characteristics of TPs have enabled researchers to develop computational models that analyse or even predict molecular and cellular behaviour under different conditions. Coupled with new analytical tools, these models are increasingly used to systemise and expedite the design and optimisation of protein production processes throughout the discovery and development stages.