Addressing raw material variability: In-line FTIR sugar composition analysis of lignocellulosic process streams.

For a sustainable economy, biorefineries that use second-generation feedstocks to produce biochemicals and biofuels are essential. However, the exact composition of renewable feedstocks depends on the natural raw materials used and is therefore highly variable. In this contribution, a process analytical technique (PAT) strategy for determining the sugar composition of lignocellulosic process streams in real-time to enable better control of bioprocesses is presented. An in-line mid-IR probe was used to acquire spectra of ultra-filtered spent sulfite liquor (UF-SSL). Independent partial least squares models were developed for the most abundant sugars in the UF-SSL. Up to 5 sugars were quantified simultaneously to determine the sugar concentration and composition of the UF-SSL. The lowest root mean square errors of the predicted values obtained per analyte were 1.02 g/L arabinose, 1.25 g/L galactose, 0.50 g/L glucose, 1.60 g/L mannose, and 0.85 g/L xylose. Equipped with this novel PAT tool, new bioprocessing strategies can be developed for UF-SSL.

A Unique Response Behavior in the Dissolved Oxygen Tension in E. coli Minibioreactor Cultivations with Intermittent Feeding.

Intermittent bolus feeding for E. coli cultivations in minibioreactor systems (MBRs) profoundly affects the cell metabolism. Bolus feeding leads to temporal substrate surplus and transient oxygen limitation, which triggers the formation of inhibitory byproducts. Due to the high oxygen demand right after the injection of the substrate, the dissolved oxygen tension (DOT) signal exhibits a negative pulse. This contribution describes and analyzes this DOT response in E. coli minibioreactor cultivations. In addition to gaining information on culture conditions, a unique response behavior in the DOT signal was observed in the analysis. This response appeared only at a dilution ratio per biomass unit higher than a certain threshold. The analysis highlights a plausible relationship between a metabolic adaptation behavior and the newly observed DOT signal segment not reported in the literature. A hypothesis that links particular DOT segments to specific metabolic states is proposed. The quantitative analysis and mechanistic model simulations support this hypothesis and show the possibility of obtaining cell physiological and growth parameters from the DOT signal.

Online estimation of changing metabolic capacities in continuous Corynebacterium glutamicum cultivations growing on a complex sugar mixture.

Model-based state estimators enable online monitoring of bioprocesses and, thereby, quantitative process understanding during running operations. During prolonged continuous bioprocesses strain physiology is affected by selection pressure. This can cause time-variable metabolic capacities that lead to a considerable model-plant mismatch reducing monitoring performance if model parameters are not adapted accordingly. Variability of metabolic capacities therefore needs to be integrated in the in silico representation of a process using model-based monitoring approaches. To enable online monitoring of multiple concentrations as well as metabolic capacities during continuous bioprocessing of spent sulfite liquor with Corynebacterium glutamicum, this study presents a particle filtering framework that takes account of parametric variability. Physiological parameters are continuously adapted by Bayesian inference, using noninvasive off-gas measurements. Additional information on current parameter importance is derived from time-resolved sensitivity analysis. Experimental results show that the presented framework enables accurate online monitoring of long-term culture dynamics, whereas state estimation without parameter adaption failed to quantify substrate metabolization and growth capacities under conditions of high selection pressure. Online estimated metabolic capacities are further deployed for multiobjective optimization to identify time-variable optimal operating points. Thereby, the presented monitoring system forms a basis for adaptive control during continuous bioprocessing of lignocellulosic by-product streams.

Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses.

During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error propagation and regression approach applied to two different, industrially relevant cultivation processes. Based on measurement uncertainties, errors for cell-specific growth, uptake, and production rates were determined across an evaluation chain, with interlinked inputs and outputs. These uncertainties were subsequently included in regression analysis to derive the covariance of the regression coefficients and the confidence bounds for prediction. The usefulness of the approach is shown within two case studies, based on the relations across biomass-specific rate control limits to guarantee high productivities in E. coli, and low lactate formation in a CHO cell fed-batch could be established. Besides the possibility to determine realistic errors on the evaluated process data, the presented approach helps to differentiate between reliable and unreliable correlations and prevents the wrong interpretations of relations based on uncertain data.

Establishing recombinant production of pediocin PA-1 in Corynebacterium glutamicum.

Bacteriocins are antimicrobial peptides produced by bacteria to inhibit competitors in their natural environments. Some of these peptides have emerged as commercial food preservatives and, due to the rapid increase in antibiotic resistant bacteria, are also discussed as interesting alternatives to antibiotics for therapeutic purposes. Currently, commercial bacteriocins are produced exclusively with natural producer organisms on complex substrates and are sold as semi-purified preparations or crude fermentates. To allow clinical application, efficacy of production and purity of the product need to be improved. This can be achieved by shifting production to recombinant microorganisms. Here, we identify Corynebacterium glutamicum as a suitable production host for the bacteriocin pediocin PA-1. C. glutamicum CR099 shows resistance to high concentrations of pediocin PA-1 and the bacteriocin was not inactivated when spiked into growing cultures of this bacterium. Recombinant C. glutamicum expressing a synthetic pedACDCgl operon releases a compound that has potent antimicrobial activity against Listeria monocytogenes and Listeria innocua and matches size and mass:charge ratio of commercial pediocin PA-1. Fermentations in shake flasks and bioreactors suggest that low levels of dissolved oxygen are favorable for production of pediocin. Under these conditions, however, reduced activity of the TCA cycle resulted in decreased availability of the important pediocin precursor l-asparagine suggesting options for further improvement. Overall, we demonstrate that C. glutamicum is a suitable host for recombinant production of bacteriocins of the pediocin family.

Advances in monitoring and control of refolding kinetics combining PAT and modeling.

Overexpression of recombinant proteins in Escherichia coli results in misfolded and non-active protein aggregates in the cytoplasm, so-called inclusion bodies (IB). In recent years, a change in the mindset regarding IBs could be observed: IBs are no longer considered an unwanted waste product, but a valid alternative to produce a product with high yield, purity, and stability in short process times. However, solubilization of IBs and subsequent refolding is necessary to obtain a correctly folded and active product. This protein refolding process is a crucial downstream unit operation-commonly done as a dilution in batch or fed-batch mode. Drawbacks of the state-of-the-art include the following: the large volume of buffers and capacities of refolding tanks, issues with uniform mixing, challenging analytics at low protein concentrations, reaction kinetics in non-usable aggregates, and generally low re-folding yields. There is no generic platform procedure available and a lack of robust control strategies. The introduction of Quality by Design (QbD) is the method-of-choice to provide a controlled and reproducible refolding environment. However, reliable online monitoring techniques to describe the refolding kinetics in real-time are scarce. In our view, only monitoring and control of re-folding kinetics can ensure a productive, scalable, and versatile platform technology for re-folding processes. For this review, we screened the current literature for a combination of online process analytical technology (PAT) and modeling techniques to ensure a controlled refolding process. Based on our research, we propose an integrated approach based on the idea that all aspects that cannot be monitored directly are estimated via digital twins and used in real-time for process control. KEY POINTS: • Monitoring and a thorough understanding of refolding kinetics are essential for model-based control of refolding processes. • The introduction of Quality by Design combining Process Analytical Technology and modeling ensures a robust platform for inclusion body refolding.

Usage of Digital Twins Along a Typical Process Development Cycle.

Digital methods for process design, monitoring, and control can convert classical trial-and-error bioprocess development to a quantitative engineering approach. By interconnecting hardware, software, data, and humans currently untapped process optimization potential can be accessed. The key component within such a framework is a digital twin interacting with its physical process counterpart. In this chapter, we show how digital twin guided process development can be applied on an exemplary microbial cultivation process. The usage of digital twins is described along a typical process development cycle, ranging from early strain characterization to real-time control applications. Along an illustrative case study on microbial upstream bioprocessing, we emphasize that digital twins can integrate entire process development cycles if the digital twin itself and the underlying models are continuously adapted to newly available data. Therefore, the digital twin can be regarded as a powerful knowledge management tool and a decision support system for efficient process development. Its full potential can be deployed in a real-time environment where targeted control actions can further improve process performance.

Noninvasive online monitoring of Corynebacterium glutamicum fed-batch bioprocesses subject to spent sulfite liquor raw material uncertainty.

In this study the use of a particle filter algorithm to monitor Corynebacterium glutamicum fed-batch bioprocesses with uncertain raw material input composition is shown. The designed monitoring system consists of a dynamic model describing biomass growth on spent sulfite liquor. Based on particle filtering, model simulations are aligned with continuously and noninvasively measured carbon evolution and oxygen uptake rates, giving an estimate of the most probable culture state. Applied on two validation experiments, culture states were accurately estimated during batch and fed-batch operations with root mean square errors below 1.1 g L-1 for biomass, 0.6 g L-1 for multiple substrate concentrations and 0.01 g g-1 h-1 for biomass specific substrate uptake rates. Additionally, upon fed-batch start uncertain feedstock concentrations were corrected by the estimator without the need of any additional measurements. This provides a solid basis towards a more robust operation of bioprocesses utilizing lignocellulosic side streams.

Optimal process design space to ensure maximum viability and productivity in Penicillium chrysogenum pellets during fed-batch cultivations through morphological and physiological control.

BACKGROUND: Biomass growth of Pencillium chrysogenum is characterised by a distinct pellet morphology consisting of compact hyphal agglomerates. Fungal pellets are advantageous in industrial process control due to rheological advantages but lead to biomass degradation due to diffusional limitations of oxygen and substrate in the pellet's core. Several fermentation parameters are known to affect key pellet characteristics regarding morphology, viability and productivity. Pellet morphology and size are affected by agitation. Biomass viability and productivity are tightly interlinked with substrate uptake and dissolved oxygen concentration. RESULTS: The goal of this study was to study the impact of the fermentation parameters power input, dissolved oxygen content and specific substrate uptake rate on morphology, biomass viability and productivity. A design of experiments (DoE) approach was conducted and corresponding responses were analysed using novel morphological descriptors analysed by a previously established flow cytometry method. Results clearly display inverse correlations between power input and pellet size, specific morphological parameters related to pellet density can be increased in direct proportion to power input. Biomass viability and productivity are negatively affected by high specific substrate uptake rates. CONCLUSIONS: Based upon multiple linear regression, it was possible to obtain an optimal design space for enhanced viability and productivity at beneficial morphological conditions. We could maintain a high number of pellets with favourable morphology at a power input of 1500 W/m3. A sound compromise between viability and high productivity is possible at a specific glucose uptake rate of 0.043 g/g/h at dissolved oxygen levels of 40% minimum.

Microbial technologies for biotherapeutics production: Key tools for advanced biopharmaceutical process development and control.

Current trends in the biopharmaceutical market such as the diversification of therapies as well as the increasing time-to-market pressure will trigger the rethinking of bioprocess development and production approaches. Thereby, the importance of development time and manufacturing costs will increase, especially for microbial production. In the present review, we investigate three technological approaches which, to our opinion, will play a key role in the future of biopharmaceutical production. The first cornerstone of process development is the generation and effective utilization of platform knowledge. Building processes on well understood microbial and technological platforms allows to accelerate early-stage bioprocess development and to better condense this knowledge into multi-purpose technologies and applicable mathematical models. Second, the application of verified scale down systems and in silico models for process design and characterization will reduce the required number of large scale batches before dossier submission. Third, the broader availability of mathematical process models and the improvement of process analytical technologies will increase the applicability and acceptance of advanced control and process automation in the manufacturing scale. This will reduce process failure rates and subsequently cost of goods. Along these three aspects we give an overview of recently developed key tools and their potential integration into bioprocess development strategies.

Generic Workflow for the Setup of Mechanistic Process Models.

Process models, consisting of transferable and applicable knowledge, can be used for various tasks such as process development and optimization, and for predicting and controlling critical process variables. In this regard, mechanistic process models, describing the mechanism of a system with a distinct model structure and characteristic parameters, are very promising.The development of a reliable and applicable model is usually the critical step, before model simulation and application show beneficial effects. In this chapter, a workflow for the generation of mechanistic process models is presented and applied on a typical cell culture process. The workflow includes the definition of critical reactions and the identification of their kinetics. By an iterative approach different reactions and kinetics are tested and model quality is assessed, leading to a final, target-oriented model.

Probeless non-invasive near-infrared spectroscopic bioprocess monitoring using microspectrometer technology.

Real-time measurements and adjustments of critical process parameters are essential for the precise control of fermentation processes and thus for increasing both quality and yield of the desired product. However, the measurement of some crucial process parameters such as biomass, product, and product precursor concentrations usually requires time-consuming offline laboratory analysis. In this work, we demonstrate the in-line monitoring of biomass, penicillin (PEN), and phenoxyacetic acid (POX) in a Penicilliumchrysogenum fed-batch fermentation process using low-cost microspectrometer technology operating in the near-infrared (NIR). In particular, NIR reflection spectra were taken directly through the glass wall of the bioreactor, which eliminates the need for an expensive NIR immersion probe. Furthermore, the risk of contaminations in the reactor is significantly reduced, as no direct contact with the investigated medium is required. NIR spectra were acquired using two sensor modules covering the spectral ranges 1350-1650 nm and 1550-1950 nm. Based on offline reference analytics, partial least squares (PLS) regression models were established for biomass, PEN, and POX either using data from both sensors separately or jointly. The established PLS models were tested on an independent validation fed-batch experiment. Root mean squared errors of prediction (RMSEP) were 1.61 g/L, 1.66 g/L, and 0.67 g/L for biomass, PEN, and POX, respectively, which can be considered an acceptable accuracy comparable with previously published results using standard process spectrometers with immersion probes. Altogether, the presented results underpin the potential of low-cost microspectrometer technology in real-time bioprocess monitoring applications. Graphical abstract.

Monitoring and control strategies for inclusion body production in E. coli based on glycerol consumption.

The Gram-negative bacterium E. coli is the host of choice for the production of a multitude of recombinant proteins in industry. Generally, cultivation is easy, media are cheap and a high product titer can be obtained. However, harsh induction procedures using IPTG as inducer are often referred to cause stress reactions, leading to a phenomenon known as metabolic burden and expression of inclusion bodies. In this contribution, we present different strategies for determination of critical timepoints for product stability in an E. coli IB bioprocess. As non-controlled feeding during induction regularly led to undesired product loss, we applied physiological feeding control. We found that the feeding strategy has indeed high impact on IB productivity. However, high applied qs,C increased IB product titer, but subsequently stressed the cells and finally led to product degradation. Calculating the cumulated glycerol uptake of the cells during induction phase (dSn), we found an empirical value, which serves as a strong indicator for process performance and can be used as process analytical tool. We tested different approaches starting from offline control. Glycerol accumulation could be used as trigger to establish a model-based approach to predict titer and viable cell concentration for a model protein. This straight forward control and model-based approach is high beneficial for upstream development and for increasing stability.

Inclusion Body Bead Size in E. coli Controlled by Physiological Feeding.

The Gram-negative bacterium E. coli is the host of choice for producing a multitude of recombinant proteins relevant in the pharmaceutical industry. Generally, cultivation is easy, media are cheap, and a high product titer can be obtained. However, harsh induction procedures combined with the usage of IPTG (isopropyl ß-d-1 thiogalactopyranoside) as an inducer are often believed to cause stress reactions, leading to intracellular protein aggregates, which are so known as so-called inclusion bodies (IBs). Downstream applications in bacterial processes cause the bottleneck in overall process performance, as bacteria lack many post-translational modifications, resulting in time and cost-intensive approaches. Especially purification of inclusion bodies is notoriously known for its long processing times and low yields. In this contribution, we present screening strategies for determination of inclusion body bead size in an E. coli-based bioprocess producing exclusively inclusion bodies. Size can be seen as a critical quality attribute (CQA), as changes in inclusion body behavior have a major effect on subsequent downstream processing. A model-based approach was used, aiming to trigger a distinct inclusion body size: Physiological feeding control, using qs,C as a critical process parameter, has a high impact on inclusion body size and could be modelled using a hyperbolic saturation mechanism calculated in form of a cumulated substrate uptake rate. Within this model, the sugar uptake rate of the cells, in the form of the cumulated sugar uptake-value, was simulated and considered being a key performance indicator for determination of the desired size. We want to highlight that the usage of the mentioned screening strategy in combination with a model-based approach will allow tuning of the process towards a certain inclusion body size using a qs based control only. Optimized inclusion body size at the time-point of harvest should stabilize downstream processing and, therefore, increase the overall time-space yield. Furthermore, production of distinct inclusion body size may be interesting for application as a biocatalyst and nanoparticulate material.

Impact of Glycerol as Carbon Source onto Specific Sugar and Inducer Uptake Rates and Inclusion Body Productivity in E. coli BL21(DE3).

The Gram-negative bacterium E. coli is the host of choice for a multitude of used recombinant proteins. Generally, cultivation is easy, media are cheap, and a high product titer can be obtained. However, harsh induction procedures using isopropyl ß-d-1 thiogalactopyranoside as inducer are often referred to cause stress reactions, leading to a phenomenon known as "metabolic" or "product burden". These high expressions of recombinant proteins mainly result in decreased growth rates and cell lysis at elevated induction times. Therefore, approaches tend to use "soft" or "tunable" induction with lactose and reduce the stress level of the production host. The usage of glucose as energy source in combination with lactose as induction reagent causes catabolite repression effects on lactose uptake kinetics and as a consequence reduced product titer. Glycerol-as an alternative carbon source-is already known to have positive impact on product formation when coupled with glucose and lactose in auto-induction systems, and has been referred to show no signs of repression when cultivated with lactose concomitantly. In recent research activities, the impact of different products on the lactose uptake using glucose as carbon source was highlighted, and a mechanistic model for glucose-lactose induction systems showed correlations between specific substrate uptake rate for glucose or glycerol (qs,C) and the maximum specific lactose uptake rate (qs,lac,max). In this study, we investigated the mechanistic of glycerol uptake when using the inducer lactose. We were able to show that a product-producing strain has significantly higher inducer uptake rates when being compared to a non-producer strain. Additionally, it was shown that glycerol has beneficial effects on viability of cells and on productivity of the recombinant protein compared to glucose.

Model-Based Methods in the Biopharmaceutical Process Lifecycle.

Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods adopted from different scientific domains accelerates this trend further. In addition, model-based methods can help to implement regulatory requirements as suggested by recent Quality by Design and validation initiatives. The aim of this review is to give an overview of the state of the art of model-based methods, their applications, further challenges and possible solutions in the biopharmaceutical process life cycle. Today, despite these advantages, the potential of model-based methods is still not fully exhausted in bioprocess technology. This is due to a lack of (i) acceptance of the users, (ii) user-friendly tools provided by existing methods, (iii) implementation in existing process control systems and (iv) clear workflows to set up specific process models. We propose that model-based methods be applied throughout the lifecycle of a biopharmaceutical process, starting with the set-up of a process model, which is used for monitoring and control of process parameters, and ending with continuous and iterative process improvement via data mining techniques.