Economic and ecological analysis of plastic-degrading enzyme production

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Process development for a flexible vaccine vector platform based on recombinant life virus

Vaccines are one of the most important, safe and efficient interventions to protect people from illness, disability and death. In recent years several new viral outbreaks where no vaccines are currently available were reported worldwide. Therefore, the development of flexible processes for the production of vaccines is urgently needed. This project aims at developing a platform process for the production of different viral vaccines. The core technology is based on the fact that large recombinant genes coding for selected, foreign antigens can be inserted into the genome of a well-established virus vaccination vector. The vaccine delivers the selected antigens directly to macrophages and dendritic cells, the most potent and effective antigen-presenting cells, thereby triggering a specific immune response to the selected antigens. As a replicating vector, the vaccine continuously expresses antigens even after immunization. This setup results in a powerful, antigen-focused immune response, which is expected to confer long-term immunity as shown for the measles vaccine. The challenges in production process design for such a vaccine are the establishment of a robust cell expansion and infection strategy as well the development of efficient downstream processing methods including several chromatography principals, ultra-diafiltration and employment of bio recognition principles. The implementation of a meaningful real-time process monitoring/characterization concept furthermore serves as a basis for reliable in-process control strategies (e.g. the prediction of the optimal infection/harvesting time point)

Elution profile from periodic counter current capture step as an on-line monitoring and control tool for perfusion bioreactors

Current trends in bioprocessing move towards the implementation of more on-line sensors such as Raman spectroscopy for titer monitoring in perfusion bioreactors. However, process performance data from one downstream unit operation can also be used to monitor and control the unit operation directly upstream. Despite several authors demonstrated a successful integration of continuous up-and downstream processes little attempts have been made to leverage the information derived from downstream processing as a real time feedback loop for upstream processing. We have developed a simple and robust approach in which protein A periodic counter current chromatography (PCCC) can function as an on-line monitoring tool for protein titer in continuous upstream fermentations. For a proof of concept, we exploit the fact that performance and binding capacities of state of the art protein A chromatography material do not significantly decrease throughout hundreds of cycles. Therefore, it is possible to predict the concentration of antibodies in the feed material from the elution pool and the volume loaded onto the column. We use the breakthrough curve during the interconnected phase of the PCCC, which is key for this approach. In the interconnected phase, the first column was loaded to 80% breakthrough, and the breakthrough curve modelled for a number of different concentrations in the feed material. Using the breakthrough curve, the time of the breakthrough can be modelled against the increase of product present in the feed stream, allowing the prediction of the concentration of antibody in the perfusion fermentation. This information feedback loop through the integration of PCCC and fermentation into effectively one single unit operation makes the titer determination in the fermentation obsolete, using the PCCC effectively as online monitoring tool itself. Future work after this proof of concept will include the prediction of protein A binding capacities through the lifetime of the resin and determination of accuracy and quantification limits the of interconnected units. Please click Additional Files below to see the full abstract

Combinatorial Fusion TAG yields powerful platform process for the production of pharmaceutically relevant proteins

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Microbial Cell Factories / Evaluation of three industrial Escherichia coli strains in fed-batch cultivations during high-level SOD protein production

Background: In the biopharmaceutical industry, Escherichia coli (E. coli) strains are among the most frequently used bacterial hosts for producing recombinant proteins because they allow a simple process set-up and they are Food and Drug Administration (FDA)-approved for human applications. Widespread use of E. coli in biotechnology has led to the development of many different strains, and selecting an ideal host to produce a specific protein of interest is an important step in developing a production process. E. coli B and K\u201312 strains are frequently employed in large-scale production processes, and therefore are of particular interest. We previously evaluated the individual cultivation characteristics of E. coli BL21 and the K\u201312 hosts RV308 and HMS174. To our knowledge, there has not yet been a detailed comparison of the individual performances of these production strains in terms of recombinant protein production and system stability. The present study directly compared the T7-based expression hosts E. coli BL21(DE3), RV308(DE3), and HMS174(DE3), focusing on evaluating the specific attributes of these strains in relation to high-level protein production of the model protein recombinant human superoxide dismutase (SOD). The experimental setup was an exponential carbon-limited fed-batch cultivation with minimal media and single-pulse induction. Results: The host strain BL21(DE3) produced the highest amounts of specific protein, followed by HMS174(DE3) and RV308(DE3). The expression system HMS174(DE3) exhibited system stability by retaining the expression vector over the entire process time; however, it entirely stopped growing shortly after induction. In contrast, BL21(DE3) and RV308(DE3) encountered plasmid loss but maintained growth. RV308(DE3) exhibited the lowest ppGpp concentration, which is correlated with the metabolic stress level and lowest degradation of soluble protein fraction compared to both other strains. Conclusions: Overall, this study provides novel data regarding the individual strain properties and production capabilities, which will enable targeted strain selection for producing a specific protein of interest. This information can be used to accelerate future process design and implementation

Growth-decoupled recombinant protein production in Escherichia coli

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Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli

<p>Abstract</p> <p>Background</p> <p>Interpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by <monospace>gcExplorer</monospace> an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results.</p> <p>Results</p> <p>In this study the interactive visualization toolbox <monospace>gcExplorer</monospace> is applied to the interpretation of <it>E. coli </it>microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased.</p> <p>Conclusion</p> <p>It was shown that <monospace>gcExplorer</monospace> is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases.</p

Antibody charge heterogeneity formation in a mammalian cell culture fed-batch process

The charge heterogeneity of a monoclonal antibody (mAb) is as a sum factor of several post translational modifications and most of them are of high importance regarding product quality and efficacy. For this reason monitoring and controlling of this sum factor can be beneficial. The work presented here builds the basis for on-line monitoring and will help to achieve Quality by Control (Sommeregger et al., 2017). The aim of this work was to develop a method that allows fast and accurate determination of the charge profile of monoclonal antibodies directly from cell culture supernatants. We were able to circumvent a pre-purification step by adapting a cation exchange method (CEX) using a highly linear pH gradient (Lingg et al, 2013). The established method was then used to gain information about the formation of charge variants during a fed-batch process of an industrial relevant mAb produced by a Chinese Hamster Ovary (CHO) cell line. Please click Additional Files below to see the full abstract

combining First Principles with deep neural networks

JP acknowledges PhD grant SFRD/BD14610472019, This work has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no 101000733 (PROMICON).Numerous studies have reported the use of hybrid semiparametric systems that combine shallow neural networks with First Principles for bioprocess modeling. Here we revisit the general bioreactor hybrid model and introduce some deep learning techniques. Multi-layer networks with varying depths were combined with First Principles equations in the form of deep hybrid models. Deep learning techniques, namely the adaptive moment estimation method (ADAM), stochastic regularization and depth-dependent weights initialization were evaluated in a hybrid modeling context. Modified sensitivity equations are proposed for the computation of gradients in order to reduce CPU time for the training of deep hybrid models. The methods are illustrated with applications to a synthetic dataset and a pilot 50 L MUT+ Pichia pastoris process expressing a single chain antibody fragment. All in all, the results point to a systematic generalization improvement of deep hybrid models over its shallow counterpart. Moreover, the CPU cost to train the deep hybrid models is shown to be lower than for the shallow counterpart. In the pilot 50L MUT+ Pichia pastoris data set, the prediction accuracy was increased by 18.4% and the CPU decreased by 43.4%.publishersversionpublishe