Florian Glauche

Consistent development of bioprocesses from microliter cultures to the industrial scale

Bioprocess development today is slow and expensive compared to chemical process development. A drastic paradigm shift is necessary and possible by the consistent application of engineering strategies that are typically used in the process development phase already in the early product development. Aside from providing a consistent pathway, strategies such as statistical‐based design of experiments, fed‐batch, minibioreactors, new on‐line sensors, process modeling, and control tools in combination with automation of manual steps offer a higher success rate and the opportunity to find the optimum parameters and operation point. This also directly benefits the early phases of biomolecular screening and initial production of small amounts of the target molecule. The paper reviews the bioprocess developmental phases from a business perspective and the available systems and technologies.

Glucose-limited high cell density cultivations from small to pilot plant scale using an enzyme-controlled glucose delivery system

The enzyme controlled substrate delivery cultivation technology EnBase(®) Flo allows a fed-batch-like growth in batch cultures. It has been previously shown that this technology can be applied in small cultivation vessels such as micro- and deep well plates and also shake flasks. In these scales high cell densities and improved protein production for Escherichia coli cultures were demonstrated. This current study aims to evaluate the scalability of the controlled glucose release technique to pilot scale bioreactors. Throughout all scales, that is, deep well plates, 3 L bioreactor and 150 L bioreactor cultivations, the growth was very similar and the model protein, a recombinant alcohol dehydrogenase (ADH) was produced with a high yield in soluble form. Moreover, EnBase Flo also was successfully used as a controlled starter culture in high cell density fed-batch cultivations with external glucose feeding. Here the external feeding pump was started after overnight cultivation with EnBase Flo. Final optical densities in these cultivations reached 120 (corresponding to about 40 g L(-1) dry cell weight) and a high expression level of ADH was obtained. The EnBase cultivation technology ensures a controlled initial cultivation under fed-batch mode without the need for a feeding pump. Because of the linear cell growth under glucose limitation it provides optimal and robust starting conditions for traditional external feed-based processes.

Design of experiments‐based high‐throughput strategy for development and optimization of efficient cell disruption protocols

Efficient and reproducible cell lysis is a crucial step during downstream processing of intracellular products. The composition of an optimal lysis buffer should be chosen depending on the organism, its growth status, the applied detection methods, and even the target molecule. Especially for high‐throughput applications, where sample volumes are limited, the adaptation of a lysis buffer to the specific campaign is an urgent need. Here, we present a general design of experiments‐based strategy suitable for eight constituents and demonstrate the strength of this approach by the development of an efficient lysis buffer for Gram‐negative bacteria, which is applicable in a high‐throughput format in a short time. The concentrations of four lysis‐inducing chemical agents EDTA, lysozyme, Triton X‐100, and polymyxin B were optimized for maximal soluble protein concentration and ß‐galactosidase activity in a 96‐well format on a Microlab Star liquid handling platform under design of experiments methodology. The resulting lysis buffer showed the same performance as a commercially available lysis buffer. The developed protocol resulted in an optimized buffer within only three runs. The established procedure can be easily applied to adapt the lysis buffer to other strains and target molecules.

Online optimal experimental re-design in robotic parallel fed-batch cultivation facilities: Online Optimal Experimental Re-Design in Robotic

We present an integrated framework for the online optimal experimental re‐design applied to parallel nonlinear dynamic processes that aims to precisely estimate the parameter set of macro kinetic growth models with minimal experimental effort. This provides a systematic solution for rapid validation of a specific model to new strains, mutants, or products. In biosciences, this is especially important as model identification is a long and laborious process which is continuing to limit the use of mathematical modeling in this field. The strength of this approach is demonstrated by fitting a macro‐kinetic differential equation model for Escherichia coli fed‐batch processes after 6 h of cultivation. The system includes two fully‐automated liquid handling robots; one containing eight mini‐bioreactors and another used for automated at‐line analyses, which allows for the immediate use of the available data in the modeling environment. As a result, the experiment can be continually re‐designed while the cultivations are running using the information generated by periodical parameter estimations. The advantages of an online re‐computation of the optimal experiment are proven by a 50‐fold lower average coefficient of variation on the parameter estimates compared to the sequential method (4.83% instead of 235.86%). The success obtained in such a complex system is a further step towards a more efficient computer aided bioprocess development. Biotechnol. Bioeng. 2017;114: 610–619.

Toward Microbioreactor Arrays: A Slow-Responding Oxygen Sensor for Monitoring of Microbial Cultures in Standard 96-Well Plates.

In this study, a slow-responding chemo-optical sensor for dissolved oxygen (DO) integrated into a 96-well plate was developed. The slow response time ensures that the measured oxygen value does not change much during plate transport to the microplate reader. The sensor therefore permits at-line DO measurement of microbial cultures. Moreover, it eliminates the necessity of individual optical measurement systems for each culture plate, as many plates can be measured successively. Combined with the 96-well format, this increases the experimental throughput enormously. The novel sensor plate (Slow OxoPlate) consists of fluorophores suspended in a polymer matrix that were placed into u-bottom 96-well plates. Response time was measured using sodium sulfite, and a t90 value of 9.7 min was recorded. For application, DO values were then measured in Escherichia coli and Saccharomyces cerevisiae cultures grown under fed-batch–like conditions. Depending on the DO sensor’s response time, different information on the oxygenation state of the culture plate was obtained: a fast sensor variant detects disturbance through sampling, whereas the slow sensor indicates oxygen limitation during incubation. A combination of the commercially available OxoPlate and the Slow OxoPlate enables operators of screening facilities to validate their cultivation procedures with regard to oxygen availability.

Lactose autoinduction with enzymatic glucose release: characterization of the cultivation system in bioreactor.

The lactose autoinduction system for recombinant protein production was combined with enzymatic glucose release as a method to provide a constant feed of glucose instead of using glycerol as a carbon substrate. Bioreactor cultivation confirmed that the slow glucose feed does not prevent the induction by lactose. HPLC studies showed that with successful recombinant protein production only a very low amount of lactose was metabolized during glucose-limited fed-batch conditions by the Escherichia coli strain BL21(DE3)pLysS in well-aerated conditions, which are problematic for glycerol-based autoinduction systems. We propose that slow enzymatic glucose feed does not cause a full activation of the lactose operon. However recombinant PDI-A protein (A-domain of human disulfide isomerase) was steadily produced until the end of the cultivation. The results of the cultivations confirmed our earlier observations with shaken cultures showing that lactose autoinduction cultures based on enzymatic glucose feed have good scalability, and that this system can be applied also to bioreactor cultivations.

Detection of growth rate-dependent product formation in miniaturized parallel fed-batch cultivations

Saccharomyces cerevisiae is a popular expression system for recombinant proteins. In most cases, production processes are performed as carbon‐limited fed‐batch cultures to avoid aerobic ethanol formation. Especially for constitutive expression systems, the specific product formation rate depends on the specific growth rate. The development of optimal feeding strategies strongly depends on laboratory‐scale cultivations, which are time and resource consuming, especially when continuous experiments are carried out. It is therefore beneficial for accelerated process development to look at alternatives. In this study, S. cerevisiae AH22 secreting a heterologous endo‐polygalacturonase (EPG) was characterized in microwell plates with an enzyme‐based fed‐batch medium. Through variation of the glucose release rate, different growth profiles were established and the impact on EPG secretion was analyzed. Product formation rates of 200–400 U (gx h)−1 were determined. As a reference, bioreactor experiments using the change‐stat cultivation technique were performed. The growth‐dependent product formation was analyzed over dilution rates of D = 0.01–0.35 with smooth change of D at a rate of 0.003 h−2. EPG production was found to be comparable with a qp of 400 U (gx h)−1 at D = 0.27 h−1. The presented results indicate that parallel miniaturized fed‐batch cultures can be applied to determine product formation profiles of putative production strains. With further automation and parallelization of the concept, strain characterization can be performed in shorter time.

Online optimal experimental re‐design in robotic parallel fed‐batch cultivation facilities

We present an integrated framework for the online optimal experimental re‐design applied to parallel nonlinear dynamic processes that aims to precisely estimate the parameter set of macro kinetic growth models with minimal experimental effort. This provides a systematic solution for rapid validation of a specific model to new strains, mutants, or products. In biosciences, this is especially important as model identification is a long and laborious process which is continuing to limit the use of mathematical modeling in this field. The strength of this approach is demonstrated by fitting a macro‐kinetic differential equation model for Escherichia coli fed‐batch processes after 6 h of cultivation. The system includes two fully‐automated liquid handling robots; one containing eight mini‐bioreactors and another used for automated at‐line analyses, which allows for the immediate use of the available data in the modeling environment. As a result, the experiment can be continually re‐designed while the cultivations are running using the information generated by periodical parameter estimations. The advantages of an online re‐computation of the optimal experiment are proven by a 50‐fold lower average coefficient of variation on the parameter estimates compared to the sequential method (4.83% instead of 235.86%). The success obtained in such a complex system is a further step towards a more efficient computer aided bioprocess development. Biotechnol. Bioeng. 2017;114: 610–619.

Editorial: Bioprocess Development in the era of digitalization

In the last few decades, a vast number of research findings in biotechnological processes were reported with high commercial relevance. Still only a small fraction of these molecules was successfully brought to industrial production. The development of production processes in biotechnology still requires a lot of time and money compared to other industries. Due to the complexity of biological systems, the commercialization of cell-derived products needs a combination of deep scientific knowledge and a thorough understanding of process engineering. In addition, regulatory requirements must be met, which may increase the overall risk of success. Process development in biotechnology is still mainly driven by experienced personnel creating and evaluating experimental data. However, in recent times, automation, miniaturization and data science are setting new paths for drastic changes in bioprocess development. Today, hundreds of automated experiments can be performed in parallel miniaturized cultivation and purification systems per day. Additionally, statistical experimental planning and evaluation is applied to utilize the experimental capacity of these facilities efficiently. Still, to exploit the full potential of automated laboratories, innovative software concepts and workflows are needed. Moreover, the conditions in which small-scale experiments are performed need to resemble the production scale as closely as possible [1]. Today, there are numerous possibilities to create, collect, store, and share data. This has a significant potential in

Online optimal experimental re‐design in robotic parallel fed‐batch cultivation facilities for validation of macro‐kinetic growth models using E. coli as an example

We present an integrated framework for the online optimal experimental re‐design applied to parallel nonlinear dynamic processes that aims to precisely estimate the parameter set of macro kinetic growth models with minimal experimental effort. This provides a systematic solution for rapid validation of a specific model to new strains, mutants, or products. In biosciences, this is especially important as model identification is a long and laborious process which is continuing to limit the use of mathematical modeling in this field. The strength of this approach is demonstrated by fitting a macro‐kinetic differential equation model for Escherichia coli fed‐batch processes after 6 h of cultivation. The system includes two fully‐automated liquid handling robots; one containing eight mini‐bioreactors and another used for automated at‐line analyses, which allows for the immediate use of the available data in the modeling environment. As a result, the experiment can be continually re‐designed while the cultivations are running using the information generated by periodical parameter estimations. The advantages of an online re‐computation of the optimal experiment are proven by a 50‐fold lower average coefficient of variation on the parameter estimates compared to the sequential method (4.83% instead of 235.86%). The success obtained in such a complex system is a further step towards a more efficient computer aided bioprocess development. Biotechnol. Bioeng. 2017;114: 610–619.