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.

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. © 2016 Wiley Periodicals, Inc.

The enormous variation possibilities of bioprocesses challenge process development to fix a commercial process with respect to costs and time. Although some cultivation systems and some devices for unit operations combine the latest technology on miniaturization, parallelization, and sensing, the degree of automation in upstream and downstream bioprocess development is still limited to single steps. We aim to face this challenge by an interdisciplinary approach to significantly shorten development times and costs. As a first step, we scaled down analytical assays to the microliter scale and created automated procedures for starting the cultivation and monitoring the optical density (OD), pH, concentrations of glucose and acetate in the culture medium, and product formation in fed-batch cultures in the 96-well format. Then, the separate measurements of pH, OD, and concentrations of acetate and glucose were combined to one method. This method enables automated process monitoring at dedicated intervals (e.g., also during the night). By this approach, we managed to increase the information content of cultivations in 96-microwell plates, thus turning them into a suitable tool for high-throughput bioprocess development. Here, we present the flowcharts as well as cultivation data of our automation approach.

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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 (g x 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 q p of 400 U (g x 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.