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Technological developments require the transfer to their location of application to make use of them. We describe the transfer of a real‐time monitoring system for lab‐scale preparative chromatography to two new sites where it will be used and developed further. Equivalent equipment was used. The capture of a biopharmaceutical model protein, human fibroblast growth factor 2 (FGF‐2) was used to evaluate the system transfer. Predictive models for five quality attributes based on partial least squares regression were transferred. Six out of seven online sensors (UV/VIS, pH, conductivity, IR, RI, and MALS) showed comparable signals between the sites while one sensor (fluorescence) showed different signal profiles. A direct transfer of the models for real‐time monitoring was not possible, mainly due to differences in sensor signals. Adaptation of the models was necessary. Then, among five prediction models, the prediction errors of the test run at the new sites were on average twice as high as at the training site (model‐wise 0.9–5.7 times). Additionally, new prediction models for different products were trained at each new site. These allowed monitoring the critical quality attributes of two new biopharmaceutical products during their purification processes with mean relative deviations between 1% and 33%., The authors show the challenges to transfer real time monitoring systems based on highly sensitive sensors and predictive models from the process science lab to two sites. Even though the exact same sensor set‐up was used at all three sites, the main challenge was the different sensitivity of sensors and data deviations due to non‐standardized process protocols. Partially, such deviations were compensated by data pre‐processing. Adaptions of the models considering those challenges enabled monitoring of in‐house processes with good precision.

Parameter estimation for scale-up of downstream operations from microtiter plates (MTPs) is mostly done empirically because engineering correlations between microplates and stirred tank reactors (STRs) are not yet available. It is challenging to change the operation mode from shaken MTPs to large-scale STRs. For the scale-up of STRs, volumetric power input is well-established although it is unclear whether this parameter can be used to transfer the operations from MTPs. We determine the volumetric power input in MTPs via the temperature increase caused by the motion of the liquid. The hydrodynamics in MTPs are studied with computational fluid dynamics (CFD). Mixing is investigated in 96-, 24-, and 6-well MTPs to cover different geometries, filling volumes, shaking diameters, and shaking frequencies. All CFD simulations are validated by experimental results, which now allows prediction of the volumetric power input and hydrodynamics at various conditions in MTPs without the need for further experiments. We provide a map of the power input achievable in MTPs. Based on this map, from knowing about large-scale conditions, adequate microscale conditions can be adjusted for process development. This enables the direct scale-up of downstream unit operations from MTPs to STRs.

The aim of this study was to semi-automate process analytics for the quantification of common impurities in downstream processing such as host cell DNA, host cell proteins and endotoxins using a commercial liquid handling station. By semi-automation, the work load to fully analyze the elution peak of a purification run was reduced by at least 2.41 h. The relative standard deviation of results among different operators over a time span of up to 6 months was at the best reduced by half, e.g. from 13.7 to 7.1% in dsDNA analysis. Automation did not improve the reproducibility of results produced by one operator but released time for data evaluation and interpretation or planning of experiments. Overall, semi-automation of process analytics reduced operator-specific influence on test results. Such robust and reproducible analytics is fundamental to establish process analytical technology and get downstream processing ready for Quality by Design approaches.

Process analytical technology combines understanding and control of the process with real‐time monitoring of critical quality and performance attributes. The goal is to ensure the quality of the final product. Currently, chromatographic processes in biopharmaceutical production are predominantly monitored with UV/Vis absorbance and a direct correlation with purity and quantity is limited. In this study, a chromatographic workstation was equipped with additional online sensors, such as multi‐angle light scattering, refractive index, attenuated total reflection Fourier‐transform infrared, and fluorescence spectroscopy. Models to predict quantity, host cell proteins (HCP), and double‐stranded DNA (dsDNA) content simultaneously were developed and exemplified by a cation exchange capture step for fibroblast growth factor 2 expressed in Escherichia coliOnline data and corresponding offline data for product quantity and co‐eluting impurities, such as dsDNA and HCP, were analyzed using boosted structured additive regression. Different sensor combinations were used to achieve the best prediction performance for each quality attribute. Quantity can be adequately predicted by applying a small predictor set of the typical chromatographic workstation sensor signals with a test error of 0.85 mg/ml (range in training data: 0.1–28 mg/ml). For HCP and dsDNA additional fluorescence and/or attenuated total reflection Fourier‐transform infrared spectral information was important to achieve prediction errors of 200 (2–6579 ppm) and 340 ppm (8–3773 ppm), respectively.

BACKGROUND Evaluation of optimized upstream conditions is often solely based on titers that do not reflect the downstream process and, consequently, the final purity and yield. This is especially critical for proteins expressed as inclusion bodies (IBs) because the subsequent downstream process is more complex than for soluble proteins. A miniaturized process development platform representing the entire downstream process at a microscale level combined with a multi-fermenter system for bioprocess screening enables fast investigation, using quality by design (QbD) measures, of the quality of feedstock compositions that are altered by upstream conditions. RESULTS We integrated high-throughput methods for an entire process chain, including upstream and downstream processing, for production of a recombinant protein as IBs. The miniaturized process chain consisted of cell disruption, IB harvesting, solubilization, refolding, and chromatographic purification. This enables processing and evaluation of 16 different fermentation conditions within 7 days. Only 2 g of initial biomass was required for evaluation of an individual fermentation. This resulted in a 15-fold reduction of time and a 100-fold reduction of material compared with common bench scale experiments. CONCLUSION This microscale process chain mimics the bench process and is able to differentiate purity variations as low as 2%.

Npro fusion technology, a highly efficient system for overexpression of proteins and peptides in Escherichia coli, was further developed by splitting the autoprotease Npro into two fragments to generate a functional complementation system. The size of the expression tag is thus reduced from 168 to 58 amino acids, so by 66%. Upon complementation of the fragments auto-proteolytic activity is restored. This process has been shown for three model proteins of different size, a short 16 aa-peptide, MCP-1, and lysozyme. Moreover, the complementation was still functional after immobilization of the N-terminal fragment to a solid support which enables recycling of the immobilized fragment. This strategy enhances overall productivity of Npro Fusion Technology and thus allows more efficient production of recombinant proteins with reduced costs and in higher yields. Overall, the Npro complementation system has, depending on the size of the target molecule, potential to increase the productivity up to 4 fold for batch refolding and even more for on-column refolding strategies by the proven possibility of regeneration of the immobilized fragment.

The aim of this study was to semi-automate process analytics for the quantification of common impurities in downstream processing such as host cell DNA, host cell proteins and endotoxins using a commercial liquid handling station. By semi-automation, the work load to fully analyze the elution peak of a purification run was reduced by at least 2.41 h. The relative standard deviation of results among different operators over a time span of up to 6 months was at the best reduced by half, e.g. from 13.7 to 7.1% in dsDNA analysis. Automation did not improve the reproducibility of results produced by one operator but released time for data evaluation and interpretation or planning of experiments. Overall, semi-automation of process analytics reduced operator-specific influence on test results. Such robust and reproducible analytics is fundamental to establish process analytical technology and get downstream processing ready for Quality by Design approaches. Electronic supplementary material The online version of this article (10.1007/s00449-019-02254-y) contains supplementary material, which is available to authorized users.

Neither the influence of high shear rates nor the impact of cavitation on protein aggregation is fully understood. The effect of cavitation bubble collapse‐derived hydroxyl radicals on the aggregation behavior of human serum albumin (HSA) was investigated. Radicals were generated by pumping through a micro‐orifice, ultra‐sonication, or chemically by Fenton's reaction. The amount of radicals produced by the two mechanical methods (0.12 and 11.25 nmol/(L min)) was not enough to change the protein integrity. In contrast, Fenton's reaction resulted in 382 nmol/(L min) of radicals, inducing protein aggregation. However, the micro‐orifice promoted the formation of soluble dimeric HSA aggregates. A validated computational fluid dynamic model of the orifice revealed a maximum and average shear rate on the order of 108 s−1 and 1.2 × 106 s−1, respectively. Although these values are among the highest ever reported in the literature, dimer formation did not occur when we used the same flow rate but suppressed cavitation. Therefore, aggregation is most likely caused by the increased surface area due to cavitation‐mediated bubble growth, not by hydroxyl radical release or shear stress as often reported.

Inclusion bodies (IBs) were solubilized in a µ-scale system using shaking microtiter plates or a stirred tank reactor in a laboratory setting. Characteristic dimensionless numbers for mixing, the Phase number Ph and Reynolds number Re did not correlate with the kinetics and equilibrium of protein solubilization. The solubilization kinetics was independent of the mixing system, stirring or shaking rate, shaking diameter, and energy input. Good agreement was observed between the solubilization kinetics and yield on the µ-scale and laboratory setting. We show that the IB solubilization process is controlled predominantly by pore diffusion. Thus, for the process it is sufficient to keep the IBs homogeneously suspended, and additional power input will not improve the process. The high-throughput system developed on the µ-scale can predict solubilization in stirred reactors up to a factor of 500 and can therefore be used to determine optimal solubilization conditions on laboratory and industrial scale.

Inclusion bodies (IBs) were solubilized in a µ-scale system using shaking microtiter plates or a stirred tank reactor in a laboratory setting. Characteristic dimensionless numbers for mixing, the Phase number Ph and Reynolds number Re did not correlate with the kinetics and equilibrium of protein solubilization. The solubilization kinetics was independent of the mixing system, stirring or shaking rate, shaking diameter, and energy input. Good agreement was observed between the solubilization kinetics and yield on the µ-scale and laboratory setting. We show that the IB solubilization process is controlled predominantly by pore diffusion. Thus, for the process it is sufficient to keep the IBs homogeneously suspended, and additional power input will not improve the process. The high-throughput system developed on the µ-scale can predict solubilization in stirred reactors up to a factor of 500 and can therefore be used to determine optimal solubilization conditions on laboratory and industrial scale.

The autoprotease Npro significantly enhances expression of fused peptides and proteins and drives the formation of inclusion bodies during protein expression. Upon refolding, the autoprotease becomes active and cleaves itself specifically at its own C-terminus releasing the target protein with its authentic N-terminus. Npro wild-type and its mutant EDDIE, respectively, were fused N-terminally to the model proteins green fluorescent protein, staphylococcus Protein A domain D, inhibitory peptide of senescence-evasion-factor, and the short 16 amino acid peptide pep6His. In comparison with the Npro wild-type, the tailored mutant EDDIE displayed an increased rate constant for refolding and cleavage from 1.3 × 10−4 s−1 to 3.5 × 10−4 s−1, and allowed a 15-fold higher protein concentration of 1.1 mg/mL when studying pep6His as a fusion partner. For green fluorescent protein, the rate constant was increased from 2.4 × 10−5 s−1 to 1.1 × 10−4 s−1 when fused to EDDIE. When fused to small target peptides, refolding and cleavage yields were independent of initial protein concentration, even at high concentrations of 3.9 mg/mL, although cleavage rates were strongly influenced by the fusion partner. This behavior differed from conventional 1st order refolding kinetics, where yield strongly depends on initial protein concentration due to an aggregation reaction of higher order. Refolding and cleavage of EDDIE fusion proteins follow a monomolecular reaction for the autoproteolytic cleavage over a wide concentration range. At high protein concentrations, deviations from the model assumptions were observed and thus smaller rate constants were required to approximate the data. Biotechnol. Bioeng. 2009; 104: 774–784 © 2009 Wiley Periodicals, Inc.