Mucins purified from porcine stomachs have gained importance in biomedical applications as they exhibit unique features such as hydrogel formation, lubricity and antivirality. Because commercially available porcine gastric mucins (PGM) are neither gel forming, nor do they reduce friction, a robust purification process for functional mucins from porcine tissue (mainly Muc5AC) is necessary. Based on our former investigations (V. J. Schömig, B. T. Käsdorf, C. Scholz, K. Bidmon, O. Lieleg and S. Berensmeier, RSC Adv., 2016, 6, 44932-44943), we further optimized the established purification process in terms of productivity and overall yield as well as ease of scalability. We therefore introduced a novel extraction method -solid liquid liquid extraction (SLLE) -as an early capture step from homogenized porcine stomach, which combines conventional solid liquid extraction with a second, immiscible solvent, to simultaneously delipidate the tissue and extract hydrophilic proteins into the polar phase. Using Design of Experiments (DoE) the parameters incubation time and ratio of solvent phases (hexane/water) were identified to 3 h and 1/15 for SLLE, respectively. PGM was collected in the polar phase and further purified by size exclusion chromatography and diafiltration. With the homogenization of porcine stomach and the introduction of the SLLE, up to 3570 mg mucin per stomach can be purified, resulting in 55 times more PGM compared to the former published reference process. The productivity of the process was up to 25 mg mucin per stomach and per h, representing an improved process productivity by a factor of 4. Lubricity and gel formation of the purified mucin were retained with the optimized extraction protocol.
Magnetophoretic velocity of magnetic nano-objects for biomedical applications was characterized by measuring space-and time-resolved extinction profiles (STEP-Technology) using a customized LUMiReader device equipped with a set of permanent magnets (STEP-MAG). The resulting magnetic fields and gradients in a sample volume enable the operator to choose measurement conditions for magnetic micro-and nanoparticles and their assemblies. The dependence of magnetophoretic velocity on concentration and optical wavelengths indicated assembly of the nano-objects upon magnetophoresis. The method has potential applications in biomedicine to develop advanced materials and protocols for cell separation, tissue engineering, and drug/nucleic acid targeting.
Optimization of experimental problems is a challenging task in both engineering and science. In principle, two different design of experiments (DOE) strategies exist: statistical and stochastic methods. Both aim to efficiently and precisely identify optimal solutions inside the problem-specific search space. Here, we evaluate and compare both strategies on the same experimental problem, the optimization of the refolding conditions of the lipase from Thermomyces lanuginosus with 26 variables under study. Protein refolding is one of the main bottlenecks in the process development for recombinant proteins. Despite intensive effort, the prediction of refolding from sequence information alone is still not applicable today. Instead, suitable refolding conditions are typically derived empirically in large screening experiments. Thus, protein refolding should constitute a good performance test for DOE strategies. We compared an iterative stochastic optimization applying a genetic algorithm and a standard statistical design consisting of a D-optimal screening step followed by an optimization via response surface methodology. Our results revealed that only the stochastic optimization was able to identify optimal refolding conditions (~1.400 U g(-1) refolded activity), which were 3.4-fold higher than the standard. Additionally, the stochastic optimization proved quite robust, as three independent optimizations performed similar. In contrast, the statistical DOE resulted in a suboptimal solution and failed to identify comparable activities. Interactions between process variables proved to be pivotal for this optimization. Hence, the linear screening model was not able to identify the most important process variables correctly. Thereby, this study highlighted the limits of the classic two-step statistical DOE.
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