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In biopharmaceutical production processes, freeze-thaw operations are used to ensure product integrity during long hold times, but they also introduce additional stresses such as freeze concentration gradients that might lead to a loss of protein activity. Process characterization of freeze-thaw operations at different scales should be conducted with attention to freezing time and boundary effects to ensure the product stability throughout the process and process development. Currently, process characterization often relies on one or very few temperature probes that detect freezing times based on raw temperature, which is largely influenced by freezing-point depression in case of concentrated solutions. A method to detect freezing based on the second derivative of temperature measurements from Fiber-Bragg-Grating sensors is presented to overcome this issue. The applicability of the method is demonstrated by process characterization of a novel small-scale freeze-thaw device with minimized boundary effects using freezing times of purified water and concentrated formulations. Freezing times varied from 35 to 81 min for temperatures between −60 and −20°C and impacted freeze concentration profiles. Furthermore, freezing time estimations based on the Plank equation revealed model limitations due to start-up temperature gradients, that can be corrected by an empirically extended Plank model. As a hypothesis, we conclude that freezing temperature, from a freeze concentration view, is less important in containers with small characteristic freezing distances such as freeze bags. Using a 2D-resolved temperature profile, a shift of the last point to freeze position from top to bottom of a container was observed when freezing above −30°C.

The immobilization of enzymes in biocatalytic flow reactors is a common strategy to increase enzyme reusability and improve biocatalytic performance. Extrusion-based 3D bioprinting has recently emerged as a versatile tool for the fabrication of perfusable hydrogel grids containing entrapped enzymes for the use in such reactors. This study demonstrates the suitability of water-in-oil high internal phase emulsions (HIPEs) as 3D-printable bioinks for the fabrication of composite materials with a porous polymeric scaffold (polyHIPE) filled with enzyme-laden hydrogel. The prepared HIPEs exhibited excellent printability and are shown to be suitable for the printing of complex three-dimensional structures without the need for sacrificial support material. An automated activity assay method for the systematic screening of different material compositions in small-scale batch experiments is presented. The monomer mass fraction in the aqueous phase and the thickness of printed objects were found to be the most important parameters determining the apparent activity of the immobilized enzyme. Mass transfer limitations and enzyme inactivation were identified as probable factors reducing the apparent activity. The presented HIPE-based bioinks enable the fabrication of flow-optimized and more efficient biocatalytic reactors while the automated activity assay method allows the rapid screening of materials to optimize the biocatalytic efficiency further without time-consuming flow-through experiments involving whole printed reactors.

Virus-like particles (VLPs) are protein-based nanoscale structures that show high potential as immunotherapeutics or cargo delivery vehicles. Chimeric VLPs are decorated with foreign peptides resulting in structures that confer immune responses against the displayed epitope. However, insertion of foreign sequences often results in insoluble proteins, calling for methods capable of assessing a VLP candidate’s solubility in silico. The prediction of VLP solubility requires a model that can identify critical hydrophobicity-related parameters, distinguishing between VLP-forming aggregation and aggregation leading to insoluble virus protein clusters. Therefore, we developed and implemented a soft ensemble vote classifier (sEVC) framework based on chimeric hepatitis B core antigen (HBcAg) amino acid sequences and 91 publicly available hydrophobicity scales. Based on each hydrophobicity scale, an individual decision tree was induced as classifier in the sEVC. An embedded feature selection algorithm and stratified sampling proved beneficial for model construction. With a learning experiment, model performance in the space of model training set size and number of included classifiers in the sEVC was explored. Additionally, seven models were created from training data of 24–384 chimeric HBcAg constructs, which were validated by 100-fold Monte Carlo cross-validation. The models predicted external test sets of 184–544 chimeric HBcAg constructs. Best models showed a Matthew’s correlation coefficient of >0.6 on the validation and the external test set. Feature selection was evaluated for classifiers with best and worst performance in the chimeric HBcAg VLP solubility scenario. Analysis of the associated hydrophobicity scales allowed for retrieval of biological information related to the mechanistic backgrounds of VLP solubility, suggesting a special role of arginine for VLP assembly and solubility. In the future, the developed sEVC could further be applied to hydrophobicity-related problems in other domains, such as monoclonal antibodies.