All-atom modeling of methacrylate-based multi-modal chromatography resins for Langmuir constant prediction of peptides.

In downstream processing, the intricate nature of the interactions between biomolecules and adsorbent materials presents a significant challenge in the prediction of their binding and elution behaviors. This complexity is further heightened in multi-modal chromatography (MMC), which employs two distinct binding mechanisms. To gain a deeper understanding of the involved interactions, simulating the adsorption of biomolecules on resin surfaces is a focal point of ongoing research. However, previous studies often simplified the adsorbent surface, modeling it as a flat or slightly curved plane without including a realistic backbone structure. Here, we introduce and validate two novel workflows aimed at predicting peptide binding behaviors in MMC, specifically targeting methacrylate-based resins. Our first achievement was the development of an all-atom model of a commercial MMC resin surface, incorporating its polymethacrylic backbone. Furthermore, we established and tested a workflow for rapid calculations of binding free energies (ΔG) with 10 linear peptides as target molecules. These ΔG calculations were effectively used to predict Langmuir constants, achieving a high coefficient of determination (R²) of 0.96. In subsequent benchmarking tests, our model outperformed established, simpler resin surface models in terms of predictive capabilities.

Tailoring polishing steps for effective removal of polysorbate-degrading host cell proteins in antibody purification.

Ensuring the quality and safety of biopharmaceutical products requires the effective separation of monoclonal antibodies (mAbs) from host cell proteins (HCPs). A major challenge in this field is the enzymatic hydrolysis of polysorbates (PS) in drug products. This study addresses this issue by investigating the removal of polysorbate-degrading HCPs during the polishing steps of downstream purification, an area where knowledge about individual HCP behavior is still limited. We investigated the separation of different mAb formats from four individual polysorbate degrading hydrolases (CES1F, CES2C, LPLA2, and PAF-AH) using cation exchange (CEX) and mixed-mode chromatography (MMC) polishing steps. Our research identified a key challenge: The similar elution behavior of mAbs and HCPs during chromatographic separation. To investigate this phenomenon, we performed high-throughput binding screenings for recombinant polysorbate degrading hydrolases and representative mAb candidates on CEX and MMC chromatography resins. We then employed a three-step strategy that also served as a scale-up process, optimizing separation conditions and leading to the successful removal of specific HCPs while maintaining high mAb recovery rates (>96%). This strategy involved the use of surface response models and miniature columns for screening, followed by validation on larger columns using a chromatography system. Our results highlight the critical role of the inherent properties of mAbs for successful separation from HCPs. These results underscore the need to tailor the purification process to leverage the slight differences in binding behavior and elution profiles between mAbs and specific HCPs. This approach lays the foundation for developing more effective strategies for overcoming the challenge of enzymatic polysorbate degradation, paving the way for improved quality and safety in biopharmaceutical products.

Regression Metamodel-Based Digital Twin for an Industrial Dynamic Crossflow Filtration Process.

Dynamic crossflow filtration (DCF) is the state-of-the-art technology for solid-liquid separation from viscous and sensitive feed streams in the food and biopharma industry. Up to now, the potential of industrial processes is often not fully exploited, because fixed recipes are usually applied to run the processes. In order to take the varying properties of biological feed materials into account, we aim to develop a digital twin of an industrial brownfield DCF plant, allowing to optimize setpoint decisions in almost real time. The core of the digital twin is a mechanistic-empirical process model combining fundamental filtration laws with process expert knowledge. The effect of variation in the selected process and model parameters on plant productivity has been assessed using a model-based design-of-experiments approach, and a regression metamodel has been trained with the data. A cyclic program that bidirectionally communicates with the DCF asset serves as frame of the digital twin. It monitors the process dynamics membrane torque and transmembrane pressure and feeds back the optimum permeate flow rate setpoint to the physical asset in almost real-time during process runs. We considered a total of 24 industrial production batches from the filtration of grape juice from the years 2022 and 2023 in the study. After implementation of the digital twin on site, the campaign mean productivity increased by 15% over the course of the year 2023. The presented digital twin framework is a simple example how an industrial established process can be controlled by a hybrid model-based algorithm. With a digital process dynamics model at hand, the presented metamodel optimization approach can be easily transferred to other (bio)chemical processes.

Application of Raman spectroscopy during pharmaceutical process development for determination of critical quality attributes in Protein A chromatography.

Raman spectroscopy is considered a Process Analytical Technology (PAT) tool in biopharmaceutical downstream processes. In the past decade, researchers have shown Raman spectroscopy's feasibility in determining Critical Quality Attributes (CQAs) in bioprocessing. This study verifies the feasibility of implementing a Raman-based PAT tool in Protein A chromatography as a CQA monitoring technique, for the purpose of accelerating process development and achieving real-time release in manufacturing. A system connecting Raman to a Tecan liquid handling station enables high-throughput model calibration. One calibration experiment collects Raman spectra of 183 samples with 8 CQAs within 25 h. After applying Butterworth high-pass filters and k-nearest neighbor (KNN) regression for model training, the model showed high predictive accuracy for fragments (Q2 = 0.965) and strong predictability for target protein concentration, aggregates, as well as charge variants (Q2≥ 0.922). The model's robustness was confirmed by varying the elution pH, load density, and residence time using 19 external validation preparative Protein A chromatography runs. The model can deliver elution profiles of multiple CQAs within a set point ± 0.3 pH range. The CQA readouts were presented as continuous chromatograms with a resolution of every 28 s for enhanced process understanding. In external validation datasets, the model maintained strong predictability especially for target protein concentration (Q2 = 0.956) and basic charge variants (Q2 = 0.943), except for overpredicted HCP (Q2 = 0.539). This study demonstrates a rapid, effective method for implementing Raman spectroscopy for in-line CQA monitoring in process development and biomanufacturing, eliminating the need for labor-intensive sample pooling and handling.

Nano- and Microscale Confinements in DNA-Scaffolded Enzyme Cascade Reactions.

Artificial reconstruction of naturally evolved principles, such as compartmentalization and cascading of multienzyme complexes, offers enormous potential for the development of biocatalytic materials and processes. Due to their unique addressability at the nanoscale, DNA origami nanostructures (DON) have proven to be an exceptionally powerful tool for studying the fundamental processes in biocatalytic cascades. To systematically investigate the diffusion-reaction network of (co)substrate transfer in enzyme cascades, a model system of stereoselective ketoreductase (KRED) with cofactor regenerating enzyme is assembled in different spatial arrangements on DNA nanostructures and is located in the sphere of microbeads (MB) as a spatially confining nano- and microenvironment, respectively. The results, obtained through the use of highly sensitive analytical methods, Western blot-based quantification of the enzymes, and mass spectrometric (MS) product detection, along with theoretical modeling, provide strong evidence for the presence of two interacting compartments, the diffusion layers around the microbead and the DNA scaffold, which influence the catalytic efficiency of the cascade. It is shown that the microscale compartment exerts a strong influence on the productivity of the cascade, whereas the nanoscale arrangement of enzymes has no influence but can be modulated by the insertion of a diffusion barrier.

Micro simulated moving bed chromatography-mass spectrometry as a continuous on-line process analytical tool.

Continuous manufacturing is becoming increasingly important in the (bio-)pharmaceutical industry, as more product can be produced in less time and at lower costs. In this context, there is a need for powerful continuous analytical tools. Many established off-line analytical methods, such as mass spectrometry (MS), are hardly considered for process analytical technology (PAT) applications in biopharmaceutical processes, as they are limited to at-line analysis due to the required sample preparation and the associated complexity, although they would provide a suitable technique for the assessment of a wide range of quality attributes. In this study, we investigated the applicability of a recently developed micro simulated moving bed chromatography system (µSMB) for continuous on-line sample preparation for MS. As a test case, we demonstrate the continuous on-line MS measurement of a protein solution (myoglobin) containing Tris buffer, which interferes with ESI-MS measurements, by continuously exchanging this buffer with a volatile ammonium acetate buffer suitable for MS measurements. The integration of the µSMB significantly increases MS sensitivity by removing over 98% of the buffer substances. Thus, this study demonstrates the feasibility of on-line µSMB-MS, providing a versatile PAT tool by combining the detection power of MS for various product attributes with all the advantages of continuous on-line analytics.

Strategies for Automated Enzymatic Glycan Synthesis (AEGS).

Glycans are the most abundant biopolymers on earth and are constituents of glycoproteins, glycolipids, and proteoglycans with multiple biological functions. The availability of different complex glycan structures is of major interest in biotechnology and basic research of biological systems. High complexity, establishment of general and ubiquitous synthesis techniques, as well as sophisticated analytics, are major challenges in the development of glycan synthesis strategies. Enzymatic glycan synthesis with Leloir-glycosyltransferases is an attractive alternative to chemical synthesis as it can achieve quantitative regio- and stereoselective glycosylation in a single step. Various strategies for synthesis of a wide variety of different glycan structures has already be established and will exemplarily be discussed in the scope of this review. However, the application of enzymatic glycan synthesis in an automated system has high demands on the equipment, techniques, and methods. Different automation approaches have already been shown. However, while these techniques have been applied for several glycans, only a few strategies are able to conserve the full potential of enzymatic glycan synthesis during the process - economical and enzyme technological recycling of enzymes is still rare. In this review, we show the major challenges towards Automated Enzymatic Glycan Synthesis (AEGS). First, we discuss examples for immobilization of glycans or glycosyltransferases as an important prerequisite for the embedment and implementation in an enzyme reactor. Next, improvement of bioreactors towards automation will be described. Finally, analysis and monitoring of the synthesis process are discussed. Furthermore, automation processes and cycle design are highlighted. Accordingly, the transition of recent approaches towards a universal automated glycan synthesis platform will be projected. To this end, this review aims to describe essential key features for AEGS, evaluate the current state-of-the-art and give thought- encouraging impulses towards future full automated enzymatic glycan synthesis.

Influence of the hierarchical architecture of multi-core iron oxide nanoflowers on their magnetic properties.

Magnetic properties of superparamagnetic iron oxide nanoparticles are controlled mainly by their particle size and by their particle size distribution. Magnetic properties of multi-core iron oxide nanoparticles, often called iron oxide nanoflowers (IONFs), are additionally affected by the interaction of magnetic moments between neighboring cores. The knowledge about the hierarchical structure of IONFs is therefore essential for understanding the magnetic properties of IONFs. In this contribution, the architecture of multi-core IONFs was investigated using correlative multiscale transmission electron microscopy (TEM), X-ray diffraction and dynamic light scattering. The multiscale TEM measurements comprised low-resolution and high-resolution imaging as well as geometric phase analysis. The IONFs contained maghemite with the average chemical composition [Formula: see text]-Fe[Formula: see text]O[Formula: see text]. The metallic vacancies located on the octahedral lattice sites of the spinel ferrite structure were partially ordered. Individual IONFs consisted of several cores showing frequently a specific crystallographic orientation relationship between direct neighbors. This oriented attachment may facilitate the magnetic alignment within the cores. Individual cores were composed of partially coherent nanocrystals having almost the same crystallographic orientation. The sizes of individual constituents revealed by the microstructure analysis were correlated with the magnetic particle sizes that were obtained from fitting the measured magnetization curve by the Langevin function.

Development of a 3D printed micro simulated moving bed chromatography system.

In the 1960s, chromatography processes were revolutionized by the invention of simulated moving bed chromatography. This method not only enhances the separation performance and resin utilization in comparison to batch-chromatography, it has also a much lower buffer consumption. While simulated moving bed chromatography nowadays is applied for a wide range of industrial applications, it was never transferred to the micro-scale (in regards to column and system volume). In our opinion a micro simulated moving bed chromatography system (µSMB) would be a useful tool for many applications, ranging from early process development and long term studies to downstream processing of speciality products. We implemented such a µSMB with a 3D printed central rotary valve and a microfluidic flow controller as flow source. We tested the system with a four zone open loop setup for the separation of bovine serum albumin and ammonium sulfate with size exclusion chromatography. We used four process points and could achieve desalting levels of BSA ranging from 94% to 99%, with yields ranging form 65% to 88%. Thus, we were able to achieve comparable results to common lab scale processes. With a total dead volume of 358 µL, including all sensors, connections and the valve, this is, to the best of our knowledge, the smallest SMB system that was ever built and we were able to perform experiments with feed flow rates reaching as low as 15 µL/min.

Redox Polyelectrolytes with pH-Sensitive Electroactive Functionality in Aqueous Media.

A framework of ferrocene-containing polymers bearing adjustable pH- and redox-active properties in aqueous electrolyte environments was developed. The electroactive metallopolymers were designed to possess enhanced hydrophilicity compared to the vinylferrocene (VFc) homopolymer, poly(vinylferrocene) (PVFc), by virtue of the comonomer incorporated into the macromolecule, and could also be prepared as conductive nanoporous carbon nanotube (CNT) composites that offered a variety of different redox potentials spanning a ca. 300 mV range. The presence of charged non-redox-active moieties such as methacrylate (MA) in the polymeric structure endowed it with acid dissociation properties that interacted synergistically with the redox activity of the ferrocene moieties to impart pH-dependent electrochemical behavior to the overall polymer, which was subsequently studied and compared to several Nernstian relationships in both homogeneous and heterogeneous configurations. This zwitterionic characteristic was leveraged for the enhanced electrochemical separation of several transition metal oxyanions using a P(VFc0.63-co-MA0.37)-CNT polyelectrolyte electrode, which yielded an almost twofold preference for chromium as hydrogen chromate versus its chromate form, and also exemplified the electrochemically mediated and innately reversible nature of the separation process through the capture and release of vanadium oxyanions. These investigations into pH-sensitive redox-active materials provide insight for future developments in stimuli-responsive molecular recognition, with extendibility to areas such as electrochemical sensing and selective separation for water purification.

MOF-Hosted Enzymes for Continuous Flow Catalysis in Aqueous and Organic Solvents.

Fully exploiting the potential of enzymes in cell-free biocatalysis requires stabilization of the catalytically active proteins and their integration into efficient reactor systems. Although in recent years initial steps towards the immobilization of such biomolecules in metal-organic frameworks (MOFs) have been taken, these demonstrations have been limited to batch experiments and to aqueous conditions. Here we demonstrate a MOF-based continuous flow enzyme reactor system, with high productivity and stability, which is also suitable for organic solvents. Under aqueous conditions, the stability of the enzyme was increased 30-fold, and the space-time yield exceeded that obtained with other enzyme immobilization strategies by an order of magnitude. Importantly, the infiltration of the proteins into the MOF did not require additional functionalization, thus allowing for time- and cost-efficient fabrication of the biocatalysts using label-free enzymes.

Magnetic/flow controlled continuous size fractionation of magnetic nanoparticles using simulated moving bed chromatography.

The use of magnetic nanoparticles shows a steadily increasing technical importance. Particularly in medical technology disciplines such as cancer treatment, the potential of these special particles is increasing rapidly. Magnetic nanoparticles are particles with a submicron size, and consist mostly of magnetite-containing composites. An important quality parameter of such particles is a particle size distribution as narrow as possible, which can only be obtained to a certain degree by synthesis. Apart from ultracentrifugation, there are so far only methods on an analytical scale to narrow the size distribution as a post-processing step. We present a method based on magnetic chromatography, by which high separation efficiencies at yields of up to 99.9% are achieved. The novel technique is based on a competition between the magnetic interaction of the nanoparticles and the separation matrix, as well as the hydrodynamic forces. Furthermore, the method is extended using a continuous mode, namely simulated moving bed chromatography, to obtain potent space-time yields of up to 2.94 g/(L*h). For those reasons, this novel continuous magnetic chromatography method offers high potential for large-scale refinement of magnetic nanoparticles while fulfilling sophisticated quality criteria for high-technology applications.

Predicting the potential of capacitive deionization for the separation of pH-dependent organic molecules.

One of the main steps in the biotechnological production of chemical building blocks, such as, e.g. bio-based succinic acid which is used for lubricants, cosmetics, food, and pharmaceuticals, is the isolation and purification of the target molecule. A new approach to isolate charged, bio-based chemicals is by electrosorption onto carbon surfaces. In contrast to ion exchange, electrosorption does not require additional chemicals for elution and regeneration. However, while the electrosorption of inorganic salts is well understood and in commercial use, the knowledge about electrosorption of weak organic acids including the strong implications of the pH-dependent dissociation and their affinity towards physical adsorption must be expanded. Here, we show a detailed discussion of the main pH-dependent effects determining the achievable charge efficiencies and capacities. An explicit set of equations allows the fast prediction of the named key figures for constant voltage and constant current operation. The calculated and experimental results obtained for the electrosorption of maleic acid show that the potential-free adsorption of differently protonated forms of the organic acid play a dominating role in the process. At pH 8 and a voltage threshold of 1.3 V, charge efficiencies of 25% and capacities around 40 mmol/kg could be reached for a constant current experiment. While this capacity is clearly below that of ion exchange resins, the required carbon materials are inexpensive and energy costs are only about 0.013 €/mol. Therefore, we anticipate that electrosorption has the potential to become an interesting alternative to conventional unit operations for the isolation of charged target molecules.

Configurable 3D Printed Microfluidic Multiport Valves with Axial Compression.

In the last decade, the fabrication of microfluidic chips was revolutionized by 3D printing. It is not only used for rapid prototyping of molds, but also for manufacturing of complex chips and even integrated active parts like pumps and valves, which are essential for many microfluidic applications. The manufacturing of multiport injection valves is of special interest for analytical microfluidic systems, as they can reduce the injection to detection dead volume and thus enhance the resolution and decrease the detection limit. Designs reported so far use radial compression of rotor and stator. However, commercially available nonprinted valves usually feature axial compression, as this allows for adjustable compression and the possibility to integrate additional sealing elements. In this paper, we transfer the axial approach to 3D-printed valves and compare two different printing techniques, as well as six different sealing configurations. The tightness of the system is evaluated with optical examination, weighing, and flow measurements. The developed system shows similar performance to commercial or other 3D-printed valves with no measurable leakage for the static case and leakages below 0.5% in the dynamic case, can be turned automatically with a stepper motor, is easy to scale up, and is transferable to other printing methods and materials without design changes.

Adsorption of organic molecules on carbon surfaces: Experimental data and molecular dynamics simulation considering multiple protonation states.

Owing to their high specific surface and low production cost, carbon materials are among the most important adsorption materials. Novel usages, for instance in pharmaceutical applications, challenge existing methods because charged and strongly polar substances need to be adsorbed. Here, we systematically investigate the highly complex adsorption equilibria of organic molecules having multiple protonation states as a function of pH. The adsorption behavior depends on intermolecular interactions within the solution (dissociation equilibria) and between adsorbed molecules on the carbon surface (electrostatic forces). For the model substances maleic acid and phenylalanine, we demonstrate that a custom-made genetic algorithm is able to extract up to nine parameters of a multispecies isotherm from experimental data covering a broad pH-range. The parameters, including adsorption affinities, interaction energies, and maximum loadings were also predicted by molecular dynamics simulations. Both approaches obtained a good qualitative and mostly also quantitative description of the adsorption behavior within a pH-range of 2-12. By combining the determined isotherms with mass balances, the final concentrations and pH-shifts of batch adsorption experiments can be predicted. The developed modeling tools can be easily adapted to other types of pH-dependent, multispecies adsorbates and therefore will help to optimize adsorption-based processes in different fields.

Enzyme Scaffolds with Hierarchically Defined Properties via 3D Jet Writing.

The immobilization of enzymes into polymer hydrogels is a versatile approach to improve their stability and utility in biotechnological and biomedical applications. However, these systems typically show limited enzyme activity, due to unfavorable pore dimensions and low enzyme accessibility. Here, 3D jet writing of water-based bioinks, which contain preloaded enzymes, is used to prepare hydrogel scaffolds with well-defined, tessellated micropores. After 3D jet writing, the scaffolds are chemically modified via photopolymerization to ensure mechanical stability. Enzyme loading and activity in the hydrogel scaffolds is fully retained over 3 d. Important structural parameters of the scaffolds such as pore size, pore geometry, and wall diameter are controlled with micrometer resolution to avoid mass-transport limitations. It is demonstrated that scaffold pore sizes between 120 µm and 1 mm can be created by 3D jet writing approaching the length scales of free diffusion in the hydrogels substrates and resulting in high levels of enzyme activity (21.2% activity relative to free enzyme). With further work, a broad range of applications for enzyme-laden hydrogel scaffolds including diagnostics and enzymatic cascade reactions is anticipated.

On Demand Light-Degradable Polymers Based on 9,10-Dialkoxyanthracenes.

Light induced degradation of polymers has drawn increasing interest due to the need for externally controllable modulation of materials properties. However, the portfolio of polymers, that undergo precisely controllable degradation, is limited and typically requires UV light. A novel class of backbone-degradable polymers that undergo aerobic degradation in the presence of visible light, yet remain stable against broad-spectrum light under anaerobic conditions is reported. In this design, the polymer backbone is comprised of 9,10-dialkoxyanthracene units that are selectively cleaved by singlet oxygen in the presence of green light as confirmed by NMR and UV/vis spectroscopy. The resulting polymers have been processed by electrohydrodynamic (EHD) co-jetting into bicompartmental microfibers, where one hemisphere is selectively degraded on demand.

Simulative Minimization of Mass Transfer Limitations Within Hydrogel-Based 3D-Printed Enzyme Carriers.

In biotechnology, immobilization of functional reactants is often done as a surface immobilization on small particles. Examples are chromatography columns and fixed-bed reactors. However, the available surface for immobilization is directly linked to particle diameter and bed porosity for these systems, leading to high backpressure for small particle sizes. When larger molecules, such as enzymes are immobilized, physical entrapment within porous materials like hydrogels is an alternative. An emerging technique for the production of geometrically structured, three-dimensional and scalable hollow bodies is 3D-printing. Different bioprinting methods are available to produce structures of the desired size, resolution and solids content. However, in case of entrapped enzymes mass transfer limitations often determine the achievable reactivities. With increasing complexity of the system, for example a fixed-bed reactor, 3D-simulation is indispensable to understand the local reaction conditions to be able to highlight the optimization potential. Based on experimental data, this manuscript shows the application of the dimensionless numbers effectiveness factor and Thiele modulus for the design of a 3D-printed flow-through reactor. Within the reactor, enzymes are physically entrapped in 3D-printed hydrogel lattices. The local reaction rate of the enzymes is directly dependent on the provided substrate amount at the site of reaction which is limited by the diffusion properties of the hydrogel matrix and the diffusion distance. All three parameters can be summed up by one key figure, the Thiele modulus, which, in short, quantifies mass transfer limitations of a catalytic system. Depending on the rate of the enzymatic reaction in correlation to the diffusional transport, mass transfer limitations will shift the optimum of the system, favoring slow enzyme kinetics and small diffusion distances. Comparison with the enzymatic reaction rate in solution yields the effectiveness factor of the system. As a result, the optimization potential of varying the 3D-printed geometries or the reaction rate within the experimentally available design space can be estimated.

Magnetism and Afterglow United: Synthesis of Novel Double Core-Shell Eu2+ -Doped Bifunctional Nanoparticles.

Afterglow-magnetic nanoparticles (NPs) offer enormous potential for bioimaging applications, as they can be manipulated by a magnetic field, as well as emitting light after irradiation with an excitation source, thus distinguishing themselves from fluorescent living cells. In this work, a novel double core-shell strategy is presented, uniting co-precipitation with combustion synthesis routes to combine an Fe3 O4 magnetic core (≈15 nm) with an afterglow SrAl2 O4 :Eu2+ ,Dy3+ outer coat (≈10 nm), and applying a SiO2 protective middle layer (≈16 nm) to reduce the luminescence quenching caused by the Fe core ions. The resulting Fe3 O4 @SiO2 @SrAl2 O4 :Eu2+ ,Dy3+ NPs emit green light attributed to the 4f6 5d1 →4f7 (8 S7/2 ) transition of Eu2+ under UV radiation and for a few seconds afterwards. This bifunctional nanocomposite can potentially be applied for the detection and separation of cells or diagnostically relevant molecules.