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The conjugation reaction is the central step in the manufacturing process of antibody-drug conjugates (ADCs). This reaction generates a heterogeneous and complex mixture of differently conjugated sub-species depending on the chosen conjugation chemistry. The parametrization of the conjugation reaction through mechanistic kinetic models offers a chance to enhance valuable reaction knowledge and ensure process robustness. This study introduces a versatile modeling framework for the conjugation reaction of cysteine-conjugated ADC modalities-site-specific and interchain disulfide conjugation. Various conjugation kinetics involving different maleimide-functionalized payloads were performed, while controlled gradual payload feeding was employed to decelerate the conjugation, facilitating a more detailed investigation of the reaction mechanism. The kinetic data were analyzed with a reducing reversed phase (RP) chromatography method, that can readily be implemented for the accurate characterization of ADCs with diverse drug-to-antibody ratios, providing the conjugation trajectories of the single chains of the monoclonal antibody (mAb). Possible kinetic models for the conjugation mechanism were then developed and selected based on multiple criteria. When calibrating the established model to kinetics involving different payloads, conjugation rates were determined to be payload-specific. Further conclusions regarding the kinetic comparability across the two modalities could also be derived. One calibrated model was used for an exemplary in silico screening of the initial concentrations offering valuable insights for profound understanding of the conjugation process in ADC development., Competing Interests: Authors MW, RB and KH are employed by AstraZeneca and report stock ownership and/or stock options or interest in AstraZeneca. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Weggen, Bean, Hui, Wendeler and Hubbuch.)

Virus-like particles (VLPs) are a promising class of biopharmaceuticals for vaccines and targeted delivery. Starting from clarified lysate, VLPs are typically captured by selective precipitation. While VLP precipitation is induced by step-wise or continuous precipitant addition, current monitoring approaches do not support the direct product quantification, and analytical methods usually require various, time-consuming processing and sample preparation steps. Here, the application of Raman spectroscopy combined with chemometric methods may allow the simultaneous quantification of the precipitated VLPs and precipitant owing to its demonstrated advantages in analyzing crude, complex mixtures. In this study, we present a Raman spectroscopy-based Process Analytical Technology (PAT) tool developed on batch and fed-batch precipitation experiments of Hepatitis B core Antigen VLPs. We conducted small-scale precipitation experiments providing a diversified data set with varying precipitation dynamics and backgrounds induced by initial dilution or spiking of clarified Escherichia coli -derived lysates. For the Raman spectroscopy data, various preprocessing operations were systematically combined allowing the identification of a preprocessing pipeline, which proved to effectively eliminate initial lysate composition variations as well as most interferences attributed to precipitates and the precipitant present in solution. The calibrated partial least squares models seamlessly predicted the precipitant concentration with R 2 of 0.98 and 0.97 in batch and fed-batch experiments, respectively, and captured the observed precipitation trends with R 2 of 0.74 and 0.64. Although the resolution of fine differences between experiments was limited due to the observed non-linear relationship between spectral data and the VLP concentration, this study provides a foundation for employing Raman spectroscopy as a PAT sensor for monitoring VLP precipitation processes with the potential to extend its applicability to other phase-behavior dependent processes or molecules., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Dietrich, Schiemer, Kurmann, Zhang and Hubbuch.)

Protein crystallization as opposed to well-established chromatography processes has the benefits to reduce production costs while reaching a comparable high purity. However, monitoring crystallization processes remains a challenge as the produced crystals may interfere with analytical measurements. Especially for capturing proteins from complex feedstock containing various impurities, establishing reliable process analytical technology (PAT) to monitor protein crystallization processes can be complicated. In heterogeneous mixtures, important product characteristics can be found by multivariate analysis and chemometrics, thus contributing to the development of a thorough process understanding. In this project, an analytical set-up is established combining offline analytics, on-line ultraviolet visible light (UV/Vis) spectroscopy, and in-line Raman spectroscopy to monitor a stirred-batch crystallization process with multiple phases and species being present. As an example process, the enzyme Lactobacillus kefir alcohol dehydrogenase (L k ADH) was crystallized from clarified Escherichia coli ( E. coli ) lysate on a 300 mL scale in five distinct experiments, with the experimental conditions changing in terms of the initial lysate solution preparation method and precipitant concentration. Since UV/Vis spectroscopy is sensitive to particles, a cross-flow filtration (cross-flow filtration)-based bypass enabled the on-line analysis of the liquid phase providing information on the lysate composition regarding the nucleic acid to protein ratio. A principal component analysis (PCA) of in situ Raman spectra supported the identification of spectra and wavenumber ranges associated with productspecific information and revealed that the experiments followed a comparable, spectral trend when crystals were present. Based on preprocessed Raman spectra, a partial least squares (PLS) regression model was optimized to monitor the target molecule concentration in real-time. The off-line sample analysis provided information on the crystal number and crystal geometry by automated image analysis as well as the concentration of Lk ADH and host cell proteins (HCPs) In spite of a complex lysate suspension containing scattering crystals and various impurities, it was possible to monitor the target molecule concentration in a heterogeneous, multi-phase process using spectroscopic methods. With the presented analytical set-up of off-line, particle-sensitive on-line, and in-line analyzers, a crystallization capture process can be characterized better in terms of the geometry, yield, and purity of the crystals., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wegner, Eming, Walla, Bischoff, Weuster-Botz and Hubbuch.)

Virus particle (VP) quantification plays a pivotal role in the development of production processes of VPs for virus-based therapies. The yield based on total VP count serves as a process performance indicator for evaluating process efficiency and consistency. Here, a label-free particle quantification method for enveloped VPs was developed, with potential applications in oncolytic virotherapy, vaccine development, and gene therapy. The method comprises size-exclusion chromatography (SEC) separation using high-performance liquid chromatography (HPLC) instruments. Ultraviolet (UV) was used for particle quantification and multi-angle light scattering (MALS) for particle characterization. Consistent recoveries of over 97% in the SEC were achieved upon mobile phase screenings and addition of bovine serum albumin (BSA) as sample stabilizer. A calibration curve was generated, and the method's performance and applicability to in-process samples were characterized. The assay's repeatability variation was <1% and its intermediate precision variation was <3%. The linear range of the method spans from 7.08 × 10 8 to 1.72 × 10 11 VP/mL, with a limit of detection (LOD) of 7.72 × 10 7 VP/mL and a lower limit of quantification (LLOQ) of 4.20 × 10 8 VP/mL. The method, characterized by its high precision, requires minimal hands-on time and provides same-day results, making it efficient for process development., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

Multimodal chromatography has emerged as a powerful method for the purification of therapeutic antibodies. However, process development of this separation technique remains challenging because of an intricate and molecule-specific interaction towards multimodal ligands, leading to time-consuming and costly experimental optimization. This study presents a multiscale modeling approach to predict the multimodal chromatographic behavior of therapeutic antibodies based on their sequence information. Linear gradient elution (LGE) experiments were performed on an anionic multimodal resin for 59 full-length antibodies, including five different antibody formats at pH 5.0, 6.0, and 7.0 that were used for parameter determination of a linear adsorption model at low loading density conditions. Quantitative structure-property relationship (QSPR) modeling was utilized to correlate the adsorption parameters with up to 1374 global and local physicochemical descriptors calculated from antibody homology models. The final QSPR models employed less than eight descriptors per model and demonstrated high training accuracy (R² > 0.93) and reasonable test set prediction accuracy (Q² > 0.83) for the adsorption parameters. Model evaluation revealed the significance of electrostatic interaction and hydrophobicity in determining the chromatographic behavior of antibodies, as well as the importance of the HFR3 region in antibody binding to the multimodal resin. Chromatographic simulations using the predicted adsorption parameters showed good agreement with the experimental data for the vast majority of antibodies not employed during the model training. The results of this study demonstrate the potential of sequence-based prediction for determining chromatographic behavior in therapeutic antibody purification. This approach leads to more efficient and cost-effective process development, providing a valuable tool for the biopharmaceutical industry., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Chemometric modeling for spectral data is considered a key technology in biopharmaceutical processing to realize real-time process control and release testing. Machine learning (ML) models have been shown to increase the accuracy of various spectral regression and classification tasks, remove challenging preprocessing steps for spectral data, and promise to improve the transferability of models when compared to commonly applied, linear methods. The training and optimization of ML models require large data sets which are not available in the context of biopharmaceutical processing. Generative methods to extend data sets with realistic in silico samples, so-called data augmentation, may provide the means to alleviate this challenge. In this study, we develop and implement a novel data augmentation method for generating in silico spectral data based on local estimation of pure component profiles for training convolutional neural network (CNN) models using four data sets. We simultaneously tune hyperparameters associated with data augmentation and the neural network architecture using Bayesian optimization. Finally, we compare the optimized CNN models with partial least-squares regression models (PLS) in terms of accuracy, robustness, and interpretability. The proposed data augmentation method is shown to produce highly realistic spectral data by adapting the estimates of the pure component profiles to the sampled concentration regimes. Augmenting CNNs with the in silico spectral data is shown to improve the prediction accuracy for the quantification of monoclonal antibody (mAb) size variants by up to 50% in comparison to single-response PLS models. Bayesian structure optimization suggests that multiple convolutional blocks are beneficial for model accuracy and enable transfer across different data sets. Model-agnostic feature importance methods and synthetic noise perturbation are used to directly compare the optimized CNNs with PLS models. This enables the identification of wavelength regions critical for model performance and suggests increased robustness against Gaussian white noise and wavelength shifts of the CNNs compared to the PLS models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Schiemer, Rüdt and Hubbuch.)

Effective process development towards intensified processing for gene delivery applications using Hepatitis B core Antigen (HBcAg) virus-like particles (VLPs) relies on analytical methods for the absolute quantification of HBcAg VLP proteins and bound nucleic acids. We investigated a silica spin column (SC)-based extraction procedure, including proteinase K lysis and silica chromatography, for the absolute quantification of different species of nucleic acids bound to HBcAg VLPs analyzed by dye-based fluorescence assays. This revealed load-dependent nucleic acid recoveries of the silica-SC-based extraction. We also developed a reversed-phase high-performance liquid chromatography (RP-HPLC) method to separate and quantify the HBcAg proteins and the bound nucleic acids simultaneously without prior sample treatment by dissociation reagents. The method demonstrated sufficient linearity, accuracy, and precision coefficients and is suited for determining absolute protein and nucleic acid concentrations and HBcAg protein purities at various purification stages. Both the silica-SC-based extraction and the RP-based extraction presented overcome the limitations of analytical techniques, which are restricted to relative or qualitative analyses for HBcAg VLPs with bound nucleic acids. In combination with existing analytics, the methods for an absolute quantification of HBcAg VLPs and bound nucleic acids presented here are required to evaluate downstream purification steps, such as the removal of host cell-derived nucleic acids, concurrent protein loss, and efficient loading with therapeutic nucleic acids. Hence, the methods are key for effective process development when using HBcAg VLP as potential gene delivery vehicles.

Gelatin methacryloyl (GelMA) is widely used for the formulation of hydrogels in diverse biotechnological applications. After the derivatization of raw gelatin, the degree of functionalization (DoF) is an attribute of particular interest as the functional residues are necessary for crosslinking. Despite progress in the optimization of the process found in the literature, a comparison of the effect of raw gelatin on the functionalization is challenging as various approaches are employed. In this work, the modification of gelatin was performed at room temperature (RT), and eight different gelatin products were employed. The DoF proved to be affected by the bloom strength and by the species of gelatin at an equal reactant ratio. Furthermore, batch-to-batch variability of the same gelatin source had an effect on the produced GelMA. Moreover, the elasticity of GelMA hydrogels depended on the DoF of the protein as well as on bloom strength and source of the raw material. Additionally, GelMA solutions were used for the microfluidic production of droplets and subsequent crosslinking to hydrogel. This process was developed as a single pipeline at RT using protein concentrations up to 20% ( w / v ). Droplet size was controlled by the ratio of the continuous to dispersed phase. The swelling behavior of hydrogel particles depended on the GelMA concentration.

Multimodal chromatography has emerged as a promising technique for antibody purification, owing to its capacity to selectively capture and separate target molecules. However, the optimization of chromatography parameters remains a challenge due to the intricate nature of protein-ligand interactions. To tackle this issue, efficient predictive tools are essential for the development and optimization of multimodal chromatography processes. In this study, we introduce a methodology that predicts the elution behavior of antibodies in multimodal chromatography based on their amino acid sequences. We analyzed a total of 64 full-length antibodies, including IgG1, IgG4, and IgG-like multispecific formats, which were eluted using linear pH gradients from pH 9.0 to 4.0 on the anionic mixed-mode resin Capto adhere. Homology models were constructed, and 1312 antibody-specific physicochemical descriptors were calculated for each molecule. Our analysis identified six key structural features of the multimodal antibody interaction, which were correlated with the elution behavior, emphasizing the antibody variable region. The results show that our methodology can predict pH gradient elution for a diverse range of antibodies and antibody formats, with a test set R² of 0.898. The developed model can inform process development by predicting initial conditions for multimodal elution, thereby reducing trial and error during process optimization. Furthermore, the model holds the potential to enable an in silico manufacturability assessment by screening target antibodies that adhere to standardized purification conditions. In conclusion, this study highlights the feasibility of using structure-based prediction to enhance antibody purification in the biopharmaceutical industry. This approach can lead to more efficient and cost-effective process development while increasing process understanding., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Recombinant adeno-associated viruses (rAAVs) are promising gene delivery vectors in the emerging field of in vivo gene therapies. To ensure their consistent quality during manufacturing and process development, multiple analytical techniques have been proposed for the characterization and quantification of rAAV capsids. Despite their indisputable capabilities for performing this task, current analytical methods are rather time-consuming, material intensive, complicated, and costly, restricting their suitability for process development in which time and sample throughput are severe constraints. To eliminate this bottleneck, we introduce here an affinity-based high-performance liquid chromatography method that allows the determination of the capsid titer and the full/empty ratio of rAAVs within less than 5 min. By packing the commercially available AAVX affinity resin into small analytical columns, the rAAV fraction of diverse serotypes can be isolated from process-related impurities and analyzed by UV and fluorescence detection. As demonstrated by both method qualification data and side-by-side comparison with AAV enzyme-linked immunosorbent assay results for rAAV8 samples as well as by experiments using additional rAAV2, rAAV8, and rAAV9 constructs, our approach showed good performance, indicating its potential as a fast, simple and efficient tool for supporting the development of rAAV gene therapies., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

The outcome of three-dimensional (3D) bioprinting heavily depends, amongst others, on the interaction between the developed bioink, the printing process, and the printing equipment. However, if this interplay is ensured, bioprinting promises unmatched possibilities in the health care area. To pave the way for comparing newly developed biomaterials, clinical studies, and medical applications (i.e. printed organs, patient-specific tissues), there is a great need for standardization of manufacturing methods in order to enable technology transfers. Despite the importance of such standardization, there is currently a tremendous lack of empirical data that examines the reproducibility and robustness of production in more than one location at a time. In this work, we present data derived from a round robin test for extrusion-based 3D printing performance comprising 12 different academic laboratories throughout Germany and analyze the respective prints using automated image analysis (IA) in three independent academic groups. The fabrication of objects from polymer solutions was standardized as much as currently possible to allow studying the comparability of results from different laboratories. This study has led to the conclusion that current standardization conditions still leave room for the intervention of operators due to missing automation of the equipment. This affects significantly the reproducibility and comparability of bioprinting experiments in multiple laboratories. Nevertheless, automated IA proved to be a suitable methodology for quality assurance as three independently developed workflows achieved similar results. Moreover, the extracted data describing geometric features showed how the function of printers affects the quality of the printed object. A significant step toward standardization of the process was made as an infrastructure for distribution of material and methods, as well as for data transfer and storage was successfully established., (Creative Commons Attribution license.)

In the production of antibody-drug conjugates (ADCs), the conjugation reaction is a central step defining the final product composition and, hence, directly affecting product safety and efficacy. To enable real-time monitoring, spectroscopic sensors in combination with multivariate regression models have gained popularity in recent years. The extended Kalman filter (EKF) can be used as so-called soft-sensor to fuse sensor predictions with long-horizon forecasts by process models. This enables the dynamic update of the current state and provides increased robustness against experimental noise or model errors. Due to the uncertainty associated with sensor and process models in biopharmaceutical applications, the deployment of such soft-sensors is challenging. In this study, we demonstrate the combination of an uncertainty-aware sensor model with a kinetic reaction model using an EKF to monitor a site-directed ADC conjugation reaction. As the sensor model, a Gaussian process regression model is presented to realize a time-variant determination of the sensor uncertainty. The EKF fuses the time-discrete predictions of the amount of conjugated drug from the sensor model with the time-continuous predictions from the kinetic model. While the ADC species are not distinguishable by on-line recorded UV/Vis spectra, the developed soft-sensor is able to dynamically update all relevant reaction species. It could be shown that the use of time-variant process and sensor noise computation approaches improved the performance of the EKF and achieved a reduction of the prediction error of up to 23% compared with the kinetic model. The developed framework proved to enhance robustness against noisy sensor measurements or wrong model initialization and was successfully transferred from batch to fed-batch mode. In future, this framework could be implemented for model-based process control and be adopted for other ADC conjugation reaction types., (© 2023 Wiley Periodicals LLC.)

Non-enveloped virus-like particles (VLPs) are versatile protein nanoparticles with great potential for biopharmaceutical applications. However, conventional protein downstream processing (DSP) and platform processes are often not easily applicable due to the large size of VLPs and virus particles (VPs) in general. The application of size-selective separation techniques offers to exploit the size difference between VPs and common host-cell impurities. Moreover, size-selective separation techniques offer the potential for wide applicability across different VPs. In this work, basic principles and applications of size-selective separation techniques are reviewed to highlight their potential in DSP of VPs. Finally, specific DSP steps for non-enveloped VLPs and their subunits are reviewed as well as the potential applications and benefits of size-selective separation techniques are shown., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Hillebrandt and Hubbuch.)

Three-dimensional bioprinting and especially extrusion-based printing as a most frequently employed method in this field is constantly evolving as a discipline in regenerative medicine and tissue engineering. However, the lack of relevant standardized analytics does not yet allow an easy comparison and transfer of knowledge between laboratories regarding newly developed bioinks and printing processes. This work revolves around the establishment of a standardized method, which enables the comparability of printed structures by controlling for the extrusion rate based on the specific flow behavior of each bioink. Furthermore, printing performance was evaluated by image-processing tools to verify the printing accuracy for lines, circles, and angles. In addition, and complementary to the accuracy metrics, a dead/live staining of embedded cells was performed to investigate the effect of the process on cell viability. Two bioinks, based on alginate and gelatin methacryloyl, which differed in 1% ( w / v ) alginate content, were tested for printing performance. The automated image processing tool reduced the analytical time while increasing reproducibility and objectivity during the identification of printed objects. During evaluation of the processing effect of the mixing of cell viability, NIH 3T3 fibroblasts were stained and analyzed after the mixing procedure and after the extrusion process using a flow cytometer, which evaluated a high number of cells. It could be observed that the small increase in alginate content made little difference in the printing accuracy but had a considerable strong effect on cell viability after both processing steps.

The manufacturing of antibody-drug conjugates (ADCs) involves the addition of a cytotoxic small-molecule linker-drug (= payload) to a solution of functionalized antibodies. For the development of robust conjugation processes, initially small-scale reaction tubes are used which requires a lot of manual handling. Scale-up to larger reaction vessels is often knowledge-driven and scale-comparability is solely assessed based on final product quality which does not account for the dynamics of the reaction. In addition, information about the influence of process parameters, such as stirrer speed, temperature, or payload addition rates, is limited due to high material costs. Given these limitations, there is a need for a modeling-based approach to investigate conjugation scale-up. In this work, both experimental kinetic studies and computational fluid dynamics (CFD) conjugation simulations were performed to understand the influence of scale and mixing parameters. In the experimental part, conjugation kinetics in small-scale reaction tubes with different mixing types were investigated for two ADC systems and compared to larger bench-scale reactions. It was demonstrated that more robust kinetics can be achieved through internal stirrer mixing instead of external mixing devices, such as orbital shakers. In the simulation part, 3D-reactor models were created by coupling CFD-models for three large-scale reaction vessels with a kinetic model for a site-specific conjugation reaction. This enabled to study the kinetics in different vessels, as well as the effect of process parameter variations in silico . Overall, it was found that for this conjugation type sufficient mixing can be achieved at all scales and the studied parameters cause only deviations during the payload addition period. An additional time-scale analysis demonstrated to aid the assessment of mixing effects during ADC process scale-up when mixing times and kinetic rates are known. In summary, this work highlights the benefit of kinetic models for enhanced conjugation process understanding without the need for large-scale experiments., Competing Interests: Authors MW and RB are employed by AstraZeneca PLC. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Weggen, Seidel, Bean, Wendeler and Hubbuch.)

Hydrogels based on natural polymers such as proteins are considered biocompatible and, therefore, represent an interesting class of materials for application in the field of biomedicine and high-performance materials. However, there is a lack of understanding of the proteins which are able to form hydrogel networks by photoinduced dityrosine crosslinking as well as a profound knowledge of the formed network itself and the mechanisms which are responsible for the resulting mechanical properties of such protein-based hydrogels. In this study, casein, bovine serum albumin, α-amylase, and a hydrophobic elastin-like protein were used to prepare binary protein mixtures with defined concentration ratios. After polymerization, the mechanical properties of the resulting homopolymeric and copolymeric hydrogels were determined using rheological methods depending on the protein shares used. In additional uniaxial compression tests, the fracture strain was shown to be independent of the protein shares, while hydrogel toughness and compressive strength were increased for protein-based hydrogels containing casein.

Multimodal chromatography offers an increased selectivity compared to unimodal chromatographic methods and is often employed for challenging separation tasks in industrial downstream processing (DSP). Unfortunately, the implementation of multimodal polishing into a generic downstream platform can be hampered by non-robust platform conditions leading to a time and cost intensive process development. Mechanistic modeling can assist experimental process development but readily applicable and easy to calibrate multimodal chromatography models are lacking. In this work, we present a mechanistic modeling aided approach that paves the way for an accelerated development of anionic mixed-mode chromatography (MMC) for biopharmaceutical purification. A modified multimodal isotherm model was calibrated using only three chromatographic experiments and was employed in the retention prediction of four antibody formats including a Fab, a bispecific, as well as an IgG1 and IgG4 antibody subtype at pH 5.0 and 6.0. The chromatographic experiments were conducted using the anionic mixed-mode resin Capto adhere at industrial relevant process conditions to enable flow through purification. An existing multimodal isotherm model was reduced to hydrophobic interactions in the linear range of the adsorption isotherm and successfully employed in the simulation of six chromatographic experiments per molecule in concert with the transport dispersive model (TDM). The model reduction to only three parameters did prevent structural parameter non-identifiability and enabled an analytical isotherm parameter determination that was further refined by incorporation of size exclusion effects of the selected multimodal resin. During the model calibration, three linear salt gradient elution experiments were performed for each molecule followed by an isotherm parameter uncertainty assessment. Lastly, each model was validated with a set of step and isocratic elution experiments. This standardized modeling approach facilitates the implementation of multimodal chromatography as a key unit operation for the biopharmaceutical downstream platform, while increasing the mechanistic insight to the multimodal adsorption behavior of complex biologics., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Monitoring the protein concentration and buffer composition during the Ultrafiltration/Diafiltration (UF/DF) step enables the further automation of biopharmaceutical production and supports Real-time Release Testing (RTRT). Previously, in-line Ultraviolet (UV) and Infrared (IR) measurements have been used to successfully monitor the protein concentration over a large range. The progress of the diafiltration step has been monitored with density measurements and Infrared Spectroscopy (IR). Raman spectroscopy is capable of measuring both the protein and excipient concentration while being more robust and suitable for production measurements in comparison to Infrared Spectroscopy (IR). Regardless of the spectroscopic sensor used, the low concentration of excipients poses a challenge for the sensors. By combining sensor measurements with a semi-mechanistic model through an Extended Kalman Filter (EKF), the sensitivity to determine the progress of the diafiltration can be improved. In this study, Raman measurements are combined with an EKF for three case studies. The advantages of Kalman-filtered Raman measurements for excipient monitoring are shown in comparison to density measurements. Furthermore, Raman measurements showed a higher measurement speed in comparison to Variable Pathlength (VP) UV measurement at the trade-off of a slightly worse prediction accuracy for the protein concentration. However, the Raman-based protein concentration measurements relied mostly on an increase in the background signal during the process and not on proteinaceous features, which could pose a challenge due to the potential influence of batch variability on the background signal. Overall, the combination of Raman spectroscopy and EKF is a promising tool for monitoring the UF/DF step and enables process automation by using adaptive process control., (© 2023. The Author(s).)

The development of biopharmaceutical downstream processes relies on exhaustive experimental studies. The root cause is the poorly understood relationship between the protein structure of monoclonal antibodies (mAbs) and their macroscopic process behavior. Especially the development of preparative chromatography processes is challenged by the increasing structural complexity of novel antibody formats and accelerated development timelines. This study introduces a multiscale in silico model consisting of homology modeling, quantitative structure-property relationships (QSPR), and mechanistic chromatography modeling leading from the amino acid sequence of a mAb to the digital representation of its cation exchange chromatography (CEX) process. The model leverages the mAbs' structural characteristics and experimental data of a diverse set of 21 therapeutic antibodies to predict elution profiles of two mAbs that were removed from the training data set. QSPR modeling identified mAb-specific protein descriptors relevant for the prediction of the thermodynamic equilibrium and the stoichiometric coefficient of the adsorption reaction. The consideration of two discrete conformational states of IgG4 mAbs enabled prediction of split-peak elution profiles. Starting from the sequence, the presented multiscale model allows in silico development of chromatography processes before protein material is available for experimental studies., (© 2022 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC.)

When developping selective crystallization or precipitation processes, biopharmaceutical modalities require empirical screenings and analytics tailored to the specific needs of the target molecule. The multi-way chemometric approach called parallel factor analysis (PARAFAC) coupled with ultraviolet visible light (UV/Vis) spectroscopy is able to predict specific concentrations and spectra from highly structured data sets without the need for calibration samples and reference analytics. These calculated models can provide exploratory information on pure species spectra and concentrations in all analyzed samples by representing one model component with one species. In this work, protein mixtures, monoclonal antibodies, and virus-like particles in chemically defined and complex solutions were investigated in three high-throughput crystallization or precipitation screenings with the aim to construct one PARAFAC model per case. Spectroscopic data sets of samples after the selective crystallization or precipitation, washing, and redissolution were recorded and arranged into a four-dimensional data set per case study. Different reference analytics and pure species spectra served as validation. Appropriate spectral preprocessing parameters were found for all case studies allowing even the application of this approach to the third case study in which quantitative concentration analytics are missing. Regardless of the modality or the number of species present in complex solutions, all models were able to estimate the specific concentration and find the optimal process condition regarding yield and product purity. It was shown that in complex solutions, species demonstrating similar phase behavior can be clustered as one component and described in the model. PARAFAC as a calibration-free approach coupled with UV/Vis spectroscopy provides a fast overview of species present in complex solution and of their concentration during selective crystallization or precipitation, washing, and redissolution., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wegner and Hubbuch.)

Gelatin and its derivatives contain cell adhesion moieties as well as sites that enable proteolytic degradation, thus allowing cellular proliferation and migration. The processing of gelatin to its derivatives and/or gelatin-containing products is challenged by its gelation below 30 ∘C. In this study, a novel strategy was developed for the dissolution and subsequent modification of gelatin to its derivative gelatin-methacryloyl (GelMA). This approach was based on the presence of urea in the buffer media, which enabled the processing at room temperature, i.e., lower than the sol-gel transition point of the gelatin solutions. The degree of functionalization was controlled by the ratio of reactant volume to the gelatin concentration. Hydrogels with tailored mechanical properties were produced by variations of the GelMA concentration and its degree of functionalization. Moreover, the biocompatibility of hydrogels was assessed and compared to hydrogels formulated with GelMA produced by the conventional method. NIH 3T3 fibroblasts were seeded onto hydrogels and the viability showed no difference from the control after a three-day incubation period.

Extrusion-based 3D bioprinting enables the production of customized hydrogel structures that can be employed in flow reactors when printing with enzyme-containing inks. The present study compares inks based on either low-melt agarose or agar at different concentrations (3-6%) and loaded with the thermostable enzyme esterase 2 from the thermophilic organism Alicyclobacillus acidocaldarius (AaEst2) with regard to their suitability for the fabrication of such enzymatically active hydrogels. A customized printer setup including a heatable nozzle and a cooled substrate was established to allow for clean and reproducible prints. The inks and printed hydrogel samples were characterized using rheological measurements and compression tests. All inks were found to be sufficiently printable to create lattices without overhangs, but printing quality was strongly enhanced at 4.5% polymer or more. The produced hydrogels were characterized regarding mechanical strength and diffusibility. For both properties, a strong correlation with polymer concentration was observed with highly concentrated hydrogels being more stable and less diffusible. Agar hydrogels were found to be more stable and show higher diffusion rates than comparable agarose hydrogels. Enzyme leaching was identified as a major drawback of agar hydrogels, while hardly any leaching from agarose hydrogels was detected. The poor ability of agar hydrogels to permanently immobilize enzymes indicates their limited suitability for their employment in perfused biocatalytic reactors. Batch-based activity assays showed that the enzymatic activity of agar hydrogels was roughly twice as high as the activity of agarose hydrogels which was mostly attributed to the increased amount of enzyme leaching. Agarose bioinks with at least 4.5% polymer were identified as the most suitable of the investigated inks for the printing of biocatalytic reactors with AaEst2. Drawbacks of these inks are limited mechanical and thermal stability, not allowing the operation of a reactor at the optimum temperature of AaEst2 which is above the melting point of the employed low-melt agarose., (Copyright © 2022 Wenger, Radtke, Gerisch, Kollmann, Niemeyer, Rabe and Hubbuch.)

A time-dependent understanding of swelling characteristics and external stimuli behavior is crucial for the development and understanding of functional hydrogels. Magnetic resonance imaging (MRI) offers the opportunity to study three-dimensional (3D) soft materials nondestructively. This technique is already widely used as an image-based medical diagnostic tool and is applied here to evaluate complex structures of a hydrogel-a double network of chemically crosslinked casein enhanced with alginate-fabricated by 3D printing. When hydrogel disks immersed in four different liquid systems were analyzed, the material exhibited distinct system-dependent behavior characterized by rheological and mechanical measurements. Further material functionalization was achieved by macroscopic structuring of the hydrogel as an auxetic material based on a re-entrant honeycomb structure. MRI offers the advantage of monitoring overall changes in the area of the analyzed specimen and internal structural changes simultaneously. To assess the behavior of this complex structure, a series of short MRI measurements, each lasting 1.7 min, captured liquid diffusion and thus structural swelling behavior. A clear dependence of external and internal structural changes as a function of liquid properties causing these changes was observed. In conclusion, this approach might pave the way for prospective applications to monitor liquid diffusion into (e.g., vascularization) and swelling behavior of functional hydrogels.

A fundamental process understanding of an entire downstream process is essential for achieving and maintaining the high-quality standards demanded for biopharmaceutical drugs. A holistic process model based on mechanistic insights could support process development by identifying dependencies between process parameters and critical quality attributes across unit operations to design a holistic control strategy. In this study, state-of-the-art mechanistic models were calibrated and validated as digital representations of a biopharmaceutical manufacturing process. The polishing ion exchange chromatography steps (Q Sepharose FF, Poros 50 HS) were described by a transport-dispersive model combined with a colloidal particle adsorption model. The elution behavior of four size variants was analyzed and included in the model. Titration curves of pH adjustments were simulated using a mean-field approach considering interactions between the protein of interest and other ions in solution. By including adjustment steps the important process control inputs ionic strength, dilution, and pH were integrated. The final process model was capable to predict online and offline data at manufacturing scale. Process variations at manufacturing scale of 94 runs were adequately reproduced by the model. Furthermore, the process robustness against a 20% input variation of concentration, size variant and ion composition, volume, and pH could be confirmed with the model. The presented model demonstrates the potential of the integrated approach for predicting manufacturing process performance across scales and operating units., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2022. Published by Elsevier B.V.)

Hydrogels based on renewable resources are a promising class of materials for future applications in pharmaceutics, drug delivery and personalized medicine. Thus, optional adjustments of mechanical properties such as swelling behavior, elasticity and network strength are desired. In this context, hydrogels based on the biological raw materials bovine serum albumin and casein were prepared by dityrosine-crosslinking of their tyrosine residues through visible light-induced photopolymerization. Changing the tyrosine accessibility by urea addition before photopolymerization increased the storage modulus of the hydrogels by 650% while simultaneously being more elastic. Furthermore, contributions of the buffer system composition, variation of protein concentration and storage medium towards mechanical properties of the hydrogel such as storage moduli, elasticity, fracture strain, compressive strength and relative weight swelling ratio are discussed. It could be shown, that changes in precursor solution and storage medium characteristics are crucial parameters towards tuning the mechanical properties of protein-based hydrogels., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Haas, Körner, Zintel and Hubbuch.)

During the filling process of a biopharmaceutical drug product (DP), a liquid DP film might creep up the inner vial wall which is barely discernible, appears as milky-white haze after lyophilisation and is known as fogging. Creeping and fogging are mainly dependent on the primary packaging material surface and its hydration, vial preparation process as well as DP composition. The occurrence of both can impede visual inspection and might lead to DP rejection. Hence, our studies focused on the early detection of liquid solution and glass vial surface interaction directly after filling. For a fast and highly sensitive evaluation a novel video-based analysis was used. To our knowledge, this is the first time a MATLAB®-algorithm-based video analysis was applied to quantitatively determine creeping time-resolved. Furthermore, creeping in dependence of vial processing sites, surfactant type and concentration, filling temperature, and vial format were investigated. The results were verified using orthogonal conventional methods such as surface tension, wetting behaviour, and contact angle measurements, as well as ToF-SIMS, ICP-MS, and SEM. Additionally, the methods applied were assessed regarding their cross-validation capability. The observations indicate that the vial preparation process can have a pronounced impact on alteration of the glass vial surface and related creeping behaviour of the filled solution., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

High-throughput screening (HTS) approaches are commonly used to accelerate downstream process development. Although most HTS approaches use batch isothermal data (K P screen) or bind and elute mode as screening procedure, different or new process designs are rarely investigated. In this paper, a mechanistic model case study for the separation of two different two-component solutions was conducted and confirmed prior evidence. With these outcomes, a novel HTS screening procedure was developed including the determination of competitive adsorption-based displacement effects and key parameter identification. The screening procedure employing an overload bind and elute (OBE) mode is presented in a case study dealing with IgG aggregate removal in a typical monoclonal antibody purification step, applying a Sartobind® S membrane adsorber (MA). Based on a MA scale down device, the OBE mode allows the determination of classical process parameters and dynamic effects, such as displacement effects. Competitive adsorption-based displacement effects are visualized by introducing a displacement identifier leading to a displacement process map. Based on this map, the approach is transferred to and confirmed by the OBE recycle experiments with 4.6 and 8.2 ml benchtop scsale devices resulting in 45% reduced IgG monomer and 88% increased higher molecular weight species binding capacities., (© 2021 International Union of Biochemistry and Molecular Biology, Inc.)

Enveloped viral vectors like lentiviral vectors pose purification challenges due to their low stability. A gentle purification method is considered one of the major bottlenecks for lentiviral vector bioprocessing. To overcome these challenges, a promising method is steric exclusion chromatography which has been used to purify a variety of target molecules. In this study, we successfully identified optimal process parameters for steric exclusion chromatography to purify lentiviral vectors. Lentiviral vector particle recoveries and infectious recoveries of 86% and 88%, respectively, were achieved. The process parameters optimal for steric exclusion chromatography were determined as follows: polyethylene glycol with a molecular weight of 4000 Da, a polyethylene glycol concentration of 12.5%, and a flow rate of 7 mL⋅min -1 using 5 layers of stabilized cellulose membranes as a stationary phase. High protein and dsDNA removal of approximately 80% were obtained. The remaining polyethylene glycol concentration in the eluate was determined. We defined the maximum loading capacity as 7.5 × 10 12 lentiviral particles for the lab device used and provide deeper insights into loading strategies. Furthermore, we determined critical process parameters like pressure. We demonstrated in our experiments that steric exclusion chromatography is a gentle purification method with high potential for fragile enveloped viral vectors as it yields high recoveries while efficiently removing impurities., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Virus-like particles (VLPs) are macromolecular structures with great potential as vehicles for the targeted administration of functional molecules. Loaded with nucleic acids, VLPs are a promising approach for nanocarriers needed for gene therapy. There is broad knowledge of the manufacturing of the truncated wild-type lacking a nucleic acid binding region, which is mainly being investigated for vaccine applications. Whereas for their potential application as a nanocarrier for gene therapy, hepatitis B core antigen (HBcAg) VLPs with a nucleic acid binding region for efficient cargo-loading are being investigated. VLP structure, loading, and phase behavior are of central importance to their therapeutic efficacy and thereby considerably affecting the production process. Therefore, HBcAg VLPs with different lengths of the nucleic acid binding region were produced in E. coli . VLP attributes such as size, zeta potential, and loading with host cell-derived nucleic acids were evaluated. Capsid's size and zeta potential of the VLP constructs did not differ remarkably, whereas the analysis of the loading with host cell-derived nucleic acids revealed strong differences in the binding of host cell-derived nucleic acids dependent on the length of the binding region of the constructs, with a non-linear correlation but a two-zone behavior. Moreover, the phase behavior and purification process of the HBcAg VLPs as a function of the liquid phase conditions and the presence of host cell-derived nucleic acids were investigated. Selective VLP precipitation using ammonium sulfate was scarcely affected by the encapsulated nucleic acids. However, the disassembly reaction, which is crucial for structure homogeneity, separation of encapsulated impurities, and effective loading of the VLPs with therapeutic nucleic acids, was affected both by the studied liquid phase conditions, varying pH and concentration of reducing agents, and the different VLP constructs and amount of bound nucleic acids, respectively. Thereby, capsid-stabilizing effects of the bound nucleic acids and capsid-destabilizing effects of the nucleic acid binding region were observed, following the two-zone behavior of the construct's loading, and a resulting correlation between the capsid stability and disassembly yields could be derived., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Valentic, Müller and Hubbuch.)

Elastin-like proteins (ELPs) are polypeptides with potential applications as renewable bio-based high-performance polymers, which undergo a stimulus-responsive reversible phase transition. The ELP investigated in this manuscript-ELP[V2Y-45]-promises fascinating mechanical properties in biomaterial applications. Purification process scalability and purification performance are important factors for the evaluation of potential industrial-scale production of ELPs. Salt-induced precipitation, inverse transition cycling (ITC), and immobilized metal ion affinity chromatography (IMAC) were assessed as purification protocols for a polyhistidine-tagged hydrophobic ELP showing low-temperature transition behavior. IMAC achieved a purity of 86% and the lowest nucleic acid contamination of all processes. Metal ion leakage did not propagate chemical modifications and could be successfully removed through size-exclusion chromatography. The simplest approach using a high-salt precipitation resulted in a 60% higher target molecule yield compared to both other approaches, with the drawback of a lower purity of 60% and higher nucleic acid contamination. An additional ITC purification led to the highest purity of 88% and high nucleic acid removal. However, expensive temperature-dependent centrifugation steps are required and aggregation effects even at low temperatures have to be considered for the investigated ELP. Therefore, ITC and IMAC are promising downstream processes for biomedical applications with scale-dependent economical costs to be considered, while salt-induced precipitation may be a fast and simple alternative for large-scale bio-based polymer production., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Haas, Desombre, Kirschhöfer, Huber, Schiller and Hubbuch.)

As virtual reality (VR) has drastically evolved over the past few years, the field of applications of VR flourished way beyond the gaming industry. While commercial VR solutions might be available, there is a need to develop a workflow for specific applications. Bioprinting represents such an example. Here, complex 3D data is generated and needs to be visualized in the context of quality control. We demonstrate that the transfer to a commercially available VR software is possible by introducing an optimized workflow. In the present work, we developed a workflow for the visualization of the critical quality attribute (cQA) cell distribution in bioprinted (extrusion-based) samples in VR. The cQA cell distribution is directly influenced by the pre-processing step mixing of cell material in the bioink. Magnetic Resonance Imaging (MRI) was used as an analytical tool to generate spatially resolved 2.5 and 3D data of the bioprinted objects. A sample with poor quality in respect of the cQA cell distribution was identified as its inhomogeneous cell distribution could be displayed spatially resolved in VR. The described workflow facilitates the usage of VR as a tool for quality inspection in the field of bioprinting and represents a powerful tool for visualization of complex 3D MRI data., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gretzinger, Schmieg, Guthausen and Hubbuch.)

Virus-like particles (VLPs) are an emerging biopharmaceutical modality with great potential as a platform technology. VLPs can be applied as gene therapy vectors and prophylactic or therapeutic vaccines. For non-enveloped VLPs, recombinant production of the protein subunits leads to intracellular self-assembly. The subsequent purification process includes VLP dis- and reassembly which aim at removing encapsulated impurities and improving particle properties. Filtration-based separation and processing has proven successful for VLPs but requires large product quantities and laborious experiments in early development stages. Both challenges can be tackled by implementation of process analytical technology (PAT) to efficiently obtain extensive process information. In this study, an existing PAT setup was extended to comprehensively monitor the diafiltration-based disassembly of hepatitis B core antigen (HBcAg) VLPs. Process-related signals were monitored in-line, while product-related signals, such as ultraviolet light (UV) spectra as well as static and dynamic light scattering (SLS and DLS), were monitored on-line. The applicability of the sensors for disassembly monitoring was evaluated under varying processing conditions. HBcAg VLP subunit concentrations were accurately predicted based on UV data using ordinary and partial least squares regression models (Q 2 from 0.909 to 0.976). DLS data were used for aggregation monitoring while the SLS intensity qualitatively reflected the disassembly progress., (© 2022 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC.)

Bioprinting is gaining importance for the manufacturing of tailor-made hydrogel scaffolds in tissue engineering, pharmaceutical research and cell therapy. However, structure fidelity and geometric deviations of printed objects heavily influence mass transport and process reproducibility. Fast, three-dimensional and nondestructive quality control methods will be decisive for the approval in larger studies or industry. Magnetic resonance imaging (MRI) meets these requirements for characterizing heterogeneous soft materials with different properties. Complementary to the idea of decentralized 3D printing, magnetic resonance tomography is common in medicine, and image data processing tools can be transferred system-independently. In this study, a MRI measurement and image analysis protocol was evaluated to jointly assess the reproducibility of three different hydrogels and a reference material. Critical parameters for object quality, namely porosity, hole areas and deviations along the height of the scaffolds are discussed. Geometric deviations could be correlated to specific process parameters, anomalies of the ink or changes of ambient conditions. This strategy allows the systematic investigation of complex 3D objects as well as an implementation as a process control tool. Combined with the monitoring of metadata this approach might pave the way for future industrial applications of 3D printing in the field of biopharmaceutics., (© 2022 The Authors. Biotechnology Journal published by Wiley-VCH GmbH.)

Hydrogels are polymer-based materials with a high water content. Due to their biocompatible and cell-friendly nature, they play a major role in a variety of biotechnological applications. For many of these applications, diffusibility is an essential property influencing the choice of material. We present an approach to estimate diffusion coefficients in hydrogels based on absorbance measurements of a UV area imaging system. A microfluidic chip with a y-junction was employed to generate a fluid-hydrogel interface and the diffusion of lysozyme from the fluid into the hydrogel phase was monitored. Employing automated image and data processing, analyte concentration profiles were generated from the absorbance measurements and fits with an analytical solution of Fick's second law of diffusion were applied to estimate diffusion coefficients. As a case study, the diffusion of lysozyme in hydrogels made from different concentrations (0.5-1.5% (w/w)) of an unmodified and a low-melt agarose was investigated. The estimated diffusion coefficients for lysozyme were between 0.80 ± 0.04×10 -10  m 2  s -1 for 1.5% (w/w) low-melt agarose and 1.14 ± 0.02×10 -10  m 2  s -1 for 0.5% (w/w) unmodified agarose. The method proved sensitive enough to resolve significant differences between the diffusion coefficients in different concentrations and types of agarose. The microfluidic approach offers low consumption of analyte and hydrogel and requires only relatively simple instrumentation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wenger and Hubbuch.)

Bioprinting is increasingly regarded as a suitable additive manufacturing method in biopharmaceutical process development and formulation. In order to manage the leap from research to industrial application, higher levels of reproducibility and a standardized bioprinting process are prerequisites. This said, the concept of process analytical technologies, standard in the biopharmaceutical industry, is still at its very early steps. To date most extrusion-based printing processes are controlled over penumatic pressure and thus not adaptive to environmental or system related changes over several experimental runs. A constant set pressure applied over a number of runs, might lead to variations in flow rate and thus to unreliable printed constructs. With this in mind, the simple question arises whether a printing process based on a set flow rate could improve reproduciblity and transfer to different printing systems. The control and monitoring of flow rate aim to introduce the concept of PAT in the field of bioprinting. This study investigates the effect of different processing modes (set pressure vs. set flow rate) on printing reproducibility occurring during an extrusion-based printing process consisting of 6 experimental runs consisting of 3 printed samples each. Additionally, the influence of different filling levels of the ink containing cartridge during a printing process was determined. Different solutions based on a varying amount of alginate polymer and Kolliphor hydrogels in varying concentrations showed the need for individual setting of printing parameter. To investigate parameter transferability among different devices two different printers were used and the flow was monitored using a flow sensor attached to the printing unit. It could be demonstrated that a set flow rate controlled printing process improved accuracy and the filling level also affects the accuracy of printing, the magnitude of this effects varies as the cartridge level declined. The transferability between printed devices was eased by setting the printing parameters according to a set flow rate of each bioink disregarding the value of the set pressure. Finally, by a bioprinting porcess control based on a set flow rate, the coefficient of variance for printed objects could be reduced from 0.2 to 0.02 for 10% (w/v) alginate polymer solutions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Strauß, Schroth and Hubbuch.)

In biopharmaceutical process development time, cost and reliability are the relevant keywords. During the development of chromatographic processes these targets are challenged by many possible scaffolds, ligands and process parameters. The common response to this diversity is the establishment of platform processes in the development of chromatographic unit operations. However, while developing a platform library to simplify and accelerate chromatographic processes, the potential combination of scaffold, ligands and process parameters need to be characterized. This challenge is addressed in a case study on novel mixed mode (MM) adsorber for the removal of monoclonal antibody (mAb) aggregates. We propose a rigorous strategy to reduce the various experimental design space resulting from possible combinations in scaffolds, backbones and ligands. This strategy is based on theoretical considerations, identification of adsorber selectivity and capacity for the identification of a suitable membrane system. For this system, each potential MM membrane adsorber candidate is investigated in its high molecular weight species reduction potential for a given mAb feed stream and referenced to the performance of Capto™ Adhere. The introduced strategy can reduce the developmental effort in an early stage from three to two possible stationary phases. Thereafter, initial examinations at different ionic capacities enlighten one favorable stationary phase. Finalizing the development strategy procedure by studying five different MM ligands by HTS and confirming the study with a 2-3 MV higher dynamic breakthrough capacity in benchtop experiments and provides an insight in the benefits of a living process platform library., (© 2021 American Institute of Chemical Engineers.)

Hybrid nanomaterials possess the properties of both organic and inorganic components and find applications in various fields of research and technology. In this study, aerosol photopolymerization is used in combination with thiol-ene chemistry to produce silver poly(thio-ether) hybrid nanospheres. In aerosol photopolymerization, a spray solution of monomers is atomized, forming a droplet aerosol, which then polymerizes, producing spherical polymer nanoparticles. To produce silver poly(thio-ether) hybrids, silver nanoparticles were introduced to the spray solution. Diverse methods of stabilization were used to produce stable dispersions of silver nanoparticles to prevent their agglomeration before the photopolymerization process. Successfully stabilized silver nanoparticle dispersion in the spray solution subsequently formed nanocomposites with non-agglomerated silver nanoparticles inside the polymer matrix. Nanocomposite particles were analyzed via scanning and transmission electron microscopy to study the degree of agglomeration of silver nanoparticles and their location inside the polymer spheres. The nanoparticle hybrids were then introduced onto various biofunctionalization reactions. A two-step bioconjugation process was developed involving the hybrid nanoparticles: (1) conjugation of (biotin)-maleimide to thiol-groups on the polymer network of the hybrids, and (2) biotin-streptavidin binding. The biofunctionalization with gold-nanoparticle-conjugates was carried out to confirm the reactivity of -SH groups on each conjugation step. Fluorescence-labeled biomolecules were conjugated to the spherical nanoparticle hybrids (applying the two-step bioconjugation process) verified by Fluorescence Spectroscopy and Fluorescence Microscopy. The presented research offers an effective method of synthesis of smart systems that can further be used in biosensors and various other biomedical applications.

Increasing the shelf life of enzymes and making them reusable is a prominent topic in biotechnology. The encapsulation inside hydrogel microparticles (HMPs) can enhance the enzyme's stability by preserving its native conformation and facilitating continuous biocatalytic processes and enzyme recovery. In this study, we present a method to immobilize β-galactosidase by, first, conjugating the enzyme onto the surface of polymer nanoparticles, and then encapsulating these enzyme-conjugated nanoparticles (ENPs) inside HMPs using microfluidic device paired with UV-LEDs. Polymer nanoparticles act as anchors for enzyme molecules, potentially preventing their leaching through the hydrogel network especially during swelling. The affinity binding (through streptavidin-biotin interaction) was used as an immobilization technique of β-galactosidase on the surface of polymer nanoparticles. The hydrogel microparticles of roughly 400 μm in size (swollen state) containing unbound enzyme and ENPs were produced. The effects of encapsulation and storage in different conditions were evaluated. It was discovered that the encapsulation in acrylamide (AcAm) microparticles caused an almost complete loss of enzymatic activity. Encapsulation in poly(ethylene glycol) (PEG)-diacrylate microparticles, on the other hand, showed a residual activity of 15-25%, presumably due to a protective effect of PEG during polymerization. One of the major factors that affected the enzyme activity was presence of photoinitiator exposed to UV-irradiation. Storage studies were carried out at room temperature, in the fridge and in the freezer throughout 1, 7 and 28 days. The polymer nanoparticles showcased excellent immobilization properties and preserved the activity of the conjugated enzyme at room temperature (115% residual activity after 28 days), while a slight decrease was observed for the unbound enzyme (94% after 28 days). Similar trends were observed for encapsulated ENPs and unbound enzyme. Nevertheless, storage at -26°C resulted in an almost complete loss of enzymatic activity for all samples., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Suvarli, Wenger, Serra, Perner-Nochta, Hubbuch and Wörner.)

Spherical, individual polymer nanoparticles with functional -SH groups were synthesized via aerosol photopolymerization (APP) employing radically initiated thiol-ene chemistry. A series of various thiol and alkene monomer combinations were investigated based on di-, tri-, and tetrafunctional thiols with difunctional allyl and vinyl ethers, and di- and trifunctional acrylates. Only thiol and alkene monomer combinations able to build cross-linked poly(thio-ether) networks were compatible with APP, which requires fast polymerization of the generated droplet aerosol during the photoreactor passage within a residence time of half-minute. Higher monomer functionalities and equal overall stoichiometry of functional groups resulted in the best nanoparticles being spherical and individual, proven by scanning electron microscopy (SEM). The presence of reactive -SH groups in the synthesized nanoparticles as a basis for post-polymerization modifications was verified by Ellman's test.

Freezing processes are a well-established unit operation in the biopharmaceutical industry to increase the shelf-life of protein-based drugs. While freezing reduces degradation reaction rates, it may also exert stresses such as freeze concentration. Macroscopic freeze concentration in large-scale freezing processes has been described thoroughly by examination of frozen bulk material, but the transient process leading to such freeze concentration profiles has not been monitored yet for biopharmaceutical solutions. In this study, Raman spectroscopy as a process analytical technology is demonstrated for model formulations containing monoclonal antibodies (mAbs) or bovine serum albumin (BSA) in varying concentrations of sucrose and buffer salts. Therefore, a Raman probe was immersed into a bulk volume at different heights, monitoring the freeze concentration in the liquid phase during the freezing processes. Partial least square regression models were used to quantitatively discriminate between the protein and excipients simultaneously. The freeze concentration profiles were dependend on freezing temperature and formulation with freeze concentrations up to 2.4-fold. Convection currents at the bottom of the freezing container were observed with a maximum height of 1 mm. Furthermore, freeze concentration was correlated with the sucrose concentration in a formulation. Analysis of the freeze concentration slope indicated diffusion from the bottom to the top of the container. In summary, Raman spectroscopy is a valuable tool for process validation of freeze concentration simulations and to overcome scale-dependent challenges., (© 2021 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals LLC.)

The automated transport of cells can enable far-reaching cell culture research. However, to date, such automated transport has been achieved with large pump systems that often come with long fluidic connections and a large power consumption. Improvement is possible with space- and energy-efficient piezoelectric micro diaphragm pumps, though a precondition for a successful use is to enable transport with little to no mechanical stress on the cell suspension. This study evaluates the impact of the microfluidic transport of cells with the piezoelectric micro diaphragm pump developed by our group. It includes the investigation of different actuation signals. Therewith, we aim to achieve optimal fluidic performance while maximizing the cell viability. The investigation of fluidic properties proves a similar performance with a hybrid actuation signal that is a rectangular waveform with sinusoidal flanks, compared to the fluidically optimal rectangular actuation. The comparison of the cell transport with three actuation signals, sinusoidal, rectangular, and hybrid actuation shows that the hybrid actuation causes less damage than the rectangular actuation. With a 5% reduction of the cell viability it causes similar strain to the transport with sinusoidal actuation. Piezoelectric micro diaphragm pumps with the fluidically efficient hybrid signal actuation are therefore an interesting option for integrable microfluidic workflows.

With the quality by design (QbD) initiative, regulatory authorities demand a consistent drug quality originating from a well-understood manufacturing process. This study demonstrates the application of a previously published mechanistic chromatography model to the in silico process characterization (PCS) of a monoclonal antibody polishing step. The proposed modeling workflow covered the main tasks of traditional PCS studies following the QbD principles, including criticality assessment of 11 process parameters and establishment of their proven acceptable ranges of operation. Analyzing effects of multi-variate sampling of process parameters on the purification outcome allowed identification of the edge-of-failure. Experimental validation of in silico results demanded approximately 75% less experiments compared to a purely wet-lab based PCS study. Stochastic simulation, considering the measured variances of process parameters and loading material composition, was used to estimate the capability of the process to meet the acceptance criteria for critical quality attributes and key performance indicators. The proposed workflow enables the implementation of digital process twins as QbD tool for improved development of biopharmaceutical manufacturing processes., (© 2021 The Authors. Biotechnology Progress published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers.)

A promising application of Process Analytical Technology to the downstream process of monoclonal antibodies (mAbs) is the monitoring of the Protein A load phase as its control promises economic benefits. Different spectroscopic techniques have been evaluated in literature with regard to the ability to quantify the mAb concentration in the column effluent. Raman and Ultraviolet (UV) spectroscopy are among the most promising techniques. In this study, both were investigated in an in-line setup and directly compared. The data of each sensor were analyzed independently with Partial-Least-Squares (PLS) models and Convolutional Neural Networks (CNNs) for regression. Furthermore, data fusion strategies were investigated by combining both sensors in hierarchical PLS models or in CNNs. Among the tested options, UV spectroscopy alone allowed for the most precise and accurate prediction of the mAb concentration. A Root Mean Square Error of Prediction (RMSEP) of 0.013 g L -1 was reached with the UV-based PLS model. The Raman-based PLS model reached an RMSEP of 0.232 g L -1 . The different data fusion techniques did not improve the prediction accuracy above the prediction accuracy of the UV-based PLS model. Data fusion by PLS models seems meritless when combining a very accurate sensor with a less accurate signal. Furthermore, the application of CNNs for UV and Raman spectra did not yield significant improvements in the prediction quality. For the presented application, linear regression techniques seem to be better suited compared with advanced nonlinear regression techniques, like, CNNs. In summary, the results support the application of UV spectroscopy and PLS modeling for future research and development activities aiming to implement spectroscopic real-time monitoring of the Protein A load phase., (© 2021 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals LLC.)

Virus-like particles (VLPs) are particulate structures, which are applied as vaccines or delivery vehicles. VLPs assemble from subunits, named capsomeres, composed of recombinantly expressed viral structural proteins. During downstream processing, in vivo-assembled VLPs are typically dis- and reassembled to remove encapsulated impurities and to improve particle morphology. Disassembly is achieved in a high-pH solution and by the addition of a denaturant or reducing agent. The optimal disassembly conditions depend on the VLP amino acid sequence and structure, thus requiring material-consuming disassembly experiments. To this end, we developed a low-volume and high-resolution disassembly screening that provides time-resolved insight into the VLP disassembly progress. In this study, two variants of C-terminally truncated hepatitis B core antigen were investigated showing different disassembly behaviors. For both VLPs, the best capsomere yield was achieved at moderately high urea concentration and pH. Nonetheless, their disassembly behaviors differed particularly with respect to disassembly rate and aggregation. Based on the high-throughput screening results, a diafiltration-based disassembly process step was developed. Compared with mixing-based disassembly, it resulted in higher yields of up to 0.84 and allowed for integrated purification. This process step was embedded in a filtration-based process sequence of disassembly, capsomere separation, and reassembly, considerably reducing high-molecular-weight species., (© 2021 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC.)

Freezing of cell culture supernatant (CCS) is a standard procedure in process development of monoclonal antibody (mAb) platform processes as up- and downstream development are usually separated. In the manufacturing process of mAb, however, freezing is avoided, which poses the question of comparability and transferability from process development to manufacturing. In this case study, mAb CCS from Chinese hamster ovary (CHO) cells is frozen and thawed in a novel active freezing device and subsequently captured by protein A chromatography. Critical quality attributes such as host cell protein (HCP) concentration and soluble mAb dimer shares have been monitored throughout the case study. Furthermore, cryo-concentration of individual proteins was investigated. The main factors that drive cryo-concentration are diffusion and natural convection. Natural convection in freezing processes was found to increase at warmer freezing temperatures and thus slower freezing, leading to higher concentration gradients from top to bottom of a freezing chamber. The freeze concentration was dependent on protein size and correlated to diffusivity, where smaller proteins are exposed to higher cryo-concentration. Our results suggest that as a result of freezing processes, large particles based on mAb and specific host cell proteins (HCPs) expressing a certain affinity to mAbs are formed that have to be removed before purification. This leads to a significant improvement in HCP reduction by the protein A step, when compared with reference samples, where twice as much HCP remained in the eluate. Furthermore, HCP and mAb dimer concentrations in protein A eluate were dependent on the freezing temperature. As a conclusion, CCS should be frozen as rapidly as possible during process development to minimize issues of transferability from process development to manufacturing., (© 2021 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC.)

A fundamental understanding of the protein retention mechanism in preparative ion exchange (IEX) chromatography columns is essential for a model-based process development approach. For the past three decades, the mechanistic description of protein retention has been based predominantly on the steric mass action (SMA) model. In recent years, however, retention profiles of proteins have been reported more frequently for preparative processes that are not consistent with the mechanistic understanding relying on the SMA model. In this work, complex elution behavior of proteins in preparative IEX processes is analyzed using a colloidal particle adsorption (CPA) model. The CPA model is found to be capable of reproducing elution profiles that cannot be described by the traditional SMA model. According to the CPA model, the reported complex behavior can be ascribed to a strong compression and concentration of the elution front in the lower unsaturated part of the chromatography column. As the unsaturated part of the column decreases with increasing protein load density, exceeding a critical load density can lead to the formation of a shoulder in the peak front. The general applicability of the model in describing preparative IEX processes is demonstrated using several industrial case studies including multiple monoclonal antibodies on different IEX adsorber systems. In this context, the work covers both salt controlled and pH-controlled protein elution., Competing Interests: Declaration of Competing Interest The authors affiliated to GoSilico declare that the software ChromX used in this study is a commercial product of GoSilico. The presented research does not depend on the usage of ChromX, the model can be implemented in any kind of modeling software., (Copyright © 2021. Published by Elsevier B.V.)

Cation exchange chromatography (CEX) is a widely used technique for the removal of monoclonal antibody (mAb) aggregates. At present, resins are mainly used for this purpose, as convective types of adsorbents such as membrane adsorbers (MAs) have often not demonstrated overall comparable performance for this particular application. Fiber-based adsorbents can overcome the current limitations of MAs with respect to permeability, binding capacity, and adsorbent cost, and could therefore be a viable alternative to resins for the removal of mAb aggregates. It has not been evaluated, however, whether and under which conditions the use of such adsorbents is feasible for this purpose. In the present study, the use of fiber-based CEX adsorbents for mAb aggregate removal was examined. Two types of fiber-based adsorbents, an uncontrolled grafted and a controlled grafted fiber-based adsorbent, were evaluated with respect to permeability, dynamic mAb binding capacity (DBC), resolution capabilities, and the performance in bind and elute (B/E) and frontal chromatography mode with respect to typical performance indicators. The permeabilities of the fiber-based adsorbents ranged from 200 to 1700 mD, making it possible to use the fiber-based adsorbents at larger bed heights than membrane adsorbers with fast mobile phase velocities. Antibody DBCs ranged from 20 to 41 g/L at 150 cm/h, and at higher mobile phase velocities exceeded the DBC of an existing resin material, Poros 50 HS, which has frequently been used for aggregate removal. Both fiber types showed good resolution capabilities of monomer and aggregates, and provided better resolution per column length than Poros 50 HS. Typical purity and yield constraints were fulfilled for both fiber types in both B/E and frontal chromatography mode for mobile phase velocities ranging up to 480 cm/h and 1060 cm/h. The overall performance of the controlled grafted fibers was found to be superior to the performance of uncontrolled grafted fiber-based adsorbents due to higher productivity and lower buffer consumption. The overall performance of the fiber-based adsorbents was found to be comparable to the performance of Poros 50 HS at typical operating conditions. The results in this study indicate that the use of fiber-based adsorbents for mAb aggregate removal is feasible with a performance that is comparable to the performance of an existing resin material. Depending on the cost of the adsorbents and the use scenario, the usage of such adsorbents could be beneficial., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

There has been a growing interest in fibers and fiber-based adsorbents as alternative adsorbents for preparative chromatography. While the benefits of fiber-based adsorbents in terms of productivity have been highlighted in several recent studies, microscale tools that enable a fast characterization of these novel adsorbents, and an easy integration into process development workflows, are still lacking. In the present study an automated high-throughput screening (HTS) for fiber-based adsorbents was established on a robotic liquid handling station in 96 well filter plates. Two techniques - punching and weighing - were identified as techniques that enabled accurate and reproducible portioning of short-cut fiber-based adsorbents. The impact of several screening parameters such as phase ratio, shaking frequency, and incubation time were investigated and optimized for different types of fiber-based adsorbents. The data from the developed HTS correlated with data from packed fiber columns, and binding capacities from both scales matched closely. Subsequently, the developed HTS was utilized to optimize the hydrogel architecture of anion exchange (AEX) fiber-based adsorbent prototypes. A novel AEX fiber-based adsorbent was developed that compared favorably with existing resin and membrane adsorbents in terms of productivity and DNA binding capacity. In addition, the developed HTS was also successfully employed in order to identify step elution conditions for the purification of a monoclonal antibody from product- and process-related impurities with a cation exchange (CEX) fiber-based adsorbent. Trends from the HTS were found to be in good agreement with trends from lab scale column runs. The tool developed in this paper will enable a faster and more complete characterization of fiber-based adsorbents, easier tailoring of such adsorbents towards specific process applications, and an easier integration of such materials into processes. In comparison to previous lab scale experiments, material requirements are reduced by a factor of 3-40 and time requirements are reduced by a factor of 2-5., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)