Hydrophobic interaction chromatography (HIC) is a widely used separation method in biopharmaceutical downstream processing. For process development, mechanistic modeling can be used to reduce timelines by simulating protein transport and adsorption during chromatography. Accuracy of the parameters used in the model is essential for successful deployment. This work compares three isotherm parameter determination methods for a simplified linear HIC isotherm: the Parente and Wetlaufer method, the Yamamoto method, and the inverse method. These methods were tested for two proteins, using the same linear gradient elution (LGE) experiments. Accuracy of the obtained parameters was determined via cross-validation using three LGEs. Finally, the obtained parameters were tested for alternative linear gradients with varying initial and final salt concentrations. While all results were comparable, parameters obtained by the inverse method showed the greatest accuracy. This method requires high quality chromatograms, while the other methods only need retention volumes. Therefore, it is less suitable when signal quality is compromised. The Yamamoto method showed similar robustness as the inverse method while outperforming the Parente and Wetlaufer method. Therefore, the Yamamoto method is a good alternative for parameter determination. This comparison offers practical guidance for method selection for isotherm determination, thereby enabling reliable mechanistic modeling of HIC processes.

In-line Raman spectroscopy combined with accurate quantification models can offer detailed real-time insights into a bioprocess by monitoring key process parameters. However, traditional approaches for model calibration require extensive data collection from multiple bioreactor runs, resulting in process-specific models that are sensitive to operational changes. These challenges can be tackled by simplifying experimental data generation or implementation of computational methods to obtain synthetic and augmented Raman spectra. In this study, we utilized a small experimental dataset of 16 single compound spectra to calibrate quantification models by using partial least squares (PLS) and indirect hard modeling (IHM), leading to comparable rRMSEP values for glucose (4.8% and 4.2%), ethanol (11.6% and 6.3%), and biomass (16.2% and 10.0%) when applied to yeast batch and fed-batch bioprocesses. Subsequently, isolated spectral features extracted during IHM were used to generate fully synthetic spectral datasets for PLS model calibration, resulting in rRMSEPs of 3.2% and 14.5% for glucose and ethanol, respectively. Finally, spectra from a single batch process were augmented with the same isolated spectral features, and calibration with these augmented spectra reduced rRMSEP by 18.6% point (glucose) and 4.3% point (ethanol) compared to process-only calibrated models. This study demonstrates how different approaches may support robust development and rapid implementation of Raman spectroscopy-based models while minimizing experimental efforts, where even complete independence of process data can be achieved.

In-line Raman spectroscopy combined with chemometric modeling is a valuable process analytical technology (PAT) providing real-time quantitative information on cell culture compounds. Considering that compound quantification through chemometric models depends on pre-processing to maintain consistent changes in intensity at certain wavenumbers, all causes of signal distortion should be well understood to prevent quantification inaccuracies. This work investigated spectral distortion caused by the changing bioreactor parameters temperature, bubble quantity, and medium viscosity. In addition, the isolated spectral contribution of Saccharomyces cerevisiae cells in suspension was also determined. A temperature range from 20 to 40°C resulted in peak shifts up to 0.8 cm⁻¹ to lower wavenumbers, bubbles generated under standard bioreactor operation conditions led to signal attenuation of up to 7.93% reduction in peak intensity, and changes in liquid viscosity resulted in complex peak shift behavior. Isolated biomass concentrations reaching 5 g/L caused up to 44.6% reduction in distinct peak intensity, which was similar to spectra from batch process fermentations. Correcting for the attenuation revealed spectral features of biomass associated with proteins and lipids in the 1000–1500 cm⁻¹ region. However, the spectral contribution of yeast biomass is dominated by signal extinction, which attenuates Raman spectra in a non-linear manner as biomass accumulates. The obtained knowledge on different sources of spectral distortion aids in the development of robust pre-processing and modeling strategies to obtain chemometric models applicable across experimental setups.

Raman spectroscopy is a valuable analytical tool for real-time analyte quantification in fermentation processes. Quantification is performed with chemometric models that translate Raman spectra into concentration values, which are typically calibrated with process data from multiple comparable fermentations. However, process-specific models underperform for minor process variation(s) or different operation modes due to the integration of cross-correlations, resulting in low target analyte specificity. Thus, model transferability is poor and labor-intensive (re-)calibration of models is required for related processes. In this work, partial least-squares models for glucose, ethanol, and biomass were calibrated with Saccharomyces cerevisiae batch fermentation data and subsequently transferred to a fed-batch operation. To enhance model transferability without additional process runs, single compound data supplementation was performed. The supplemented models increased overall target analyte specificity and demonstrated sufficient prediction accuracy for the fed-batch process (root-mean-square errors of prediction (RMSEP) of 3.06 mM, 8.65 mM, and 0.99 g/L for glucose, ethanol, and biomass), while maintaining high prediction accuracy for the batch process (RMSEP of 1.71 mM, 4.20 mM, and 0.17 g/L for glucose, ethanol, and biomass). This work showcases that process data in combination with single compound spectra is a fast and efficient strategy to apply Raman spectroscopy for real-time process monitoring across related processes.

Cell therapies based on inducible pluripotent stem cells offer promising new treatments for a variety of different illnesses. However, the sensitivity of stem cells to hydrodynamic stress makes developing reliable stem cell production processes challenging. Understanding hydrodynamic stress conditions experienced by stem cells during early-stage process development is important to guide scale-up and design scale-down experiments. We characterize the hydrodynamic stresses in a 125 mL shake flask using Lattice-Boltzmann implicit large eddy simulations (LB-ILES). First, we validated the LB-ILES shake flask simulations using volumetric power input measurements and experimental liquid distribution data showing good overall agreement, while also numerical challenges of the LB-ILES method regarding grid and time step dependencies are discussed. The mean shear stress in the shake flask increases from 0.01 to 0.24 Pa when increasing the shaking frequency from 55 to 250 rpm, and the mean Kolmogorov length scale decreases from 185 to 51 μm. Furthermore, time-averaged distributions of the shear stress and Kolmogorov length scales were evaluated and compared to reported stress thresholds for stem cells. Based on the shear stress and Kolmogorov length scale distributions, our developed shake flask CFD model can help to design small-scale experiments to characterize stem cell cultures in terms of their hydrodynamic stress tolerance, and ultimately guide scale-up stem cell cultures to larger cultivation systems.

The competition in the biopharmaceutical market is increasing due to the market entry of biosimilars and rising costs in research and development of new drugs. Hence, continuous manufacturing gained significant attention due to its potential in reducing production cycle times and costs, as well as the possibility of real-time release testing. As a consequence, active monitoring and/or control systems are required for quantitative product quality measurements and in-process control. Process analytical technology emerged as a robust strategy for the development and implementation of in situ real-time testing, instead of the standard batch testing of end product. Through the evaluation of state-of-the-art applications, this review highlights future opportunities in the field of quantitative real-time analytical techniques for the characterization of monoclonal antibodies in continuous downstream biomanufacturing.

Integrated Continuous Biomanufacturing reduces manufacturing costs while maintaining product quality. A key contributor to high biopharmaceutical costs, specifically monoclonal antibodies (mAbs), is chromatography. Protein A ligands are usually preferred but still expensive in the manufacturing context, and batch chromatography under-utilizes the columns' capacity, compromising productivity to maintain high yields. Continuous chromatography increases columns' Capacity Utilization (CU) without sacrificing yield or productivity. This work presents the in-silico optimization of a 3 Column Periodic Counter-current Chromatography (3C-PCC) of a capture and polishing step for mAbs from a high titer harvest (cmAb = 5 g/L). The 3C-PCC was modeled and Pareto-fronts for continuous and batch modes were used to optimize the 3C-PCC steps varying the flow rate and percentage of breakthrough achieved in the interconnected loading, maximizing Productivity and CU, for varying concentrations of mAb (batch mode concentration of 5 g/L and continuous mode concentration of 2.5, 5, 7.5, and 10 g/L). The shape of the breakthrough curve significantly impacts the optimization of 3C-PCC. The model output was validated for three different protein A ligands using a pure mAb solution. MAb Select SuRe pcc was selected to continuously capture mAb from a high-titer clarified cell culture supernatant (harvest). The product eluates were pooled and used for continuous polishing using a Cation-Exchange resin (CaptoS ImpAct). Experimental results validated model predictions (<7% deviation in the worst case) and a process with two 3C-PCC in sequence was proposed, with a productivity of approximately 100 mg/mL res/h.

The challenging task of designing biopharmaceutical downstream processes is initially to select the type of unit operations, followed by optimizing their operating conditions. For complex flowsheet optimizations, the strategy becomes crucial in terms of duration and outcome. In this study, we compared three optimization strategies, namely, simultaneous, top-to-bottom, and superstructure decomposition. Moreover, all strategies were evaluated by either using chromatographic Mechanistic Models (MMs) or Artificial Neural Networks (ANNs). An overall evaluation of 39 flowsheets was performed, including a buffer-exchange step between the chromatography operations. All strategies identified orthogonal structures to be optimal, and the weighted overall performance values were generally consistent between the MMs and ANNs. In terms of time-efficiency, the decomposition method with MMs stands out when utilizing multiple cores on a multiprocessing system for simulations. This study analyses the influence of different optimization strategies on flowsheet optimization and advices on suitable strategies and modeling techniques for specific scenarios.

BACKGROUND: Selecting an optimal chromatography resin during biopharmaceutical downstream process development is a great challenge. This is especially the case for recombinant subunit vaccines, where product properties vary greatly and recovery often involves cell lysis, which yields a complex mixture of different host cell materials. Host cell protein (HCP) impurities may remain similar for platform processes, but their critical impact on separation efficiency is relative to specific product properties. Therefore, every process needs to be designed per product. Prior knowledge on the elution behavior of HCPs would support the identification of critical compounds. However, determining chromatographic behavior of HCPs experimentally is a time-consuming approach. RESULTS: In this work, we leverage quantitative structure–property relationship (QSPR) models calibrated with retention data of 13 commercial proteins, collected at pH 7, 8, 9 and 10 to predict the anion-exchange retention of Escherichia coli HCPs. These models use features calculated from the molecular structure to describe protein behavior, like chromatographic retention. A multilinear regression model containing two features (isoelectric point and sum of negative surface electrostatics) was able to predict the retention times of 288 HCPs accurately (error ≤ 5%). Moreover, we identified the key attributes missing in the training dataset, which is important to increase model performance in the future. CONCLUSION: This work showcases how chromatographic data obtained using commercial proteins can be translated to a clarified E. coli lysate to accelerate chromatography resin selection for new products.

The main objectives of bioprocesses are to reliably deliver drugs in a relatively short time frame with high quality within a tight regulatory framework. Bioprocesses are highly complex, the level of automation is moderate, and there is constant pressure to improve efficiency and costs. In addition, climate change and resource scarcity mandate a reduction in the environmental footprint of bioprocesses and production facilities. In the biopharmaceutical industry, two extreme production scenarios are applied: a fully disposable factory with the characteristics of full flexibility and speed, or a fixed large-scale plant with high capacity. Forward-looking solutions and ideas will be discussed how to combine new processes and environmental friendliness for the benefit of the patient, security of supply and profitability. The concept will be extended to large scale production of proteins for food and non-pharma applications, e.g., in material science and a roadmap towards a future plant will be laid out.

Protein-based biopharmaceuticals require high purity before final formulation to ensure product safety, making process development time consuming. Implementation of computational approaches at the initial stages of process development offers a significant reduction in development efforts. By preselecting process conditions, experimental screening can be limited to only a subset. One such computational selection approach is the application of Quantitative Structure Property Relationship (QSPR) models that describe the properties exploited during purification. This work presents a novel open-source Python tool capable of extracting a range of features from protein 3D models on a local computer allowing total transparency of the calculations. As open-source tool, it also impacts initial investments in constructing a QSPR workflow for protein property prediction for third parties, making it widely applicable within the field of bioprocess development. The focus of current calculated molecular features is projection onto the protein surface by constructing surface grid representations. Linear regression models were trained with the calculated features to predict chromatographic retention times/volumes. Model validation shows a high accuracy for anion and cation exchange chromatography data (cross-validated R² of 0.87 and 0.95). Hence, these models demonstrate the potential of the use of QSPR to accelerate process design.

Purification of recombinantly produced biopharmaceuticals involves removal of host cell material, such as host cell proteins (HCPs). For lysates of the common expression host Escherichia coli (E. coli) over 1500 unique proteins can be identified. Currently, understanding the behavior of individual HCPs for purification operations, such as preparative chromatography, is limited. Therefore, we aim to elucidate the elution behavior of individual HCPs from E. coli strain BLR(DE3) during chromatography. Understanding this complex mixture and knowing the chromatographic behavior of each individual HCP improves the ability for rational purification process design. Specifically, linear gradient experiments were performed using ion exchange (IEX) and hydrophobic interaction chromatography, coupled with mass spectrometry-based proteomics to map the retention of individual HCPs. We combined knowledge of protein location, function, and interaction available in literature to identify trends in elution behavior. Additionally, quantitative structure–property relationship models were trained relating the protein 3D structure to elution behavior during IEX. For the complete data set a model with a cross-validated R2 of 0.55 was constructed, that could be improved to a R2 of 0.70 by considering only monomeric proteins. Ultimately this study is a significant step toward greater process understanding.

Mechanistic models mostly focus on the target protein and some selected process- or product-related impurities. For a better process understanding, however, it is advantageous to describe also reoccurring host cell protein impurities. Within the purification of biopharmaceuticals, the binding of host cell proteins to a chromatographic resin is far from being described comprehensively. For a broader coverage of the binding characteristics, large-scale proteomic data and systems level knowledge on protein interactions are key. However, a method for determining binding parameters of the entire host cell proteome to selected chromatography resins is still lacking. In this work, we have developed a method to determine binding parameters of all detected individual host cell proteins in an Escherichia coli harvest sample from large-scale proteomics experiments. The developed method was demonstrated to model abundant and problematic proteins, which are crucial impurities to be removed. For these 15 proteins covering varying concentration ranges, the model predicts the independently measured retention time during the validation gradient well. Finally, we optimized the anion exchange chromatography capture step in silico using the determined isotherm parameters of the persistent host cell protein contaminants. From these results, strategies can be developed to separate abundant and problematic impurities from the target antigen.

Optimizing a biopharmaceutical chromatographic purification process is currently the greatest challenge during process development. A lack of process understanding calls for extensive experimental efforts in pursuit of an optimal process. In silico techniques, such as mechanistic or data driven modeling, enhance the understanding, allowing more cost-effective and time efficient process optimization. This work presents a modeling strategy integrating quantitative structure property relationship (QSPR) models and chromatographic mechanistic models (MM) to optimize a cation exchange (CEX) capture step, limiting experiments. In QSPR, structural characteristics obtained from the protein structure are used to describe physicochemical behavior. This QSPR information can be applied in MM to predict the chromatogram and optimize the entire process. To validate this approach, retention profiles of six proteins were determined experimentally from mixtures, at different pH (3.5, 4.3, 5.0, and 7.0). Four proteins at different pH's were used to train QSPR models predicting the retention volumes and characteristic charge, subsequently the equilibrium constant was determined. For an unseen protein knowing only the protein structure, the retention peak difference between the modeled and experimental peaks was 0.2% relative to the gradient length (60 column volume). Next, the CEX capture step was optimized, demonstrating a consistent result in both the experimental and QSPR-based methods. The impact of model parameter confidence on the final optimization revealed two viable process conditions, one of which is similar to the optimization achieved using experimentally obtained parameters. The multiscale modeling approach reduces the required experimental effort by identification of initial process conditions, which can be optimized.

The monoclonal antibody (mAb) industry is becoming increasingly digitalized. Digital twins are becoming increasingly important to test or validate processes before manufacturing. High-Throughput Process Development (HTPD) has been progressively used as a tool for process development and innovation. The combination of High-Throughput Screening with fast computational methods allows to study processes in-silico in a fast and efficient manner. This paper presents a hybrid approach for HTPD where equal importance is given to experimental, computational and decision-making stages. Equilibrium adsorption isotherms of 13 protein A and 16 Cation-Exchange resins were determined with pure mAb. The influence of other components in the clarified cell culture supernatant (harvest) has been under-investigated. This work contributes with a methodology for the study of equilibrium adsorption of mAb in harvest to different protein A resins and compares the adsorption behavior with the pure sample experiments. Column chromatography was modelled using a Lumped Kinetic Model, with an overall mass transfer coefficient parameter (k_ov). The screening results showed that the harvest solution had virtually no influence on the adsorption behavior of mAb to the different protein A resins tested. k_ov was found to have a linear correlation with the sample feed concentration, which is in line with mass transfer theory. The hybrid approach for HTPD presented highlights the roles of the computational, experimental, and decision-making stages in process development, and how it can be implemented to develop a chromatographic process. The proposed white-box digital twin helps to accelerate chromatographic process development.

Parkinson's Disease (PD) is a common neurodegenerative disorder affecting millions of people worldwide for which there are only symptomatic therapies. Small molecules able to target key pathological processes in PD have emerged as interesting options for modifying disease progression. We have previously shown that a (poly)phenol-enriched fraction (PEF) of Corema album L. leaf extract modulates central events in PD pathogenesis, namely α-synuclein (αSyn) toxicity, aggregation and clearance. PEF was now subjected to a bio-guided fractionation with the aim of identifying the critical bioactive compound. We identified genipin, an iridoid, which relieves αSyn toxicity and aggregation. Furthermore, genipin promotes metabolic alterations and modulates lipid storage and endocytosis. Importantly, genipin was able to prevent the motor deficits caused by the overexpression of αSyn in a Drosophila melanogaster model of PD. These findings widens the possibility for the exploitation of genipin for PD therapeutics.

The hydrodynamics of the Expanded Bed Adsorption process is studied through simulations combining Computational Fluid Dynamics and the Discrete Element Method. A representative base case is defined, based on process design parameters commonly encountered in literature. Then, 19 other cases are defined, each representing a singular adjustment to the column design, material properties, or operating conditions. The parameters that are varied are the expansion factor, liquid viscosity, bed aspect ratio, mean particle density, width of the particle density distribution, width of the particle size distribution, column taper angle, and column alignment angle. The impact of each adjustment on the bed behaviour is discussed, using the local particle size distribution and solids dispersion coefficient as main indicators of bed stability. Optimal performance was found for an expansion factor of two to three, and the combination of particle size distribution and particle density distribution was found to greatly improve bed stability. The mixing process of the liquid and solid phases is concluded to be of highly complex nature, and cannot simply be predicted from the liquid flow velocity.

Mass-spectrometry-based proteomics is increasingly employed to monitor purification processes or to detect critical host cell proteins in the final drug substance. This approach is inherently unbiased and can be used to identify individual host cell proteins without prior knowledge. In process development for the purification of new biopharmaceuticals, such as protein subunit vaccines, a broader knowledge of the host cell proteome could promote a more rational process design. Proteomics can establish qualitative and quantitative information on the complete host cell proteome before purification (i.e., protein abundances and physicochemical properties). Such information allows for a more rational design of the purification strategy and accelerates purification process development. In this study, we present an extensive proteomic characterisation of two E. coli host cell strains widely employed in academia and industry to produce therapeutic proteins, BLR and HMS174. The established database contains the observed abundance of each identified protein, information relating to their hydrophobicity, the isoelectric point, molecular weight, and toxicity. These physicochemical properties were plotted on proteome property maps to showcase the selection of suitable purification strategies. Furthermore, sequence alignment allowed integration of subunit information and occurrences of post-translational modifications from the well-studied E. coli K12 strain.

An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches enable to screen a broad range of the design-space, in contrast to traditional statistical or heuristic-based approaches. Though, chromatographic mechanistic modeling (MM), one of the advanced model-based approaches, can be speed-limiting for flowsheet optimization, which evaluates every purification possibility (e.g., type and order of purification techniques, and their operating conditions). Therefore, we propose to use artificial neural networks (ANNs) during global optimization to select the most optimal flowsheets. So, the number of flowsheets for final local optimization is reduced and consequently the overall optimization time. Employing ANNs during global optimization proved to reduce the number of flowsheets from 15 to only 3. From these three, one flowsheet was optimized locally and similar final results were found when using the global outcome of either the ANN or MM as starting condition. Moreover, the overall flowsheet optimization time was reduced by 50% when using ANNs during global optimization. This approach accelerates the early purification process design; moreover, it is generic, flexible, and regardless of sample material's type.

Lubrication forces play a major role in the behaviour of fluid–solid systems, where they affect the collisions between particles. Current implementations of lubrication forces in unresolved simulations often suffer from shortcomings, such as neglecting parts of the physics or relying on arbitrarily defined parameters. In this short communication, we propose a novel implementation, rigorously defined based on physical and numerical factors. Both particle roughness and deformation are considered, and the model accuracy is demonstrated through comparison with experimental results.