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 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.

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.

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.

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.

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.

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.

Membrane technology is commonly used within food, bio- and pharmaceutical processes. Beside single-stage membranes, multi-stage membrane systems are become more popular to improve separation performance. In this review, we present a unified four-phase model-based optimization framework to optimize these systems, using mechanistic models, empirical models including machine learning models, or a combination of them. We begin by providing a general overview and outlining the steps to construct each phase in the framework. The importance of each stage and critical points to consider are discussed. We then provide detailed information for each phase, including the governing equations from known literature models. Finally, we explore the platform's potential applications and outlook. Despite the great potential of an integrated approach, studies thus far focus either on extensive membrane modeling with brute-force optimization via simple comparison or on meticulous optimization using an oversimplified membrane model. We believe that the integrated framework can bridge the well justified approaches in both filtration modeling and mathematical optimization and help in designing multi-unit processes.

The ultrafiltration/diafiltration (UF/DF) process is a crucial step in the canola protein isolation process from rapeseed meal. The process involves using a multi-stage membrane system to separate components of the mixture. As diafiltration dilutes the feed stream in the ultrafiltration system, a large amount of diafiltration water is required. Reducing the diafiltrate for sustainability reasons can be done by carefully selecting process variables or using recycle streams. However, finding the optimum process variables can be a meticulous process if performed experimentally or via trial and error. In this study, we performed an optimization using a mechanistic model integrated with a genetic algorithm to aid in finding an optimum combination of process variables. The mechanistic ultrafiltration model was derived by taking into account transport phenomena within the filtration system. Parameters were characterized experimentally in term of viscosity coefficient, membrane resistance, cake porosity, aggregate diameter equivalent, and material compressibility factors. Using the mechanistic model-based optimization in combination with actual experimental values, the performance of a four-stage UF/DF system could already be improved despite fixing the configuration, albeit at the cost of a reduction in purity. Further improvement could be achieved by using recycle streams. The optimized system achieved a diafiltrate reduction of up to 79%, an increase of purity of up to 31%, and an increase of dry matter content of up to 18%, while maintaining the product purity of the reference set-up.

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.