Vaccination is a strong and effective way to prevent spreading of infectious diseases and promotes global health. In the future, the importance of vaccines is expected only to increase, driven by factors such as increased international traveling, higher healthcare expenditures, and a growing population. To meet the growing demands, it is necessary to shorten process development timelines for the production of new vaccines without compromising on safety, efficacy, consistency, and stability of the product. Therefore, it is necessary to advance process development approaches of vaccines to respond quickly and in event of an emerging infectious disease. The work in this thesis employed mathematical modeling and simulation techniques to accelerate this process development. The developed methods are particularly valuable for early phase process development, aiming to enhance process knowledge and minimize the consumption of valuable resources and material. While the project has a focus on vaccine production processes, the modeling tools and methods developed are also applicable to other (bio)pharmaceutical processes. In Chapter 2, we discussed the present and future process development approaches in (bio)pharmaceutical purification with an emphasis on vaccines. The primary needs are to establish standardized processes and to improve understanding of both production processes and host cell impurities. Modeling, when combined with high throughput experimentation, can play a crucial role in achieving these goals...@en

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.@en

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.@en

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.

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The safety requirements for vaccines are extremely high since they are administered to healthy people. For that reason, vaccine development is time-consuming and very expensive. Reducing time-to-market is key for pharmaceutical companies, saving lives and money. Therefore the need is raised for systematic, general and efficient process development strategies to shorten development times and enhance process understanding. High throughput technologies tremendously increased the volume of process-related data available and, combined with statistical and mechanistic modeling, new high throughput process development (HTPD) approaches evolved. The introduction of model-based HTPD enabled faster and broader screening of conditions, and furthermore increased knowledge. Model-based HTPD has particularly been important for chromatography, which is a crucial separation technique to attain high purities. This review provides an overview of downstream process development strategies and tools used within the (bio)pharmaceutical industry, focusing attention on (protein subunit) vaccine purification processes. Subsequently high throughput process development and other combinatorial approaches are discussed and compared according to their experimental effort and understanding. Within a growing sea of information, novel modeling tools and artificial intelligence (AI) gain importance for finding patterns behind the data and thereby acquiring a deeper process understanding.

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Efficient recovery and utilization of valuable components from industrial food side streams is a main driver towards a circular economy. Among different available purification techniques, adsorption can effectively recover these components. However, the conventional batch mode of operation can limit its applicability in food processes due to limited efficiency. This work compares conventional batch packed bed adsorption with semi-continuous adsorption (so-called CaptureSMB) for the recovery of sinapic acid at industrial scale, using a food grade resin Amberlite^TM FPX66. A mathematical mechanistic model able to describe semi-continuous operation is successfully validated and used to identify optimum operating parameters to maximize productivity and resin capacity utilization in batch and semi-continuous operating modes. The results indicate that CaptureSMB outperforms batch operation, increasing productivity from 5.18 g/L/h to 10.3 g/L/h for a given yield (>97%). A resin capacity utilization (RU) of around 70% is observed in both operating modes when productivity is maximized. A 92% RU can be accomplished for a given yield using the CaptureSMB process at a productivity of 7.0 g/L/h, higher than for conventional batch operation. The use of semi-continuous adsorption operation in food industry contributes to more efficient processes at reduced purification costs.

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Sinapic acid is a potential valuable compound to be recovered from rapeseed meal extracts as it processes antioxidant and antimicrobial properties. However, the concentration of this compound might be low and the presence of other low value compounds could complicate its downstream processing. Adsorption is an alternative technique that might allow selective recovery of this compound. This work was focused on establishing the foundation of an industrial process design to recover sinapic acid by adsorption. The obtained results from multicomponent experiments indicate that, resin Amberlite^TM FPX66 is the best performing one showing a maximum adsorption capacity of 102.6 + 11.7 mg/g_resin, easy sinapic acid recovery by desorbing it with 70% ethanol and high selectivity to sinapic acid over glucose, phytic acid and glucosinolates. The obtained equilibrium information was applied as input in a dynamic column model and compared with experimental results, showing a good agreement (r² = 0.98). The model can be further applied for a large-scale chromatography process design to recover sinapic acid from rapeseed/canola meal extracts.

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