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.

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.

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.

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.