Model based process optimization of an industrial chromatographic process for separation of lactoferrin from bovine milk
Model based process development using predictive mechanistic models is a powerful tool for in-silico downstream process development. It allows to obtain a thorough understanding of the process reducing experimental effort. While in pharma industry, mechanistic modeling becomes more common in the las...
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| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
8 November 2023
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| In: |
Journal of chromatography
Year: 2023, Jahrgang: 1710, Pages: 1-13 |
| ISSN: | 1873-3778 |
| DOI: | 10.1016/j.chroma.2023.464428 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.chroma.2023.464428 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0021967323006532 
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| Verfasserangaben: | Lukas Gerstweiler, Paulina Schad, Tatjana Trunzer, Lena Enghauser, Max Mayr, Jagan Billakanti |
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| 245 | 1 | 0 | |a Model based process optimization of an industrial chromatographic process for separation of lactoferrin from bovine milk |c Lukas Gerstweiler, Paulina Schad, Tatjana Trunzer, Lena Enghauser, Max Mayr, Jagan Billakanti |
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| 520 | |a Model based process development using predictive mechanistic models is a powerful tool for in-silico downstream process development. It allows to obtain a thorough understanding of the process reducing experimental effort. While in pharma industry, mechanistic modeling becomes more common in the last years, it is rarely applied in food industry. This case study investigates risk ranking and possible optimization of the industrial process of purifying lactoferrin from bovine milk using SP Sepharose Big Beads with a resin particle diameter of 200 µm, based on a minimal number of lab-scale experiments combining traditional scale-down experiments with mechanistic modeling. Depending on the location and season, process water pH and the composition of raw milk can vary, posing a challenge for highly efficient process development. A predictive model based on the general rate model with steric mass action binding, extended for pH dependence, was calibrated to describe the elution behavior of lactoferrin and main impurities. The gained model was evaluated against changes in flow rate, step elution conditions, and higher loading and showed excellent agreement with the observed experimental data. The model was then used to investigate the critical process parameters, such as water pH, conductivity of elution steps, and flow rate, on process performance and purity. It was found that the elution behavior of lactoferrin is relatively consistent over the pH range of 5.5 to 7.6, while the elution behavior of the main impurities varies greatly with elution pH. As a result, a significant loss in lactoferrin is unavoidable to achieve desired purities at pH levels below pH 6.0. Optimal process parameters were identified to reduce water and salt consumption and increase purity, depending on water pH and raw milk composition. The optimal conductivity for impurity removal in a low conductivity elution step was found to be 43 mS/cm, while a conductivity of 95 mS/cm leads to the lowest overall salt usage during lactoferrin elution. Further increasing the conductivity during lactoferrin elution can only slightly lower the elution volume thus can also lead to higher total salt usage. Low flow rates during elution of 0.2 column volume per minute are beneficial compared to higher flow rates of 1 column volume per minute. The, on lab-scale, calibrated model allows predicting elution volume and impurity removal for large-scale experiments in a commercial plant processing over 106 liters of milk per day. The successful model extrapolation was possible without recalibration or detailed knowledge of the manufacturing plant. This study therefore provides a possible pathway for rapid process development of chromatographic purification in the food industries combining traditional scale-down experiments with mechanistic modeling. | ||
| 650 | 4 | |a chromatography | |
| 650 | 4 | |a dairy | |
| 650 | 4 | |a Lactoferrin | |
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| 650 | 4 | |a process optimization | |
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