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 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.

Predictive scaling of fiber-based protein A capture chromatography using mechanistic modeling.

Protein A affinity chromatography is an important step in the purification of monoclonal antibodies (mAbs) and mAb-derived biotherapeutics. While the biopharma industry has extensive expertise in the operation of protein A chromatography, the mechanistic understanding of the adsorption/desorption processes is still limited, and scaling up and scaling down can be challenging because of complex mass transfer effects in bead-based resins. In convective media, such as fiber-based technologies, complex mass transfer effects such as film and pore diffusions do not occur which facilitates the study of the adsorption phenomena in more detail and simplifies the process scale-up. In the present study, the experimentation with small-scale fiber-based protein A affinity adsorber units using different flow rates forms the basis for modeling of mAb adsorption and elution behavior. The modeling approach combines aspects of both stoichiometric and colloidal adsorption models, and an empirical part for the pH. With this type of model, it was possible to describe the experimental chromatograms on a small scale very well. An in silico scale-up could be carried out solely with the help of system and device characterization without feedstock. The adsorption model could be transferred without adaption. Although only a limited number of runs were used for modeling, the predictions of up to 37 times larger units were accurate.

Adsorption of organic molecules on carbon surfaces: Experimental data and molecular dynamics simulation considering multiple protonation states.

Owing to their high specific surface and low production cost, carbon materials are among the most important adsorption materials. Novel usages, for instance in pharmaceutical applications, challenge existing methods because charged and strongly polar substances need to be adsorbed. Here, we systematically investigate the highly complex adsorption equilibria of organic molecules having multiple protonation states as a function of pH. The adsorption behavior depends on intermolecular interactions within the solution (dissociation equilibria) and between adsorbed molecules on the carbon surface (electrostatic forces). For the model substances maleic acid and phenylalanine, we demonstrate that a custom-made genetic algorithm is able to extract up to nine parameters of a multispecies isotherm from experimental data covering a broad pH-range. The parameters, including adsorption affinities, interaction energies, and maximum loadings were also predicted by molecular dynamics simulations. Both approaches obtained a good qualitative and mostly also quantitative description of the adsorption behavior within a pH-range of 2-12. By combining the determined isotherms with mass balances, the final concentrations and pH-shifts of batch adsorption experiments can be predicted. The developed modeling tools can be easily adapted to other types of pH-dependent, multispecies adsorbates and therefore will help to optimize adsorption-based processes in different fields.