Modeling of Biopharmaceutical Processes. Part 2: Process Chromatography Unit Operation - How to apply the latest thinking in process modeling to your process using a Quality by Design approach. - BioP

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Modeling of Biopharmaceutical Processes. Part 2: Process Chromatography Unit Operation
How to apply the latest thinking in process modeling to your process using a Quality by Design approach.


BioPharm International
Volume 21, Issue 8

MODELING TO ESTIMATE DYNAMIC CAPACITY IN PROCESS CHROMATOGRAPHY


Figure 4. Schematic of the competitive, binary adsorption process for a monomer and aggregate mixture. The aggregate species binds irreversibly in the case shown
Models describing protein adsorption within a chromatography column can be used to predict the performance of the chromatography step, including the dynamic binding capacity, effects of column scale-up, and separation of complex multicomponent protein mixtures.1,3,11,12 Once the model parameters are known, dynamic binding capacity can be predicted over a range of operating conditions, such as different column bed heights and operating velocities. Because experimental measurement of the dynamic binding in high capacity resins can involve significant amounts of feed material (because protein breakthrough must be achieved), the prediction of the dynamic binding capacity can result in significant savings in development time and protein-feed requirements. Modeling predictions can also be used to determine which adsorbents will have the highest capacities over a range of operating conditions.


Figure 5. Comparison of a simulated and an experimental purification of the four closely related components. the ordinate axis on the right hand side of the figure shows the concentration scale of the gradient
McCue, et al., have used modeling to predict the separation of highly complex, multicomponent protein mixtures after adsorption onto the column.1 As a result of recent advancements in computational power, this type of approach can be used to rapidly predict the separation of two or more components possessing similar adsorptive properties, such as the separation of protein and monomer species using hydrophobic interaction chromatography (HIC).1 Table 2 shows an example of model predictions and experimental results for the separation of monomer and aggregate species using Phenyl Sepharose Fast Flow (GE Healthcare) to evaluate product yield and aggregate removal over a range of operating conditions. As with all model formulations, the governing adsorption isotherm model must first be chosen. In the case of the described monomer or aggregate separation, a competitive Langmuir binary adsorption isotherm was selected, in which the aggregate species bound irreversibly to the HIC adsorbent, as shown schematically in Figure 4. When formulating model predictions, a sensitivity analysis should be performed to determine which input parameters have the greatest impact on the model predictions. Once this is known, highly sensitive parameters should be measured as accurately as possible to minimize the uncertainty in the predictions. A model parameter sensitivity analysis can also add further insight into the fundamental mechanisms that govern the particular adsorption or separation. For example, a sensitivity analysis may show the dynamic binding capacity is highly sensitive to moderate changes in the operating velocity. This information could be highly useful for resin selection, as it may be desirable to select a resin in which the performance is insensitive to flow rate, so that the process throughput could be increased without a loss in dynamic binding capacity. A sensitivity analysis could also help in troubleshooting investigations or manufacturing deviations involving chromatography steps. For example, if during the course of an investigation an input parameter was found to be above or below an expected range, the model could be used to predict the effect on column performance, as well as what corrective actions should be taken. Model predictions could be especially useful for troubleshooting if experimental data are not available, as could be the case when unexpected deviations occur in a manufacturing environment.


Figure 6. Comparison of results from traditional screening approach and modeling approach. Bars indicate individual contributions of variance.
Mollerup, et al., used a thermodynamic model to simulate elution of several closely related compounds in process chromatography characterized by four key components.3 They assumed that all activity coefficients except the activity coefficients of the solute proteins are unity or constant. In the case of ion-exchange, the data presented demonstrate that the activity coefficients in this case are of minor importance. As seen in Figure 5, the agreement between the adsorption models and the experimental adsorption data is good with respect to the actual chromatogram, impurity profile, yield, and collected fraction volume, thus indicating that the chosen model describes the actual process quite well. Though the conclusions made on the basis of simulation need to be confirmed experimentally, the authors state that proper use of simulations can reduce the number of experiments substantially.


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