MODELING TO ESTIMATE DYNAMIC CAPACITY IN PROCESS CHROMATOGRAPHY
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
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 4. Schematic of the competitive, binary adsorption process for a monomer and aggregate mixture. The aggregate species
binds irreversibly in the case shown
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 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
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.
Figure 6. Comparison of results from traditional screening approach and modeling approach. Bars indicate individual contributions