Combined effects of potassium chloride and ethanol as mobile phase modulators on hydrophobic interaction and reversed-phase chromatography of three insulin variants.

The two main chromatographic modes based on hydrophobicity, hydrophobic interaction chromatography (HIC) and reversed-phase chromatography (RPC), are widely used for both analytical and preparative chromatography of proteins in the pharmaceutical industry. Despite the extensive application of these separation methods, and the vast amount of studies performed on HIC and RPC over the decades, the underlying phenomena remain elusive. As part of a systematic study of the influence of mobile phase modulators in hydrophobicity-based chromatography, we have investigated the effects of both KCl and ethanol on the retention of three insulin variants on two HIC adsorbents and two RPC adsorbents. The focus was on the linear adsorption range, separating the modulator effects from the capacity effects, but some complementary experiments at higher load were included to further investigate observed phenomena. The results show that the modulators have the same effect on the two RPC adsorbents in the linear range, indicating that the modulator concentration only affects the activity of the solute in the mobile phase, and not that of the solute-ligand complex, or that of the ligand. Unfortunately, the HIC adsorbents did not show the same behavior. However, the insulin variants displayed a strong tendency toward self-association on both HIC adsorbents; on one in particular. Since this causes peak fronting, the retention is affected, and this could probably explain the lack of congruity. This conclusion was supported by the results from the non-linear range experiments which were indicative of double-layer adsorption on the HIC adsorbents, while the RPC adsorbents gave the anticipated increased tailing at higher load.

Prediction of IgG1 aggregation in solution.

Interest in monoclonal antibody aggregation is increasing as aggregates of biopharmaceuticals can cause an immunogenic response when injected into the body. In this work, a stoichiometric reaction model from concentration-time data is developed to predict the dimer ratio in stored antibody solutions over time. IgG1 was incubated at pH from 4.5 to 5.5, salt concentrations from 100 to 600 mmol/kg and protein concentrations of 10.6-26.3 g/L; samples were taken at intervals of 20 min to 5 h over time periods from 4 h to 7.6 days, and analyzed with size-exclusion chromatography. The experiments showed the formation of dimers from monomers, but no higher order aggregates. Dilution of samples containing dimers led to the reversal of the dimerization reaction. Measurements of the concentrations of each component were made by fitting exponentially modified Gaussian peaks to the chromatograms used to measure the concentrations of the different forms of protein. This stoichiometric reaction model was able to predict the formation of dimers by the antibody studied. The equilibrium constant was found to be dependent on the salt concentration, and the kinetic constant showed a dependence on the pH of the solution. The prediction of the aggregation leads to a possibility of optimizing the conditions in order to prevent the dimer formation and to maximize the monomer concentration.

Model-based design and integration of a two-step biopharmaceutical production process.

This paper presents the design of a two-step process in which the first step is PEGylation of a protein, and the second step is chromatographic purification of the target mono-PEGylated protein from the unreacted and the di-PEGylated impurities. The difference between optimizing each process step separately and optimizing them simultaneously is studied. It was found that by optimizing the steps simultaneously up to a 100 % increase in productivity could be obtained without reduction in yield. Optimizing both steps at the same time makes it possible for the optimization method to take into account that the di-PEGylated protein is much easier to separate than the non-PEGylated protein. The easier separation makes it possible to get a higher yield and productivity at the same time. The effect of recycling was also studied and the yield could be increased by 30 % by recycling the unreacted protein. However, if maximum productivity is required, batch mode is preferable.

Pooling control in variable preparative chromatography processes.

Preparative chromatographic columns that run at high loads are highly sensitive to batch-to-batch disturbances of the process parameters, placing high demands on the strategy used for pooling of the product fractions. A new approach to pooling control is presented in a proof-of-concept study. A model-based sensitivity analysis was performed identifying the critical process parameters to product purity and optimal cut points. From this, the robust fixed cut points were found and pooling control strategies for variations in the critical parameters were designed. Direct measurements and indirect measurements based on the UV detector signal were used as control signals. The method is demonstrated for two case studies of preparative protein chromatography: hydrophobic interaction and reversed phase chromatography. The yield improved from 88.18 to 92.88% when changing from fixed to variable pooling in hydrophobic interaction chromatography, and from 35.15 to 76.27% in the highly sensitive reversed phase chromatography.

Modeling and optimization of preparative reversed-phase liquid chromatography for insulin purification.

This paper presents a model for reversed-phase purification of insulin from desamido insulin. The system is described by a reaction dispersive model with a competitive Langmuir isotherm. A model building and calibration method is presented and the model's region of validity is defined. The model is calibrated using only two-component experiments on the raw mixture by the inverse method and then experimentally validated. The model is then used to optimize the system's production rate with both purity and yield requirements. The yield requirement is varied between 80 and 95% to study the effect on the production rate and the operating point. The operating points found with the optimization were found outside the model's region of validity, but the experimental validation of the operating points shows that the model can be extrapolated to the interesting operating points.

Model based robustness analysis of an ion-exchange chromatography step.

Process development, optimization and robustness analysis for chromatographic separation are often entirely based on experimental work and generic knowledge. This paper describes a model-based approach that can be used to gain process knowledge and assist in the robustness analysis of an ion-exchange chromatography step using a model-based approach. A kinetic dispersive model, where the steric mass action model accounts for the adsorption is used to describe column performance. Model calibration is based solely on gradient elution experiments at different gradients, flow rates, pH and column loads. The position and shape of the peaks provide enough information to calibrate the model and thus single-component experiments can be avoided. The model is calibrated to the experiments and the confidence intervals for the estimated parameters are used to account for the model error throughout the analysis. The model is used to predict the result of a robustness analysis conducted as a factorial experiment and to design a robust pooling approach. The confidence intervals are used in a "worst case" approach where the parameters for the components are set at the edge of their confidence intervals to create a worst case for the removal of impurities at each point in the factorial experiment. The pooling limit was changed to ensure product quality at every point in the factorial analysis. The predicted purities and yields were compared to the experimental results to ensure that the prediction intervals cover the experimental results.

Constrained optimization of a preparative ion-exchange step for antibody purification.

Today, the optimization of chromatographic separation is usually based on experimental work and rule of thumb. The process and analytical technology (PAT) initiative, of the US Food and Drug Administration, has provided the opportunity of using model-based approach when designing downstream processing of pharmaceutical substances. A nonlinear chromatography model was used in this study to optimize a preparative ion-exchange separation step involving two components. Separation was simulated with the general rate model employing Langmuir kinetics. Optimization was performed with an indirect method allowing constraints on the purity, thus avoiding sub-optimization, which can lead to noisy objective functions. The six decision variables used in the optimizations were flow rate, loading volume, initial salt concentration in the elution, final salt concentration in the linear elution gradient and the two cut points. A graphical representation of the effect of the decision variables on the objective function was used to verify that the optimization had converged to the true optimum. The optimal operating points, using productivity and yield separately as objective functions, were found and compared with the product of productivity and yield as objective function. The optimum obtained with this objective function had a lower productivity, than the productivity function, but much higher yield, which makes it a good substitute for a cost function.

Optimisation and robustness analysis of a hydrophobic interaction chromatography step.

Process development, optimisation and robustness analysis for chromatography separations are often entirely based on experimental work and generic knowledge. The present study proposes a method of gaining process knowledge and assisting in the robustness analysis and optimisation of a hydrophobic interaction chromatography step using a model-based approach. Factorial experimental design is common practice in industry today for robustness analysis. The method presented in this study can be used to find the critical parameter variations and serve as a basis for reducing the experimental work. In addition, the calibrated model obtained with this approach is used to find the optimal operating conditions for the chromatography column. The methodology consists of three consecutive steps. Firstly, screening experiments are performed using a factorial design. Secondly, a kinetic-dispersive model is calibrated using gradient elution and column load experiments. Finally, the model is used to find optimal operating conditions and a robustness analysis is conducted at the optimal point. The process studied in this work is the separation of polyclonal IgG from BSA using hydrophobic interaction chromatography.