Stefan Hepbildikler

Determination of parameters for the steric mass action model—A comparison between two approaches

The application of mechanistic modeling for the optimization of chromatographic steps increased recently due to time efficiency of algorithms and rising calculation power. In the modeling of ion exchange chromatography steps, the sorption processes occurring on adsorbent particle surfaces can be simulated with the steric mass action (SMA) model introduced by Brooks and Cramer (1992) [14]. In this paper, two approaches for the determination of SMA parameters will be carried out and discussed concerning their specific experimental effort, quality of results, method differences, reasons for uncertainties and consequences for SMA parameter determination: Approach I: estimation of SMA parameters based on gradient and frontal experiments according to instructions in Brooks and Cramer (1992) [14] and Shukla et al. (1998) [16]. Approach II: application of an inverse method for parameter estimation, resulting in SMA parameters that induce a best fit of chromatographic data to a mechanistic model for column chromatography. These approaches for SMA parameter determination were carried out for three proteins (ribonuclease A, cytochrome c and lysozyme) at pH 5 and pH 7. The results were comparable and the order of parameter values and their relations to the chromatographic data similar. Nevertheless, differences in the complexity and effort of methods as well as the parameter values themselves were observed. The comparison of methods demonstrated that discrepancies depend mainly on model sensitivities and additional parameters influencing the calculations. However, the discrepancies do not affect predictivity; predictivity is high in both approaches. The approach based on an inverse method and the mechanistic model has the advantage that not only retention times but also complete elution profiles can be predicted. Thus, the inverse method based on a mechanistic model for column chromatography is the most comfortable way to establish highly predictive SMA parameters lending themselves for the optimization of chromatography steps and process control.

Optimizing a chromatographic three component separation: A comparison of mechanistic and empiric modeling approaches

The search for a favorable and robust operating point of a separation process represents a complex multi-factor optimization problem. This problem is typically tackled by design of experiments (DoE) in the factor space and empiric response surface modeling (RSM); however, separation optimizations based on mechanistic modeling are on the rise. In this paper, a DoE-RSM-approach and a mechanistic modeling approach are compared with respect to their performance and predictive power by means of a case study - the optimization of a multicomponent separation of proteins in an ion exchange chromatography step with a nonlinear gradient (ribonuclease A, cytochrome c and lysozyme on SP Sepharose FF). The results revealed that at least for complex problems with low robustness, the performance of the DoE-approach is significantly inferior to the performance of the mechanistic model. While some influential factors of the system could be detected with the DoE-RSM-approach, predictions concerning the peak resolutions were mostly inaccurate and the optimization failed. The predictions of the mechanistic model for separation results were very accurate. Influences of the experimental factors could be quantified and the separation was optimized with respect to several objectives. However, the discussion of advantages and disadvantages of empiric and mechanistic modeling generates synergies of both methods and leads to a new optimization concept, which is promising with respect to an efficient employment of high throughput screening data.

Calibration-free inverse modeling of ion-exchange chromatography in industrial antibody purification

The identification of optimal process parameters for the isolation of a target component from multicomponent mixtures is especially challenging in industrial applications. With constantly increasing time‐to‐market pressure, screening a large parameter space is not feasible and design‐of‐experiment approaches with few experiments might fail due to dynamic and nonlinear reactions to small parameter changes. Model‐based optimization can determine optimal operating conditions, once the model has been calibrated to the specific process step. In this work, parameters for the steric mass action model were estimated for the target protein and three impurities of an industrial antibody cation‐exchange purification step using only chromatograms at different wavelengths and additional fraction analyses with size exclusion chromatography. Information on the molar or mass concentrations in the feed are not available. The model‐based optimization results coincide with conventional chromatogram‐based optimization.

Proceedings of the 2017 Viral Clearance Symposium: Conclusion

This report provides a high-level summary of the key outcomes and gaps based on the research presented at the Viral Clearance Symposium 2017 and identifies new areas for future study and improvements. The 2017 conference structure extended the framework from the preceding conferences, focusing on the key gaps and associated developments and including the additional focus areas of facility risk mitigation and continuous processing, and ways to improve the efficiency of the overall adventitious agent strategy. LAY ABSTRACT: This report provides a high-level summary of the key outcomes and gaps based on the research presented at the Viral Clearance Symposium 2017 and identifies new areas for future study and improvements.

Proceedings of the 2017 Viral Clearance Symposium - Introduction

This article introduces the white paper from the 2017 Viral Clearance Symposium. The 5th Viral Clearance Symposium in Penzberg, Germany, addressed regulatory perspectives presented by officials from Health Canada, the US Food and Drug Administration, and Paul-Ehrlich-Institut as well as upstream and facility risk mitigation, downstream unit operations, and viral clearance strategies to support novel molecule formats, accelerated scenarios, and continuous processing. LAY ABSTRACT: This article introduces the summarized findings and next steps from the 2017 Viral Clearance Symposium in Penzberg, Germany.

Modeling of complex antibody elution behavior under high protein load densities in ion exchange chromatography using an asymmetric activity coefficient

A main requirement for the implementation of model-based process development in industry is the capability of the model to predict high protein load densities. The frequently used steric mass action isotherm assumes a thermodynamically ideal system and, hence constant activity coefficients. In this manuscript, an industrial antibody purification problem under high load conditions is considered where this assumption does not hold. The high protein load densities, as commonly applied in industrial downstream processing, may lead to complex elution peak shapes. Using Mollerups generalized ion-exchange isotherm (GIEX), the observed elution peak shapes could be modeled. To this end, the GIEX isotherm introduced two additional parameters to approximate the asymmetric activity coefficient. The effects of these two parameters on the curvature of the adsorption isotherm and the resulting chromatogram are investigated. It could be shown that they can be determined by inverse peak fitting and conform with the mechanistic demands of model-based process development.