Michel H.M. Eppink

Multi‐dimensional fractionation and characterization of crude protein mixtures: Toward establishment of a database of protein purification process development parameters

A multi‐dimensional fractionation and characterization scheme was developed for fast acquisition of the relevant molecular properties for protein separation from crude biological feedstocks by ion‐exchange chromatography (IEX), hydrophobic interaction chromatography (HIC), and size‐exclusion chromatography. In this approach, the linear IEX isotherm parameters were estimated from multiple linear salt‐gradient IEX data, while the nonlinear IEX parameters as well as the HIC isotherm parameters were obtained by the inverse method under column overloading conditions. Collected chromatographic fractions were analyzed by gel electrophoresis for estimation of molecular mass, followed by mass spectrometry for protein identification. The usefulness of the generated molecular properties data for rational decision‐making during downstream process development was equally demonstrated. Monoclonal antibody purification from crude hybridoma cell culture supernatant was used as case study. The obtained chromatographic parameters only apply to the employed stationary phases and operating conditions, hence prior high throughput screening of different chromatographic resins and mobile phase conditions is still a prerequisite. Nevertheless, it provides a quick, knowledge‐based approach for rationally synthesizing purification cascades prior to more detailed process optimization and evaluation. Biotechnol. Bioeng. 2012; 109: 3070–3083.

Size-exclusion chromatographic protein refolding: Fundamentals, modeling and operation

Size-exclusion chromatography (SEC) has proven its capability to refold a variety of proteins using a range of gel filtration column materials, demonstrated in the growing body of experimental evidence. However, little effort has been allocated to the development of mechanistic models describing size-exclusion chromatographic refolding reactors (SECRR). Mechanistic models are important since they provide a link between process variables like denatured and reduced protein feed concentration (C(f,D&R)), flow rate, column length, etc., and performance indicators like refolding yield (Y(N)), thereby opening the possibility for in silico design of SECRRs. A critical step, in the formulation of such models, is the selection of an adequate reaction mechanism, which provides the direct link between the separation and the refolding yield. Therefore, in this work we present a methodology using a SEC refolding reactor model, supported by a library of reaction mechanisms, to estimate a suitable reaction scheme using experimental SEC refolding data. SEC refolding data is used since it provides information about the mass distribution of monomers and aggregates after refolding, information not readily available from batch dilution refolding data alone. Additionally, this work presents (1) a systematic analysis of the reaction mechanisms considered using characteristic time analysis and Damköhler maps, revealing (a) the direct effect of a given reaction mechanism on the shape of the SEC refolding chromatogram (number of peaks and resolution) and (b) the effect that the competition between convection, refolding and aggregation is likely to have on the SEC refolding yield; (2) a comparison between the SECR reactor and the batch dilution refolding reactor based on mechanistic modeling, quantitatively showing the advantages of the former over the latter; and (3) the successful application of the modeling based strategy to study the SEC refolding data of an industrially relevant protein. In principle, the presented modeling strategy can be applied to any protein refolded using any gel filtration material, providing the proper mass balances and activity measurements are available.

Optimization of Biopharmaceutical Downstream Processes Supported by Mechanistic Models and Artificial Neural Networks

Downstream process development is a major area of importance within the field of bioengineering. During the design of such a downstream process, important decisions have to be made regarding the type of unit operations as well as their sequence and their operating conditions. Current computational approaches addressing these issues either show a high level of simplification or struggle with computational speed. Therefore, this article presents a new approach that combines detailed mechanistic models and speed‐enhancing artificial neural networks. This approach was able to simultaneously optimize a process with three different chromatographic columns toward yield with a minimum purity of 99.9%. The addition of artificial neural networks greatly accelerated this optimization. Due to high computational speed, the approach is easily extendable to include more unit operations. Therefore, it can be of great help in the acceleration of downstream process development.

Prediction of protein retention times in hydrophobic interaction chromatography by robust statistical characterization of their atomic‐level surface properties

The correlation between the dimensionless retention times (DRT) of proteins in hydrophobic interaction chromatography (HIC) and their surface properties were investigated. A ternary atomic‐level hydrophobicity scale was used to calculate the distribution of local average hydrophobicity across the proteins surfaces. These distributions were characterized by robust descriptive statistics to reduce their sensitivity to small changes in the three‐dimensional structure. The applicability of these statistics for the prediction of protein retention behaviour was looked into. A linear combination of robust statistics describing the central tendency, heterogeneity and frequency of highly hydrophobic clusters was found to have a good predictive capability (R2 = 0.78), when combined a factor to account for protein size differences. The achieved error of prediction was 35% lower than for a similar model based on a description of the protein surface on an amino acid level. This indicates that a robust and mathematically simple model based on an atomic description of the protein surface can be used for the prediction of the retention behaviour of conformationally stable globular proteins with a well determined 3D structure in HIC.

Fourier transform assisted deconvolution of skewed peaks in complex multi-dimensional chromatograms

Lower order peak moments of individual peaks in heavily fused peak clusters can be determined by fitting peak models to the experimental data. The success of such an approach depends on two main aspects: the generation of meaningful initial estimates on the number and position of the peaks, and the choice of a suitable peak model. For the detection of meaningful peaks in multi-dimensional chromatograms, a fast data scanning algorithm was combined with prior resolution enhancement through the reduction of column and system broadening effects with the help of two-dimensional fast Fourier transforms. To capture the shape of skewed peaks in multi-dimensional chromatograms a formalism for the accurate calculation of exponentially modified Gaussian peaks, one of the most popular models for skewed peaks, was extended for direct fitting of two-dimensional data. The method is demonstrated to successfully identify and deconvolute peaks hidden in strongly fused peak clusters. Incorporation of automatic analysis and reporting of the statistics of the fitted peak parameters and calculated properties allows to easily identify in which regions of the chromatograms additional resolution is required for robust quantification.

3D-liquid chromatography as a complex mixture characterization tool for knowledge-based downstream process development.

Knowledge‐based development of chromatographic separation processes requires efficient techniques to determine the physicochemical properties of the product and the impurities to be removed. These characterization techniques are usually divided into approaches that determine molecular properties, such as charge, hydrophobicity and size, or molecular interactions with auxiliary materials, commonly in the form of adsorption isotherms. In this study we demonstrate the application of a three‐dimensional liquid chromatography approach to a clarified cell homogenate containing a therapeutic enzyme. Each separation dimension determines a molecular property relevant to the chromatographic behavior of each component. Matching of the peaks across the different separation dimensions and against a high‐resolution reference chromatogram allows to assign the determined parameters to pseudo‐components, allowing to determine the most promising technique for the removal of each impurity. More detailed process design using mechanistic models requires isotherm parameters. For this purpose, the second dimension consists of multiple linear gradient separations on columns in a high‐throughput screening compatible format, that allow regression of isotherm parameters with an average standard error of 8%.