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Abstract: BACKGROUND: The bioavailability of therapeutic proteins is improved through PEGylation. This chemical modification involves the production of isomers with different numbers and sites of attached PEG chains, which are difficult to separate efficiently. Their purification with chromatography requires an understanding of the operation and the evaluation of different operational conditions. The General Rate Model (GRM) was applied for modelling the linear salt gradient elution of mono‐PEGylated and native lysozyme in Heparin Affinity Chromatography (HAC) considering mass balance equations for proteins in the bulk‐fluid phase, in the particle phase and the kinetic adsorption. RESULTS: The model was able to simulate the individual proteins and the separation of these in a PEGylation reaction using as proof‐of‐concept a mono‐PEGylated and native lysozyme mixture under changes of operational parameters such as the gradient length (5, 13, 25 column volumes) and flow (0.8 and 1.2 mL min‐1) with a relative error in retention times of less than 6% and correlation coefficients greater than 0.78. CONCLUSION: Simulation of the elution curves of PEGylated lysozyme in HAC was performed in this work and the diverse information generated by the model is explained through the physicochemical protein properties. This simulation represents a tool for optimization, prediction and future scale‐up of PEGylated proteins purification, which would reduce the investment in time and resources to test several operating conditions. © 2017 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Hydrophobic interaction chromatography (HIC) is widely used in the downstream processing of proteins. Resolution of HIC is very good, but sometimes not as high as expected. Resolution values could be increased if good operating conditions were selected. In this paper we present a methodology for selecting good operating conditions. First, it is necessary to predict the dimensionless retention time (DRT) of each protein in the mixture. We use a correlation such as DRT=A+Bφ+Cφ2, where φ is the superficial hydrophobicity of the protein, which is calculated considering the hydrophobicity of the superficial amino acids using the Miyazawa–Jernigan scale. Considering that there was little interaction amongst proteins in a mixture at the concentrations investigated (2 g/l of each protein), the behaviour of the proteins in the mixture was considered to be similar to that of the individual proteins. Using simulations it was possible to test different operating conditions for the purification of a target protein from a mixture of proteins and it was possible to select ideal conditions. The methodology developed was also tested for the purification of a recombinant protein from a fermentation extract of yeast producing human superoxide dismutase and the results have been satisfactory. [Copyright &y& Elsevier]

This paper reports how a novel method for determining the surface hydrophobicity of a protein can be used to predict the dimensionless retention time (DRT) in hydrophobic interaction chromatography. The methodology has three steps. First, it is necessary to know the three-dimensional structure of the target protein. Then, it is necessary to calculate the surface hydrophobicity of the protein, considering that each amino acid has a relative contribution to the surface properties, using an appropriate equation. This will depend largely on the relative scale used to evaluate the hydrophobicity of each amino acid. Forty-two scales were investigated and two gave the best correlation, the Miyazawa–Jernigan and the Cowan–Whittaker scales. Finally, a “quadratic model” (DRT=a+bΦsurface+cΦsurface2) is used to predict the retention time of the target protein. The methodology was able to correlate adequately the retention data for monomeric proteins not included in the generation of the model. [Copyright &y& Elsevier]

The development of systematic methods for the synthesis of downstream protein processing operations has seen growing interest in recent years, as purification is often the most complex and costly stage in biochemical production plants. The objective of the work presented here is to develop mathematical models based on mixed integer optimization techniques, which integrate the selection of optimal peptide purification tags into an established framework for the synthesis of protein purification processes. Peptide tags are comparatively short sequences of amino acids fused onto the protein product, capable of reducing the required purification steps. The methodology is illustrated through its application on two example protein mixtures involving up to 13 contaminants and a set of 11 candidate chromatographic steps. The results are indicative of the benefits resulting by the appropriate use of peptide tags in purification processes and provide a guideline for both optimal tag design and downstream process synthesis.