Chromatographic parameter determination for complex biological feedstocks

Abstract

The application of mechanistic models for chromatography requires accurate model parameters. Especially for complex feedstocks such as a clarified cell harvest, this can still be an obstacle limiting the use of mechanistic models. Another commonly encountered obstacle is a limited amount of sample material and time to determine all needed parameters. Therefore, this study aimed at implementing an approach on a robotic liquid handling system that starts directly with a complex feedstock containing a monoclonal antibody. The approach was tested by comparing independent experimental data sets with predictions generated by the mechanistic model using all parameters determined in this study. An excellent agreement between prediction and experimental data was found verifying the approach. Thus, it can be concluded that RoboColumns with a bed volume of 200 μL can well be used to determine isotherm parameters for predictions of larger scale columns. Overall, this approach offers a new way to determine crucial model input parameters for mechanistic modelling of chromatography for complex biological feedstocks. © 2018 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:1006–1018, 2018

The use of predictive models to develop chromatography-based purification processes

“…In our hands, purities of >50% are typically required to obtain a Gaussian curve-shaped peak for fitting ( Bernau et al, 2021 ), yet higher purities may be necessary depending on the type, number and individual abundance of non-target protein impurities. The presence of such impurities may result in shoulders or even several peak maxima, which in the simplest case can falsify the fitted transport parameters, and in the worst case can result in incorrect protein-specific isotherm parameters due to the selection of inappropriate peak properties, e.g., maxima, for fitting ( Pirrung et al, 2018 ). Nevertheless, the inverse method can determine parameter values for multicomponent isotherms, even for separation factors of 0.9–1.1 ( Kaczmarski 2007 ; Hahn et al, 2016a ), if the corresponding proteins do not interact with each other.…”

Automation and miniaturization: enabling tools for fast, high‐throughput process development in integrated continuous biomanufacturing

“…The power of automation is clear in this study, as it was shown that with the same setup it was possible to study the purification of ovalbumin from a mixture with conalbumin and BSA and capture of mAbs. Recently, implementation of a HTS setup coupled to mechanistic modelling showed how data retrieved from MiniColumns can be translated to laboratory‐scale chromatography 60,88,89 . By analyzing the Pe number at different scales, the authors concluded that an increased axial dispersion is observed at smaller scales, compared to larger scales, leading to larger elution pool volumes 60 .…”

“…Recently, implementation of a HTS setup coupled to mechanistic modelling showed how data retrieved from MiniColumns can be translated to laboratory-scale chromatography. 60,88,89 By analyzing the Pe number at different scales, the authors concluded that an increased axial dispersion is observed at smaller scales, compared to larger scales, leading to larger elution pool volumes. 60 The results then were used to correct the model, allowing for accurate prediction of elution pool volumes at larger scales using the MiniColumns for experimentation.…”

Prediction of the elution profiles of proteins in mixed salt systems in hydrophobic interaction chromatography

“…The elution profile is then obtained by plotting the total protein concentration at the exit of the column of length L (∑ p c p (z = L, t)) against the elution volume V elution , i.e. the total volume of the mobile phase that has eluted from the column between t = 0 and time t. Models of this type have been used before in the literature [23,[26][27][28][29][30]. In the present work, v p , ε * p , and k p are considered to be model parameters that depend on the protein p but are otherwise state-independent.…”