MAB - Biomolecular Separation Engineering

Digitalization

Modelling of biomolecular systems offers the possibility to better understand the behaviour of proteins at the molecular and process level. We are dealing with two different approaches. Molecular dynamics simulation (MD simulation) allows us to simulate individual proteins in their atomic environment and to predict their behavior during various purification steps. With the mechanistic modeling of chromatographic systems, the purification process is viewed macroscopically. Transport, binding and diffusion are described using differential equations. This enables an optimal process design.

Process analytical technology (PAT)

In the production of biopharmaceuticals, high and consistent product quality is key. However, in current downstream processes, real-time process monitoring and control play a minor roll in assuring the product quality. Instead, industry widely relies on offline analytics to ensure product quality (e.g. product content, concentration of co-eluting contaminants, host cell proteins) and to define important process parameters. This approach is however time-demanding and may introduce unwanted variability into the process.

In 2004, US Food and Drug Administration (FDA) introduced the Process Analytical Technology (PAT) initiative, which aims to promote (near) realtime process monitoring and control. The research group MAB works on chemometric methods for realtime monitoring. A special focus is set on:

  • Process monitoring by spectroscopy in conjunction with chemometrics
  • Realtime process control based on the obtained information
  • Root-cause investigations
Literature
2020
Rolinger, L.; Rüdt, M.; Hubbuch, J. (2020). A critical review of recent trends, and a future perspective of optical spectroscopy as PAT in biopharmaceutical downstream processing. Analytical and bioanalytical chemistry, 412 (9), 2047–2064. doi:10.1007/s00216-020-02407-z
Rolinger, L.; Rüdt, M.; Diehm, J.; Chow-Hubbertz, J.; Heitmann, M.; Schleper, S.; Hubbuch, J. (2020). Multi-attribute PAT for UF/DF of Proteins—Monitoring Concentration, particle sizes, and Buffer Exchange. Analytical and bioanalytical chemistry, 412 (9), 2123–2136. doi:10.1007/s00216-019-02318-8
2019
Rüdt, M.; Vormittag, P.; Hillebrandt, N.; Hubbuch, J. (2019). Process monitoring of virus-like particle reassembly by diafiltration with UV/Vis spectroscopy and light scattering. Biotechnology & bioengineering, 116 (6), 1366–1379. doi:10.1002/bit.26935
2018
Andris, S.; Rüdt, M.; Rogalla, J.; Wendeler, M.; Hubbuch, J. (2018). Monitoring of antibody-drug conjugation reactions with UV/Vis spectroscopy. Journal of biotechnology, 288, 15–22. doi:10.1016/j.jbiotec.2018.10.003
Großhans, S.; Rüdt, M.; Sanden, A.; Brestrich, N.; Morgenstern, J.; Heissler, S.; Hubbuch, J. (2018). In-line Fourier-transform infrared spectroscopy as a versatile process analytical technology for preparative protein chromatography. Journal of chromatography / A, 1547, 37–44. doi:10.1016/j.chroma.2018.03.005
2017
Hansen, S.; Brestrich, N.; Staby, A.; Hubbuch, J. (2017). Mid-UV Protein Absorption Spectra and Partial Least Squares Regression as Screening and PAT Tool. Preparative Chromatography for Separation of Proteins. Ed.: Arne Staby, 501–536, John Wiley & Sons, Hoboken (NJ). doi:10.1002/9781119031116.ch17
Rüdt, M.; Brestrich, N.; Rolinger, L.; Hubbuch, J. (2017). Real-time monitoring and control of the load phase of a protein A capture step. Biotechnology & bioengineering, 114 (2), 368–373. doi:10.1002/bit.26078
2016
Baumann, P.; Huuk, T.; Hahn, T.; Osberghaus, A.; Hubbuch, J. (2016). Deconvolution of high-throughput multicomponent isotherms using multivariate data analysis of protein spectra. Engineering in life sciences, 16 (2), 194–201. doi:10.1002/elsc.201400243
2015
Brestrich, N.; Sanden, A.; Kraft, A.; McCann, K.; Bertolini, J.; Hubbuch, J. (2015). Advances in inline quantification of co-eluting proteins in chromatography: Process-data-based model calibration and application towards real-life separation issues. Biotechnology and Bioengineering / Symposium, 112 (7), 1406–1416. doi:10.1002/bit.25546
Baumann, P.; Bluthardt, N.; Renner, S.; Burghardt, H.; Osberghaus, A.; Hubbuch, J. (2015). Integrated development of up- and downstream processes supported by the Cherry-Tag™ for real-time tracking of stability and solubility of proteins. Journal of biotechnology, 200, 27–37. doi:10.1016/j.jbiotec.2015.02.024
2014
Brestrich, N.; Briskot, T.; Osberghaus, A.; Hubbuch, J. (2014). A tool for selective inline quantification of co-eluting proteins in chromatography using spectral analysis and partial least squares regression. Biotechnology and Bioengineering, 111 (7), 1365–373. doi:10.1002/bit.25194
2013
Hansen, S. K.; Jamali, B.; Hubbuch, J. (2013). Selective high throughput protein quantification based on UV absorption spectra. Biotechnology & bioengineering, 110 (2), 448–460. doi:10.1002/bit.24712
Dismer, F.; Hansen, S.; Oelmeier, S. A.; Hubbuch, J. (2013). Accurate retention time determination of co-eluting proteins in analytical chromatography by means of spectral data. Biotechnology & bioengineering, 110 (3), 683–693. doi:10.1002/bit.24738
2012
Hansen, S. K.; Maiser, B.; Hubbuch, J. (2012). Rapid quantification of protein-polyethylene glycol conjugates by multivariate evaluation of chromatographic data. Journal of chromatography / A, 1257, 41–47. doi:10.1016/j.chroma.2012.07.089
2011
Hansen, S. K.; Skibsted, E.; Staby, A.; Hubbuch, J. (2011). A label-free methodology for selective protein quantification by means of absorption measurements. Biotechnology and Bioengineering, 108 (11), 2661–2669. doi:10.1002/bit.23229

Data science and data visualization

The field of data science and visualization works on advanced approaches to establish and connect data handling steps such as extraction, processing, visualization, and application. At MAB we strive to generate a broader understanding of bioprocessing by means of these data science strategies. This includes automated data extraction protocols for in-house labware and subsequent data cleaning and processing required for further usage. Algorithms are established to present the processed data by means of (multidimensional) data visualization to display data patterns in large and otherwise incomprehensive data sets. Data application is realized by developing machine learning and deep learning algorithms for classification and regression problems, which can be used to establish predictive and prescriptive models.

Literature
2019

Mechanistic Modeling of Chromatography

Preparative chromatography is one of the most important technologies for the purification of biologics, e.g. therapeutic proteins. Model-based chromatography process development is the response to Quality-by-Design (QbD) approach required by regulatory authorities such as FDA, EMA, etc. A mechanistic model consists of differential equations of varying complexities, which describe the mass transfer effects and the protein-ligand interaction within a chromatography column. In the last years, the in-house developed software ChromX could be successfully used for model-based process development to purify virus-like particles, monoclonal antibodies and other proteins. The benefits of model-based process development are obvious: up to 95% lab experiments for process optimization, robustness studies, and worst-case analysis can be replaced by computer-aided simulations. From the academic point of view, the gain of mechanistic understanding of the process is a great advantage.
 

ChromX

ChromX is a simulation toolbox for liquid chromatography of proteins. The conceptual goal of ChromX is to be a flexible multi-purpose software package for research while maintaining a high level of user-friendliness. ChromX was originally developed at MAB and is now commercialized by the startup GoSilico GmbH.

Literature
2020
Saleh, D.; Wang, G.; Müller, B.; Rischawy, F.; Kluters, S.; Studts, J.; Hubbuch, J. (2020). Straightforward method for calibration of mechanistic cation exchange chromatography models for industrial applications. Biotechnology progress, Art.Nr. e2984. doi:10.1002/btpr.2984
Briskot, T.; Hahn, T.; Huuk, T.; Hubbuch, J. (2020). Adsorption of colloidal proteins in ion-exchange chromatography under consideration of charge regulation. Journal of chromatography / A, 1611, Art.Nr. 460608. doi:10.1016/j.chroma.2019.460608
2019
Briskot, T.; Stückler, F.; Wittkopp, F.; Williams, C.; Yang, J.; Konrad, S.; Doninger, K.; Griesbach, J.; Bennecke, M.; Hepbildikler, S.; Hubbuch, J. (2019). Prediction uncertainty assessment of chromatography models using Bayesian inference. Journal of chromatography / A, 1587, 101–110. doi:10.1016/j.chroma.2018.11.076
2017
Morgenstern, J.; Wang, G.; Baumann, P.; Hubbuch, J. J. (2017). Model-Based Investigation on the Mass Transfer and Adsorption Mechanisms of Mono-Pegylated Lysozyme in Ion-Exchange Chromatography. Biotechnology journal, 12 (9), Art.Nr.: 1700255. doi:10.1002/biot.201700255
Wang, G.; Briskot, T.; Hahn, T.; Baumann, P.; Hubbuch, J. (2017). Root cause investigation of deviations in protein chromatography based on mechanistic models and artificial neural networks. Journal of chromatography / A, 1515, 146–153. doi:10.1016/j.chroma.2017.07.089
Huuk, T. C.; Hahn, T.; Doninger, K.; Griesbach, J.; Hepbildikler, S.; Hubbuch, J. (2017). Modeling of complex antibody elution behavior under high protein load densities in ion exchange chromatography using an asymmetric activity coefficient. Biotechnology journal, 12 (3), 1600336/1–8. doi:10.1002/biot.201600336
Wang, G.; Briskot, T.; Hahn, T.; Baumann, P.; Hubbuch, J. (2017). Estimation of adsorption isotherm and mass transfer parameters in protein chromatography using artificial neural networks. Journal of chromatography / A, 1487, 211–217. doi:10.1016/j.chroma.2017.01.068
2016
Effio, C. L.; Hahn, T.; Seiler, J.; Oelmeier, S. A.; Asen, I.; Silberer, C.; Villain, L.; Hubbuch, J. (2016). Modeling and simulation of anion-exchange membrane chromatography for purification of Sf9 insect cell-derived virus-like particles. Journal of chromatography, 1429, 142–154. doi:10.1016/j.chroma.2015.12.006
Winderl, J.; Hahn, T.; Hubbuch, J. (2016). A mechanistic model of ion-exchange chromatography on polymer fiber stationary phases. Journal of chromatography / A, 1475, 18–30. doi:10.1016/j.chroma.2016.10.057
Hahn, T.; Huuk, T.; Osberghaus, A.; Doninger, K.; Nath, S.; Hepbildikler, S.; Heuveline, V.; Hubbuch, J. (2016). Calibration-free inverse modeling of ion-exchange chromatography in industrial antibody purification. Engineering in life sciences, 16 (2), 107–113. doi:10.1002/elsc.201400248
Hahn, T.; Baumann, P.; Huuk, T.; Heuveline, V.; Hubbuch, J. (2016). UV absorption-based inverse modeling of protein chromatography. Engineering in life sciences, 16 (2), 99–106. doi:10.1002/elsc.201400247
Wang, G.; Hahn, T.; Hubbuch, J. (2016). Water on hydrophobic surfaces: Mechanistic modeling of hydrophobic interaction chromatography. Journal of chromatography / A, 1465, 71–78. doi:10.1016/j.chroma.2016.07.085
2015
Hahn, T.; Huuk, T.; Heuveline, V.; Hubbuch, J. (2015). Simulating and Optimizing Preparative Protein Chromatography with ChromX. Journal of chemical education, 92 (9), 1497–1502. doi:10.1021/ed500854a
Baumann, P.; Hahn, T.; Hubbuch, J. (2015). High-throughput micro-scale cultivations and chromatography modeling: Powerful tools for integrated process development. Biotechnology and Bioengineering / Symposium, 112 (10), 2123–2133. doi:10.1002/bit.25630
2014
Hahn, T.; Sommer, A.; Osberghaus, A.; Heuveline, V.; Hubbuch, J. (2014). Adjoint-based estimation and optimization for column liquid chromatography models. Computers & chemical engineering, 64, 41–54. doi:10.1016/j.compchemeng.2014.01.013
Huuk, T.; Hahn, T.; Osberghaus, A.; Hubbuch, J. (2014). Model-based integrated optimization and evaluation of a multi-step ion exchange chromatography. Separation and Purification Technology, 136, 207–222. doi:10.1016/j.seppur.2014.09.012
2012
Osberghaus, A.; Hepbildikler, S.; Nath, S.; Haindl, M.; Lieres, E. von; Hubbuch, J. (2012). Determination of parameters for the steric mass action model - A comparison between experimental and modeling approaches. Journal of Chromatography A, 1233, 54–64. doi:10.1016/j.chroma.2012.02.004
Osberghaus, A.; Hepbildikler, S.; Nath, S.; Haindl, M.; Lieres, E. von; Hubbuch, J. (2012). Optimizing a chromatographic three component separation: A comparison of mechanistic and empiric modeling approaches. Journal of Chromatography A, 1237, 86–95. doi:10.1016/j.chroma.2012.03.029
2010
Dismer, F.; Hubbuch, J. (2010). 3D structure-based protein retention prediction for ion-exchange chromatography. Journal of Chromatography A, 1217 (8), 1343–1354. doi:10.1016/j.chroma.2009.12.061

3D-Structure determination

By developing powerful computers with parallel architecture it has become possible to simulate the dynamics of molecular systems. The Molecular dynamics simulation (MD) of proteins offers a wide range of possibilities: from the reduction of the experimental effort, to access to experimentally difficult to access sizes up to a deeper understanding of the behavior of complex systems. Researchers at the institute developed an automated workflow of MD simulations includes some steps in which the molecule is immobilized in target process conditions (e.g. pH and ionic strength) and simulated in a data-dependent manner. These 3D structures are also the starting point for the application of quantitative Structure-activity relationship (QSAR). The goal of QSAR is the prediction of chemical and biological properties and activities not yet of synthesized substances. This theory is based on the assumption that properties and activities completely through the molecular structure of a protein can be determined. This is def ined by various descriptors, such as shape, size, electrostatics and hydrophobicity. These data form the starting point for multivariate data analysis, which molecular properties of known structures based on suitable descriptors and their experimental behaviour and predictive models are created with which the properties and activities for new molecules can be predicted.

Literature
2019
2017
Baumann, P.; Schermeyer, M.-T.; Burghardt, H.; Dürr, C.; Gärtner, J.; Hubbuch, J. (2017). Prediction and characterization of the stability enhancing effect of the Cherry-Tag™ in highly concentrated protein solutions by complex rheological measurements and MD simulations. International journal of pharmaceutics, 531 (1), 360–371. doi:10.1016/j.ijpharm.2017.08.068
Wang, G.; Briskot, T.; Hahn, T.; Baumann, P.; Hubbuch, J. (2017). Root cause investigation of deviations in protein chromatography based on mechanistic models and artificial neural networks. Journal of chromatography / A, 1515, 146–153. doi:10.1016/j.chroma.2017.07.089
Hämmerling, F.; Pieler, M. M.; Hennig, R.; Serve, A.; Rapp, E.; Wolff, M. W.; Reichl, U.; Hubbuch, J. (2017). Influence of the production system on the surface properties of influenza A virus particles. Engineering in life sciences, 17 (10), 1071–1077. doi:10.1002/elsc.201700058
Bauer, K. C.; Hämmerling, F.; Kittelmann, J.; Dürr, C.; Görlich, F.; Hubbuch, J. (2017). Influence of structure properties on protein–protein interactions—QSAR modeling of changes in diffusion coefficients. Biotechnology & bioengineering, 114 (4), 821–831. doi:10.1002/bit.26210
Hämmerling, F.; Ladd Effio, C.; Andris, S.; Kittelmann, J.; Hubbuch, J. (2017). Investigation and prediction of protein precipitation by polyethylene glycol using quantitative structure–activity relationship models. Journal of biotechnology, 241, 87–97. doi:10.1016/j.jbiotec.2016.11.014
2015
Schaller, A.; Connors, N. K.; Oelmeier, S. A.; Hubbuch, J.; Middelberg, A. P. J. (2015). Predicting recombinant protein expression experiments using molecular dynamics simulation. Chemical engineering science, 121, 340–350. doi:10.1016/j.ces.2014.09.044
Lang, K. M. H.; Kittelinann, J.; Duerr, C.; Osberghaus, A.; Hubbuch, J. (2015). A comprehensive molecular dynamics approach to protein retention modeling in ion exchange chromatography. Journal of chromatography / A, 1381, 184–193. doi:10.1016/j.chroma.2015.01.018
Lang, K. M. H.; Kittelmann, J.; Pilgram, F.; Osberghaus, A.; Hubbuch, J. (2015). Custom-tailored adsorbers: A molecular dynamics study on optimal design of ion exchange chromatography material. Journal of chromatography / A, 1413, 60–67. doi:10.1016/j.chroma.2015.08.021
Schaller, A.; Connors, N. K.; Dwyer, M. D.; Oelmeier, S. A.; Hubbuch, J.; Middelberg, A. P. J. (2015). Computational study of elements of stability of a four-helix bundle protein biosurfactant. Journal of computer aided molecular design, 29 (1), 47–58. doi:10.1007/s10822-014-9803-6
2014
Amrhein, S.; Oelmeier, S. A.; Dismer, F.; Hubbuch, J. (2014). Molecular dynamics simulations approach for the characterization of peptides with respect to hydrophobicity. The journal of physical chemistry <Washington, DC> / B, 118 (7), 1707–1714. doi:10.1021/jp407390f
2013
Dismer, F.; Alexander Oelmeier, S.; Hubbuch, J. (2013). Molecular dynamics simulations of aqueous two-phase systems: Understanding phase formation and protein partitioning. Chemical engineering science, 96, 142–151. doi:10.1016/j.ces.2013.03.020
2012
Oelmeier, S. A.; Dismer, F.; Hubbuch, J. (2012). Molecular dynamics simulations on aqueous two-phase systems - Single PEG-molecules in solution. BMC Biophysics, 5 (1), 14. doi:10.1186/2046-1682-5-14
2010
Dismer, F.; Hubbuch, J. (2010). 3D structure-based protein retention prediction for ion-exchange chromatography. Journal of Chromatography A, 1217 (8), 1343–1354. doi:10.1016/j.chroma.2009.12.061