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:
N. Brestrich, T. Hahn, J. Hubbuch, Application of spectral deconvolution and inverse mechanistic modelling as a tool for root cause investigation in protein chromatography, Journal of Chromatography A 1437 (2016), p. 158–167.
N. Brestrich, A. Sanden, A. Kraft, K. McCann, J. Bertolini, J. Hubbuch, 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 112 (2015), p. 1406-1416.
N. Brestrich, T. Briskot, A. Osberghaus, J. Hubbuch, A tool for selective inline quantification of co-eluting proteins in chromatography using spectral analysis and partial least squares regression, Biotechnology and Bioengineering 111 (2014), p. 1365-1373.
Data science and data visualization
Der Bereich Datenwissenschaft und Visualisierung arbeitet an fortgeschrittenen Ansätzen zur Etablierung und Verbindung von Datenverarbeitungsschritten wie Extraktion, Verarbeitung, Visualisierung und Anwendung. Im MAB streben wir mit Hilfe dieser datenwissenschaftlichen Strategien ein breiteres Verständnis der Bioverarbeitung an. Dazu gehören automatisierte Datenextraktionsprotokolle für hauseigene Laborgeräte und die anschließende Datenbereinigung und -verarbeitung, die für die weitere Verwendung erforderlich sind. Es werden Algorithmen etabliert, um die verarbeiteten Daten mittels (multidimensionaler) Datenvisualisierung zu präsentieren, um Datenmuster in großen und sonst unübersichtlichen Datensätzen darzustellen. Die Datenanwendung wird durch die Entwicklung von Algorithmen für maschinelles Lernen und tiefes Lernen für Klassifizierungs- und Regressionsprobleme realisiert, die zur Erstellung von prädiktiven und präskriptiven Modellen verwendet werden können.
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 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.
T. Hahn, T. Huuk, A. Osberghaus, K. Doninger, S. Nath, S. Hepbildikler, V. Heuveline, J. Hubbuch, Calibration-free inverse modeling of ion-exchange chromatography in industrial antibody purification, Eng. Life Sci. (2015), DOI: 10.1002/elsc.201400248.
T. Hahn, P. Baumann, T. Huuk, V. Heuveline, J. Hubbuch, UV absorption-based inverse modeling of protein chromatography, Eng. Life Sci. (2015), DOI: 10.1002/elsc.201400247.
T. Huuk, T. Hahn, A. Osberghaus, J. Hubbuch, Model-based integrated optimization and evaluation of a multi-step ion exchange chromatography, Separation and Purification Technology (2014), DOI: 10.1016/j.seppur.2014.09.012.
P. Baumann, T. Hahn, J. Hubbuch, High-throughput Micro-scale Cultivations and Chromatography Modeling: Powerful Tools for Integrated Process Development, Biotechnol. Bioeng. (2015), DOI: 10.1002/bit.25630.
C. Ladd Effio, T. Hahn, J. Seiler, S. A. Oelmeier, I. Asen, C. Silberer, L. Villain, J. Hubbuch, Modeling and simulation of anion-exchange membrane chromatography for purification of Sf9 insect cell-derived virus-like particles. J. Chrom. A (2015), DOI: 10.1016/j.chroma.2015.12.006.
Molecular Dynamics Simulations
With increasing computational power, there is a growing interest in molecular dynamics (MD) simulations of biological macromolecules. MD simulations calculate the movement and dynamic of atomic systems on basis of covalent and non-covalent interactions such as the movement of a protein in aqueous solution. MD simulations enable to investigate and to understand experimental observables on an atomic level of detail. The computationally demanding in-silico approach is applied with our research group for the atomic assessment of proteins and peptides and to process related issues such as retention and binding behavior within chromatographic systems or protein phase behavior of.
F. Dismer, J. Hubbuch, 3D structure-based protein retention prediction for ion-exchange chromatography, J. Chrom. A 1217 (2010), p. 1343-1353.
S. A. Oelmeier, F. Dismer, J. Hubbuch, Molecular dynamics simulations on aqueous two-phase systems-single PEG-molecules in solution, BMC Biophysics, 5 (2012), p. 1.
K. M .Lang, J. Kittelmann, F. Pilgram, A .Osberghaus, J. Hubbuch, Custom-tailored adsorbers: A molecular dynamics study on optimal design of ion exchange chromatography material, J. Chrom. A, 1413 (2015), p. 60-67.
K. M. Lang, J. Kittelmann, C. Dürr, A. Osberghaus, J. Hubbuch, A comprehensive molecular dynamics approach to protein retention modeling in ion exchange chromatography, J. Chrom. A, 1381 (2015), p. 184-193.
S. Amrhein, S. A. Oelmeier, F. Dismer, J. Hubbuch, Molecular dynamics simulations approach for the characterization of peptides with respect to hydrophobicity, J. Phys. Chem. B, 118 (2014), p. 1707-1714.
L. Galm, S. Amrhein, J. Hubbuch, Predictive approach for protein aggregation: Correlation of protein surface characteristics and conformational flexibility to protein aggregation propensity, Biotechnology and Bioengineering, Biotechnology and bioengineering (2016).
Quantitative Structure Activity Relationship (QSAR)
QSAR aims to predict the chemical and biological properties and activities of substances that have not been synthesized yet. This theory is based on the assumption that properties and activities are completely determined by the molecular structure of a protein. The protein structure is characterized by various descriptors, e.g. for shape, size, electrostatics and hydrophobicity. The generated dataset constitutes the basis for the multi-variate data analysis that combines the molecular properties of known compounds expressed through descriptors, and the experimental behavior and thereby creates predictive models. With these models, the activities of new molecules can be predicted.
For the successful generation of a QSAR model it is necessary to adapt the protein structure in silico to the respective process conditions like pH and ionic strength. This structure is used for descriptor calculation using our in-house developed software. With the aid of the final QSAR model process parameters e.g. for chromatography processes can be assessed.