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Wegner CH, Eming SM, Walla B, Bischoff D, Weuster-Botz D, Hubbuch J. Spectroscopic insights into multi-phase protein crystallization in complex lysate using Raman spectroscopy and a particle-free bypass. Front Bioeng Biotechnol 2024; 12:1397465. [PMID: 38812919 PMCID: PMC11133712 DOI: 10.3389/fbioe.2024.1397465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 04/23/2024] [Indexed: 05/31/2024] Open
Abstract
Protein crystallization as opposed to well-established chromatography processes has the benefits to reduce production costs while reaching a comparable high purity. However, monitoring crystallization processes remains a challenge as the produced crystals may interfere with analytical measurements. Especially for capturing proteins from complex feedstock containing various impurities, establishing reliable process analytical technology (PAT) to monitor protein crystallization processes can be complicated. In heterogeneous mixtures, important product characteristics can be found by multivariate analysis and chemometrics, thus contributing to the development of a thorough process understanding. In this project, an analytical set-up is established combining offline analytics, on-line ultraviolet visible light (UV/Vis) spectroscopy, and in-line Raman spectroscopy to monitor a stirred-batch crystallization process with multiple phases and species being present. As an example process, the enzyme Lactobacillus kefir alcohol dehydrogenase (LkADH) was crystallized from clarified Escherichia coli (E. coli) lysate on a 300 mL scale in five distinct experiments, with the experimental conditions changing in terms of the initial lysate solution preparation method and precipitant concentration. Since UV/Vis spectroscopy is sensitive to particles, a cross-flow filtration (cross-flow filtration)-based bypass enabled the on-line analysis of the liquid phase providing information on the lysate composition regarding the nucleic acid to protein ratio. A principal component analysis (PCA) of in situ Raman spectra supported the identification of spectra and wavenumber ranges associated with productspecific information and revealed that the experiments followed a comparable, spectral trend when crystals were present. Based on preprocessed Raman spectra, a partial least squares (PLS) regression model was optimized to monitor the target molecule concentration in real-time. The off-line sample analysis provided information on the crystal number and crystal geometry by automated image analysis as well as the concentration of LkADH and host cell proteins (HCPs) In spite of a complex lysate suspension containing scattering crystals and various impurities, it was possible to monitor the target molecule concentration in a heterogeneous, multi-phase process using spectroscopic methods. With the presented analytical set-up of off-line, particle-sensitive on-line, and in-line analyzers, a crystallization capture process can be characterized better in terms of the geometry, yield, and purity of the crystals.
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Affiliation(s)
- Christina Henriette Wegner
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Sebastian Mathis Eming
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Brigitte Walla
- Institute of Biochemical Engineering, Technical University of Munich, Garching, Germany
| | - Daniel Bischoff
- Institute of Biochemical Engineering, Technical University of Munich, Garching, Germany
| | - Dirk Weuster-Botz
- Institute of Biochemical Engineering, Technical University of Munich, Garching, Germany
| | - Jürgen Hubbuch
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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Wang Y, Cai L, Du S, Cheng Y, Zhang P, Li Y, Xue F, Gong J. Solid-liquid equilibrium of ropivacaine in fourteen organic solvents: An experimental and molecular simulation study. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2021.118163] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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3
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Castro F, Cunha I, Ferreira A, Teixeira JA, Rocha F. Towards an enhanced control of protein crystallization: Seeded batch lysozyme crystallization in a meso oscillatory flow reactor. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2021.12.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Klijn ME, Hubbuch J. Influence of image analysis strategy, cooling rate, and sample volume on apparent protein cloud-point temperature determination. Bioprocess Biosyst Eng 2021; 44:525-536. [PMID: 33237399 PMCID: PMC7889528 DOI: 10.1007/s00449-020-02465-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 10/16/2020] [Indexed: 01/08/2023]
Abstract
The protein cloud-point temperature (TCloud) is a known representative of protein-protein interaction strength and provides valuable information during the development and characterization of protein-based products, such as biopharmaceutics. A high-throughput low volume TCloud detection method was introduced in preceding work, where it was concluded that the extracted value is an apparent TCloud (TCloud,app). As an understanding of the apparent nature is imperative to facilitate inter-study data comparability, the current work was performed to systematically evaluate the influence of 3 image analysis strategies and 2 experimental parameters (sample volume and cooling rate) on TCloud,app detection of lysozyme. Different image analysis strategies showed that TCloud,app is detectable by means of total pixel intensity difference and the total number of white pixels, but the latter is also able to extract the ice nucleation temperature. Experimental parameter variation showed a TCloud,app depression for increasing cooling rates (0.1-0.5 °C/min), and larger sample volumes (5-24 μL). Exploratory thermographic data indicated this resulted from a temperature discrepancy between the measured temperature by the cryogenic device and the actual sample temperature. Literature validation confirmed that the discrepancy does not affect the relative inter-study comparability of the samples, regardless of the image analysis strategy or experimental parameters. Additionally, high measurement precision was demonstrated, as TCloud,app changes were detectable down to a sample volume of only 5 μL and for 0.1 °C/min cooling rate increments. This work explains the apparent nature of the TCloud detection method, showcases its detection precision, and broadens the applicability of the experimental setup.
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Affiliation(s)
- Marieke E Klijn
- Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany
| | - Jürgen Hubbuch
- Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany.
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Gao Y, Zhang T, Yu C, Li P, Tian N, Wu S. The effect of solvents on solid-liquid phase equilibrium of 1,3-Di-o-tolylguanidine. J Mol Liq 2020. [DOI: 10.1016/j.molliq.2020.113147] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Klijn ME, Hubbuch J. Time-Dependent Multi-Light-Source Image Classification Combined With Automated Multidimensional Protein Phase Diagram Construction for Protein Phase Behavior Analysis. J Pharm Sci 2019; 109:331-339. [PMID: 31369742 DOI: 10.1016/j.xphs.2019.07.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 07/10/2019] [Accepted: 07/23/2019] [Indexed: 11/29/2022]
Abstract
Image-based protein phase diagram analysis is key for understanding and exploiting protein phase behavior in the biopharmaceutical field. However, required data analysis has become a notorious time-consuming task since high-throughput screening approaches were implemented. A variety of computational tools have been developed to support analysis, but these tools primarily use end point visible light images. This study investigates the combined effect of end point and time-dependent image features obtained from cross-polarized and ultraviolet light features, supplementary to visible light, on protein phase diagram image classification. In addition, external validation was performed to evaluate the classification algorithm's applicability to support protein phase diagram scoring. The predicted protein phase behavior classes were subsequently used to automatically construct multidimensional protein phase diagrams to prevent image information loss without complicating the used image classification algorithm. Combining end point and time-dependent features from 3 light sources resulted in a balanced accuracy of 86.4 ± 4.3%, which is comparable to or better than more complex classifiers reported in literature. External validation resulted in a correct formulation classification rate of 91.7%. Subsequent automated construction of the multidimensional protein phase diagrams, using predicted classes, allowed visualization of details such as crystallization rate and protein phase behavior type coexistence.
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Affiliation(s)
- Marieke E Klijn
- Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 2, 76131 Karlsruhe, Germany
| | - Jürgen Hubbuch
- Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 2, 76131 Karlsruhe, Germany.
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Poplewska I, Łyskowski A, Kołodziej M, Szałański P, Piątkowski W, Antos D. Determination of protein crystallization kinetics by a through-flow small-angle X-ray scattering method. Chem Eng Res Des 2019. [DOI: 10.1016/j.cherd.2018.11.030] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Klijn ME, Hubbuch J. Application of Empirical Phase Diagrams for Multidimensional Data Visualization of High-Throughput Microbatch Crystallization Experiments. J Pharm Sci 2018; 107:2063-2069. [PMID: 29709489 DOI: 10.1016/j.xphs.2018.04.018] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 04/20/2018] [Indexed: 01/18/2023]
Abstract
Protein phase diagrams are a tool to investigate the cause and consequence of solution conditions on protein phase behavior. The effects are scored according to aggregation morphologies such as crystals or amorphous precipitates. Solution conditions affect morphologic features, such as crystal size, as well as kinetic features, such as crystal growth time. Commonly used data visualization techniques include individual line graphs or phase diagrams based on symbols. These techniques have limitations in terms of handling large data sets, comprehensiveness or completeness. To eliminate these limitations, morphologic and kinetic features obtained from crystallization images generated with high throughput microbatch experiments have been visualized with radar charts in combination with the empirical phase diagram method. Morphologic features (crystal size, shape, and number, as well as precipitate size) and kinetic features (crystal and precipitate onset and growth time) are extracted for 768 solutions with varying chicken egg white lysozyme concentration, salt type, ionic strength, and pH. Image-based aggregation morphology and kinetic features were compiled into a single and easily interpretable figure, thereby showing that the empirical phase diagram method can support high-throughput crystallization experiments in its data amount as well as its data complexity.
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Affiliation(s)
- Marieke E Klijn
- Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
| | - Jürgen Hubbuch
- Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany.
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Malwade CR, Buchholz H, Rong BG, Qu H, Christensen LP, Lorenz H, Seidel-Morgenstern A. Crystallization of Artemisinin from Chromatography Fractions of Artemisia annua Extract. Org Process Res Dev 2016. [DOI: 10.1021/acs.oprd.5b00399] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Chandrakant Ramkrishna Malwade
- Department
of Chemical Engineering, Biotechnology and Environmental Technology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
| | - Hannes Buchholz
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany
| | - Ben-Guang Rong
- Department
of Chemical Engineering, Biotechnology and Environmental Technology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
| | - Haiyan Qu
- Department
of Chemical Engineering, Biotechnology and Environmental Technology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
| | - Lars Porskjær Christensen
- Department
of Chemical Engineering, Biotechnology and Environmental Technology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
| | - H. Lorenz
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany
| | - A. Seidel-Morgenstern
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany
- Institute
of Process Engineering, Otto von Guericke University, 39106 Magdeburg, Germany
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Liu G, Zhang L, Mao S, Rohani S, Ching C, Lu J. Zwitterionic chitosan–silica–PVA hybrid ultrafiltration membranes for protein separation. Sep Purif Technol 2015. [DOI: 10.1016/j.seppur.2015.08.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Hekmat D. Large-scale crystallization of proteins for purification and formulation. Bioprocess Biosyst Eng 2015; 38:1209-31. [PMID: 25700885 DOI: 10.1007/s00449-015-1374-y] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Accepted: 02/02/2015] [Indexed: 12/17/2022]
Abstract
Since about 170 years, salts were used to create supersaturated solutions and crystallize proteins. The dehydrating effect of salts as well as their kosmotropic or chaotropic character was revealed. Even the suitability of organic solvents for crystallization was already recognized. Interestingly, what was performed during the early times is still practiced today. A lot of effort was put into understanding the underlying physico-chemical interaction mechanisms leading to protein crystallization. However, it was understood that already the solvation of proteins is a highly complex process not to mention the intricate interrelation of electrostatic and hydrophobic interactions taking place. Although many basic questions are still unanswered, preparative protein crystallization was attempted as illustrated in the presented case studies. Due to the highly variable nature of crystallization, individual design of the crystallization process is needed in every single case. It was shown that preparative crystallization from impure protein solutions as a capture step is possible after applying adequate pre-treatment procedures like precipitation or extraction. Protein crystallization can replace one or more chromatography steps. It was further shown that crystallization can serve as an attractive alternative means for formulation of therapeutic proteins. Crystalline proteins can offer enhanced purity and enable highly concentrated doses of the active ingredient. Easy scalability of the proposed protein crystallization processes was shown using the maximum local energy dissipation as a suitable scale-up criterion. Molecular modeling and target-oriented protein engineering may allow protein crystallization to become part of a platform purification process in the near future.
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Affiliation(s)
- Dariusch Hekmat
- Institute of Biochemical Engineering, Technische Universität München, Boltzmannstr. 15, 85748, Garching, Germany,
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