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Dürauer A, Jungbauer A, Scharl T. Sensors and chemometrics in downstream processing. Biotechnol Bioeng 2024; 121:2347-2364. [PMID: 37470278 DOI: 10.1002/bit.28499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 06/14/2023] [Accepted: 07/07/2023] [Indexed: 07/21/2023]
Abstract
The biopharmaceutical industry is still running in batch mode, mostly because it is highly regulated. In the past, sensors were not readily available and in-process control was mainly executed offline. The most important product parameters are quantity, purity, and potency, in addition to adventitious agents and bioburden. New concepts using disposable single-use technologies and integrated bioprocessing for manufacturing will dominate the future of bioprocessing. To ensure the quality of pharmaceuticals, initiatives such as Process Analytical Technologies, Quality by Design, and Continuous Integrated Manufacturing have been established. The aim is that these initiatives, together with technology development, will pave the way for process automation and autonomous bioprocessing without any human intervention. Then, real-time release would be realized, leading to a highly predictive and robust biomanufacturing system. The steps toward such automated and autonomous bioprocessing are reviewed in the context of monitoring and control. It is possible to integrate real-time monitoring gradually, and it should be considered from a soft sensor perspective. This concept has already been successfully implemented in other industries and requires relatively simple model training and the use of established statistical tools, such as multivariate statistics or neural networks. This review describes a scenario for integrating soft sensors and predictive chemometrics into modern process control. This is exemplified by selective downstream processing steps, such as chromatography and membrane filtration, the most common unit operations for separation of biopharmaceuticals.
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Affiliation(s)
- Astrid Dürauer
- Institute of Bioprocessing Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Alois Jungbauer
- Institute of Bioprocessing Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Theresa Scharl
- Institute of Statistics, University of Natural Resources and Life Sciences, Vienna, Austria
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Klausser R, Kopp J, Prada Brichtova E, Gisperg F, Elshazly M, Spadiut O. State-of-the-art and novel approaches to mild solubilization of inclusion bodies. Front Bioeng Biotechnol 2023; 11:1249196. [PMID: 37545893 PMCID: PMC10399460 DOI: 10.3389/fbioe.2023.1249196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 07/12/2023] [Indexed: 08/08/2023] Open
Abstract
Throughout the twenty-first century, the view on inclusion bodies (IBs) has shifted from undesired by-products towards a targeted production strategy for recombinant proteins. Inclusion bodies can easily be separated from the crude extract after cell lysis and contain the product in high purity. However, additional solubilization and refolding steps are required in the processing of IBs to recover the native protein. These unit operations remain a highly empirical field of research in which processes are developed on a case-by-case basis using elaborate screening strategies. It has been shown that a reduction in denaturant concentration during protein solubilization can increase the subsequent refolding yield due to the preservation of correctly folded protein structures. Therefore, many novel solubilization techniques have been developed in the pursuit of mild solubilization conditions that avoid total protein denaturation. In this respect, ionic liquids have been investigated as promising agents, being able to solubilize amyloid-like aggregates and stabilize correctly folded protein structures at the same time. This review briefly summarizes the state-of-the-art of mild solubilization of IBs and highlights some challenges that prevent these novel techniques from being yet adopted in industry. We suggest mechanistic models based on the thermodynamics of protein unfolding with the aid of molecular dynamics simulations as a possible approach to solve these challenges in the future.
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Affiliation(s)
- Robert Klausser
- Research Division Integrated Bioprocess Development, Institute of Chemical, Environmental and Bioscience, Vienna, Austria
- Christian Doppler Laboratory IB Processing 4.0, Technische Universität Wien, Vienna, Austria
| | - Julian Kopp
- Research Division Integrated Bioprocess Development, Institute of Chemical, Environmental and Bioscience, Vienna, Austria
- Christian Doppler Laboratory IB Processing 4.0, Technische Universität Wien, Vienna, Austria
| | - Eva Prada Brichtova
- Research Division Integrated Bioprocess Development, Institute of Chemical, Environmental and Bioscience, Vienna, Austria
- Christian Doppler Laboratory IB Processing 4.0, Technische Universität Wien, Vienna, Austria
| | - Florian Gisperg
- Research Division Integrated Bioprocess Development, Institute of Chemical, Environmental and Bioscience, Vienna, Austria
- Christian Doppler Laboratory IB Processing 4.0, Technische Universität Wien, Vienna, Austria
| | - Mohamed Elshazly
- Research Division Integrated Bioprocess Development, Institute of Chemical, Environmental and Bioscience, Vienna, Austria
- Christian Doppler Laboratory IB Processing 4.0, Technische Universität Wien, Vienna, Austria
| | - Oliver Spadiut
- Research Division Integrated Bioprocess Development, Institute of Chemical, Environmental and Bioscience, Vienna, Austria
- Christian Doppler Laboratory IB Processing 4.0, Technische Universität Wien, Vienna, Austria
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Helleckes LM, Hemmerich J, Wiechert W, von Lieres E, Grünberger A. Machine learning in bioprocess development: from promise to practice. Trends Biotechnol 2023; 41:817-835. [PMID: 36456404 DOI: 10.1016/j.tibtech.2022.10.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/20/2022] [Accepted: 10/27/2022] [Indexed: 11/30/2022]
Abstract
Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.
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Affiliation(s)
- Laura M Helleckes
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Johannes Hemmerich
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Wolfgang Wiechert
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Eric von Lieres
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Alexander Grünberger
- Multiscale Bioengineering, Technical Faculty, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Institute of Process Engineering in Life Sciences, Section III: Microsystems in Bioprocess Engineering, Karlsruhe Institute of Technology, Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany.
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Duong-Trung N, Born S, Kim JW, Schermeyer MT, Paulick K, Borisyak M, Cruz-Bournazou MN, Werner T, Scholz R, Schmidt-Thieme L, Neubauer P, Martinez E. When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Bernau CR, Knödler M, Emonts J, Jäpel RC, Buyel JF. The use of predictive models to develop chromatography-based purification processes. Front Bioeng Biotechnol 2022; 10:1009102. [PMID: 36312533 PMCID: PMC9605695 DOI: 10.3389/fbioe.2022.1009102] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 09/23/2022] [Indexed: 11/13/2022] Open
Abstract
Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.
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Affiliation(s)
- C. R. Bernau
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Aachen, Germany
| | - M. Knödler
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Aachen, Germany
- Institute for Molecular Biotechnology, RWTH Aachen University, Aachen, Germany
| | - J. Emonts
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Aachen, Germany
| | - R. C. Jäpel
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Aachen, Germany
- Institute for Molecular Biotechnology, RWTH Aachen University, Aachen, Germany
| | - J. F. Buyel
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Aachen, Germany
- Institute for Molecular Biotechnology, RWTH Aachen University, Aachen, Germany
- University of Natural Resources and Life Sciences, Vienna (BOKU), Department of Biotechnology (DBT), Institute of Bioprocess Science and Engineering (IBSE), Vienna, Austria
- *Correspondence: J. F. Buyel,
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Need for a Next Generation of Chromatography Models—Academic Demands for Thermodynamic Consistency and Industrial Requirements in Everyday Project Work. Processes (Basel) 2022. [DOI: 10.3390/pr10040715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Process chromatography modelling for process development, design, and optimization as well as process control has been under development for decades. Still, the discussion of scientific potential and industrial applications needs is open to innovation. The discussion of next-generation modelling approaches starting from Langmuirian to steric mass action and multilayer or thermodynamic consistent real and ideal adsorption theory or colloidal particle adsorption approaches is continued.
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