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Igwe CL, Müller DF, Gisperg F, Pauk JN, Kierein M, Elshazly M, Klausser R, Kopp J, Spadiut O, Přáda Brichtová E. Online monitoring of protein refolding in inclusion body processing using intrinsic fluorescence. Anal Bioanal Chem 2024; 416:3019-3032. [PMID: 38573344 PMCID: PMC11045631 DOI: 10.1007/s00216-024-05249-1] [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: 01/08/2024] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 04/05/2024]
Abstract
Inclusion bodies (IBs) are protein aggregates formed as a result of overexpression of recombinant protein in E. coli. The formation of IBs is a valuable strategy of recombinant protein production despite the need for additional processing steps, i.e., isolation, solubilization and refolding. Industrial process development of protein refolding is a labor-intensive task based largely on empirical approaches rather than knowledge-driven strategies. A prerequisite for knowledge-driven process development is a reliable monitoring strategy. This work explores the potential of intrinsic tryptophan and tyrosine fluorescence for real-time and in situ monitoring of protein refolding. In contrast to commonly established process analytical technology (PAT), this technique showed high sensitivity with reproducible measurements for protein concentrations down to 0.01 g L- 1 . The change of protein conformation during refolding is reflected as a shift in the position of the maxima of the tryptophan and tyrosine fluorescence spectra as well as change in the signal intensity. The shift in the peak position, expressed as average emission wavelength of a spectrum, was correlated to the amount of folding intermediates whereas the intensity integral correlates to the extent of aggregation. These correlations were implemented as an observation function into a mechanistic model. The versatility and transferability of the technique were demonstrated on the refolding of three different proteins with varying structural complexity. The technique was also successfully applied to detect the effect of additives and process mode on the refolding process efficiency. Thus, the methodology presented poses a generic and reliable PAT tool enabling real-time process monitoring of protein refolding.
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Affiliation(s)
- Chika Linda Igwe
- Competence Center CHASE GmbH, Hafenstraße 47-51, Linz, 4020, Austria
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
| | - Don Fabian Müller
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
| | - Florian Gisperg
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
- Christian Doppler Laboratory for Inclusion Body Processing 4.0, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
| | - Jan Niklas Pauk
- Competence Center CHASE GmbH, Hafenstraße 47-51, Linz, 4020, Austria
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
| | - Matthias Kierein
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
| | - Mohamed Elshazly
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
- Christian Doppler Laboratory for Inclusion Body Processing 4.0, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
| | - Robert Klausser
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
- Christian Doppler Laboratory for Inclusion Body Processing 4.0, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
| | - Julian Kopp
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
- Christian Doppler Laboratory for Inclusion Body Processing 4.0, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
| | - Oliver Spadiut
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
- Christian Doppler Laboratory for Inclusion Body Processing 4.0, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria
| | - Eva Přáda Brichtová
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria.
- Christian Doppler Laboratory for Inclusion Body Processing 4.0, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Gumpendorferstraße 1A, Vienna, 1060, Austria.
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Kager J, Herwig C. Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses. Bioengineering (Basel) 2021; 8:160. [PMID: 34821726 PMCID: PMC8614739 DOI: 10.3390/bioengineering8110160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/14/2021] [Accepted: 10/19/2021] [Indexed: 11/29/2022] Open
Abstract
During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error propagation and regression approach applied to two different, industrially relevant cultivation processes. Based on measurement uncertainties, errors for cell-specific growth, uptake, and production rates were determined across an evaluation chain, with interlinked inputs and outputs. These uncertainties were subsequently included in regression analysis to derive the covariance of the regression coefficients and the confidence bounds for prediction. The usefulness of the approach is shown within two case studies, based on the relations across biomass-specific rate control limits to guarantee high productivities in E. coli, and low lactate formation in a CHO cell fed-batch could be established. Besides the possibility to determine realistic errors on the evaluated process data, the presented approach helps to differentiate between reliable and unreliable correlations and prevents the wrong interpretations of relations based on uncertain data.
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Affiliation(s)
- Julian Kager
- Competence Center CHASE GmbH, 4040 Linz, Austria
- Research Division Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, 1060 Vienna, Austria
| | - Christoph Herwig
- Research Division Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, 1060 Vienna, Austria
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, Technische Universität Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria
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Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses. Bioprocess Biosyst Eng 2021; 44:683-700. [PMID: 33471162 PMCID: PMC7997827 DOI: 10.1007/s00449-020-02478-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 11/05/2020] [Indexed: 10/26/2022]
Abstract
Bioprocess development and optimization are still cost- and time-intensive due to the enormous number of experiments involved. In this study, the recently introduced model-assisted Design of Experiments (mDoE) concept (Möller et al. in Bioproc Biosyst Eng 42(5):867, https://doi.org/10.1007/s00449-019-02089-7 , 2019) was extended and implemented into a software ("mDoE-toolbox") to significantly reduce the number of required cultivations. The application of the toolbox is exemplary shown in two case studies with Saccharomyces cerevisiae. In the first case study, a fed-batch process was optimized with respect to the pH value and linearly rising feeding rates of glucose and nitrogen source. Using the mDoE-toolbox, the biomass concentration was increased by 30% compared to previously performed experiments. The second case study was the whole-cell biocatalysis of ethyl acetoacetate (EAA) to (S)-ethyl-3-hydroxybutyrate (E3HB), for which the feeding rates of glucose, nitrogen source, and EAA were optimized. An increase of 80% compared to a previously performed experiment with similar initial conditions was achieved for the E3HB concentration.
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Arndt L, Wiegmann V, Kuchemüller KB, Baganz F, Pörtner R, Möller J. Model-based workflow for scale-up of process strategies developed in miniaturized bioreactor systems. Biotechnol Prog 2021; 37:e3122. [PMID: 33438830 DOI: 10.1002/btpr.3122] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 12/02/2020] [Accepted: 12/29/2020] [Indexed: 11/06/2022]
Abstract
Miniaturized bioreactor (MBR) systems are routinely used in the development of mammalian cell culture processes. However, scale-up of process strategies obtained in MBR- to larger scale is challenging due to mainly non-holistic scale-up approaches. In this study, a model-based workflow is introduced to quantify differences in the process dynamics between bioreactor scales and thus enable a more knowledge-driven scale-up. The workflow is applied to two case studies with antibody-producing Chinese hamster ovary cell lines. With the workflow, model parameter distributions are estimated first under consideration of experimental variability for different scales. Second, the obtained individual model parameter distributions are tested for statistical differences. In case of significant differences, model parametric distributions are transferred between the scales. In case study I, a fed-batch process in a microtiter plate (4 ml working volume) and lab-scale bioreactor (3750 ml working volume) was mathematically modeled and evaluated. No significant differences were identified for model parameter distributions reflecting process dynamics. Therefore, the microtiter plate can be applied as scale-down tool for the lab-scale bioreactor. In case study II, a fed-batch process in a 24-Deep-Well-Plate (2 ml working volume) and shake flask (40 ml working volume) with two feed media was investigated. Model parameter distributions showed significant differences. Thus, process strategies were mathematically transferred, and model predictions were simulated for a new shake flask culture setup and confirmed in validation experiments. Overall, the workflow enables a knowledge-driven evaluation of scale-up for a more efficient bioprocess design and optimization.
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Affiliation(s)
- Lukas Arndt
- Hamburg University of Technology, Bioprocess and Biosystems Engineering, Hamburg, Germany
| | - Vincent Wiegmann
- University College London, The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, London, UK
| | - Kim B Kuchemüller
- Hamburg University of Technology, Bioprocess and Biosystems Engineering, Hamburg, Germany
| | - Frank Baganz
- University College London, The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, London, UK
| | - Ralf Pörtner
- Hamburg University of Technology, Bioprocess and Biosystems Engineering, Hamburg, Germany
| | - Johannes Möller
- Hamburg University of Technology, Bioprocess and Biosystems Engineering, Hamburg, Germany
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Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2021. [PMID: 33381857 DOI: 10.1007/10_2020_154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Typically, bioprocesses on an industrial scale are dynamic systems with a certain degree of variability, system inhomogeneities, and even population heterogeneities. Therefore, the scaling of such processes from laboratory to industrial scale and vice versa is not a trivial task. Traditional scale-down methodologies consider several technical parameters, so that systems on the laboratory scale tend to qualitatively reflect large-scale effects, but not the dynamic situation in an industrial bioreactor over the entire process, from the perspective of a cell. Supported by the enormous increase in computing power, the latest scientific focus is on the application of dynamic models, in combination with computational fluid dynamics to quantitatively describe cell behavior. These models allow the description of possible cellular lifelines which in turn can be used to derive a regime analysis for scale-down experiments. However, the approaches described so far, which were for a very few process examples, are very labor- and time-intensive and cannot be validated easily. In parallel, alternatives have been developed based on the description of the industrial process with hybrid process models, which describe a process mechanistically as far as possible in order to determine the essential process parameters with their respective variances. On-line analytical methods allow the characterization of population heterogeneity directly in the process. This detailed information from the industrial process can be used in laboratory screening systems to select relevant conditions in which the cell and process related parameters reflect the situation in the industrial scale. In our opinion, these technologies, which are available in research for modeling biological systems, in combination with process analytical techniques are so far developed that they can be implemented in industrial routines for faster development of new processes and optimization of existing ones.
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Möller J, Hernández Rodríguez T, Müller J, Arndt L, Kuchemüller KB, Frahm B, Eibl R, Eibl D, Pörtner R. Model uncertainty-based evaluation of process strategies during scale-up of biopharmaceutical processes. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2019.106693] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Kroll P, Hofer A, Ulonska S, Kager J, Herwig C. Model-Based Methods in the Biopharmaceutical Process Lifecycle. Pharm Res 2017; 34:2596-2613. [PMID: 29168076 PMCID: PMC5736780 DOI: 10.1007/s11095-017-2308-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 09/21/2017] [Indexed: 12/18/2022]
Abstract
Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods adopted from different scientific domains accelerates this trend further. In addition, model-based methods can help to implement regulatory requirements as suggested by recent Quality by Design and validation initiatives. The aim of this review is to give an overview of the state of the art of model-based methods, their applications, further challenges and possible solutions in the biopharmaceutical process life cycle. Today, despite these advantages, the potential of model-based methods is still not fully exhausted in bioprocess technology. This is due to a lack of (i) acceptance of the users, (ii) user-friendly tools provided by existing methods, (iii) implementation in existing process control systems and (iv) clear workflows to set up specific process models. We propose that model-based methods be applied throughout the lifecycle of a biopharmaceutical process, starting with the set-up of a process model, which is used for monitoring and control of process parameters, and ending with continuous and iterative process improvement via data mining techniques.
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Affiliation(s)
- Paul Kroll
- Research Area Biochemical Engineering, Institute of Chemical Environmental and Biological Engineering, Vienna University of Technology, Gumpendorfer Straße 1a - 166/4, A-1060, Vienna, Austria
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, TU Wien, Vienna, Austria
| | - Alexandra Hofer
- Research Area Biochemical Engineering, Institute of Chemical Environmental and Biological Engineering, Vienna University of Technology, Gumpendorfer Straße 1a - 166/4, A-1060, Vienna, Austria
| | - Sophia Ulonska
- Research Area Biochemical Engineering, Institute of Chemical Environmental and Biological Engineering, Vienna University of Technology, Gumpendorfer Straße 1a - 166/4, A-1060, Vienna, Austria
| | - Julian Kager
- Research Area Biochemical Engineering, Institute of Chemical Environmental and Biological Engineering, Vienna University of Technology, Gumpendorfer Straße 1a - 166/4, A-1060, Vienna, Austria
| | - Christoph Herwig
- Research Area Biochemical Engineering, Institute of Chemical Environmental and Biological Engineering, Vienna University of Technology, Gumpendorfer Straße 1a - 166/4, A-1060, Vienna, Austria.
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, TU Wien, Vienna, Austria.
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Ehgartner D, Hartmann T, Heinzl S, Frank M, Veiter L, Kager J, Herwig C, Fricke J. Controlling the specific growth rate via biomass trend regulation in filamentous fungi bioprocesses. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2017.06.020] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Ponte X, Montesinos-Seguí JL, Valero F. Bioprocess efficiency in Rhizopus oryzae lipase production by Pichia pastoris under the control of PAOX1 is oxygen tension dependent. Process Biochem 2016. [DOI: 10.1016/j.procbio.2016.08.030] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Reichelt WN, Haas F, Sagmeister P, Herwig C. Bioprocess development workflow: Transferable physiological knowledge instead of technological correlations. Biotechnol Prog 2016; 33:261-270. [PMID: 27690336 DOI: 10.1002/btpr.2377] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Revised: 09/28/2016] [Indexed: 11/06/2022]
Abstract
Microbial bioprocesses need to be designed to be transferable from lab scale to production scale as well as between setups. Although substantial effort is invested to control technological parameters, usually the only true constant parameter is the actual producer of the product: the cell. Hence, instead of solely controlling technological process parameters, the focus should be increasingly laid on physiological parameters. This contribution aims at illustrating a workflow of data life cycle management with special focus on physiology. Information processing condenses the data into physiological variables, while information mining condenses the variables further into physiological descriptors. This basis facilitates data analysis for a physiological explanation for observed phenomena in productivity. Targeting transferability, we demonstrate this workflow using an industrially relevant Escherichia coli process for recombinant protein production and substantiate the following three points: (1) The postinduction phase is independent in terms of productivity and physiology from the preinduction variables specific growth rate and biomass at induction. (2) The specific substrate uptake rate during induction phase was found to significantly impact the maximum specific product titer. (3) The time point of maximum specific titer can be predicted by an easy accessible physiological variable: while the maximum specific titers were reached at different time points (19.8 ± 7.6 h), those maxima were reached all within a very narrow window of cumulatively consumed substrate dSn (3.1 ± 0.3 g/g). Concluding, this contribution provides a workflow on how to gain a physiological view on the process and illustrates potential benefits. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:261-270, 2017.
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Affiliation(s)
- Wieland N Reichelt
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, Institute of Chemical Engineering, Vienna University of Technology, Getreidemarkt 9/166, Vienna, A-1060, Austria
| | - Florian Haas
- Research Division Biochemical Engineering, Institute of Chemical Engineering, Vienna University of Technology, Gumpendorfer Strasse 1A/166-4, Vienna, 1060, Austria
| | - Patrick Sagmeister
- Research Division Biochemical Engineering, Institute of Chemical Engineering, Vienna University of Technology, Gumpendorfer Strasse 1A/166-4, Vienna, 1060, Austria
| | - Christoph Herwig
- Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, Institute of Chemical Engineering, Vienna University of Technology, Getreidemarkt 9/166, Vienna, A-1060, Austria
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Steinwandter V, Zahel T, Sagmeister P, Herwig C. Propagation of measurement accuracy to biomass soft-sensor estimation and control quality. Anal Bioanal Chem 2016; 409:693-706. [PMID: 27376358 PMCID: PMC5233751 DOI: 10.1007/s00216-016-9711-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Revised: 06/06/2016] [Accepted: 06/09/2016] [Indexed: 12/04/2022]
Abstract
In biopharmaceutical process development and manufacturing, the online measurement of biomass and derived specific turnover rates is a central task to physiologically monitor and control the process. However, hard-type sensors such as dielectric spectroscopy, broth fluorescence, or permittivity measurement harbor various disadvantages. Therefore, soft-sensors, which use measurements of the off-gas stream and substrate feed to reconcile turnover rates and provide an online estimate of the biomass formation, are smart alternatives. For the reconciliation procedure, mass and energy balances are used together with accuracy estimations of measured conversion rates, which were so far arbitrarily chosen and static over the entire process. In this contribution, we present a novel strategy within the soft-sensor framework (named adaptive soft-sensor) to propagate uncertainties from measurements to conversion rates and demonstrate the benefits: For industrially relevant conditions, hereby the error of the resulting estimated biomass formation rate and specific substrate consumption rate could be decreased by 43 and 64 %, respectively, compared to traditional soft-sensor approaches. Moreover, we present a generic workflow to determine the required raw signal accuracy to obtain predefined accuracies of soft-sensor estimations. Thereby, appropriate measurement devices and maintenance intervals can be selected. Furthermore, using this workflow, we demonstrate that the estimation accuracy of the soft-sensor can be additionally and substantially increased.
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Affiliation(s)
| | | | | | - Christoph Herwig
- Institute of Chemical Engineering, Research Area Biochemical Engineering, Vienna University of Technology, Gumpendorferstrasse 1a, Vienna, Austria.
- CD Laboratory on Mechanistic and Physiological Methods for Improved Bioprocesses, Vienna University of Technology, Gumpendorferstrasse 1a, Vienna, Austria.
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Zahel T, Sagmeister P, Suchocki S, Herwig C. Accurate Information from Fermentation Processes - Optimal Rate Calculation by Dynamic Window Adaptation. CHEM-ING-TECH 2016. [DOI: 10.1002/cite.201500085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Klein T, Heinzel N, Kroll P, Brunner M, Herwig C, Neutsch L. Quantification of cell lysis during CHO bioprocesses: Impact on cell count, growth kinetics and productivity. J Biotechnol 2015; 207:67-76. [DOI: 10.1016/j.jbiotec.2015.04.021] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2014] [Revised: 04/18/2015] [Accepted: 04/27/2015] [Indexed: 02/02/2023]
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Ex situonline monitoring: application, challenges and opportunities for biopharmaceuticals processes. ACTA ACUST UNITED AC 2014. [DOI: 10.4155/pbp.14.22] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Tunable recombinant protein expression with E. coli in a mixed-feed environment. Appl Microbiol Biotechnol 2013; 98:2937-45. [DOI: 10.1007/s00253-013-5445-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2013] [Revised: 11/28/2013] [Accepted: 11/28/2013] [Indexed: 11/26/2022]
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16
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17
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Dynamic Experiments for Bioprocess Parameter Optimization with Extreme Halophilic Archaea. Bioengineering (Basel) 2013. [DOI: 10.3390/bioengineering1010001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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18
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Microbials for the production of monoclonal antibodies and antibody fragments. Trends Biotechnol 2013; 32:54-60. [PMID: 24183828 PMCID: PMC3906537 DOI: 10.1016/j.tibtech.2013.10.002] [Citation(s) in RCA: 154] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Revised: 10/03/2013] [Accepted: 10/04/2013] [Indexed: 01/31/2023]
Abstract
Glycosylated full length antibodies are currently produced in mammalian cells. Antibody fragments can be produced in microbial organisms. Strain engineering allows production of full length antibodies in microbials. Microbials provide several advantages over mammalian cells.
Monoclonal antibodies (mAbs) and antibody fragments represent the most important biopharmaceutical products today. Because full length antibodies are glycosylated, mammalian cells, which allow human-like N-glycosylation, are currently used for their production. However, mammalian cells have several drawbacks when it comes to bioprocessing and scale-up, resulting in long processing times and elevated costs. By contrast, antibody fragments, that are not glycosylated but still exhibit antigen binding properties, can be produced in microbial organisms, which are easy to manipulate and cultivate. In this review, we summarize recent advances in the expression systems, strain engineering, and production processes for the three main microbials used in antibody and antibody fragment production, namely Saccharomyces cerevisiae, Pichia pastoris, and Escherichia coli.
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A dynamic method for the investigation of induced state metabolic capacities as a function of temperature. Microb Cell Fact 2013; 12:94. [PMID: 24127686 PMCID: PMC4015482 DOI: 10.1186/1475-2859-12-94] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2013] [Accepted: 09/27/2013] [Indexed: 11/19/2022] Open
Abstract
Background Science-based recombinant bioprocess designs as well as the design of statistical experimental plans for process optimization (Design of Experiments, DoE) demand information on physiological bioprocess boundaries, such as the onset of acetate production, adaptation times, mixed feed metabolic capabilities or induced state maximum metabolic rates as at the desired cultivation temperature. Dynamic methods provide experimental alternatives to determine this information in a fast and efficient way. Information on maximum metabolic capabilities as a function of temperature is needed in case a reduced cultivation temperature is desirable (e.g. to avoid inclusion body formation) and an appropriate feeding profile is to be designed. Results Here, we present a novel dynamic method for the determination of the specific growth rate as a function of temperature for induced recombinant bacterial bioprocesses. The method is based on the control of the residual substrate concentration at non-limiting conditions with dynamic changes in cultivation temperature. The presented method was automated in respect to information extraction and closed loop control by means of in-line Fourier Transformation Infrared Spectroscopy (FTIR) residual substrate measurements and on-line first principle rate-based soft-sensors. Maximum induced state metabolic capabilities as a function of temperature were successfully extracted for a recombinant E. coli C41 fed-batch bioprocess without the need for sampling in a time frame of 20 hours. Conclusions The presented method was concluded to allow the fast and automated extraction of maximum metabolic capabilities (specific growth rate) as a function of temperature. This complements the dynamic toolset necessary for science-based recombinant bacterial bioprocess design and DoE design.
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Royle KE, Jimenez del Val I, Kontoravdi C. Integration of models and experimentation to optimise the production of potential biotherapeutics. Drug Discov Today 2013; 18:1250-5. [PMID: 23850703 DOI: 10.1016/j.drudis.2013.07.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2013] [Revised: 06/29/2013] [Accepted: 07/02/2013] [Indexed: 12/17/2022]
Abstract
Despite decades of clinical and commercial success, the current paradigm for drug discovery and development is still empirical and costly. The many hundreds of therapeutic proteins (TPs) in the development pipeline and the FDA-led quality-by-design initiative represent opportunities to address this issue. Advances in our understanding of cellular mechanisms as well as the physicochemical and biological characteristics of TPs have enabled researchers to develop computational models that analyse or even predict molecular and cellular behaviour under different conditions. Coupled with new analytical tools, these models are increasingly used to systemise and expedite the design and optimisation of protein production processes throughout the discovery and development stages.
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Affiliation(s)
- Kate E Royle
- Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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Sagmeister P, Wechselberger P, Jazini M, Meitz A, Langemann T, Herwig C. Soft sensor assisted dynamic bioprocess control: Efficient tools for bioprocess development. Chem Eng Sci 2013. [DOI: 10.1016/j.ces.2013.02.069] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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