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Yang YX, Lin ZY, Chen YC, Yao SJ, Lin DQ. Modeling multi-component separation in hydrophobic interaction chromatography with improved parameter-by-parameter estimation method. J Chromatogr A 2024; 1730:465121. [PMID: 38959659 DOI: 10.1016/j.chroma.2024.465121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/10/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024]
Abstract
Mechanistic models are powerful tools for chromatographic process development and optimization. However, hydrophobic interaction chromatography (HIC) mechanistic models lack an effective and logical parameter estimation method, especially for multi-component system. In this study, a parameter-by-parameter method for multi-component system (called as mPbP-HIC) was derived based on the retention mechanism to estimate the six parameters of the Mollerup isotherm for HIC. The linear parameters (ks,i and keq,i) and nonlinear parameters (ni and qmax,i) of the isotherm can be estimated by the linear regression (LR) and the linear approximation (LA) steps, respectively. The remaining two parameters (kp,i and kkin,i) are obtained by the inverse method (IM). The proposed method was verified with a two-component model system. The results showed that the model could accurately predict the protein elution at a loading of 10 g/L. However, the elution curve fitting was unsatisfactory for high loadings (12 g/L and 14 g/L), which is mainly attributed to the demanding experimental conditions of the LA step and the potential large estimation error of the parameter qmax. Therefore, the inverse method was introduced to further calibrate the parameter qmax, thereby reducing the estimation error and improving the curve fitting. Moreover, the simplified linear approximation (SLA) was proposed by reasonable assumption, which provides the initial guess of qmax without solving any complex matrix and avoids the problem of matrix unsolvable. In the improved mPbP-HIC method, qmax would be initialized by the SLA and finally determined by the inverse method, and this strategy was named as SLA+IM. The experimental validation showed that the improved mPbP-HIC method has a better curve fitting, and the use of SLA+IM reduces the error accumulation effect. In process optimization, the parameters estimated by the improved mPbP-HIC method provided the model with excellent predictive ability and reasonable extrapolation. In conclusion, the SLA+IM strategy makes the improved mPbP-HIC method more rational and can be easily applied to the practical separation of protein mixture, which would accelerate the process development for HIC in downstream of biopharmaceuticals.
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Affiliation(s)
- Yu-Xiang Yang
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Zhi-Yuan Lin
- Zhejiang University-University of Edinburgh Institute, Zhejiang University, Haining 314400, China
| | - Yu-Cheng Chen
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Shan-Jing Yao
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Dong-Qiang Lin
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China.
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Yang YX, Chen YC, Yao SJ, Lin DQ. Parameter-by-parameter estimation method for adsorption isotherm in hydrophobic interaction chromatography. J Chromatogr A 2024; 1716:464638. [PMID: 38219627 DOI: 10.1016/j.chroma.2024.464638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/06/2024] [Accepted: 01/08/2024] [Indexed: 01/16/2024]
Abstract
Hydrophobic interaction chromatography (HIC) is used as a critical polishing step in the downstream processing of biopharmaceuticals. Normally the process development of HIC is a cumbersome and time-consuming task, and the mechanical models can provide a powerful tool to characterize the process, assist process design and accelerate process development. However, the current estimation of model parameters relies on the inverse method, which lacks an efficient and logical parameter estimation strategy. In this study, a parameter-by-parameter (PbP) method based on the theoretical derivation and simplifying assumptions was proposed to estimate the Mollerup isotherm parameters for HIC. The method involves three key steps: (1) linear regression (LR) to estimate the salt-protein interaction parameter and the equilibrium constant; (2) linear approximation (LA) to estimate the stoichiometric parameter and the maximum binding capacity; and (3) inverse method to estimate the protein-protein interaction parameter and the kinetic coefficient. The results indicated that the LR step should be used for dilution condition (loading factor below 5%), while the LA step should be conducted when the isotherm is in the transition or nonlinear regions. Six numerical experiments were conducted to implement the PbP method. The results demonstrated that the PbP method developed allows for the systematic estimation of HIC parameters one-by-one, effectively reducing the number of parameters required for inverse method estimation from six to two. This helps prevent non-identifiability of structural parameters. The feasibility of the PbP-HIC method was further validated by real-world experiments. Moreover, the PbP method enhances the mechanistic understanding of adsorption behavior of HIC and shows a promising application to other stoichiometric displacement model-derived isotherms.
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Affiliation(s)
- Yu-Xiang Yang
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yu-Cheng Chen
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Shan-Jing Yao
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Dong-Qiang Lin
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China.
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Ghiasvand Mohammadkhani L, Ghorbani J, Kompany-Zareh M. Resampling for estimation of parameters uncertainty in genetic algorithm based model fitting. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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4
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Coupling of multivariate curve resolution-alternating least squares and mechanistic hard models to investigate antibody purification from human plasma using ion exchange chromatography. J Chromatogr A 2022; 1675:463168. [PMID: 35667219 DOI: 10.1016/j.chroma.2022.463168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 05/19/2022] [Accepted: 05/19/2022] [Indexed: 11/21/2022]
Abstract
A two steps proposal for the purification of immunoglobulin G from human blood plasma is investigated. The first step is precipitation using cold ethanol based on the Cohn method with some modification and the second step is a chromatographic separation by DEAE-Sepharose FF resin as a weak anion exchanger. The presence of interferent in the region3 of chromatographic fractions, which is co-eluted with IgG, restricts the application of the mechanistic chromatography model. Therefore, multivariate cure resolution-alternating least squares (MCR-ALS) as a soft method is employed on measured absorbance data matrix from eluted fractions to recover pure concentration and spectral profiles. Besides, possible solutions for resolved concentration and spectral profiles are investigated. The reaction-dispersive model as a mechanistic hard model for the column is utilized for the evaluation of the ion exchange chromatography. Using a genetic algorithm as a global optimization method, mobile phase modulator (MPM) adsorption model parameters such as β, kdes,0, and Keq,0, were fitted to the concentration profiles from MCR-ALS as 1.96, 2.87×10-4 m3 mol-1s-1, and 1883, respectively. Furthermore, a new resampling incorporated non-parametric statistics is conducted to assess parameters' uncertainty. Values of 2.00, 1.10×10-3 m3 mol-1s-1, and 549.80 are estimated median, and values of 0.05, 2.5×10-3, and 691.00 are calculated interquartile range (IQR) for β, kdes,0, and Keq,0, respectively. Finally, results show three and two outliers for β and kdes,0, respectively.
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Harada H, Suzuki K, Sato K, Okada K, Tsuruta M, Yajima T, Kawajiri Y. Process development for advanced simulated moving bed (ASMB) chromatography by parameter refinement using pilot plant experimental data. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2021.119932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Kumar V, Leweke S, Heymann W, von Lieres E, Schlegel F, Westerberg K, Lenhoff AM. Robust mechanistic modeling of protein ion-exchange chromatography. J Chromatogr A 2021; 1660:462669. [PMID: 34800897 DOI: 10.1016/j.chroma.2021.462669] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/20/2021] [Accepted: 10/31/2021] [Indexed: 11/25/2022]
Abstract
Mechanistic models for ion-exchange chromatography of proteins are well-established and a broad consensus exists on most aspects of the detailed mathematical and physical description. A variety of specializations of these models can typically capture the general locations of elution peaks, but discrepancies are often observed in peak position and shape, especially if the column load level is in the non-linear range. These discrepancies may prevent the use of models for high-fidelity predictive applications such as process characterization and development of high-purity and -productivity process steps. Our objective is to develop a sufficiently robust mechanistic framework to make both conventional and anomalous phenomena more readily predictable using model parameters that can be evaluated based on independent measurements or well-accepted correlations. This work demonstrates the implementation of this approach for industry-relevant case studies using both a model protein, lysozyme, and biopharmaceutical product monoclonal antibodies, using cation-exchange resins with a variety of architectures (SP Sepharose FF, Fractogel EMD SO3-, Capto S and Toyopearl SP650M). The modeling employs the general rate model with the extension of the surface diffusivity to be variable, as a function of ionic strength or binding affinity. A colloidal isotherm that accounts for protein-surface and protein-protein interactions independently was used, with each characterized by a parameter determined as a function of ionic strength and pH. Both of these isotherm parameters, along with the variable surface diffusivity, were successfully estimated using breakthrough data at different ionic strengths and pH. The model developed was used to predict overloads and elution curves with high accuracy for a wide variety of gradients and different flow rates and protein loads. The in-silico methodology used in this work for parameter estimation, along with a minimal amount of experimental data, can help the industry adopt model-based optimization and control of preparative ion-exchange chromatography with high accuracy.
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Affiliation(s)
- Vijesh Kumar
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, United States
| | - Samuel Leweke
- IBG-1: Biotechnology Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - William Heymann
- IBG-1: Biotechnology Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; Amgen Process Development, One Kendall Square, 360 Binney St., Cambridge, MA 02141, United States
| | - Eric von Lieres
- IBG-1: Biotechnology Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Fabrice Schlegel
- Amgen Process Development, One Kendall Square, 360 Binney St., Cambridge, MA 02141, United States
| | - Karin Westerberg
- Amgen Process Development, One Amgen Center Drive, Thousand Oaks, CA 91360, United States
| | - Abraham M Lenhoff
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, United States.
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Yamamoto Y, Yajima T, Kawajiri Y. Uncertainty quantification for chromatography model parameters by Bayesian inference using sequential Monte Carlo method. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2021.09.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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8
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He QL, Zhao L. Bayesian inference based process design and uncertainty analysis of simulated moving bed chromatographic systems. Sep Purif Technol 2020. [DOI: 10.1016/j.seppur.2020.116856] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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9
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Simulation and experimental study of the transport of protein bands through cuboid packed-bed devices during chromatographic separations. J Chromatogr A 2020; 1615:460764. [DOI: 10.1016/j.chroma.2019.460764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 12/02/2019] [Indexed: 11/22/2022]
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10
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Shekhawat LK, Rathore AS. An overview of mechanistic modeling of liquid chromatography. Prep Biochem Biotechnol 2019; 49:623-638. [DOI: 10.1080/10826068.2019.1615504] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Lalita K. Shekhawat
- Department of Chemical Engineering, Indian Institute of Technology, New Delhi, India
| | - Anurag S. Rathore
- Department of Chemical Engineering, Indian Institute of Technology, New Delhi, India
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11
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Roch P, Sellberg A, Andersson N, Gunne M, Hauptmann P, Nilsson B, Mandenius CF. Model-based monitoring of industrial reversed phase chromatography to predict insulin variants. Biotechnol Prog 2019; 35:e2813. [PMID: 30938075 DOI: 10.1002/btpr.2813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 02/07/2019] [Accepted: 03/20/2019] [Indexed: 11/09/2022]
Abstract
Downstream processing in the manufacturing biopharmaceutical industry is a multistep process separating the desired product from process- and product-related impurities. However, removing product-related impurities, such as product variants, without compromising the product yield or prolonging the process time due to extensive quality control analytics, remains a major challenge. Here, we show how mechanistic model-based monitoring, based on analytical quality control data, can predict product variants by modeling their chromatographic separation during product polishing with reversed phase chromatography. The system was described by a kinetic dispersive model with a modified Langmuir isotherm. Solely quality control analytical data on product and product variant concentrations were used to calibrate the model. This model-based monitoring approach was developed for an insulin purification process. Industrial materials were used in the separation of insulin and two insulin variants, one eluting at the product peak front and one eluting at the product peak tail. The model, fitted to analytical data, used one component to simulate each protein, or two components when a peak displayed a shoulder. This monitoring approach allowed the prediction of the elution patterns of insulin and both insulin variants. The results indicate the potential of using model-based monitoring in downstream polishing at industrial scale to take pooling decisions.
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Affiliation(s)
- Patricia Roch
- Division of Biotechnology, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Anton Sellberg
- Department of Chemical Engineering, Lund University, Lund, Sweden
| | - Niklas Andersson
- Department of Chemical Engineering, Lund University, Lund, Sweden
| | - Matthias Gunne
- Biologics Development, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, Germany
| | - Peter Hauptmann
- Biologics Development, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, Germany
| | - Bernt Nilsson
- Department of Chemical Engineering, Lund University, Lund, Sweden
| | - Carl-Fredrik Mandenius
- Division of Biotechnology, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
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12
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Briskot T, Stückler F, Wittkopp F, Williams C, Yang J, Konrad S, Doninger K, Griesbach J, Bennecke M, Hepbildikler S, Hubbuch J. Prediction uncertainty assessment of chromatography models using Bayesian inference. J Chromatogr A 2018; 1587:101-110. [PMID: 30579636 DOI: 10.1016/j.chroma.2018.11.076] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 11/19/2018] [Accepted: 11/28/2018] [Indexed: 12/18/2022]
Abstract
Mechanistic modeling of chromatography has been around in academia for decades and has gained increased support in pharmaceutical companies in recent years. Despite the large number of published successful applications, process development in the pharmaceutical industry today still does not fully benefit from a systematic mechanistic model-based approach. The hesitation on the part of industry to systematically apply mechanistic models can often be attributed to the absence of a general approach for determining if a model is qualified to support decision making in process development. In this work a Bayesian framework for the calibration and quality assessment of mechanistic chromatography models is introduced. Bayesian Markov Chain Monte Carlo is used to assess parameter uncertainty by generating samples from the parameter posterior distribution. Once the parameter posterior distribution has been estimated, it can be used to propagate the parameter uncertainty to model predictions, allowing a prediction-based uncertainty assessment of the model. The benefit of this uncertainty assessment is demonstrated using the example of a mechanistic model describing the separation of an antibody from its impurities on a strong cation exchanger. The mechanistic model was calibrated at moderate column load density and used to make extrapolations at high load conditions. Using the Bayesian framework, it could be shown that despite significant parameter uncertainty, the model can extrapolate beyond observed process conditions with high accuracy and is qualified to support process development.
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Affiliation(s)
- Till Briskot
- Roche Pharma Technical Development, Roche Diagnostics GmbH, Nonnenwald 2, 82377, Penzberg, Germany
| | - Ferdinand Stückler
- Roche Pharma Technical Development, Roche Diagnostics GmbH, Nonnenwald 2, 82377, Penzberg, Germany
| | - Felix Wittkopp
- Roche Pharma Research and Early Development, Roche Innovation Center Munich, Roche Diagnostics GmbH, Nonnenwald 2, 82377, Penzberg, Germany
| | - Christopher Williams
- Department of Purification Development, Genentech Inc., 1 DNA Way, South San Francisco, CA, 94080, USA
| | - Jessica Yang
- Department of Purification Development, Genentech Inc., 1 DNA Way, South San Francisco, CA, 94080, USA
| | - Susanne Konrad
- Roche Pharma Technical Development, Roche Diagnostics GmbH, Nonnenwald 2, 82377, Penzberg, Germany
| | - Katharina Doninger
- Roche Pharma Technical Development, Roche Diagnostics GmbH, Nonnenwald 2, 82377, Penzberg, Germany
| | - Jan Griesbach
- Roche Pharma Technical Development, Roche Diagnostics GmbH, Nonnenwald 2, 82377, Penzberg, Germany
| | - Moritz Bennecke
- Roche Pharma Technical Development, Roche Diagnostics GmbH, Nonnenwald 2, 82377, Penzberg, Germany
| | - Stefan Hepbildikler
- Roche Pharma Technical Development, Roche Diagnostics GmbH, Nonnenwald 2, 82377, Penzberg, 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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13
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Pirrung SM, Parruca da Cruz D, Hanke AT, Berends C, Van Beckhoven RFWC, Eppink MHM, Ottens M. Chromatographic parameter determination for complex biological feedstocks. Biotechnol Prog 2018; 34:1006-1018. [PMID: 29693326 PMCID: PMC6175100 DOI: 10.1002/btpr.2642] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 04/12/2018] [Indexed: 11/23/2022]
Abstract
The application of mechanistic models for chromatography requires accurate model parameters. Especially for complex feedstocks such as a clarified cell harvest, this can still be an obstacle limiting the use of mechanistic models. Another commonly encountered obstacle is a limited amount of sample material and time to determine all needed parameters. Therefore, this study aimed at implementing an approach on a robotic liquid handling system that starts directly with a complex feedstock containing a monoclonal antibody. The approach was tested by comparing independent experimental data sets with predictions generated by the mechanistic model using all parameters determined in this study. An excellent agreement between prediction and experimental data was found verifying the approach. Thus, it can be concluded that RoboColumns with a bed volume of 200 μL can well be used to determine isotherm parameters for predictions of larger scale columns. Overall, this approach offers a new way to determine crucial model input parameters for mechanistic modelling of chromatography for complex biological feedstocks. © 2018 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:1006–1018, 2018
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Affiliation(s)
- Silvia M Pirrung
- Dept. of Biotechnology, Delft University of Technology, Van der Maasweg 9, Delft, 2629 HZ, the Netherlands
| | - Diogo Parruca da Cruz
- Dept. of Biotechnology, Delft University of Technology, Van der Maasweg 9, Delft, 2629 HZ, the Netherlands
| | - Alexander T Hanke
- Dept. of Biotechnology, Delft University of Technology, Van der Maasweg 9, Delft, 2629 HZ, the Netherlands
| | - Carmen Berends
- Dept. of Biotechnology, Delft University of Technology, Van der Maasweg 9, Delft, 2629 HZ, the Netherlands
| | | | - Michel H M Eppink
- Synthon Biopharmaceuticals BV, Microweg 22, GN Nijmegen, 6503, the Netherlands
| | - Marcel Ottens
- Dept. of Biotechnology, Delft University of Technology, Van der Maasweg 9, Delft, 2629 HZ, the Netherlands
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15
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Sellberg A, Holmqvist A, Magnusson F, Andersson C, Nilsson B. Discretized multi-level elution trajectory: A proof-of-concept demonstration. J Chromatogr A 2017; 1481:73-81. [DOI: 10.1016/j.chroma.2016.12.038] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Revised: 12/13/2016] [Accepted: 12/14/2016] [Indexed: 10/20/2022]
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16
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Improving the downstream processing of vaccine and gene therapy vectors with continuous chromatography. ACTA ACUST UNITED AC 2015. [DOI: 10.4155/pbp.15.29] [Citation(s) in RCA: 10] [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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17
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Kumar V, Leweke S, von Lieres E, Rathore AS. Mechanistic modeling of ion-exchange process chromatography of charge variants of monoclonal antibody products. J Chromatogr A 2015; 1426:140-53. [DOI: 10.1016/j.chroma.2015.11.062] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Revised: 11/18/2015] [Accepted: 11/18/2015] [Indexed: 12/29/2022]
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18
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Nestola P, Silva RJ, Peixoto C, Alves PM, Carrondo MJ, Mota JP. Robust design of adenovirus purification by two-column, simulated moving-bed, size-exclusion chromatography. J Biotechnol 2015; 213:109-19. [DOI: 10.1016/j.jbiotec.2015.01.030] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Revised: 01/19/2015] [Accepted: 01/26/2015] [Indexed: 11/30/2022]
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19
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Methods and Tools for Robust Optimal Control of Batch Chromatographic Separation Processes. Processes (Basel) 2015. [DOI: 10.3390/pr3030568] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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20
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Huuk TC, Hahn T, Osberghaus A, Hubbuch J. Model-based integrated optimization and evaluation of a multi-step ion exchange chromatography. Sep Purif Technol 2014. [DOI: 10.1016/j.seppur.2014.09.012] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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21
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A model based approach for identifying robust operating conditions for industrial chromatography with process variability. Chem Eng Sci 2014. [DOI: 10.1016/j.ces.2014.03.010] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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22
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23
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Hanke AT, Ottens M. Purifying biopharmaceuticals: knowledge-based chromatographic process development. Trends Biotechnol 2014; 32:210-20. [DOI: 10.1016/j.tibtech.2014.02.001] [Citation(s) in RCA: 123] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2013] [Revised: 01/24/2014] [Accepted: 02/04/2014] [Indexed: 01/04/2023]
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