1
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Biselli A, Reifsteck RA, Tesanovic M, Jupke A. Model-based investigation of the pH-dependent chromatographic separation of itaconic acid from aqueous solution using strongly hydrophobic adsorbents. J Chromatogr A 2024; 1734:465251. [PMID: 39191184 DOI: 10.1016/j.chroma.2024.465251] [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: 05/20/2024] [Revised: 08/07/2024] [Accepted: 08/08/2024] [Indexed: 08/29/2024]
Abstract
In this study, we propose a model for the simulation of the pH-dependent separation of dicarboxylic acids from aqueous solutions using strongly hydrophobic adsorbents. Building upon results of our previous study, where we experimentally investigated the pH-dependent adsorption behavior of the individual acid species of itaconic acid (IA) on a strongly hydrophobic adsorbent using in-line Raman spectroscopy, we utilize a transport-dispersive model as the basis for our simulation model. Instead of considering IA as a single component in our model, we simulated each acid species of IA individually. For this purpose, we expanded the transport-dispersive model with reaction terms in all aqueous phases. The reaction terms include all dissociation reactions of all involved components for each time step and spatial discretization. This model enables the time and spatial dependent simulation of the pH value in the chromatographic column and thus the time and spatial dependent knowledge of each acid species concentration. The consideration of activity coefficients due to high local ionic strength is achieved using the Truesdell-Jones (TdJ) model. The simulation model is successfully validated using experimental data from our previous study and used in a simulation study that demonstrates the potential of the model approach for analyzing associated separation tasks.
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Affiliation(s)
- Andreas Biselli
- Fluid Process Engineering (AVT.FVT), RWTH Aachen University, 52074 Aachen, Germany
| | - Rafael A Reifsteck
- Fluid Process Engineering (AVT.FVT), RWTH Aachen University, 52074 Aachen, Germany
| | - Marko Tesanovic
- Fluid Process Engineering (AVT.FVT), RWTH Aachen University, 52074 Aachen, Germany
| | - Andreas Jupke
- Fluid Process Engineering (AVT.FVT), RWTH Aachen University, 52074 Aachen, Germany.
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2
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Altern SH, Lyall JY, Welsh JP, Burgess S, Kumar V, Williams C, Lenhoff AM, Cramer SM. High-throughput in silico workflow for optimization and characterization of multimodal chromatographic processes. Biotechnol Prog 2024:e3483. [PMID: 38856182 DOI: 10.1002/btpr.3483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 04/13/2024] [Accepted: 05/08/2024] [Indexed: 06/11/2024]
Abstract
While high-throughput (HT) experimentation and mechanistic modeling have long been employed in chromatographic process development, it remains unclear how these techniques should be used in concert within development workflows. In this work, a process development workflow based on HT experiments and mechanistic modeling was constructed. The integration of HT and modeling approaches offers improved workflow efficiency and speed. This high-throughput in silico (HT-IS) workflow was employed to develop a Capto MMC polishing step for mAb aggregate removal. High-throughput batch isotherm data was first generated over a range of mobile phase conditions and a suite of analytics were employed. Parameters for the extended steric mass action (SMA) isotherm were regressed for the multicomponent system. Model validation was performed using the extended SMA isotherm in concert with the general rate model of chromatography using the CADET modeling software. Here, step elution profiles were predicted for eight RoboColumn runs across a range of ionic strength, pH, and load density. Optimized processes were generated through minimization of a complex objective function based on key process metrics. Processes were evaluated at lab-scale using two feedstocks, differing in composition. The results confirmed that both processes obtained high monomer yield (>85%) and removed∼ 50 % $$ \sim 50\% $$ of aggregate species. Column simulations were then carried out to determine sensitivity to a wide range of process inputs. Elution buffer pH was found to be the most critical process parameter, followed by resin ionic capacity. Overall, this study demonstrated the utility of the HT-IS workflow for rapid process development and characterization.
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Affiliation(s)
- Scott H Altern
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Jessica Y Lyall
- Purification Development, Genentech, South San Francisco, California, USA
| | - John P Welsh
- Process Research and Development, Merck & Co., Inc., Rahway, New Jersey, USA
- Rivanna Bioprocess Solutions, Charlottesville, Virginia, USA
| | - Sean Burgess
- Purification Development, Genentech, South San Francisco, California, USA
| | - Vijesh Kumar
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, USA
| | - Chris Williams
- Purification Development, Genentech, South San Francisco, California, USA
| | - Abraham M Lenhoff
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware, USA
| | - Steven M Cramer
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York, USA
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3
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LeBarre JP, Chu W, Altern SH, Kocot AJ, Bhandari D, Barbieri E, Sly J, Crapanzano M, Cramer SM, Phillips M, Roush D, Carbonell R, Boi C, Menegatti S. Mixed-mode size-exclusion silica resin for polishing human antibodies in flow-through mode. J Chromatogr A 2024; 1720:464772. [PMID: 38452560 DOI: 10.1016/j.chroma.2024.464772] [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: 09/22/2023] [Revised: 02/07/2024] [Accepted: 02/25/2024] [Indexed: 03/09/2024]
Abstract
The polishing step in the downstream processing of therapeutic antibodies removes residual impurities from Protein A eluates. Among the various classes of impurities, antibody fragments are especially challenging to remove due to the broad biomolecular diversity generated by a multitude of fragmentation patterns. The current approach to fragment removal relies on ion exchange or mixed-mode adsorbents operated in bind-and-gradient-elution mode. However, fragments that bear strong similarity to the intact product or whose biophysical features deviate from the ensemble average can elude these adsorbents, and the lack of a chromatographic technology enabling robust antibody polishing is recognized as a major gap in downstream bioprocessing. Responding to this challenge, this study introduces size-exclusion mixed-mode (SEMM) silica resins as a novel chromatographic adsorbent for the capture of antibody fragments irrespective of their biomolecular features. The pore diameter of the silica beads features a narrow distribution and is selected to exclude monomeric antibodies, while allowing their fragments to access the pores where they are captured by the mixed-mode ligands. The static and dynamic binding capacity of the adsorbent ranged respectively between 30-45 and 25-33 gs of antibody fragments per liter of resin. Selected SEMM-silica resins also demonstrated the ability to capture antibody aggregates, which adsorb on the outer layer of the beads. Optimization of the SEMM-silica design and operation conditions - namely, pore size (10 nm) and ligand composition (quaternary amine and alkyl chain) as well as the linear velocity (100 cm/h), ionic strength (5.7 mS/cm), and pH (7) of the mobile phase - afforded a significant reduction of both fragments and aggregates, resulting into a final antibody yield up to 80% and monomeric purity above 97%.
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Affiliation(s)
- Jacob P LeBarre
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA
| | - Wenning Chu
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA
| | - Scott H Altern
- The Howard P. Isermann Department of Chemical and Biological Engineering and the Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, 110 8th St, Troy, NY, 12180, USA
| | - Andrew J Kocot
- The Howard P. Isermann Department of Chemical and Biological Engineering and the Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, 110 8th St, Troy, NY, 12180, USA
| | - Dipendra Bhandari
- LigaTrap Technologies, Raleigh, 1791 Varsity Dr, Raleigh, NC, 27606, USA
| | - Eduardo Barbieri
- LigaTrap Technologies, Raleigh, 1791 Varsity Dr, Raleigh, NC, 27606, USA
| | - Jae Sly
- LigaTrap Technologies, Raleigh, 1791 Varsity Dr, Raleigh, NC, 27606, USA
| | - Michael Crapanzano
- LigaTrap Technologies, Raleigh, 1791 Varsity Dr, Raleigh, NC, 27606, USA
| | - Steven M Cramer
- The Howard P. Isermann Department of Chemical and Biological Engineering and the Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, 110 8th St, Troy, NY, 12180, USA
| | | | - David Roush
- Merck & Co., Inc., 2000 Galloping Hill Rd, Kenilworth, Roush Biopharma Panacea, 20 Squire Terrace, Colts Neck, NJ, 07033, USA
| | - Ruben Carbonell
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA; Biomanufacturing Training and Education Center (BTEC), North Carolina State University, 850 Oval Dr, Raleigh, NC 27606, USA
| | - Cristiana Boi
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA; Biomanufacturing Training and Education Center (BTEC), North Carolina State University, 850 Oval Dr, Raleigh, NC 27606, USA; Department of Civil, Chemical Environmental and Materials Engineering, University of Bologna, Via Terracini 28, 40131, Bologna, Italy
| | - Stefano Menegatti
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA; LigaTrap Technologies, Raleigh, 1791 Varsity Dr, Raleigh, NC, 27606, USA; Biomanufacturing Training and Education Center (BTEC), North Carolina State University, 850 Oval Dr, Raleigh, NC 27606, USA; North Carolina Viral Vector Initiative in Research and Learning (NC-VVIRAL), North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA.
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4
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Altern SH, Kocot AJ, LeBarre JP, Boi C, Phillips MW, Roush DJ, Menegatti S, Cramer SM. Mechanistic model-based characterization of size-exclusion-mixed-mode resins for removal of monoclonal antibody fragments. J Chromatogr A 2024; 1718:464717. [PMID: 38354506 DOI: 10.1016/j.chroma.2024.464717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 01/22/2024] [Accepted: 02/03/2024] [Indexed: 02/16/2024]
Abstract
Although antibody fragments are a critical impurity to remove from process streams, few platformable purification techniques have been developed to this end. In this work, a novel size-exclusion-mixed-mode (SEMM) resin was characterized with respect to its efficacy in mAb fragment removal. Inverse size-exclusion chromatography showed that the silica-based resin had a narrow pore size distribution and a median pore radius of roughly 6.2 nm. Model-based characterization was carried out with Chromatography Analysis and Design Toolkit (CADET), using the general rate model and the multicomponent Langmuir isotherm. Model parameters were obtained from fitting breakthrough curves, performed at multiple residence times, for a mixture of mAb, aggregates, and an array of fragments (varying in size). Accurate fits were obtained to the frontal chromatographic data across a range of residence times. Model validation was then performed with a scaled-up column, altering residence time and feed composition from the calibration run. Accurate predictions were obtained, thereby illustrating the model's interpolative and extrapolative capabilities. Additionally, the SEMM resin achieved 90% mAb yield, 37% aggregate removal, 29% [Formula: see text] removal, 54% Fab/Fc removal, 100% Fc fragments removal, and a productivity of 72.3 g mAbL×h. Model predictions for these statistics were all within 5%. Simulated batch uptake experiments showed that resin penetration depth was directly related to protein size, with the exception of the aggregate species, and that separation was governed by differential pore diffusion rates. Additional simulations were performed to characterize the dependence of fragment removal on column dimension, load density, and feed composition. Fragment removal was found to be highly dependent on column load density, where optimal purification was achieved below 100 mg protein/mL column. Furthermore, fragment removal was dependent on column volume (constant load mass), but agnostic to whether column length or diameter was changed. Lastly, the dependence on feed composition was shown to be complex. While fragment removal was inversely related to fragment mass fraction in the feed, the extent depended on fragment size. Overall, the results from this study illustrated the efficacy of the SEMM resin in fragment and aggregate removal and elucidated relationships with key operational parameters through model-based characterization.
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Affiliation(s)
- Scott H Altern
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Andrew J Kocot
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Jacob P LeBarre
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA
| | - Cristiana Boi
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA; Biomanufacturing Training and Education Center (BTEC), North Carolina State University, Raleigh, NC, USA; Department of Civil, Chemical, Environmental, and Materials Engineering, University of Bologna, Bologna, Italy
| | - Michael W Phillips
- Downstream Research and Development, EMD Millipore Corporation, Burlington, MA, USA
| | - David J Roush
- Process Research and Development, Merck & Co., Inc., Rahway, NJ, USA
| | - Stefano Menegatti
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA; Biomanufacturing Training and Education Center (BTEC), North Carolina State University, Raleigh, NC, USA; North Carolina Viral Vector Initiative in Research and Learning (NC-VVIRAL), North Carolina State University, Raleigh, NC, USA
| | - Steven M Cramer
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA.
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5
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Khan A, Qamar S. Simulation of Fixed-Bed Chromatographic Processes Considering the Nonlinear Adsorption Isotherms. ACS OMEGA 2023; 8:38301-38312. [PMID: 37867701 PMCID: PMC10586318 DOI: 10.1021/acsomega.3c04641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/21/2023] [Indexed: 10/24/2023]
Abstract
This paper presents the numerical approximation of a nonlinear equilibrium-dispersive (ED) model of multicomponent mixtures for simulating single-column chromatographic processes. Using Danckwerts boundary conditions (DBCs), the ED is studied for both generalized and standard bi-Langmuir adsorption isotherms. Advection-diffusion partial differential equations are used to represent fixed-bed chromatographic processes. As the diffusion term is significantly weaker than the advection term, sophisticated numerical techniques must be applied for solving such model equations. In this study, the model equations are numerically solved by using the Runge-Kutta discontinuous Galerkin (RKDG) finite element method. The technique is designed to handle sudden changes (sharp discontinuities) in solutions and to produce highly accurate results. The method is tested with several case studies considering different parameters, and its results are compared with the high-resolution finite volume scheme. One-, two-, and three-component liquid chromatography elutions on fixed beds are among the case studies being considered. The dynamic model and its accompanying numerical case studies provide the initial step toward continuous monitoring, troubleshooting, and effectively controlling the chromatographic processes.
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Affiliation(s)
- Ambreen Khan
- Department
of Mathematics, Air University, Islamabad 44000, Pakistan
| | - Shamsul Qamar
- Department
of Mathematics, COMSATS University Islamabad, Islamabad 45550, Pakistan
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6
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Heymann W, Glaser J, Schlegel F, Johnson W, Rolandi P, von Lieres E. Advanced error modeling and Bayesian uncertainty quantification in mechanistic liquid chromatography modeling. J Chromatogr A 2023; 1708:464329. [PMID: 37714013 DOI: 10.1016/j.chroma.2023.464329] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 08/07/2023] [Accepted: 08/23/2023] [Indexed: 09/17/2023]
Abstract
Current mechanistic chromatography process modeling methods lack the ability to account for the impact of experimental errors beyond detector noise (e.g. pump delays and variable feed composition) on the uncertainty in calibrated model parameters and the resulting model-predicted chromatograms. This paper presents an uncertainty quantification method that addresses this limitation by determining the probability distribution of parameters in calibrated models, taking into consideration multiple realistic sources of experimental error. The method, which is based on Bayes' theorem and utilizes Markov chain Monte Carlo with an ensemble sampler, is demonstrated to be robust and extensible using synthetic and industrial data. The corresponding software is freely available as open-source code at https://github.com/modsim/CADET-Match.
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Affiliation(s)
- William Heymann
- Institute of Bio- and Geosciences (IBG-1), Forschungszentrum Jülich, Wilhelm-Johnen-Str., Jülich 52428, Germany; RWTH Aachen University, Aachen 52062, Germany; Operations Digital Technology and Innovation Process Development (Ops DTI PD), Amgen Research Munich, Staffelseestr. 2, München 81477, Germany
| | - Juliane Glaser
- Digital Integration and Predictive Technologies (DIPT), Amgen Research Munich, Staffelseestr. 2, München 81477, Germany
| | - Fabrice Schlegel
- Digital Integration and Predictive Technologies (DIPT), Amgen, 360 Binney St, Cambridge, MA 02142
| | - Will Johnson
- Digital Integration and Predictive Technologies (DIPT), Amgen, 360 Binney St, Cambridge, MA 02142
| | - Pablo Rolandi
- Digital Integration and Predictive Technologies (DIPT), Amgen, 360 Binney St, Cambridge, MA 02142
| | - Eric von Lieres
- Institute of Bio- and Geosciences (IBG-1), Forschungszentrum Jülich, Wilhelm-Johnen-Str., Jülich 52428, Germany.
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7
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Tang SY, Yuan YH, Chen YC, Yao SJ, Wang Y, Lin DQ. Physics-informed neural networks to solve lumped kinetic model for chromatography process. J Chromatogr A 2023; 1708:464346. [PMID: 37716084 DOI: 10.1016/j.chroma.2023.464346] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 09/18/2023]
Abstract
Numerical method is widely used for solving the mechanistic models of chromatography process, but it is time-consuming and hard to response in real-time. Physics-informed neural network (PINN) as an emerging technology combines the structure of neural network with physics laws, and is getting noticed for solving physics problems with a balanced accuracy and calculation speed. In this research, a proof-of-concept study was carried out to apply PINN to chromatography process simulation. The PINN model structure was designed for the lumped kinetic model (LKM) with all LKM parameters. The PINN structure, training data and model complexity were optimized, and an optimal mode was obtained by adopting an in-series structure with a nonuniform training data set focusing on the breakthrough transition region. A PINN for LKM (LKM-PINN) consisting of four neural networks, 12 layers and 606 neurons was then used for the simulation of breakthrough curves of chromatography processes. The LKM parameters were estimated with two breakthrough curves and used to infer the breakthrough curves at different residence times, loading concentrations and column sizes. The results were comparable to that obtained with numerical methods. With the same raw data and constraints, the average fitting error for LKM-PINN model was 0.075, which was 0.081 for numerical method. With the same initial guess, the LKM-PINN model took 160 s to complete the fitting, while the numerical method took 7 to 72 min, depending on the fitting settings. The fitting speed of LKM-PINN model was further improved to 30 s with random initial guess. Thus, the LKM-PINN model developed in this study is capable to be applied to real-time simulation for digital twin.
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Affiliation(s)
- Si-Yuan Tang
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China; Manufacturing Science and Technology, Global Manufacturing, WuXi Biologics, Wuxi 214000, China
| | - Yun-Hao Yuan
- Manufacturing Science and Technology, Global Manufacturing, WuXi Biologics, Wuxi 214000, China
| | - Yu-Cheng Chen
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Shan-Jing Yao
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Ying Wang
- Manufacturing Science and Technology, Global Manufacturing, WuXi Biologics, Wuxi 214000, China
| | - Dong-Qiang Lin
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China.
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8
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Romero JJ, Jenkins EW, Husson SM. Surrogate-based Optimization of Capture Chromatography Platforms for the Improvement of Computational Efficiency. Comput Chem Eng 2023; 173:108225. [PMID: 37064815 PMCID: PMC10100681 DOI: 10.1016/j.compchemeng.2023.108225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
In this work, we discuss the use of surrogate functions and a new optimization framework to create an efficient and robust computational framework for process design. Our model process is the capture chromatography unit operation for monoclonal antibody purification, an important step in biopharmaceutical manufacturing. Simulating this unit operation involves solving a system of non-linear partial differential equations, which can have high computational cost. We implemented surrogate functions to reduce the computational time and make the framework more attractive for industrial applications. This strategy yielded accurate results with a 93% decrease in processing time. Additionally, we developed a new optimization framework to reduce the number of simulations needed to generate a solution to the optimization problem. We demonstrate the performance of our new framework, which uses MATLAB built-in tools, by comparing its performance against individual optimization algorithms for problems with integer, continuous, and mixed-integer variables.
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Affiliation(s)
- Juan J. Romero
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC 29634 USA
| | - Eleanor W. Jenkins
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634 USA
| | - Scott M. Husson
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC 29634 USA
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9
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Zafar S, Perveen S, Qamar S. Discontinuous Galerkin finite element scheme for solving non-linear lumped kinetic model of non-isothermal reactive liquid chromatography. KOREAN J CHEM ENG 2023. [DOI: 10.1007/s11814-022-1352-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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10
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Romero JJ, Jenkins EW, Osuofa J, Husson SM. Computational framework for the techno-economic analysis of monoclonal antibody capture chromatography platforms. J Chromatogr A 2023; 1689:463755. [PMID: 36586284 PMCID: PMC9868085 DOI: 10.1016/j.chroma.2022.463755] [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: 10/13/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/25/2022]
Abstract
We developed a computational framework that integrates commercial software components to perform customizable technoeconomic feasibility analyses. The use of multiple software packages overcomes the shortcomings of each to provide a detailed simulation that can be used for sensitivity analyses and optimizations. In this paper, the framework was used to evaluate the performance of monoclonal antibody capture processes. To this end, the simulation framework incorporated dynamic models for the affinity chromatography step that were validated with experimental breakthrough curves. The results were integrated with an Intelligen SuperPro Designer process simulation for the evaluation of key performance indicators of the operations. As proof of concept, the framework was used to perform a sensitivity analysis and optimization for a case study in which we sought to compare membrane and resin chromatography for disposable and reusable batch capture platforms. Two membranes and one resin were selected for the capture media, which yielded six process alternatives to compare. The objective functions were set to be cost of goods, process time, and buffer utilization. The results of the optimization of these process alternatives were a set of operating conditions that display tradeoffs between competing objectives. From this application exercise, we conclude that the framework can handle multiple variables and objectives, and it is adaptable to platforms with different chromatography media and operating modes. Additionally, the framework is capable of providing ad hoc analyses for decision making in a specific production context.
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Affiliation(s)
- Juan J Romero
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC 29634 USA
| | - Eleanor W Jenkins
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634 USA
| | - Joshua Osuofa
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC 29634 USA
| | - Scott M Husson
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC 29634 USA.
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11
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Perveen S, Rasheed MA, Sana S, Mumtaz I, Qamar S. Theoretical Analysis of a Nonequilibrium Transport Model of Two-Dimensional Nonisothermal Reactive Chromatography Accounting for Bi-Langmuir Isotherm. ACS OMEGA 2023; 8:3057-3077. [PMID: 36713702 PMCID: PMC9878646 DOI: 10.1021/acsomega.2c06317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/28/2022] [Indexed: 06/18/2023]
Abstract
The current study investigates a nonequilibrium and nonlinear two-dimensional lumped kinetic transport model of nonisothermal reactive liquid chromatography, considering the Bi-Langmuir adsorption isotherm, heterogeneous reaction rates, radial and axial concentration variations, and the adsorption and reaction enthalpies. The mathematical models of packed bed chromatographic processes are expressed by a highly nonlinear system of coupled partial differential algebraic equations connecting the phenomena of convection, diffusion, and reaction, for mass and energy balance, the differential algebraic equations for mass balance in the solid phase, and the algebraical expressions for the adsorption isotherms and for the reaction rates. The nonlinearity of the reaction term and the adsorption isotherm preclude the derivation of an analytical solution for the model equations. For this reason, a semidiscrete, high-resolution, finite-volume technique is extended and employed in this study to obtain the numerical solution. Several consistency checks are performed to evaluate the model predictions and analyze the precision of the proposed numerical scheme. A number of heterogeneously catalyzed stoichiometric reactions are numerically simulated to examine reactor performance under the influence of temperature and Bi-Langmuir adsorption dynamics, the level of coupling between mass and energy fronts, and to study the effects of various critical parameters. The numerical results obtained are beneficial for optimal predictive control and process optimization during production and the development of methods for systematic design and fault detection of nonisothermal liquid chromatographic reactors, and hence constitute the first step to provide deeper insight into the overall evaluation of integrated reaction and separation processes.
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Affiliation(s)
- Sadia Perveen
- Department
of Mathematics, Air University, Islamabad, 44000, Pakistan
| | | | - Samra Sana
- Department
of Mathematics, Air University, Islamabad, 44000, Pakistan
| | - Iram Mumtaz
- Department
of Mathematics, Air University, Islamabad, 44000, Pakistan
| | - Shamsul Qamar
- Department
of Mathematics, COMSATS University Islamabad, Islamabad, 45550, Pakistan
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12
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Kozorog M, Caserman S, Grom M, Vicente FA, Pohar A, Likozar B. Model-based process optimization for mAb chromatography. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2022.122528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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13
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Effect of solution condition on the binding behaviors of monoclonal antibody and fusion protein therapeutics in Protein A chromatography. J Chromatogr A 2022; 1686:463652. [DOI: 10.1016/j.chroma.2022.463652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/12/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022]
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14
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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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15
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Jäpel RC, Buyel JF. Bayesian optimization using multiple directional objective functions allows the rapid inverse fitting of parameters for chromatography simulations. J Chromatogr A 2022; 1679:463408. [PMID: 35977456 DOI: 10.1016/j.chroma.2022.463408] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 07/27/2022] [Accepted: 07/29/2022] [Indexed: 11/28/2022]
Abstract
The modeling of chromatographic separations can speed up downstream process development, reducing the time to market and corresponding development costs for new products such as pharmaceuticals. However, calibrating such models by identifying suitable parameter values for mass transport and sorption is a major, time-consuming challenge that can hinder model development and improvement. We therefore designed a new approach based on Bayesian optimization (BayesOpt) and Gaussian processes that reduced the time required to compute relevant chromatography parameters by up to two orders of magnitude compared to a multistart gradient descent and a genetic algorithm. We compared the three approaches side by side to process several internal and external datasets for ion exchange chromatography (based on a steric mass action isotherm) and hydrophobic interaction chromatography (a modified version of a recently published five-parameter isotherm) as well as different input data types (gradient elution data alone vs gradient elution and breakthrough data). We found that BayesOpt computation was consistently faster than the other approaches when using either single-core or 12-cores computer processing units. The error of the BayesOpt parameter estimates was higher than that of the competing algorithms, but still two orders of magnitude less than the variability of our experimental data, indicating BayesOpts applicability for chromatography modeling. The low computational demand of BayesOpt will facilitate rapid model development and improvement even for large datasets (e.g., > 100 proteins) and increase its suitability for research laboratories or small and medium enterprises lacking access to dedicated mainframe computers.
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Affiliation(s)
- Ronald Colin Jäpel
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Forckenbeckstrasse 6, Aachen 52074, Germany
| | - Johannes Felix Buyel
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Forckenbeckstrasse 6, Aachen 52074, Germany; Institute for Molecular Biotechnology, RWTH Aachen University, Worringerweg 1, Aachen 52074, Germany.
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16
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Shekhawat LK, Tiwari A, Yamamoto S, Rathore AS. An accelerated approach for mechanistic model based prediction of linear gradient elution ion-exchange chromatography of proteins. J Chromatogr A 2022; 1680:463423. [PMID: 36001907 DOI: 10.1016/j.chroma.2022.463423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 08/12/2022] [Accepted: 08/14/2022] [Indexed: 11/30/2022]
Abstract
With growing demands for therapeutic monoclonal antibodies, in silico downstream process development based on mechanistic modeling of chromatography separation process is being increasingly used for process optimization and process characterization. Application of mechanistic modeling in biopharmaceutical industry has been sparse due to the significant investment of time and resources that are required for performing model calibration. Mechanistic modeling of the chromatography process involves a large number of mass transport and binding parameters and their initial input values are required for simulations. These input values of column parameters can be easily obtained either from experiments or from empirical correlations available in literature. On the other hand, obtaining the model input valves for binding kinetic parameters is usually a cumbersome process as it involves performing batch experiments which are not only tedious but also require significant quantities of purely isolated main product and its related impurities, which is challenging as the product related impurities are typically present in smaller quantities and hence are difficult to obtain as pure species. In the present work, a mechanistic model that is based on the general rate model coupled with extended Langmuir binding model has been used for prediction of linear gradient elution peaks of monoclonal antibody on cation exchanger chromatography. The present work describes an accelerated approach for obtaining the input values for binding kinetic parameters in the extended Langmuir binding model from the two Yamamoto coefficient A and B values obtained by Yamamoto method directly from the model calibration linear gradient elution runs of different gradient slopes and at low to moderate protein loadings. The equations that can relate the two coefficients to the extended Langmuir model equation binding kinetic parameters were derived. Therefore, once A and B are determined, the binding kinetic parameter values were determined straightforward, thereby avoiding the problem of multiple solutions for the model parameters. The estimated binding parameters were successfully validated from isocratic elution experiments performed at low loading. What we demonstrate is that the proposed approach allows us to estimate binding kinetic parameters in a significantly more efficient and accelerated manner than presently used approaches, thereby accelerating development and implementation of mechanistic modeling for process chromatography.
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Affiliation(s)
- Lalita Kanwar Shekhawat
- Department of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, India; Cytiva Sweden AB Björkgatan 30, 753 23 Uppsala
| | - Anamika Tiwari
- Biomedical Engineering Center, Yamaguchi University, Tokiwadai, Ube, 755-8611, Japan; Manufacturing Technology Association of Biologics, 2-6-16, Shinkawa, Tokyo, 104-0033, Japan
| | - Shuichi Yamamoto
- Biomedical Engineering Center, Yamaguchi University, Tokiwadai, Ube, 755-8611, Japan; Manufacturing Technology Association of Biologics, 2-6-16, Shinkawa, Tokyo, 104-0033, Japan.
| | - Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, India.
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Kumar V, Khanal O, Jin M. Modeling the Impact of Holdup Volume from Chromatographic Workstations on Ion-Exchange Chromatography. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c01266] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Vijesh Kumar
- Technical Development, Downstream and Drug Product Development, Spark Therapeutics, Inc., 3737 Market Street, Philadelphia, Pennsylvania 19104, United States
| | - Ohnmar Khanal
- Technical Development, Downstream and Drug Product Development, Spark Therapeutics, Inc., 3737 Market Street, Philadelphia, Pennsylvania 19104, United States
| | - Mi Jin
- Technical Development, Downstream and Drug Product Development, Spark Therapeutics, Inc., 3737 Market Street, Philadelphia, Pennsylvania 19104, United States
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18
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Automation of Modeling and Calibration of Integrated Preparative Protein Chromatography Systems. Processes (Basel) 2022. [DOI: 10.3390/pr10050945] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
With the increasing global demand for precise and efficient pharmaceuticals and the biopharma industry moving towards Industry 4.0, the need for advanced process integration, automation, and modeling has increased as well. In this work, a method for automatic modeling and calibration of an integrated preparative chromatographic system for pharmaceutical development and production is presented. Based on a user-defined system description, a system model was automatically generated and then calibrated using a sequence of experiments. The system description and model was implemented in the Python-based preparative chromatography control software Orbit.
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19
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Leipnitz M, Scholl N, Biselli A, Jupke A. Influences of the constraints of a separation task on the optimal selection of a cation exchanger resin. FOOD AND BIOPRODUCTS PROCESSING 2022. [DOI: 10.1016/j.fbp.2022.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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20
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Yang X, Merenda A, AL-Attabi R, Dumée LF, Zhang X, Thang SH, Pham H, Kong L. Towards next generation high throughput ion exchange membranes for downstream bioprocessing: A review. J Memb Sci 2022. [DOI: 10.1016/j.memsci.2022.120325] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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21
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Simulations of liquid chromatography using two-dimensional non-equilibrium lumped kinetic model with Bi-Langmuir Isotherm. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2022.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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22
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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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23
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Bock HG, Cebulla DH, Kirches C, Potschka A. Mixed-integer optimal control for multimodal chromatography. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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24
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Badr S, Okamura K, Takahashi N, Ubbenjans V, Shirahata H, Sugiyama H. Integrated design of biopharmaceutical manufacturing processes: Operation modes and process configurations for monoclonal antibody production. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107422] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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25
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Bernau CR, Jäpel RC, Hübbers JW, Nölting S, Opdensteinen P, Buyel JF. Precision analysis for the determination of steric mass action parameters using eight tobacco host cell proteins. J Chromatogr A 2021; 1652:462379. [PMID: 34256268 DOI: 10.1016/j.chroma.2021.462379] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 06/25/2021] [Accepted: 06/26/2021] [Indexed: 12/12/2022]
Abstract
Plants are advantageous as biopharmaceutical manufacturing platforms because they allow the economical and scalable upstream production of proteins, including those requiring post-translational modifications, but do not support the replication of human viruses. However, downstream processing can be more labor-intensive compared to fermenter-based systems because the product is often mixed with abundant host cell proteins (HCPs). Modeling chromatographic separation can minimize the number of process development experiments and thus reduce costs. An important part of such modeling is the sorption isotherm, such as the steric mass action (SMA) model, which describes the multicomponent protein-salt equilibria established in ion-exchange systems. Here we purified ten HCPs, including 2-Cys-peroxiredoxin, from tobacco (Nicotiana tabacum and N. benthamiana). For eight of these HCPs, we obtained sufficient quantities to determine the SMA binding parameters (KSMA and ν) under different production-relevant conditions. We studied the parameters for 2-Cys-peroxiredoxin on Q-Sepharose HP in detail, revealing that pH, resin batch and buffer batch had little influence on KSMA and ν, with coefficients of variation (COVs) less than 0.05 and 0.21, respectively. In contrast, the anion-exchange resins SuperQ-650S, Q-Sepharose FF and QAE-550C led to COVs of 0.69 for KSMA and 0.05 for ν, despite using the same quaternary amine functional group as Q-Sepharose HP. Plant cultivation in summer vs winter resulted in COVs of 0.09 for KSMA and 0.02 for ν, revealing a small impact compared to COVs of 17.15 for KSMA and 0.20 for ν when plants were grown in different settings (climate-controlled phytotron vs greenhouse). We conclude that plant cultivation can substantially affect protein properties and the resulting SMA parameters. Accordingly, plant growth but also protein purification and characterization for chromatography model building should be tightly controlled and well documented.
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Affiliation(s)
- C R Bernau
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Forckenbeckstrasse 6, Aachen 52074, Germany.
| | - R C Jäpel
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Forckenbeckstrasse 6, Aachen 52074, Germany.
| | - J W Hübbers
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Forckenbeckstrasse 6, Aachen 52074, Germany.
| | - S Nölting
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Forckenbeckstrasse 6, Aachen 52074, Germany.
| | - P Opdensteinen
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Forckenbeckstrasse 6, Aachen 52074, Germany; Institute for Molecular Biotechnology, RWTH Aachen University, Worringerweg 1, Aachen 52074, Germany.
| | - J F Buyel
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Forckenbeckstrasse 6, Aachen 52074, Germany; Institute for Molecular Biotechnology, RWTH Aachen University, Worringerweg 1, Aachen 52074, Germany.
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26
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Khan A, Perveen S, Qamar S. Discontinuous-Galerkin finite-element method for approximating a model of non-equilibrium liquid chromatography considering Bi-Langmuir isotherm. J LIQ CHROMATOGR R T 2021. [DOI: 10.1080/10826076.2021.1916526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Ambreen Khan
- Department of Mathematics, COMSATS University, Islamabad, Pakistan
- Department of Mathematics, Air University, Islamabad, Pakistan
| | - Sadia Perveen
- Department of Mathematics, Air University, Islamabad, Pakistan
| | - Shamsul Qamar
- Department of Mathematics, COMSATS University, Islamabad, Pakistan
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
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27
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Reinforcement learning based optimization of process chromatography for continuous processing of biopharmaceuticals. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2020.116171] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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28
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Efficient Simulation of Chromatographic Processes Using the Conservation Element/Solution Element Method. Processes (Basel) 2020. [DOI: 10.3390/pr8101316] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Chromatographic separation processes need efficient simulation methods, especially for nonlinear adsorption isotherms such as the Langmuir isotherms which imply the formation of concentration shocks. The focus of this paper is on the space–time conservation element/solution element (CE/SE) method. This is an explicit method for the solution of systems of partial differential equations. Numerical stability of this method is guaranteed when the Courant–Friedrichs–Lewy condition is satisfied. To investigate the accuracy and efficiency of this method, it is compared with the classical cell model, which corresponds to a first-order finite volume discretization using a method of lines approach (MOL). The evaluation is done for different models, including the ideal equilibrium model and a mass transfer model for different adsorption isotherms—including linear and nonlinear Langmuir isotherms—and for different chromatographic processes from single-column operation to more sophisticated simulated moving bed (SMB) processes for the separation of binary and ternary mixtures. The results clearly show that CE/SE outperforms MOL in terms of computational times for all considered cases, ranging from 11-fold for the case with linear isotherm to 350-fold for the most complicated case with ternary center-cut eight-zone SMB with Langmuir isotherms, and it could be successfully applied for the optimization and control studies of such processes.
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29
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ChromaTech: A discontinuous Galerkin spectral element simulator for preparative liquid chromatography. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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30
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Ahmad AG, Qamar S. Simulation of nonisothermal reactive liquid chromatography using two‐dimensional lumped kinetic model. INT J CHEM KINET 2020. [DOI: 10.1002/kin.21392] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Abdulaziz Garba Ahmad
- Department of MathematicsCOMSATS University IslamabadIslamabad Pakistan
- Department of Mathematics ProgrammeNational Mathematical Centre AbujaNigeria
| | - Shamsul Qamar
- Department of MathematicsCOMSATS University IslamabadIslamabad Pakistan
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31
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Hagemann F, Adametz P, Wessling M, Thom V. Modeling hindered diffusion of antibodies in agarose beads considering pore size reduction due to adsorption. J Chromatogr A 2020; 1626:461319. [DOI: 10.1016/j.chroma.2020.461319] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/27/2020] [Accepted: 06/04/2020] [Indexed: 11/17/2022]
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Chromatography bioseparation technologies and in-silico modelings for continuous production of biotherapeutics. J Chromatogr A 2020; 1627:461376. [PMID: 32823091 DOI: 10.1016/j.chroma.2020.461376] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 06/22/2020] [Accepted: 06/28/2020] [Indexed: 12/23/2022]
Abstract
The potential of continuous bioprocessing is hindered by the bottlenecks of chromatography processing, which continues to be executed in batch mode. Highlighting the critical drawbacks of batch chromatography, this review underscores the transition that the industry has made by implementing continuous upstream process without devising a working model for downstream chromatography operations. Even though multitude of process development initiatives have commenced, the review emphasizes the first principle models of chromatography on which these initiatives are built. Various models of continuous chromatography, which are essential, but not limited to multi-column systems, employed to congeal a unified process are reviewed. Advancements made by several mechanistic models and simulations to maximize productivity and performance are described, in an attempt to provide the integral tools. The modeling tools can be used for development of a strong model based control strategy and can be embedded into the continuous chromatography framework. The review addresses the limitations and challenges of the current modeling methods for development of robust mechanistic modeling and efficient unit operation platform in continuous chromatography.
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33
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Kumar V, Lenhoff AM. Mechanistic Modeling of Preparative Column Chromatography for Biotherapeutics. Annu Rev Chem Biomol Eng 2020; 11:235-255. [DOI: 10.1146/annurev-chembioeng-102419-125430] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Chromatography has long been, and remains, the workhorse of downstream processing in the production of biopharmaceuticals. As bioprocessing has matured, there has been a growing trend toward seeking a detailed fundamental understanding of the relevant unit operations, which for some operations include the use of mechanistic modeling in a way similar to its use in the conventional chemical process industries. Mechanistic models of chromatography have been developed for almost a century, but although the essential features are generally understood, the specialization of such models to biopharmaceutical processing includes several areas that require further elucidation. This review outlines the overall approaches used in such modeling and emphasizes current needs, specifically in the context of typical uses of such models; these include selection and improvement of isotherm models and methods to estimate isotherm and transport parameters independently. Further insights are likely to be aided by molecular-level modeling, as well as by the copious amounts of empirical data available for existing processes.
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Affiliation(s)
- Vijesh Kumar
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, USA
| | - Abraham M. Lenhoff
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, USA
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34
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Khanal O, Kumar V, Schlegel F, Lenhoff AM. Estimating and leveraging protein diffusion on ion-exchange resin surfaces. Proc Natl Acad Sci U S A 2020; 117:7004-7010. [PMID: 32179691 PMCID: PMC7132105 DOI: 10.1073/pnas.1921499117] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Protein mobility at solid-liquid interfaces can affect the performance of applications such as bioseparations and biosensors by facilitating reorganization of adsorbed protein, accelerating molecular recognition, and informing the fundamentals of adsorption. In the case of ion-exchange chromatographic beads with small, tortuous pores, where the existence of surface diffusion is often not recognized, slow mass transfer can result in lower resin capacity utilization. We demonstrate that accounting for and exploiting protein surface diffusion can alleviate the mass-transfer limitations on multiple significant length scales. Although the surface diffusivity has previously been shown to correlate with ionic strength (IS) and binding affinity, we show that the dependence is solely on the binding affinity, irrespective of pH, IS, and resin ligand density. Different surface diffusivities give rise to different protein distributions within the resin, as characterized using confocal microscopy and small-angle neutron scattering (length scales of micrometer and nanometer, respectively). The binding dependence of surface diffusion inspired a protein-loading approach in which the binding affinity, and hence the surface diffusivity, is modulated by varying IS. Such gradient loading increased the protein uptake efficiency by up to 43%, corroborating the importance of protein surface diffusion in protein transport in ion-exchange chromatography.
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Affiliation(s)
- Ohnmar Khanal
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716
| | - Vijesh Kumar
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716
| | | | - Abraham M Lenhoff
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716;
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35
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Osterroth S, Menstell P, Schwämmle A, Ohser J, Steiner K. Adjoint optimization for the general rate model of liquid chromatography. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2019.106657] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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36
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Leipnitz M, Biselli A, Merfeld M, Scholl N, Jupke A. Model-based selection of the degree of cross-linking of cation exchanger resins for an optimised separation of monosaccharides. J Chromatogr A 2020; 1610:460565. [PMID: 31615624 DOI: 10.1016/j.chroma.2019.460565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 09/20/2019] [Accepted: 09/21/2019] [Indexed: 01/03/2023]
Abstract
One of the main steps in designing preparative chromatographic separation units is the selection of a well-performing adsorbent. This is often based on expert knowledge or based on case studies of preselected adsorbents. Therefore, the selection is usually limited in terms of an optimised choice. In this contribution, a model-based optimisation of the selection of an adsorbent on the basis of correlations between structural adsorbent properties with model parameters of a transport dispersive model is proposed. Model parameters of glucose and xylose for five cation exchanger resins with varying degree of cross-linking are experimentally determined in a sequential approach. Void fractions and particle porosities were determined by pulse experiments with different tracers. Single-component isotherms were determined threefold via breakthrough curves with concentrations of up to 250 g l-1 at 60 °C. Mass transfer coefficients were determined by batch experiments. Correlations between the degree of cross-linking of the resins and the Henry coefficients as well as the mass transfer coefficients were derived and applied in an optimisation case study. Based on the derived mathematical formula, the process performance of experimentally not investigated resins were predicted. Further, the selection of a resin for a preparative monosaccharide separation was included into optimisation algorithms.
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Affiliation(s)
- Martin Leipnitz
- Fluid Process Engineering, AVT - Aachener Verfahrenstechnik, RWTH Aachen University, Forckenbeckstrasse 51, Aachen D-52074, Germany; Bioeconomy Science Center (BioSC), c/o Forschungszentrum Jülich, Jülich D-52425, Germany.
| | - Andreas Biselli
- Fluid Process Engineering, AVT - Aachener Verfahrenstechnik, RWTH Aachen University, Forckenbeckstrasse 51, Aachen D-52074, Germany; Bioeconomy Science Center (BioSC), c/o Forschungszentrum Jülich, Jülich D-52425, Germany
| | - Marcel Merfeld
- Fluid Process Engineering, AVT - Aachener Verfahrenstechnik, RWTH Aachen University, Forckenbeckstrasse 51, Aachen D-52074, Germany
| | - Niklas Scholl
- Fluid Process Engineering, AVT - Aachener Verfahrenstechnik, RWTH Aachen University, Forckenbeckstrasse 51, Aachen D-52074, Germany
| | - Andreas Jupke
- Fluid Process Engineering, AVT - Aachener Verfahrenstechnik, RWTH Aachen University, Forckenbeckstrasse 51, Aachen D-52074, Germany; Bioeconomy Science Center (BioSC), c/o Forschungszentrum Jülich, Jülich D-52425, Germany
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Kiran N, Perveen S, Sattar FA, Qamar S. Numerical solution of nonlinear and non-isothermal general rate model of reactive liquid chromatography. J LIQ CHROMATOGR R T 2019. [DOI: 10.1080/10826076.2019.1686705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Nadia Kiran
- Department of Mathematics, COMSATS University Islamabad, Islamabad, Pakistan
| | - Sadia Perveen
- Department of Mathematics, Air University, Islamabad, Pakistan
| | - Fouzia A. Sattar
- Department of Mathematics, COMSATS University Islamabad, Islamabad, Pakistan
| | - Shamsul Qamar
- Department of Mathematics, COMSATS University Islamabad, Islamabad, Pakistan
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
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38
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Solving hyperbolic conservation laws with active counteraction against numerical errors: Isothermal fixed-bed adsorption. Chem Eng Sci 2019. [DOI: 10.1016/j.ces.2019.07.053] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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39
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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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40
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Meyer K, Huusom JK, Abildskov J. A stabilized nodal spectral solver for liquid chromatography models. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.02.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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41
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Brhane KW, Qamar S, Seidel-Morgenstern A. Two-Dimensional General Rate Model of Liquid Chromatography Incorporating Finite Rates of Adsorption–Desorption Kinetics and Core–Shell Particles. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b00364] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Kewani Welay Brhane
- Department of Mathematics, COMSATS University Islamabad, Islamabad 45550, Pakistan
- Department of Mathematics, Mekelle University, Mekelle, Ethiopia
| | - Shamsul Qamar
- Department of Mathematics, COMSATS University Islamabad, Islamabad 45550, Pakistan
- Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Germany
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42
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Shekhawat LK, Rathore AS. Mechanistic modeling based process analytical technology implementation for pooling in hydrophobic interaction chromatography. Biotechnol Prog 2018; 35:e2758. [PMID: 30485717 DOI: 10.1002/btpr.2758] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 09/26/2018] [Accepted: 11/26/2018] [Indexed: 12/26/2022]
Abstract
A major challenge in chromatography purification of therapeutic proteins is batch-to-batch variability with respect to impurity levels and product concentration in the feed. Mechanistic model can enable process analytical technology (PAT) implementation by predicting impact of such variations and thereby improving the robustness of the resulting process and controls. This article presents one such application of mechanistic model of hydrophobic interaction chromatography (HIC) as a PAT tool for making robust pooling decisions to enable clearance of aggregates for a monoclonal antibody (mAb) therapeutic. Model predictions were performed before the actual chromatography experiments to facilitate feedforward control. The approach has been successfully demonstrated for four different feeds with varying aggregate levels (3.84%-5.54%) and feed concentration (0.6 mg/mL-1 mg/mL). The resulting pool consistently yielded a product with 1.32 ± 0.03% aggregate vs. a target of 1.5%. A comparison of the traditional approach involving column fractionation with the proposed approach indicates that the proposed approach results in achievement of satisfactory product purity (98.68 ± 0.03% for mechanistic model based PAT controlled pooling vs. 98.64 ± 0.16% for offline column fractionation based pooling). © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2758, 2019.
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Affiliation(s)
| | - Anurag S Rathore
- Dept. of Chemical Engineering, Indian Inst. of Technology, Hauz Khas, New Delhi, India
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43
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Jäpel R, Müschen C, von Lieres E, Buyel J. Using quantitative structure-activity relationship models to predict protein properties for chromatographic separation of host cell proteins. CHEM-ING-TECH 2018. [DOI: 10.1002/cite.201855364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- R. C. Jäpel
- Fraunhofer IME; Integrated Production Platforms; Forckenbeckstraße 6 52074 Aachen Deutschland
| | - C. Müschen
- Fraunhofer IME; Integrated Production Platforms; Forckenbeckstraße 6 52074 Aachen Deutschland
| | - E. von Lieres
- Forschungszentrum Jülich GmbH; Institute of Bio- and Geosciences; Wilhelm-Johnen-Straße 52428 Jülich Deutschland
| | - J. F. Buyel
- Fraunhofer IME; Integrated Production Platforms; Forckenbeckstraße 6 52074 Aachen Deutschland
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Koppejan V, Ferreira G, Lin D, Ottens M. Mathematical modelling of expanded bed adsorption - a perspective on in silico process design. JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY (OXFORD, OXFORDSHIRE : 1986) 2018; 93:1815-1826. [PMID: 30008502 PMCID: PMC6032964 DOI: 10.1002/jctb.5595] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 01/22/2018] [Accepted: 01/24/2018] [Indexed: 06/08/2023]
Abstract
Expanded bed adsorption (EBA) emerged in the early 1990s in an attempt to integrate the clarification, capture and initial product concentration/purification process. Several mathematical models have been put forward to describe its operation. However, none of the models developed specifically for EBA allows simultaneous prediction of bed hydrodynamics, mass transfer/adsorption and (unwanted) interactions and fouling. This currently limits the development and early optimization of EBA-based separation processes. In multiphase reactor engineering, the use of multiphase computational fluid dynamics has been shown to improve fundamental understanding of fluidized beds. To advance EBA technology, a combination of particle, equipment and process scale models should be used. By employing a cascade of multiscale simulations, the various challenges EBA currently faces can be addressed. This allows for optimal design and selection of equipment, materials and process conditions, and reduces risks and development times of downstream processes involving EBA. © 2018 The Authors. Journal of Chemical Technology & Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Victor Koppejan
- Delft University of TechnologyDepartment of Biotechnology, Van der Maasweg 9, 2629 HZDelftThe Netherlands
| | - Guilherme Ferreira
- DSM Biotechnology CenterCenter of Integrated BioProcessing, Alexander Fleminglaan 12613AXDelftThe Netherlands
| | - Dong‐Qiang Lin
- College of Chemical and Biological EngineeringZhejiang UniversityHangzhouChina
| | - Marcel Ottens
- Delft University of TechnologyDepartment of Biotechnology, Van der Maasweg 9, 2629 HZDelftThe Netherlands
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Pabst TM, Thai J, Hunter AK. Evaluation of recent Protein A stationary phase innovations for capture of biotherapeutics. J Chromatogr A 2018; 1554:45-60. [DOI: 10.1016/j.chroma.2018.03.060] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 03/26/2018] [Accepted: 03/29/2018] [Indexed: 11/29/2022]
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46
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47
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Protein A affinity chromatography of Chinese hamster ovary (CHO) cell culture broths containing biopharmaceutical monoclonal antibody (mAb): Experiments and mechanistic transport, binding and equilibrium modeling. J Chromatogr B Analyt Technol Biomed Life Sci 2018. [DOI: 10.1016/j.jchromb.2018.02.032] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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48
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He QL, Leweke S, von Lieres E. Efficient numerical simulation of simulated moving bed chromatography with a single-column solver. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2017.12.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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49
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Affiliation(s)
- Franziska Ortner
- Institute of Process Engineering, ETH Zurich, 8092 Zurich, Switzerland
| | - Marco Mazzotti
- Institute of Process Engineering, ETH Zurich, 8092 Zurich, Switzerland
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50
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Analytical and numerical solutions of two-dimensional general rate models for liquid chromatographic columns packed with core–shell particles. Chem Eng Res Des 2018. [DOI: 10.1016/j.cherd.2017.12.044] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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