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Wittkopp F, Welsh J, Todd R, Staby A, Roush D, Lyall J, Karkov S, Hunt S, Griesbach J, Bertran MO, Babi D. Current state of implementation of in silico tools in the biopharmaceutical industry-Proceedings of the 5th modeling workshop. Biotechnol Bioeng 2024; 121:2952-2973. [PMID: 38853778 DOI: 10.1002/bit.28768] [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: 03/20/2024] [Revised: 05/24/2024] [Accepted: 05/29/2024] [Indexed: 06/11/2024]
Abstract
The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high-level guidance in ICH Q8-Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for "mechanistic modeling." Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter-disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community.
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Affiliation(s)
- Felix Wittkopp
- Roche Diagnostics GmbH, Gene Therapy Technical Development, Penzberg, Germany
| | - John Welsh
- Rivanna Bioprocess Solutions, Charlottesville, Virginia, USA
| | - Robert Todd
- Digital Process Design, Boulder, Colorado, USA
| | - Arne Staby
- CMC Development, Novo Nordisk, Bagsværd, Denmark
| | - David Roush
- Roush Biopharma Panacea, Colts Neck, New Jersey, USA
| | - Jessica Lyall
- Purification Development, Genentech, South San Francisco, California, USA
| | - Sophie Karkov
- Purification Research, Global Research Technologies, Novo Nordisk, Måløv, Denmark
| | - Stephen Hunt
- Allogene Therapeutics, Inc., South San Francisco, California, USA
| | | | - Maria-Ona Bertran
- Product Supply API Manufacturing Development, Novo Nordisk, Bagsværd, Denmark
| | - Deenesh Babi
- Product Supply API Manufacturing Development, Novo Nordisk, Bagsværd, Denmark
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Ballweg T, Liu M, Grimm J, Sedghamiz E, Wenzel W, Franzreb M. All-atom modeling of methacrylate-based multi-modal chromatography resins for Langmuir constant prediction of peptides. J Chromatogr A 2024; 1730:465089. [PMID: 38879977 DOI: 10.1016/j.chroma.2024.465089] [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: 03/13/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 06/18/2024]
Abstract
In downstream processing, the intricate nature of the interactions between biomolecules and adsorbent materials presents a significant challenge in the prediction of their binding and elution behaviors. This complexity is further heightened in multi-modal chromatography (MMC), which employs two distinct binding mechanisms. To gain a deeper understanding of the involved interactions, simulating the adsorption of biomolecules on resin surfaces is a focal point of ongoing research. However, previous studies often simplified the adsorbent surface, modeling it as a flat or slightly curved plane without including a realistic backbone structure. Here, we introduce and validate two novel workflows aimed at predicting peptide binding behaviors in MMC, specifically targeting methacrylate-based resins. Our first achievement was the development of an all-atom model of a commercial MMC resin surface, incorporating its polymethacrylic backbone. Furthermore, we established and tested a workflow for rapid calculations of binding free energies (ΔG) with 10 linear peptides as target molecules. These ΔG calculations were effectively used to predict Langmuir constants, achieving a high coefficient of determination (R²) of 0.96. In subsequent benchmarking tests, our model outperformed established, simpler resin surface models in terms of predictive capabilities.
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Affiliation(s)
- Tim Ballweg
- Institute of Functional Interfaces, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen 76344, Germany
| | - Modan Liu
- Institute of Functional Interfaces, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen 76344, Germany
| | - Julian Grimm
- Institute of Functional Interfaces, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen 76344, Germany
| | - Elaheh Sedghamiz
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen 76344, Germany; Schrödinger, GmbH, Glücksteinallee 25, Mannheim 68163, Germany
| | - Wolfgang Wenzel
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen 76344, Germany
| | - Matthias Franzreb
- Institute of Functional Interfaces, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen 76344, Germany.
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Weggen JT, Bean R, Hui K, Wendeler M, Hubbuch J. Kinetic models towards an enhanced understanding of diverse ADC conjugation reactions. Front Bioeng Biotechnol 2024; 12:1403644. [PMID: 39070164 PMCID: PMC11274341 DOI: 10.3389/fbioe.2024.1403644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 06/07/2024] [Indexed: 07/30/2024] Open
Abstract
The conjugation reaction is the central step in the manufacturing process of antibody-drug conjugates (ADCs). This reaction generates a heterogeneous and complex mixture of differently conjugated sub-species depending on the chosen conjugation chemistry. The parametrization of the conjugation reaction through mechanistic kinetic models offers a chance to enhance valuable reaction knowledge and ensure process robustness. This study introduces a versatile modeling framework for the conjugation reaction of cysteine-conjugated ADC modalities-site-specific and interchain disulfide conjugation. Various conjugation kinetics involving different maleimide-functionalized payloads were performed, while controlled gradual payload feeding was employed to decelerate the conjugation, facilitating a more detailed investigation of the reaction mechanism. The kinetic data were analyzed with a reducing reversed phase (RP) chromatography method, that can readily be implemented for the accurate characterization of ADCs with diverse drug-to-antibody ratios, providing the conjugation trajectories of the single chains of the monoclonal antibody (mAb). Possible kinetic models for the conjugation mechanism were then developed and selected based on multiple criteria. When calibrating the established model to kinetics involving different payloads, conjugation rates were determined to be payload-specific. Further conclusions regarding the kinetic comparability across the two modalities could also be derived. One calibrated model was used for an exemplary in silico screening of the initial concentrations offering valuable insights for profound understanding of the conjugation process in ADC development.
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Affiliation(s)
- Jan Tobias Weggen
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Ryan Bean
- Purification Process Sciences, BioPharmaceuticals Development, R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Kimberly Hui
- Purification Process Sciences, BioPharmaceuticals Development, R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Michaela Wendeler
- Purification Process Sciences, BioPharmaceuticals Development, R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Jürgen Hubbuch
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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Shi C, Chen XJ, Zhong XZ, Yang Y, Lin DQ, Chen R. Realization of digital twin for dynamic control toward sample variation of ion exchange chromatography in antibody separation. Biotechnol Bioeng 2024; 121:1702-1715. [PMID: 38230585 DOI: 10.1002/bit.28660] [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: 10/24/2023] [Revised: 12/26/2023] [Accepted: 01/05/2024] [Indexed: 01/18/2024]
Abstract
Digital twin (DT) is a virtual and digital representation of physical objects or processes. In this paper, this concept is applied to dynamic control of the collection window in the ion exchange chromatography (IEC) toward sample variations. A possible structure of a feedforward model-based control DT system was proposed. Initially, a precise IEC mechanistic model was established through experiments, model fitting, and validation. The average root mean square error (RMSE) of fitting and validation was 8.1% and 7.4%, respectively. Then a model-based gradient optimization was performed, resulting in a 70.0% yield with a remarkable 11.2% increase. Subsequently, the DT was established by systematically integrating the model, chromatography system, online high-performance liquid chromatography, and a server computer. The DT was validated under varying load conditions. The results demonstrated that the DT could offer an accurate control with acidic variants proportion and yield difference of less than 2% compared to the offline analysis. The embedding mechanistic model also showed a positive predictive performance with an average RMSE of 11.7% during the DT test under >10% sample variation. Practical scenario tests indicated that tightening the control target could further enhance the DT robustness, achieving over 98% success rate with an average yield of 72.7%. The results demonstrated that the constructed DT could accurately mimic real-world situations and perform an automated and flexible pooling in IEC. Additionally, a detailed methodology for applying DT was summarized.
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Affiliation(s)
- Ce Shi
- Process Development Downstream, Shanghai Engineering Research Center of Anti-tumor Biological Drugs, Shanghai Henlius Biotech, Inc., Shanghai, China
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China
| | - Xu-Jun Chen
- Process Development Downstream, Shanghai Engineering Research Center of Anti-tumor Biological Drugs, Shanghai Henlius Biotech, Inc., Shanghai, China
| | - Xue-Zhao Zhong
- Process Development Downstream, Shanghai Engineering Research Center of Anti-tumor Biological Drugs, Shanghai Henlius Biotech, Inc., Shanghai, China
| | - Yan Yang
- Process Development Downstream, Shanghai Engineering Research Center of Anti-tumor Biological Drugs, Shanghai Henlius Biotech, Inc., Shanghai, China
| | - Dong-Qiang Lin
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China
| | - Ran Chen
- Process Development Downstream, Shanghai Engineering Research Center of Anti-tumor Biological Drugs, Shanghai Henlius Biotech, Inc., Shanghai, China
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Hess R, Faessler J, Yun D, Saleh D, Grosch JH, Schwab T, Hubbuch J. Antibody sequence-based prediction of pH gradient elution in multimodal chromatography. J Chromatogr A 2023; 1711:464437. [PMID: 37865026 DOI: 10.1016/j.chroma.2023.464437] [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: 07/11/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 10/23/2023]
Abstract
Multimodal chromatography has emerged as a promising technique for antibody purification, owing to its capacity to selectively capture and separate target molecules. However, the optimization of chromatography parameters remains a challenge due to the intricate nature of protein-ligand interactions. To tackle this issue, efficient predictive tools are essential for the development and optimization of multimodal chromatography processes. In this study, we introduce a methodology that predicts the elution behavior of antibodies in multimodal chromatography based on their amino acid sequences. We analyzed a total of 64 full-length antibodies, including IgG1, IgG4, and IgG-like multispecific formats, which were eluted using linear pH gradients from pH 9.0 to 4.0 on the anionic mixed-mode resin Capto adhere. Homology models were constructed, and 1312 antibody-specific physicochemical descriptors were calculated for each molecule. Our analysis identified six key structural features of the multimodal antibody interaction, which were correlated with the elution behavior, emphasizing the antibody variable region. The results show that our methodology can predict pH gradient elution for a diverse range of antibodies and antibody formats, with a test set R² of 0.898. The developed model can inform process development by predicting initial conditions for multimodal elution, thereby reducing trial and error during process optimization. Furthermore, the model holds the potential to enable an in silico manufacturability assessment by screening target antibodies that adhere to standardized purification conditions. In conclusion, this study highlights the feasibility of using structure-based prediction to enhance antibody purification in the biopharmaceutical industry. This approach can lead to more efficient and cost-effective process development while increasing process understanding.
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Affiliation(s)
- Rudger Hess
- Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Jan Faessler
- DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Doil Yun
- DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - David Saleh
- DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Jan-Hendrik Grosch
- DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Thomas Schwab
- DSP Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Jürgen Hubbuch
- Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
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Koch J, Scheps D, Gunne M, Boscheinen O, Frech C. Mechanistic modeling of cation exchange chromatography scale-up considering packing inhomogeneities. J Sep Sci 2023; 46:e2300031. [PMID: 36846902 DOI: 10.1002/jssc.202300031] [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: 01/16/2023] [Revised: 02/24/2023] [Accepted: 02/25/2023] [Indexed: 03/01/2023]
Abstract
In process development and characterization, the scale-up of chromatographic steps is a crucial part and brings a number of challenges. Usually, scale-down models are used to represent the process step, and constant column properties are assumed. The scaling is then typically based on the concept of linear scale-up. In this work, a mechanistic model describing an anti-Langmuirian to Langmuirian elution behavior of a polypeptide, calibrated with a pre-packed 1 ml column, is applied to demonstrate the scalability to larger column volumes up to 28.2 ml. Using individual column parameters for each column size, scaling to similar eluting salt concentrations, peak heights, and shapes is experimentally demonstrated by considering the model's relationship between the normalized gradient slope and the eluting salt concentration. Further scale-up simulations show improved model predictions when radial inhomogeneities in packing quality are considered.
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Affiliation(s)
- Jonas Koch
- Department of Biotechnology, Institute for Biochemistry, University of Applied Sciences, Mannheim, Germany
| | - Daniel Scheps
- CMC Microbial Platform, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, Germany
| | - Matthias Gunne
- IA MSAT M&I DS, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, Germany
| | - Oliver Boscheinen
- CMC Microbial Platform, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, Germany
| | - Christian Frech
- Department of Biotechnology, Institute for Biochemistry, University of Applied Sciences, Mannheim, Germany
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7
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A Comprehensive Mechanistic Yeast Model Able to Switch Metabolism According to Growth Conditions. FERMENTATION 2022. [DOI: 10.3390/fermentation8120710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
This paper proposes a general approach for building a mechanistic yeast model able to predict the shift of metabolic pathways. The mechanistic model accounts for the coexistence of several metabolic pathways (aerobic fermentation, glucose respiration, anaerobic fermentation and ethanol respiration) whose activation depends on growth conditions. This general approach is applied to a commercial strain of Saccharomyces cerevisiae. Stoichiometry and yeast kinetics were mostly determined from aerobic and completely anaerobic experiments. Known parameters were taken from the literature, and the remaining parameters were estimated by inverse analysis using the particle swarm optimization method. The optimized set of parameters allows the concentrations to be accurately determined over time, reporting global mean relative errors for all variables of less than 7 and 11% under completely anaerobic and aerobic conditions, respectively. Different affinities of yeast for glucose and ethanol tolerance under aerobic and anaerobic conditions were obtained. Finally, the model was successfully validated by simulating a different experiment, a batch fermentation process without gas injection, with an overall mean relative error of 7%. This model represents a useful tool for the control and optimization of yeast fermentation systems. More generally, the modeling framework proposed here is intended to be used as a building block of a digital twin of any bioproduction process.
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Hahn T, Geng N, Petrushevska-Seebach K, Dolan ME, Scheindel M, Graf P, Takenaka K, Izumida K, Li L, Ma Z, Schuelke N. Mechanistic modeling, simulation, and optimization of mixed-mode chromatography for an antibody polishing step. Biotechnol Prog 2022; 39:e3316. [PMID: 36471899 DOI: 10.1002/btpr.3316] [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: 09/12/2022] [Revised: 11/27/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
Mixed-mode chromatography combines features of ion-exchange chromatography and hydrophobic interaction chromatography and is increasingly used in antibody purification. As a replacement for flow-through operations on traditional unmixed resins or as a pH-controlled bind-and-elute step, the use of both interaction modes promises a better removal of product-specific impurities. However, the combination of the functionalities makes industrial process development significantly more complex, in particular the identification of the often small elution window that delivers the desired selectivity. Mechanistic modeling has proven that even difficult separation problems can be solved in a computer-optimized manner once the process dynamics have been modeled. The adsorption models described in the literature are also very complex, which makes model calibration difficult. In this work, we approach this problem with a newly constructed model that describes the adsorber saturation with the help of the surface coverage function of the colloidal particle adsorption model for ion-exchange chromatography. In a case study, a model for a pH-controlled antibody polishing step was created from six experiments. The behavior of fragments, aggregates, and host cell proteins was described with the help of offline analysis. After in silico optimization, a validation experiment confirmed an improved process performance in comparison to the historical process set point. In addition to these good results, the work also shows that the high dynamics of mixed-mode chromatography can produce unexpected results if process parameters deviate too far from tried and tested conditions.
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Affiliation(s)
| | | | | | | | | | - Pia Graf
- GoSilico GmbH, Karlsruhe, Germany
| | | | - Kyo Izumida
- Takeda Pharmaceuticals, Fujisawa, Kanagawa, Japan
| | - Lijuan Li
- Takeda Pharmaceuticals, Lexington, Massachusetts, USA
| | - Zijian Ma
- Takeda Pharmaceuticals, Lexington, Massachusetts, USA
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Doyle K, Tsopanoglou A, Fejér A, Glennon B, del Val IJ. Automated assembly of hybrid dynamic models for CHO cell culture processes. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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10
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Richelle A, Corbett B, Agarwal P, Vernersson A, Trygg J, McCready C. Model-based intensification of CHO cell cultures: One-step strategy from fed-batch to perfusion. Front Bioeng Biotechnol 2022; 10:948905. [PMID: 36072286 PMCID: PMC9443430 DOI: 10.3389/fbioe.2022.948905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/06/2022] [Indexed: 11/13/2022] Open
Abstract
There is a growing interest in continuous processing of the biopharmaceutical industry. However, the technology transfer from traditional batch-based processes is considered a challenge as protocol and tools still remain to be established for their usage at the manufacturing scale. Here, we present a model-based approach to design optimized perfusion cultures of Chinese Hamster Ovary cells using only the knowledge captured during small-scale fed-batch experiments. The novelty of the proposed model lies in the simplicity of its structure. Thanks to the introduction of a new catch-all variable representing a bulk of by-products secreted by the cells during their cultivation, the model was able to successfully predict cellular behavior under different operating modes without changes in its formalism. To our knowledge, this is the first experimentally validated model capable, with a single set of parameters, to capture culture dynamic under different operating modes and at different scales.
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Affiliation(s)
- Anne Richelle
- Sartorius Corporate Research, Brussels, Belgium
- *Correspondence: Anne Richelle,
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Matte A. Recent Advances and Future Directions in Downstream Processing of Therapeutic Antibodies. Int J Mol Sci 2022; 23:ijms23158663. [PMID: 35955796 PMCID: PMC9369434 DOI: 10.3390/ijms23158663] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 02/05/2023] Open
Abstract
Despite the advent of many new therapies, therapeutic monoclonal antibodies remain a prominent biologics product, with a market value of billions of dollars annually. A variety of downstream processing technological advances have led to a paradigm shift in how therapeutic antibodies are developed and manufactured. A key driver of change has been the increased adoption of single-use technologies for process development and manufacturing. An early-stage developability assessment of potential lead antibodies, using both in silico and high-throughput experimental approaches, is critical to de-risk development and identify molecules amenable to manufacturing. Both statistical and mechanistic modelling approaches are being increasingly applied to downstream process development, allowing for deeper process understanding of chromatographic unit operations. Given the greater adoption of perfusion processes for antibody production, continuous and semi-continuous downstream processes are being increasingly explored as alternatives to batch processes. As part of the Quality by Design (QbD) paradigm, ever more sophisticated process analytical technologies play a key role in understanding antibody product quality in real-time. We should expect that computational prediction and modelling approaches will continue to be advanced and exploited, given the increasing sophistication and robustness of predictive methods compared to the costs, time, and resources required for experimental studies.
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Affiliation(s)
- Allan Matte
- Downstream Processing Team, Bioprocess Engineering Department, Human Health Therapeutics Research Center, National Research Council Canada, 6100 Royalmount Avenue, Montreal, QC H4P 2R2, Canada
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Seelinger F, Wittkopp F, von Hirschheydt T, Hafner M, Frech C. Application of the Steric Mass Action formalism for modeling under high loading conditions: Part 1. Investigation of the influence of pH on the steric shielding factor. J Chromatogr A 2022; 1676:463265. [DOI: 10.1016/j.chroma.2022.463265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/16/2022] [Accepted: 06/18/2022] [Indexed: 11/28/2022]
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