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Model-based intensification of CHO cell cultures: One-step strategy from fed-batch to perfusion

Richelle, Anne, Corbett, Brandon, Agarwal, Piyush, Vernersson, Anton, Trygg, Johan, McCready, Chris·2022·Frontiers in Bioengineering and Biotechnology
DOI 10.3389/fbioe.2022.948905PMC PMC9443430
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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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Crossref · 10.3389/fbioe.2022.948905

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  • The terms used in this model (Eqs. 6-8) are adapted from structured CHO cell models developed in previous work of our group, where they have been shown to yield stable and biologically meaningful kinetics.

    “The terms used in this model (Eqs. 6–8) are adapted from structured CHO cell models developed in previous work of our group [8, 9], where they have been shown to yield stable and biologically meaningful kinetics.”

    From Mechanistic model for HEK293 viral vector processes and its application in a digital shadow framework (2026) · cites this paper as [9]

    Section 4.2 says the model shares a general cell-population formalism with existing models, and Section 4.1 reports that it captures viable-cell density well. Our copy does not say its terms were adapted from earlier structured CHO models by the authors’ group or that those earlier models yielded stable, biologically meaningful kinetics. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Such mechanistic models have successfully supported monoclonal antibody production and scale-up processes in mammalian systems.

    “Such models have successfully supported monoclonal antibody production and scale up processes in mammalian systems [8, 9].”

    From Mechanistic model for HEK293 viral vector processes and its application in a digital shadow framework (2026) · cites this paper as [9]

  • The mathematical model, described in detail in [8, 9], explains cell growth in three phases: viable, dead, and lysed cells.

    “The mathematical model, described in detail in [8, 9], explains the cell growth in three phases: viable (xVCD, Eq. 1), dead (xDCD, Eq. 2), and lysed cells (xLCD, Eq. 3)”

    From Mechanistic model for HEK293 viral vector processes and its application in a digital shadow framework (2026) · cites this paper as [9]

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Research works printed but not linked: 33

  1. Babi D. K. Griesbach J. Hunt S. Insaidoo F. Roush D. Todd R. (2022). Opportunities and challenges for model utilization in the biopharmaceutical industry: Current versus future state. Curr. Opin. Chem. Eng. 36, 100813. 10.1016/j.coche.2022.100813 · DOI
  2. Banner M. Alosert H. Spencer C. Cheeks M. Farid S. S. Thomas M. (2021). A decade in review: Use of data analytics within the biopharmaceutical sector. Curr. Opin. Chem. Eng. 34, 100758. 10.1016/j.coche.2021.100758 PMC8665905 34926134 · DOI
  3. Bausch M. Schultheiss C. Sieck J. B. (2019). Recommendations for comparison of productivity between fed-batch and perfusion processes. Biotechnol. J. 14, 1700721. 10.1002/biot.201700721 30024096 · DOI · Paper page
  4. Berdigaliyev N. Aljofan M. (2020). An overview of drug discovery and development. Future Med. Chem. 12 (10), 939– 947. 10.4155/fmc-2019-0307 32270704 · DOI
  5. Bielser J. M. Wolf M. Souquet J. Broly H. Morbidelli M. (2018). Perfusion mammalian cell culture for recombinant protein manufacturing – a critical review. Biotechnol. Adv. 36 (4), 1328– 1340. 10.1016/j.biotechadv.2018.04.011 29738813 · DOI
  6. Chen C. Wong H. E. Goudar C. T. (2018). Upstream process intensification and continuous manufacturingdoi:10.1016/j.coche.2018.10.006. Curr. Opin. Chem. Eng. 22, 191– 198. 10.1016/j.coche.2018.10.006 · DOI
  7. Craven S. Shirsat N. Whelan J. Glennon B. (2013). Process model comparison and transferability across bioreactor scales and modes of operation for a mammalian cell bioprocess. Biotechnol. Prog. 29, 186– 196. 10.1002/btpr.1664 23143896 · DOI
  8. Finelli L. A. Narasimhan V. (2020). Leading a digital transformation in the pharmaceutical industry: Reimagining the way we work in global drug development. Clin. Pharmacol. Ther. 108 (4), 756– 761. 10.1002/cpt.1850 32294230 PMC7540662 · DOI
  9. Janoschek S. Schulze M. Zijlstra G. Greller G. Matuszczyk J. (2018). A protocol to transfer a fed-batch platform process into semi-perfusion mode: The benefit of automated small-scale bioreactors compared to shake flasks as scale-down model. Biotechnol. Prog. 35, e2757. 10.1002/btpr.2757 30479066 PMC6667907 · DOI
  10. Klein T. Heinzel N. Kroll P. Brunner M. Herwig C. Neutsch L. (2015). Quantification of cell lysis during CHO bioprocesses: Impact on cell count, growth kinetics and productivity. J. Biotechnol. 207, 67– 76. 10.1016/j.jbiotec.2015.04.021 25956245 · DOI
  11. Kroll P. Eilers K. Fricke J. Herwig C. (2016). Impact of cell lysis on the description of cell growth and death in cell culture. Eng. Life Sci. 17 (4), 440– 447. 10.1002/elsc.201600088 32624789 PMC6999402 · DOI
  12. Landhuis E. (2018). Outsourcing is in. Nature 556 (7700), 263– 265. 10.1038/d41586-018-04163-8 29643487 · DOI
  13. Lourenço da Silva A. Marc A. Engasser J. M. Goergen J. L. (1996). Kinetic model of hybridoma cultures for the identification of rate limiting factors and process optimisation. Math. Comput. Simul. 42 (2–3), 197– 205. 10.1016/0378-4754(96)83703-7 · DOI
  14. Luo Y. Kurian V. Ogunnaike B. A. (2021). Bioprocess systems analysis, modeling, estimation, and control. Curr. Opin. Chem. Eng. 33, 100705. 10.1016/j.coche.2021.100705 · DOI
  15. Mallapaty S. (2017). Outsourcing discovery. Nature 552 (7683), S5. 10.1038/d41586-017-07419-x 29219992 · DOI
  16. Müller D. Klein L. Lemke J. Schulze M. Kruse T. Saballus M. (2022). Process intensification in the biopharma industry: Improving efficiency of protein manufacturing processes from development to production scale using synergistic approaches. Chem. Eng. Process. - Process Intensif. 171, 108727. 10.1016/j.cep.2021.108727 · DOI
  17. Narayanan H. Sponchioni M. Morbidelli M. (2022). Integration and digitalization in the manufacturing of therapeutic proteins. Chem. Eng. Sci. 248 (A), 117159. 10.1016/j.ces.2021.117159 · DOI
  18. Papathanasiou M. M. Kontoravdi C. (2020). Engineering challenges in therapeutic protein product and process design. Curr. Opin. Chem. Eng. 27, 81– 88. 10.1016/j.coche.2019.11.010 · DOI · Paper page
  19. Papathanasiou M. M. Quiroga-Campano A. L. Steinebach F. Elviro M. Mantalaris A. Pistikopoulos E. N. (2017). Advanced model-based control strategies for the intensification of upstream and downstream processing in mAb production. Biotechnol. Prog. 33, 966– 988. 10.1002/btpr.2483 28401675 · DOI
  20. Portela R. M. C. Varsakelis C. Richelle A. Giannelos N. Pence J. Dessoy S. (2021). When is an In Silico representation a digital twin? A biopharmaceutical industry approach to the digital twin concept. Adv. Biochem. Eng. Biotechnol., 176: 35– 55. 10.1007/10_2020_138 32797270 · DOI
  21. Rathore A. S. Zydney A. L. Anupa A. Nikita S. Gangwar N. (2022). Enablers of continuous processing of biotherapeutic products. Trends Biotechnol. 40, 804– 815. 10.1016/j.tibtech.2021.12.003 35034769 · DOI
  22. Richelle A. Fickers P. Bogaerts P. (2014). Macroscopic modelling of baker's yeast production in fed-batch cultures and its link with trehalose production. Comput. Chem. Eng. 2014 (61), 220– 233. 10.1016/j.compchemeng.2013.11.007 · DOI
  23. Schwarz H. Mäkinen M. E. Castan A. Chotteau V. (2022). Monitoring of amino acids and antibody N-glycosylation in high cell density perfusion culture based on Raman spectroscopy. Biochem. Eng. J. 182, 108426. 10.1016/j.bej.2022.108426 · DOI
  24. Shirahata H. Diab S. Sugiyama H. Gerogiorgis D. (2019). Dynamic modelling, simulation and economic evaluation of two CHO cell-based production modes towards developing biopharmaceutical manufacturing processes. Chem. Eng. Res. Des. 150, 218– 233. 10.1016/j.cherd.2019.07.016 · DOI
  25. Smiatek J. Jung A. Bluhmki E. (2020). Towards a digital bioprocess replica: Computational approaches in biopharmaceutical development and manufacturing. Trends Biotechnol. 38 (10), 1141– 1153. 10.1016/j.tibtech.2020.05.008 32518043 · DOI
  26. Steinwandter V. Borchert D. Herwig C. (2019). Data science tools and applications on the way to Pharma 4.0. Drug Discov. Today 24 (9), 1795– 1805. 10.1016/j.drudis.2019.06.005 31207205 · DOI
  27. Strober W. (2015). Trypan blue exclusion test of cell viability. Curr. Protoc. Immunol. 111, A3.B.1– A3.B.3. 10.1002/0471142735.ima03bs111 26529666 PMC6716531 · DOI
  28. Strube J. Ditz R. Kornecki M. Huter M. Schmidt A. Thiess H. (2018). Process intensification in biologics manufacturing. Chem. Eng. Process. - Process Intensif. 133, 278– 293. 10.1016/j.cep.2018.09.022 · DOI
  29. Tsopanoglou A. Jiménez del Val I. (2021). Moving towards an era of hybrid modelling: Advantages and challenges of coupling mechanistic and data-driven models for upstream pharmaceutical bioprocesses. Curr. Opin. Chem. Eng. 32, 100691. 10.1016/j.coche.2021.100691 · DOI
  30. Wang G. Haringa C. Noorman H. Chu J. Zhuang Y. (2020). Developing a computational framework to advance bioprocess scale-up. Trends Biotechnol. 38 (8), 846– 856. 10.1016/j.tibtech.2020.01.009 32493657 · DOI
  31. Wasalathanthri D. P. Shah R. Ding J. Leone A. Li Z. J. (2021). Process analytics 4.0: A paradigm shift in rapid analytics for biologics development. Biotechnol. Prog. 37 (4), e3177. 10.1002/btpr.3177 34036755 · DOI · Paper page
  32. Wolf M. Pechlaner A. Lorenz V. Karst D. J. Souquet J. Broly H. (2019). A two-step procedure for the design of perfusion bioreactors. Biochem. Eng. J. 151, 107295. 10.1016/j.bej.2019.107295 · DOI · Paper page
  33. Wouters O. J. McKee M. Luyten J. (2020). Estimated research and development investment needed to bring a new medicine to market, 2009-2018. JAMA 323 (9), 844. 10.1001/jama.2020.1166 32125404 PMC7054832 · DOI · Paper page

Cited by

Sorted by citations

Authors
Richelle, A., Corbett, B., Agarwal, P., Vernersson, A., Trygg, J., & McCready, C.
Venue
Frontiers in Bioengineering and Biotechnology
Publisher
Frontiers Media SA
Published
2022-08-22
Volume
10
ISSN
2296-4185
DOI
10.3389/fbioe.2022.948905
PMC
PMC9443430
Cited by
24
References
3
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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