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Inline Raman spectroscopy as process analytical technology for SARS-CoV-2 VLP production

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Abstract

The present work focused on inline Raman spectroscopy monitoring of SARS-CoV-2 VLP production using two culture media by fitting chemometric models for biochemical parameters (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, ammonium, and viral titer). For that purpose, linear, partial least square (PLS), and nonlinear approaches, artificial neural network (ANN), were used as correlation techniques to build the models for each variable. ANN approach resulted in better fitting for most parameters, except for viable cell density and glucose, whose PLS presented more suitable models. Both were statistically similar for ammonium. The mean absolute error of the best models, within the quantified value range for viable cell density (375,000–1,287,500 cell/mL), cell viability (29.76–100.00%), glucose (8.700–10.500 g/), lactate (0.019–0.400 g/L), glutamine (0.925–1.520 g/L), glutamate (0.552–1.610 g/L), viral titer (no virus quantified-7.505 log10 PFU/mL) and ammonium (0.0074–0.0478 g/L) were, respectively, 41,533 ± 45,273 cell/mL (PLS), 1.63 ± 1.54% (ANN), 0.058 ± 0.065 g/L (PLS), 0.007 ± 0.007 g/L (ANN), 0.007 ± 0.006 g/L (ANN), 0.006 ± 0.006 g/L (ANN), 0.211 ± 0.221 log10 PFU/mL (ANN), and 0.0026 ± 0.0026 g/L (PLS) or 0.0027 ± 0.0034 g/L (ANN). The correlation accuracy, errors, and best models obtained are in accord with studies, both online and offline approaches while using the same insect cell/baculovirus expression system or different cell host. Besides, the biochemical tracking throughout bioreactor runs using the models showed suitable profiles, even using two different culture media.

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Data availability

The datasets generated or analyzed during the current study are available from the corresponding author on request.

References

  1. Kushwaha ND, Mohan J, Kushwaha B et al (2023) A comprehensive review on the global efforts on vaccines and repurposed drugs for combating COVID-19. Eur J Med Chem 260:115719. https://doi.org/10.1016/j.ejmech.2023.115719

    Article  PubMed  CAS  Google Scholar 

  2. World Health Organization (WHO) (2024) WHO COVID-19 dashboard. https://data.who.int/dashboards/covid19/data. Accessed 18 May 2024

  3. World Health Organization (WHO) (2023) COVID-19 vaccine tracker and landscape. https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines. Accessed 9 Mar 2024

  4. Mahboob T, Ismail AA, Shah MR et al (2023) Development of SARS-CoV-2 vaccine: challenges and prospects. Diseases 11:64. https://doi.org/10.3390/diseases11020064

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Sadeghalvad M, Mansourabadi AH, Noori M et al (2023) Recent developments in SARS-CoV-2 vaccines: a systematic review of the current studies. Rev Med Virol. https://doi.org/10.1002/rmv.2359

    Article  PubMed  Google Scholar 

  6. Priyanka AMAH, Chopra H et al (2023) Nanovaccines: a game changing approach in the fight against infectious diseases. Biomed Pharmacother 167:115597. https://doi.org/10.1016/j.biopha.2023.115597

    Article  PubMed  CAS  Google Scholar 

  7. Puente-Massaguer E, Gòdia F, Lecina M (2020) Development of a non-viral platform for rapid virus-like particle production in Sf9 cells. J Biotechnol 322:43–53. https://doi.org/10.1016/j.jbiotec.2020.07.009

    Article  PubMed  CAS  Google Scholar 

  8. Liu F, Wu X, Li L et al (2013) Use of baculovirus expression system for generation of virus-like particles: successes and challenges. Protein Expr Purif 90:104–116. https://doi.org/10.1016/j.pep.2013.05.009

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Kornecki M, Strube J (2018) Process analytical technology for advanced process control in biologics manufacturing with the aid of macroscopic kinetic modeling. Bioengineering 5:25. https://doi.org/10.3390/bioengineering5010025

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Esmonde-White KA, Cuellar M, Lewis IR (2022) The role of Raman spectroscopy in biopharmaceuticals from development to manufacturing. Anal Bioanal Chem 414:969–991. https://doi.org/10.1007/s00216-021-03727-4

    Article  PubMed  CAS  Google Scholar 

  11. Buckley K, Ryder AG (2017) Applications of raman spectroscopy in biopharmaceutical manufacturing: a short review. Appl Spectrosc 71:1085–1116. https://doi.org/10.1177/0003702817703270

    Article  PubMed  CAS  Google Scholar 

  12. Rafferty C, Johnson K, O’Mahony J et al (2020) Analysis of chemometric models applied to Raman spectroscopy for monitoring key metabolites of cell culture. Biotechnol Prog. https://doi.org/10.1002/btpr.2977

    Article  PubMed  Google Scholar 

  13. Dong X, Yan X, Wan Y et al (2024) Enhancing real-time cell culture monitoring: automated Raman model optimization with Taguchi method. Biotechnol Bioeng. https://doi.org/10.1002/bit.28688

    Article  PubMed  Google Scholar 

  14. Takahashi MB, Leme J, Caricati CP et al (2015) Artificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes. Bioprocess Biosyst Eng 38:1045–1054. https://doi.org/10.1007/s00449-014-1346-7

    Article  PubMed  CAS  Google Scholar 

  15. Hutchinson A, Seitova A (2021) Production of recombinant PRMT proteins using the baculovirus expression vector system. J Vis Exp. https://doi.org/10.3791/62510

    Article  PubMed  Google Scholar 

  16. Drugmand J-C, Schneider Y-J, Agathos SN (2012) Insect cells as factories for biomanufacturing. Biotechnol Adv 30:1140–1157. https://doi.org/10.1016/j.biotechadv.2011.09.014

    Article  PubMed  CAS  Google Scholar 

  17. Augusto EFP, Moraes AM, Piccoli RAM et al (2010) Nomenclature and guideline to express the amount of a membrane protein synthesized in animal cells in view of bioprocess optimization and production monitoring. Biologicals 38:105–112

    Article  PubMed  CAS  Google Scholar 

  18. Hernández-López J, Vargas-Albores F (2003) A microplate technique to quantify nutrients (NO2−, NO3−, NH4+ and PO43−) in seawater. Aquac Res 34:1201–1204. https://doi.org/10.1046/j.1365-2109.2003.00928.x

    Article  Google Scholar 

  19. Hopkins RF, Esposito D (2009) A rapid method for titrating baculovirus stocks using the Sf-9 easy titer cell line. Biotechniques 47:785–788. https://doi.org/10.2144/000113238

    Article  PubMed  CAS  Google Scholar 

  20. Reed LJ, Muench H (1938) A simple method of estimating fifty per cent endpoints12. Am J Epidemiol 27:493–497. https://doi.org/10.1093/oxfordjournals.aje.a118408

    Article  Google Scholar 

  21. Dee KU, Shuler ML (1997) Optimization of an assay for baculovirus titer and design of regimens for the synchronous infection of insect cells. Biotechnol Prog 13:14–24. https://doi.org/10.1021/bp960086t

    Article  PubMed  CAS  Google Scholar 

  22. Oliveira Guardalini LG, Aragão Tejo Dias V, Leme J et al (2023) Comparison of chemometric models using Raman spectroscopy for offline biochemical monitoring throughout the VLP-making upstream process. Biochem Eng J 198:109013. https://doi.org/10.1016/j.bej.2023.109013

    Article  CAS  Google Scholar 

  23. Oliveira Guardalini LG, da Silva Cavalcante PE, Leme J et al (2023) Biochemical monitoring throughout all stages of rabies virus-like particles production by Raman spectroscopy using global models. J Biotechnol 363:19–31. https://doi.org/10.1016/j.jbiotec.2022.12.009

    Article  PubMed  CAS  Google Scholar 

  24. Sartorius Stedim Data Analytics AB (2020) SIMCA® 15 user guide. https://www.sartorius.com/download/544940/simca-15-user-guide-en-b-00076-sartorius-data.pdf. Accessed 7 Sep 2024

  25. André S, Lagresle S, Hannas Z et al (2017) Mammalian cell culture monitoring using in situ spectroscopy: is your method really optimised? Biotechnol Prog 33:308–316. https://doi.org/10.1002/btpr.2430

    Article  PubMed  CAS  Google Scholar 

  26. González-Domínguez I, Puente-Massaguer E, Cervera L, Gòdia F (2020) Quality assessment of virus-like particles at single particle level: a comparative study. Viruses 12:223. https://doi.org/10.3390/v12020223

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. André S, Lagresle S, Da Sliva A et al (2017) Developing global regression models for metabolite concentration prediction regardless of cell line. Biotechnol Bioeng 114:2550–2559. https://doi.org/10.1002/bit.26368

    Article  PubMed  CAS  Google Scholar 

  28. Yousefi-Darani A, Paquet-Durand O, von Wrochem A et al (2022) Generic chemometric models for metabolite concentration prediction based on Raman spectra. Sensors 22:5581. https://doi.org/10.3390/s22155581

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Golic M, Walsh KB (2006) Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids content. Anal Chim Acta 555:286–291. https://doi.org/10.1016/j.aca.2005.09.014

    Article  CAS  Google Scholar 

  30. Power J, Greenfield PF, Nielsen L, Reid S (1992) Modelling the growth and protein production by insect cells following infection by a recombinant baculovirus in suspension culture. Cytotechnology 9:149–155. https://doi.org/10.1007/BF02521742

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

This work was financially supported by Merck-Brazil (Installation and temporary availability of ProCellicsTM Raman Analyzer), the São Paulo Research Foundation (FAPESP) (grants no. 20/05264-0, no. 2022/02713-3), Master’s degree scholarship no. 2021/14451-0, and scientific initiation scholarships no. 2023/14085-0, no. 2022/04711-8, University of Sao Paulo (Scientific initiation scholarship-PUB 83-1/2023) and Butantan Foundation.

Funding

This work was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo, 2021/14451-0, 2023/14085-0, 2022/04711-8, 20/05264-0,2022/02713-3, Pro-Reitoria de Pesquisa,Universidade de São Paulo, PUB 83-1/2023, Fundação Butantan.

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Author notes

  1. Felipe Moura Dias and Milena Miyu Teruya contributed equally to this manuscript.

Authors and Affiliations

  1. Laboratório de Engenharia de Bioprocessos. Escola de Artes, Ciências E Humanidades (EACH), Universidade de São Paulo, Rua Arlindo Béttio, 1000, São Paulo, SP, CEP 03828-000, Brazil

    Felipe Moura Dias, Milena Miyu Teruya, Vinícius Aragão Tejo Dias & Eutimio Gustavo Fernández Núñez

  2. Laboratório de Biotecnologia Viral, Instituto Butantan, Av Vital Brasil 1500, São Paulo, SP, CEP 05503-900, Brazil

    Felipe Moura Dias, Samanta Omae Camalhonte, Luis Giovani de Oliveira Guardalini, Jaci Leme, Thaissa Consoni Bernardino & Soraia Attie Calil Jorge

  3. Merck Brasil, Alameda Xingu, 350, Alphaville Industrial, São Paulo, SP, CEP 06455-030, Brazil

    Felipe S. Sposito & Eduardo Dias

  4. Laboratório Multipropósito, Instituto Butantan, Av. Vital Brasil 1500, São Paulo, SP, CEP 05503-900, Brazil

    Renato Manciny Astray

  5. Laboratório de Células Animais, Departamento de Engenharia Química, Escola Politécnica, Universidade de São Paulo. Av. Prof. Luciano Gualberto, Travessa Do Politécnico, 380, São Paulo, SP, 05508-010, Brazil

    Aldo Tonso

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Contributions

F.M.D. took part in experimental tasks, biochemical quantifications, and spectral data acquisition. M.M.T. took part in chemometric modeling, data curation, and manuscript writing—review & editing. S.O.C. collaborated in experimental investigation. V.A.T.D. collaborated in chemometric modeling. L.G.O.G. collaborated in biochemical and immunochemical data acquisition. J.L. collaborated in experimental investigation, work conceptualization, and methodology. T.C.B. took part in experimental investigation and supervision. F.S.S. took part in work conceptualization and experimental investigation. E.D. took part in work conceptualization and experimental investigation. R.M.A. collaborated in work conceptualization and methodology. A.T. supported work conceptualization and manuscript reviewing. S.A.C.J. got investigation funding and collaborated with work conceptualization. E.G.F.N. led work conceptualization and methodology definition, the project management, and supported the project funding as well as manuscript reviewing and editing.

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Correspondence to Eutimio Gustavo Fernández Núñez.

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Moura Dias, F., Teruya, M.M., Omae Camalhonte, S. et al. Inline Raman spectroscopy as process analytical technology for SARS-CoV-2 VLP production. Bioprocess Biosyst Eng 48, 63–84 (2025). https://doi.org/10.1007/s00449-024-03094-1

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