Advertisement

Frontiers of Chemical Science and Engineering

, Volume 9, Issue 3, pp 386–406 | Cite as

Real time monitoring of bioreactor mAb IgG3 cell culture process dynamics via Fourier transform infrared spectroscopy: Implications for enabling cell culture process analytical technology

  • Huiquan WuEmail author
  • Erik Read
  • Maury White
  • Brittany Chavez
  • Kurt Brorson
  • Cyrus Agarabi
  • Mansoor Khan
Research Article

Abstract

Compared to small molecule process analytical technology (PAT) applications, biotechnology product PAT applications have certain unique challenges and opportunities. Understanding process dynamics of bioreactor cell culture process is essential to establish an appropriate process control strategy for biotechnology product PAT applications. Inline spectroscopic techniques for real time monitoring of bioreactor cell culture process have the distinct potential to develop PAT approaches in manufacturing biotechnology drug products. However, the use of inline Fourier transform infrared (FTIR) spectroscopic techniques for bioreactor cell culture process monitoring has not been reported. In this work, real time inline FTIR Spectroscopy was applied to a lab scale bioreactor mAb IgG3 cell culture fluid biomolecular dynamic model. The technical feasibility of using FTIR Spectroscopy for real time tracking and monitoring four key cell culture metabolites (including glucose, glutamine, lactate, and ammonia) and protein yield at increasing levels of complexity (simple binary system, fully formulated media, actual bioreactor cell culture process) was evaluated via a stepwise approach. The FTIR fingerprints of the key metabolites were identified. The multivariate partial least squares (PLS) calibration models were established to correlate the process FTIR spectra with the concentrations of key metabolites and protein yield of in-process samples, either individually for each metabolite and protein or globally for all four metabolites simultaneously. Applying the 2nd derivative pre-processing algorithm to the FTIR spectra helps to reduce the number of PLS latent variables needed significantly and thus simplify the interpretation of the PLS models. The validated PLS models show promise in predicting the concentration profiles of glucose, glutamine, lactate, and ammonia and protein yield over the course of the bioreactor cell culture process. Therefore, this work demonstrated the technical feasibility of real time monitoring of the bioreactor cell culture process via FTIR spectroscopy. Its implications for enabling cell culture PAT were discussed.

Keywords

process analytical technology (PAT) Fouriertransform infrared (FTIR) spectroscopy partial least squares (PLS) regression mouse IgG3 bioreactor cell culture process real time process monitoring 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    FDA/ICH. Guidance for Industry Q11 Development and Manufacture of Drug Substances. FDA, 2012. Available at: http://www.fda.gov/downloads/Drugs/Guidances/UCM261078.pdf (accessed on 07/28/2015)Google Scholar
  2. 2.
    FDA. Guidance for Industry PAT—A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance. FDA, 2004. Available at: http://www.fda.gov/downloads/Drugs/Guidances/ucm070305.pdf (accessed on 07/28/2015)Google Scholar
  3. 3.
    FDA. Advancing Regulatory Science at FDA: A Strategic Plan. FDA, 2011. Available at: http://www.fda.gov/downloads/scienceresearch/specialtopics/regulatoryscience/ucm268225.pdf (accessed on 07/28/2015)Google Scholar
  4. 4.
    Wu H Q, Khan M A, Hussain A S. Process control perspective for process analytical technology: Integration of chemical engineering practice into semiconductor and pharmaceutical industries. Chemical Engineering Communications. 2007, 194(6): 760–779CrossRefGoogle Scholar
  5. 5.
    Read E K, Park J T, Shah R B, Riley B S, Brorson K A, Rathore A S. Process analytical technology (PAT) for biopharmaceutical products: Part I. Concepts and applications. Biotechnology and Bioengineering. 2010, 105(2): 276–284CrossRefGoogle Scholar
  6. 6.
    Glassey J, Gernaey K V, Clemens C, Schulz T W, Oliveira R, Striedner G, Mandenius C F. Process analytical technology (PAT) for biopharmaceuticals. Biotechnology Journal. 2011, 6(4): 369–377CrossRefGoogle Scholar
  7. 7.
    Simon L L, Pataki H, Marosi G, Meemken F, Hungerbühler K, Baiker A, Tummala S, Glennon B, Kuentz M, Steele G, Kramer H J M, Rydzak J W, Chen Z, Morris J, Kjell F, Singh R, Gani R, Gernaey K V, Louhi-Kultanen M, O’Reilly J, Sandler N, Antikainen O, Yliruusi J, Frohberg P, Ulrich J, Braatz R D, Leyssens T, von Stosch M, Oliveira R, Tan R B H, Wu H, Khan M, O’Grady D, Pandey A, Westra R, Delle-Case E, Pape D, Angelosante D, Maret Y, Steiger O, Lenner M, Abbou-Oucherif K, Nagy Z K, Litster J D, Kamaraju V K, Chiu M S. Assessment of recent process analytical technology (PAT) trends: A multiauthor review. Organic Process Research & Development. 2015, 19(1): 3–62CrossRefGoogle Scholar
  8. 8.
    Wu H, Dong Z, Li H, Khan M A. An integrated process analytical technology (PAT) approach for pharmaceutical crystallization process understanding to ensure product quality and safety: FDA scientist’s perspective. Organic Process Research & Development. 2015, 19(1): 89–101CrossRefGoogle Scholar
  9. 9.
    Wu H, Heilweil E, Hussain A S, Khan M A. Process analytical technology (PAT): Quantification approaches in Terahertz spectroscopy for pharmaceutical application. Journal of Pharmaceutical Sciences. 2007, 97(2): 970–984CrossRefGoogle Scholar
  10. 10.
    Wu H, Khan M A. THz spectroscopy: An emerging technology for pharmaceutical development and pharmaceutical process analytical technology (PAT) applications. Journal of Molecular Structure. 2012, 1020: 112–120CrossRefGoogle Scholar
  11. 11.
    Wu H, Khan M A. Quality-by-design (QbD): An integrated approach for evaluation of powder blending process kinetics and determination of powder blending end-point. Journal of Pharmaceutical Sciences. 2009, 98(8): 2784–2798CrossRefGoogle Scholar
  12. 12.
    Wu H, White M, Khan M A. An integrated process analytical technology (PAT) approach for process dynamics-related measurement error and process design space development for a pharmaceutical powder blending bed. Organic Process Research & Development. 2015, 19(1): 215–226CrossRefGoogle Scholar
  13. 13.
    Wu H, White M, Khan M A. Quality-by-design (QbD): An integrated process analytical technology (PAT) approach for a dynamic pharmaceutical co-precipitation process characterization and process design space development. International Journal of Pharmaceutics. 2011, 405(1–2): 63–78CrossRefGoogle Scholar
  14. 14.
    Wu H, White M, Berendt R, Foringer R, Khan M A. An integrated PAT approach for nucleation induction time measurement and nucleation mechanism assessment for a dynamic multi-component pharmaceutical antisolvent crystallization system. Industrial & Engineering Chemistry Research. 2014, 53(4): 1688–1701CrossRefGoogle Scholar
  15. 15.
    Wu H, Lyon R C, Khan M A, Voytilla R, Drennen J III. Integration of near-infrared spectroscopy and mechanistic modeling for predicting film-coating and dissolution of modified release tablets. Industrial & Engineering Chemistry Research. Accepted for publication on May 19, 2015. http://pubs.acs.org/doi/abs/10.1021/ie504680mGoogle Scholar
  16. 16.
    Chung H, Arnold M A, Rhiel M, Murhammer D W. Simultaneous measurements of glucose, glutamine, ammonia, lactate, and glutamate in aqueous solutions by near-infrared spectroscopy. Applied Spectroscopy. 1996, 50(2): 270–276CrossRefGoogle Scholar
  17. 17.
    Cervera A E, Petersen N, Lantz A E, Larsen A, Gernaey K V. Application of near-infrared spectroscopy for monitoring and control of cell culture and fermentation. Biotechnology Progress. 2009, 25(6): 1561–1581Google Scholar
  18. 18.
    Liu K Z, Mantsch H H. Simultaneous quantitation from infrared spectra of glucose concentrations, lactate concentrations, and lecithin/sphingomyelin ratios in amniotic fluid. American Journal of Obstetrics and Gynecology. 1999, 180(3): 696–702CrossRefGoogle Scholar
  19. 19.
    Petersen N, Odman P, Padrell A E, Stocks S, Lantz A E, Gernaey K V. In situ near infrared spectroscopy for analyte-specific monitoring of glucose and ammonium in streptomyces coelicolor fermentations. Biotechnology Progress. 2010, 26(1): 263–271Google Scholar
  20. 20.
    Jose G E, Folque F, Menezes J C, Werz S, Strauss U, Hakemeyer C. Predicting mab product yields from cultivation media components, using near-infrared and 2D-fluorescence spectroscopies. Biotechnology Progress. 2011, 27(5): 1339–1346CrossRefGoogle Scholar
  21. 21.
    Abu-Absi N R, Kenty B M, Cuellar M E, Borys M C, Sakhamuri S, Strachan D J, Hausladen M C, Li Z J. Real time monitoring of multiple parameters in mammalian cell culture bioreactors using an in-line Raman spectroscopy probe. Biotechnology and Bioengineering. 2011, 108(5): 1215–1221CrossRefGoogle Scholar
  22. 22.
    Budinova G, Salva J, Volka K. Application of molecular spectroscopy in the mid-infrared region to the determination of glucose and cholesterol in whole blood and in blood serum. Applied Spectroscopy. 1997, 51(5): 631–635CrossRefGoogle Scholar
  23. 23.
    Petibois C, Cazorla G, Cassaigne A, Deleris G. Plasma protein contents determined by Fourier-transform infrared spectrometry. Clinical Chemistry. 2001, 47(4): 730–738Google Scholar
  24. 24.
    Ellis D I, Goodacre R. Metabolic fingerprinting in disease diagnosis: Biomedical applications of infrared and Raman spectroscopy. Analyst (London). 2006, 131(8): 875–885CrossRefGoogle Scholar
  25. 25.
    Haris P I, Chapman D. Does Fourier-transform infrared-spectroscopy provide useful information on protein structures. Trends in Biochemical Sciences. 1992, 17(9): 328–333CrossRefGoogle Scholar
  26. 26.
    Gross-Selbeck S, Margreiter G, Obinger C, Bayer K. Fast quantification of recombinant protein inclusion bodies within intact cells by FTIR spectroscopy. Biotechnology Progress. 2007, 23(3): 762–766CrossRefGoogle Scholar
  27. 27.
    Stuart B H, Ando D J, eds. Biological Applications of Infrared Spectroscopy. New York: John Wiley & Sons, Inc., 1997, 191Google Scholar
  28. 28.
    Sellick C A, Hansen R, Jarvis R M, Maqsood A R, Stephens G M, Dickson A J, Goodacre R. Rapid monitoring of recombinant antibody production by mammalian cell cultures using fourier transform infrared spectroscopy and chemometrics. Biotechnology and Bioengineering. 2010, 106(3): 432–442Google Scholar
  29. 29.
    Read E K, Bradley S A, Smitka T A, Agarabi C D, Lute S C, Brorson K A. Fermentanomics informed amino acid supplementation of an antibody producing mammalian cell culture. Biotechnology Progress. 2013, 29(3): 745–753CrossRefGoogle Scholar
  30. 30.
    Delwiche S R, Reeves J B III. A graphical method to evaluate spectral proprecessing in multivariate regression calibrations: example with Savtzky-Golay filters and partial least squares regression. Applied Spectroscopy. 2010, 64(1): 73–82CrossRefGoogle Scholar
  31. 31.
    Wu H, Khan M A. Quality-by-design (QbD): An integrated process analytical technology (PAT) approach for real-time monitoring and mapping the state of a pharmaceutical co-precipitation process. Journal of Pharmaceutical Sciences. 2010, 99(3): 1516–1534CrossRefGoogle Scholar
  32. 32.
    FDA/ICH. Guidance for Industry Q8(R2) Pharmaceutical Development. FDA, 2009. Available at: http://www.fda.gov/downloads/Drugs/Guidances/ucm073507.pdf (accessed on 07/28/2015)Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Huiquan Wu
    • 1
    • 2
    Email author
  • Erik Read
    • 3
  • Maury White
    • 1
  • Brittany Chavez
    • 3
  • Kurt Brorson
    • 3
  • Cyrus Agarabi
    • 1
  • Mansoor Khan
    • 1
  1. 1.Division of Product Quality Research (DPQR, HFD-940), Office of Testing and Research (OTR), Office of Pharmaceutical Quality (OPQ), Center for Drug Evaluation and Research (CDER), US Food and Drug Administration (FDA)Federal Research Center at White OakSilver SpringUSA
  2. 2.Process Assessment Branch II, Division of Process Assessment 1 (DPA 1), Office of Process and Facilities (OPF), Office of Pharmaceutical Quality (OPQ), Center for Drug Evaluation and Research (CDER), US Food and Drug Administration (FDA)Federal Research Center at White OakSilver SpringUSA
  3. 3.Division of Biotechnology Review and Research II (DBRRII), Office of Biotechnology Products (OBP), Office of Pharmaceutical Quality (OPQ), Center for Drug Evaluation and Research (CDER), US Food and Drug Administration (FDA)Federal Research Center at White OakSilver SpringUSA

Personalised recommendations