Advertisement

Opportunities for Process Control and Quality Assurance Using Online NIR Analysis to a Continuous Wet Granulation Tableting Line

  • J. Palmer
  • C. J. O’Malley
  • M. J. Wade
  • E. B. Martin
  • T. Page
  • G. A. Montague
Original Article
  • 68 Downloads

Abstract

This paper investigates the application of online near-infrared measurements as a means to measure blend uniformity in a continuous tableting line. Underlying all the monitoring and control methods is the ability to measure key tablet properties online at a rate suitable for control purposes. The use of NIR to determine any deviations in blend uniformity is demonstrated by interpreting the relevant spectral signature allowing quantitative information to be acquired for process monitoring and quality assurance. In addition to demonstrating the functionality of the NIR probe, the practical issues arising in the application are discussed. The composition of the blend was measured using an NIR probe over a range of concentrations and the results were calculated comparing sub unit dose scale of scrutiny of small populations. This was compared with predicted product quality for whole tablets over the whole production period. This technique has demonstrated how data collected online can be used to successfully predict the quality of the whole production run for the purposes of real-time product quality assurance.

Keywords

Near-infrared spectroscopy Tabletting Process analytical technology Quality control Pharmaceutics 

Notes

Acknowledgements

The authors would like to acknowledge the significant contribution of GSK and GEA Pharma Systems to all aspects of the technical work described.

Funding Information

The authors would like to acknowledge the financial support of the UK Engineering and Physical Sciences Research Council grant EP/G037620/1 and the UK Technology Strategy Board

References

  1. 1.
    Colón YM, Vargas J, Sánchez E, Navarro G, Romañach RJ. Assessment of robustness for a near-infrared concentration model for real-time release testing in a continuous manufacturing process. J Pharm Innov. 2017;12(1):14–25.CrossRefGoogle Scholar
  2. 2.
    Lee SL, O’Connor TF, Yang X, Cruz CN, Chatterjee S, Madurawe RD, et al. Modernizing pharmaceutical manufacturing: from batch to continuous production. J Pharm Innov. 2015;10(3):191–9.CrossRefGoogle Scholar
  3. 3.
    Yu LX, Kopcha M. The future of pharmaceutical quality and the path to get there. Int J Pharm. 2017;528(2017):354–9.CrossRefGoogle Scholar
  4. 4.
    Holý R, Pozivil J. Batch control system project for a pharmaceutical plant. ISA Trans. 2002;41(2):245–54.CrossRefGoogle Scholar
  5. 5.
    Buchholz S. Future manufacturing approaches in the chemical and pharmaceutical industry. Chem Eng Process. 2010;49(2010):993–5.CrossRefGoogle Scholar
  6. 6.
    Leuenberger H. New trends in the production of pharmaceutical granules: batch versus continuous processing. Eur J Pharm Biopharm. 2001;52(3):289–96.CrossRefGoogle Scholar
  7. 7.
    Vervaet C, Remon JP. Continuous granulation in the pharmaceutical industry. Chem Eng Sci. 2005;60(14):3949–57.CrossRefGoogle Scholar
  8. 8.
    Plumb K. Continuous processing in the pharmaceutical industry: changing the mind set. Chem Eng Res Des. 2005;83(6):730–8.CrossRefGoogle Scholar
  9. 9.
    Fonteyne M, Vercruysse J, De Leersnyder F, Van Snick B, Vervaet C, Remnon JP, et al. Process analytical technology for continuous manufacturing of solid dosage forms. Trends Anal Chem. 2015;67:159–66.CrossRefGoogle Scholar
  10. 10.
    Hattori Y, Otsuka M. Modeling of feed-forward control using the partial least squares regression method in the tablet compression process. Int J Pharm. 2017;524:407–13.CrossRefGoogle Scholar
  11. 11.
    De Beer T, Fonteyne M, Saerens L, Remon JP, Vervaet C. Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical processes. Int J Pharm. 2011;417:32–47.CrossRefGoogle Scholar
  12. 12.
    Menezes JC, Ferreira AP, Rodrigues LO, Brás LP, Alves TP. Chemometrics role within the PAT context: examples from primary pharmaceutical manufacturing. Comprehensive Chemometrics, ed. Oxford: Elsevier; 2009. p. 313–55.Google Scholar
  13. 13.
    Roggo Y, Chalus P, Maurer L, Lema-Martinez C, Edmond A, Jent N. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J Pharm Biomed Anal. 2007;44(3):683–700.CrossRefGoogle Scholar
  14. 14.
    Vanarase AU, Alacla M, Jerez Rozo JI, Muzzio FJ, Romanach RJ. Real-time monitoring of drug concentration in a continuous powder mixing process using NIR spectroscopy. Chem Eng Sci. 2010;65:5728–33.CrossRefGoogle Scholar
  15. 15.
    Koller DM, Posch A, Horl G, Voura C, Radl S, Urbanetz N, et al. Continuous quantitative monitoring of powder mixing dynamics by near-infrared spectroscopy. Powder Technol. 2011;205(2011):87–96.CrossRefGoogle Scholar
  16. 16.
    Shi Z, Cogdill RP, Short SM, Anderson CA. Process characterization of powder blending by near-infrared spectroscopy: blend end-points and beyond. J Pharm Biomed Anal. 2008;47(2008):738–45.CrossRefGoogle Scholar
  17. 17.
    Pawar P, Wang Y, Keyvan G, Callegari G, Cuitino A, Muzzio F. Enabling real time release testing by NIR prediction of dissolution of tablets made by continuous direct compression (CDC). Int J Pharm. 2016;512:96–107.CrossRefGoogle Scholar
  18. 18.
    Järvinen K, Hoehe W, Järvinen M, Poutiainen S, Juuti M, Borchert S. In-line monitoring of the drug content of powder mixtures and tablets by near-infrared spectroscopy during the continuous direct compression tableting process. Eur J Pharm Sci. 2013;48:680–8.CrossRefGoogle Scholar
  19. 19.
    Vargas JM, Nielsen S, Cárdenas V, Gonzalez A, Aymat EY, Almodovar E, et al. Process analytical technology in continuous manufacturing of a commercial pharmaceutical product. Int J Pharm. 2018;538(2018):167–78.CrossRefGoogle Scholar
  20. 20.
    Wahl PR, Fruhmann G, Sacher S, Straka G, Sowinski S, Khinast JG. PAT for tableting: inline monitoring of API and excipients via NIR spectroscopy. Eur J Pharm Biopharm. 2014;87:271–8.CrossRefGoogle Scholar
  21. 21.
    Casian T, Reznek A, Vonica-Gligor AL, Renterghem J, De Beer T, Tomuță I. Development, validation and comparison of near infrared and Raman spectroscopic methods for fast characterization of tablets with amlodipine and valsartan. Talanta. 2017;167:333–43.CrossRefGoogle Scholar
  22. 22.
    Li Y, Anderson CA, Drennen JK III, Airiau C, Igne B. Method development and validation of an inline process analytical technology method for blend monitoring in the tablet feed frame using Raman spectroscopy. Anal Chem. 2018;90(14):8436–44.CrossRefGoogle Scholar
  23. 23.
    Chen Z, Morris AJ. Process analytical technologies and real time process control a review of some spectroscopic issues and challenges. J Process Control. 2011;21:1467–82.CrossRefGoogle Scholar
  24. 24.
    Blanco M, Peguero A. Influence of physical factors on the accuracy of calibration models for NIR spectroscopy. J Pharm Biomed Anal. 2010;52(2010):59–65.CrossRefGoogle Scholar
  25. 25.
    Banfai B, Ganzier K, Kemeny S. Content uniformity and assay requirements in current regulations. J Chromatogr. 2007;1156:206–12.CrossRefGoogle Scholar
  26. 26.
    Kleinebudde P, Khinast J, Rantanen J. Continuous manufacturing of pharmaceuticals. Hoboken: Wiley; 2017.Google Scholar
  27. 27.
    Triadaphillou S, Martin EB, Montague GA, Norden A, Jeffkins P, Stimpson S. Fermentation process tracking through enhanced spectral calibration modeling. Biotechnol Bioeng. 2007;97(3):554–67.CrossRefGoogle Scholar
  28. 28.
    Gerretzen J, Szymanśka E, Jansen JJ, Bart J, van Manen HJ, van den Heuvel ER, et al. Simple and effective way for data preprocessing selection based on design of experiments. Anal Chem. 2015;87:12096–103.CrossRefGoogle Scholar
  29. 29.
    Laske S, Paudel A, Scheibelhofer O, Sacher S, Hörmann T, Khinast J. A review of PAT strategies in secondary solid oral dosage manufacturing of small molecules. J Pharm Sci. 2017;106(3):667–712.CrossRefGoogle Scholar
  30. 30.
    Silva AF, Sarraguça MC, Fonteyne M, Vercruysse J, De Leersnyder F, Vanhoorne V, et al. Multivariate statistical process control of a continuous pharmaceutical twin-screw granulation and fluid bed drying process. Int J Pharm. 2017;528(2017):242–52.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of EngineeringNewcastle UniversityNewcastle upon TyneUK
  2. 2.School of Chemical and Process EngineeringLeeds University Engineering Building, University of LeedsLeedsUK
  3. 3.GEA Pharma SystemsChandler’s FordEastleighUK
  4. 4.School of Science, Engineering and DesignTeesside UniversityMiddlesbroughUK

Personalised recommendations