Advertisement

Journal of Pharmaceutical Innovation

, Volume 7, Issue 3–4, pp 140–150 | Cite as

Hybrid Controls Combining First-Principle Calculations with Empirical Modeling for Fully Automated Fluid Bed Processing

  • Brian M. Zacour
  • James K. DrennenIII
  • Carl A. AndersonEmail author
Research Article

Abstract

Introduction

The US Food and Drug Administration has encouraged the use of the guidelines put forth by the International Conference on Harmonization (ICH-Q8) that allow for operational flexibility within a validated design space. These guidelines make possible fully automated control systems that incorporate information about a process back into the system to adjust process variables to consistently hit product quality targets. Traditionally, fluid bed control systems have used either first-principle calculations to control the internal process environment or purely empirical methods that incorporate online process measurements with process models and real-time data management. This study demonstrates the development and implementation of a novel hybrid control system that combines the two traditional approaches.

Material and Methods

Granules containing gabapentin, and hydroxypropyl cellulose were prepared in a high-shear granulator and dried in a fluid bed processing system (Diosna Minilab). The fluid bed dryer was outfitted with near-infrared (NIR), pressure, temperature, and flow sensors which were connected to a distributed control system (DCS) that was used to exercise control of the system. The control system itself consisted of a Delta V DCS (Emerson Process Management, Equipment and Controls, Inc., Lawrence, PA, USA) that was used to interface the fluid bed dryer with SynTQ (Optimal, Bristol, UK). The dried granules were characterized by median particle size and quantity of gabapentin lactam formed (a chemical degradant).

Results

Control of a fluid bed dryer utilizing both a first-principle control strategy and empirical model-based controls was demonstrated. First-principle control was based on an environmental equivalency factor model to maintain a constant thermodynamic environment. Empirical models included a pressure drop across the bed and NIR measurement of water content. These systems were combined effectively to consistently dry granules prepared by high-shear wet granulation. Utilization of this system greatly reduced the number of experiments necessary to characterize the performance of the system and facilitated control of the process with respect to the two properties of interest, median particle size and chemical stability during drying.

Keywords

Fluid bed processing Process analytical technology Multivariate modeling Online monitoring Real-time data management 

Notes

Acknowledgments

The research team at the Duquesne Center for Pharmaceutical Technology (DCPT) would like to thank the National Institute for Pharmaceutical Technology and Education (NIPTE) and the US Food and Drug Administration (FDA) for providing funds for this research. This study was funded by the FDA-sponsored grant “Development of Quality by Design (QbD) Guidance Elements on Design Space Specifications Across Scales with Stability Considerations.”(#HHSF223200819929C). The DCPT would also like to recognize Dr. Lee Kirsch’s laboratory at the University of Iowa for providing the DCPT with the lactam concentration measurements used in this study.

References

  1. 1.
    Pharmaceutical development Q8(R2). In: International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use; 2009.Google Scholar
  2. 2.
    U.S. Department of Health and Human Services, Food and Drug Administration. Guidance for Industry: Quality Systems Approach to Pharmaceutical CGMP Regulations. Rockville: U.S. Department of Health and Human Services, Food and Drug Administration; 2006.Google Scholar
  3. 3.
    Kunii D, Levenspiel O. Fluidization engineering. 2nd ed. London: Wiley; 1991.Google Scholar
  4. 4.
    Iveson SM, Litster JD, Hapgood K, Ennis BJ. Nucleation, growth and breakage phenomena in agitated wet granulation processes: a review. Powder Technol. 2001;117:3–39.CrossRefGoogle Scholar
  5. 5.
    Harbert FC. Moisture measurement and control in industrial processes. Case studies carried out by Sira Institute. Report. 1973;R505:1–24.Google Scholar
  6. 6.
    Harbert FC. Automatic control of industrial drying processes. Manuf Chem Aerosol News. 1974;45:23–4.Google Scholar
  7. 7.
    Alden M, Torkington P, Strutt ACR. Control and instrumentation of a fluidized-bed drier using the temperature-difference technique. Powder Technol. 1988;54:15–25.CrossRefGoogle Scholar
  8. 8.
    Ebey GC. A thermodynamic model for aqueous film-coating. Pharm Technol. 1987;11:40–50.Google Scholar
  9. 9.
    Walter K. Introduction to real time process determination. Pharm Eng. 2007;27:40–53.Google Scholar
  10. 10.
    Strong JC. Psychrometric analysis of the environmental equivalency factor for aqueous tablet coating. AAPS PharmSciTech. 2009;10:303–9.PubMedCrossRefGoogle Scholar
  11. 11.
    Behjat Y, Shahhosseini S, Hashemabadi SH. CFD modeling of hydrodynamic and heat transfer in fluidized bed reactors. Int Commun Heat Mass Trans. 2008;35:357–68.CrossRefGoogle Scholar
  12. 12.
    Arastoopour H, Strumendo M, Ahmadzadeh A. Numerical simulation of poly-dispersed systems, circulating fluidized bed technology IX. Proceedings of the 9th International Conference on Circulating Fluidized Beds 2008:313–8.Google Scholar
  13. 13.
    Szafran RG, Kmiec A. CFD modeling of heat and mass transfer in a spouted bed dryer. Ind Eng Chem Res. 2004;78:1025–31.Google Scholar
  14. 14.
    Wachem BGM, Shouten JC, Bleek CM, Krishna R, Sinclair JL. CFD modeling of gas-fluidized beds with a bimodal particle mixture. AICHE J. 2001;47:1292–302.CrossRefGoogle Scholar
  15. 15.
    Wang HG, Yang WQ, Senior P, Raghavan RS, Duncan SR. Investigation of batch fluidized bed drying by mathematical modeling, CFD simulation and ECT measurement. AICHE J. 2008;54:427–44.CrossRefGoogle Scholar
  16. 16.
    Groenewold H, Tsotsas E. A new model for fluid bed drying. Dry Technol. 1997;15:1687–98.CrossRefGoogle Scholar
  17. 17.
    Peglow M, Heinrich S, Tsotsas E, Morl L. Fluidized bed drying: influence of dispersion and transport phenomena. Drying. 2004;A:129–36.Google Scholar
  18. 18.
    Rambali B, Baert L, Massart DL. Using experimental design to optimize the process parameters in fluidized bed granulation on a semi-full scale. Int J Pharm. 2001; doi 10.1016/S0378-5173(01)00658-5.
  19. 19.
    Frake P, Greenhalgh D, Grierson SM, Hempenstall JM, Rudd DR. Process control and end-point determination of a fluid bed granulation by application of near infra-red spectroscopy. Int J Pharm. 1997;151:75–80.CrossRefGoogle Scholar
  20. 20.
    Rantanen J, Lehtola S, Ramet P, Mannermaa J, Yliruusi J. Online monitoring of moisture content in an instrumental fluidized bed granulator with a multi-channel NIR moisture sensor. Powder Technol. 1998;99:163–70.CrossRefGoogle Scholar
  21. 21.
    Rantanen J, Antikainen O, Mannermaa J, Yliruusi J. Use of the near-infrared reflectance method for measurement of moisture content during granulation. Pharm Dev Technol. 2000;5:209–17.PubMedCrossRefGoogle Scholar
  22. 22.
    Rantanen J, Wikstrom H, Turner R, Taylor LS. Use of in-line near-infrared spectroscopy in combination with chemometrics for improved understanding of pharmaceutical processes. Anal Chem. 2005;77:556–63.PubMedCrossRefGoogle Scholar
  23. 23.
    Watano S, Miyanami K. Image processing for on-line monitoring of granule size distribution and shape in fluidized bed granulation. Powder Technol. 1995;83:55–60.CrossRefGoogle Scholar
  24. 24.
    Watano S. Direct control of wet granulation processes by image processing system. Powder Technol. 2001;117:163–72.CrossRefGoogle Scholar
  25. 25.
    Hu X, Cunningham JC, Winstead D. Study growth kinetics in fluidized bed granulation with at-line FBRM. Int J Pharm. 2008;347:54–61.PubMedCrossRefGoogle Scholar
  26. 26.
    Portoghese F, Berruti F, Briens C. Continuous on-line measurement of solid moisture content during fluidized bed drying using triboelectric probes. Powder Technol. 2008;181:169–77.CrossRefGoogle Scholar
  27. 27.
    Rantanen J, Kansakoski M, Suhonen J, Tenhunen J, Lehtonen S, Rajalahti T, Mannermaa J, Yliruusi J. Next generation fluidized bed granulator automation. AAPS PharmSciTech. 2000;1:26–36.CrossRefGoogle Scholar
  28. 28.
    Rantanen J, Jorgensen A, Rasanen E, Luukkonen P, Airaksinen S, Raiman J, Hanninen K, Antikainen O, Yliruusi J. Process analysis of fluidized bed granulation. AAPS PharmSciTech. 2001;2:13–20.CrossRefGoogle Scholar
  29. 29.
    Rasanen E, Rantanen J, Jorgensen A, Karjalainen M, Paakkari T, Yliruusi J. Novel identification of pseudopolymorphic changes of theophylline during wet granulation using near infrared spectroscopy. J Pharm Sci. 2001;90:389–96.PubMedCrossRefGoogle Scholar
  30. 30.
    Jorgensen A, Rantanen J, Karjalainen M, Khriachtchev L, Rasanen E, Yliruusi J. Hydrate formation during wet granulation studied by spectroscopic methods and multivariate analysis. Pharm Res. 2002;19:1285–91.PubMedCrossRefGoogle Scholar
  31. 31.
    Hausman DS, Cambron RT, Sakr A. Application of on-line Raman spectroscopy for characterizing relationships between drug hydration state and tablet physical stability. Int J Pharm. 2005;299:19–33.PubMedCrossRefGoogle Scholar
  32. 32.
    Zong Z, Desai SD, Kaushal AM, Barich DH, Huang H, Munson EJ, Suryanarayanan R, Kirsch LE. The stabilizing effect of moisture on the solid-state degradation of gabapentin. AAPS PharmSciTech. 2011;12:924–31.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Brian M. Zacour
    • 1
  • James K. DrennenIII
    • 1
  • Carl A. Anderson
    • 1
    Email author
  1. 1.Center for Pharmaceutical TechnologyDuquesne UniversityPittsburghUSA

Personalised recommendations