Advanced Control of Continuous Pharmaceutical Tablet Manufacturing Processes

  • Ravendra Singh
  • Carlos Velazquez
  • Abhishek Sahay
  • Krizia M. Karry
  • Fernando J. Muzzio
  • Marianthi G. Ierapetritou
  • Rohit Ramachandran
Part of the Methods in Pharmacology and Toxicology book series (MIPT)

Abstract

A novel manufacturing strategy based on continuous processing, integrated with online/inline monitoring tools, coupled with an advanced automatic feedback control system is highly desired for efficient Quality by Design (QbD)-based manufacturing of the next generation of pharmaceutical products with optimal consumption of time, space and resources. In this work, an advanced hybrid MPC-PID control system as well as a simpler PID controller for a direct compaction continuous tablet manufacturing process has been designed and implemented for a pilot-scale pharmaceutical process. An NIR sensor, an online NIR prediction tool, a PAT data management tool, an OPC communication protocol, a standard control platform and control hardware have been used to close the control loop. A systematic methodology to design and implement the control system has been also proposed. A control framework with features such as the option to run the plant in open-loop as well as in a closed-loop scenario has been developed. Furthermore, within the closed-loop scenario, options for a simpler PID, a dead time compensator (Smith predictor) as well as an advanced model predictive controller have been included. The feature to run the control strategy in simulation mode has been added to the control platform to facilitate virtual control system design and performance evaluation. Two case studies involving a direct compaction continuous tablet manufacturing process have been considered to demonstrate the closed-loop operation. Case Study 1 was completed at Rutgers University and constituted the use of a continuous cylindrical blender with a rotating screw. Case Study 2 was based on a continuous tumble mixer and was completed at the University of Puerto Rico—Mayaguez Campus (UPRM).

Key words

Control system Model predictive control Pharmaceutical Continuous Tablet manufacturing 

Notes

Acknowledgements

This work is supported by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems (ERC-SOPS) through Grant NSF-ECC 0540855. The authors would also like to acknowledge Paul Brodbeck (CAI) and Rodolfo J. Romañach (UPRM) for their meaningful discussions.

References

  1. 1.
    PhRMA profile (2012). Washington DC, http://phrma.org/sites/default/files/pdf/PhRMA%20Profile%202013.pdf. Accessed 26 September 2013.
  2. 2.
    FDA (2004). Challenge and opportunity on the critical path to new medical products. U.S. Food and Drug Administration, http://www.fda.gov/downloads/Drugs/ScienceResearch/ResearchAreas/ucm079290.pdf. Accessed 26 September 2013.
  3. 3.
    Singh R, Godfrey A, Gregertsen B, Muller F, Gernaey KV, Gani R, Woodley JM (2013) Systematic substrate adoption methodology (SAM) for future flexible, generic pharmaceutical production processes. Comput Chem Eng 58:344–368CrossRefGoogle Scholar
  4. 4.
    Singh R, Sahay A, Muzzio F, Ierapetritou M, Ramachandran R (2013) Systematic framework for onsite design and implementation of the control system in continuous tablet manufacturing process. Comput Chem Eng 66:186–200CrossRefGoogle Scholar
  5. 5.
    Singh R, Ierapetritou M, Ramachandran R (2013) System-wide hybrid model predictive control of a continuous pharmaceutical tablet manufacturing process via direct compaction. Eur J Pharm Biopharm 85(3):1164–1182CrossRefPubMedGoogle Scholar
  6. 6.
    Singh R, Ierapetritou MG, Ramachandran R (2012) An engineering study on the enhanced control and operation of continuous manufacturing of pharmaceutical tablets via roller compaction. Int J Pharm 438(1-2):307–326CrossRefPubMedGoogle Scholar
  7. 7.
    Charoo NA, Shamsher AAA, Zidan AS, Rahman Z (2012) Quality by design approach for formulation development: a case study of dispersible tablets. Int J Pharm 423:167–178CrossRefPubMedGoogle Scholar
  8. 8.
    Singh R, Gernaey KV, Gani R (2009) Model-based computer-aided framework for design of process monitoring and analysis systems. Comput Chem Eng 33(1):22–42CrossRefGoogle Scholar
  9. 9.
    FDA/CDER (2005) Process Analytical Technology - (PAT) initiative. U.S. Food and Drug Administration, Center for Drug Evaluation and Research. http://www.fda.gov/AboutFDA/CentersOffices/OfficeofMedicalProductsandTobacco/CDER/ucm088828.htm. Accessed 19 June 2012
  10. 10.
    Gnoth S, Jenzsch M, Simutis R, Lübbert A (2007) Process Analytical Technology (PAT): batch-to-batch reproducibility of fermentation processes by robust process operational design and control. J Biotechnol 132(2):180–186CrossRefPubMedGoogle Scholar
  11. 11.
    FDA (2004) Guidance for industry: PAT-A framework for innovative pharmaceutical manufacturing & quality assurance. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm070305.pdf. Accessed 19 June 2012
  12. 12.
    Muzzio F, Singh R, Chaudhury A, Rogers A, Ramachandran R, Ierapetritou MG (2013) PharmTech magazine Europe 37(6): 40–41. http://www.pharmtech.com/pharmtech/Man-ufacturing/Model-Predictive-Design-Control-and-Optimization/ArticleStandard/Article/detail/814906. Accessed 04 October 2013
  13. 13.
    Boukouvala F, Chaudhury A, Sen M, Zhou R, Mioduszewski L, Ierapetritou M, Ramachandran R (2013) Computer-aided flowsheet simulation of a pharmaceutical tablet manufacturing process incorporating wet granulation. J Pharm Innov 8(1):11–27CrossRefGoogle Scholar
  14. 14.
    Boukouvala F, Niotis V, Ramachandran R, Muzzio F, Ierapetritou M (2012) An integrated approach for dynamic flowsheet modeling and sensitivity analysis of a continuous tablet manufacturing process: an integrated approach. Comput Chem Eng 42:30–47CrossRefGoogle Scholar
  15. 15.
    Boukouvala F, Ramachandran R, Vanarase A, Muzzio FJ, Ierapetritou M (2011) Computer aided design and analysis of continuous pharmaceutical manufacturing processes. Comput Aid Chem Eng 29:216–220CrossRefGoogle Scholar
  16. 16.
    Sen M, Dubey A, Singh R, Ramachandran R (2013). Mathematical development and comparison of a hybrid PBM-DEM description of a continuous powder mixing process. J Powder Technol. http://dx.doi.org/10.1155/2013/843784
  17. 17.
    Sen M, Ramachandran R (2012) A multi-dimensional population balance model approach to continuous powder mixing processes. Adv Powder Technol 24(1):51–59CrossRefGoogle Scholar
  18. 18.
    Sen M, Singh R, Vanarase A, John J, Ramachandran R (2012) Multi-dimensional population balance modeling and experimental validation of continuous powder mixing processes. Chem Eng Sci 80:349–360CrossRefGoogle Scholar
  19. 19.
    Barrasso D, Ramachandran R (2012) A comparison of model order reduction techniques for a four-dimensional population balance model describing multi-component wet granulation processes. Chem Eng Sci 80:380–392CrossRefGoogle Scholar
  20. 20.
    Barrasso D, Walia S, Ramachandran R (2013) Multi-component population balance modeling of continuous granulation processes: a parametric study and comparison with experimental trends. Powder Technol 241:85–97CrossRefGoogle Scholar
  21. 21.
    Portillo PM, Vanarase A, Ingram A, Seville JK, Ierapetritou MG, Muzzio FJ (2010) Investigation of the effect of impeller rotation rate, powder flow rate, and cohesion on powder flow behavior in a continuous blender using PEPT. Chem Eng Sci 65:5658–5668CrossRefGoogle Scholar
  22. 22.
    Vanarase AU, Alcala M, Rozo J, Muzzio FJ, Romanach RJ (2010) Real-time monitoring of drug concentration in a continuous powder mixing process using NIR spectroscopy. Chem Eng Sci 65:5728–5733CrossRefGoogle Scholar
  23. 23.
    Vanarase A, Muzzio FJ (2011) Effect of operating conditions and design parameters in a continuous powder mixer. Powder Technol 208:26–36CrossRefGoogle Scholar
  24. 24.
    Vanarase A, Gao Y, Muzzio FJ, Ierapetritou MG (2011) Characterizing continuous powder mixing using residence time distribution. Chem Eng Sci 66(3):417–425CrossRefGoogle Scholar
  25. 25.
    Singh R, Gernaey KV, Gani R (2010) ICAS-PAT: a software for design, analysis & validation of PAT systems. Comput Chem Eng 34(7):1108–1136CrossRefGoogle Scholar
  26. 26.
    Hsu S, Reklaitis GV, Venkatasubramanian V (2010) Modeling and control of roller compaction for pharmaceutical manufacturing. Part I: process dynamics and control framework. J Pharm Innov 5:14–23CrossRefGoogle Scholar
  27. 27.
    Hsu S, Reklaitis GV, Venkatasubramanian V (2010) Modeling and control of roller compaction for pharmaceutical manufacturing. Part II: control and system design. J Pharm Innov 5:24–36CrossRefGoogle Scholar
  28. 28.
    Ramachandran R, Chaudhury A (2012) Model-based design and control of continuous drum granulation processes. Chem Eng Res Des 90(8):1063–1073CrossRefGoogle Scholar
  29. 29.
    Burggraeve A, Tavares da Silva A, Van den Kerkhof T, Hellings M, Vervaet C, Remon JP, Vander Heyden Y, Beer TD (2012) Development of a fluid bed granulation process control strategy based on real-time process and product measurements. Talanta 100:293–302CrossRefPubMedGoogle Scholar
  30. 30.
    Bardin M, Knight PC, Seville JPK (2004) On control of particle size distribution in granulation using high-shear mixers. Powder Technol 140(3):169–175CrossRefGoogle Scholar
  31. 31.
    Sanders CFW, Hounslow MJ, Doyle FJ III (2009) Identification of models for control of wet granulation. Powder Technol 188(3):255–263CrossRefGoogle Scholar
  32. 32.
    Long CE, Polisetty PK, Gatzke EP (2007) Deterministic global optimization for nonlinear model predictive control of hybrid dynamic systems. Int J Robust Nonlin Control 17:1232–1250CrossRefGoogle Scholar
  33. 33.
    Gatzke EP, Doyle FJ III (2001) Model predictive control of a granulation system using soft output constraints and prioritized control objectives. Powder Technol 121(2–3):149–158CrossRefGoogle Scholar
  34. 34.
    Pottmann M, Ogunnaike BA, Adetayo AA, Ennis BJ (2000) Model-based control of a granulation system. Powder Technol 108(2–3):192–201CrossRefGoogle Scholar
  35. 35.
    Ramachandran R, Arjunan J, Chaudhury A, Ierapetritou M (2012) Model-based control loop performance assessment of a continuous direct compaction pharmaceutical processes. J Pharm Innov 6(3):249–263Google Scholar
  36. 36.
    Seborg DE, Edgar TF, Mellichamp DA (2004) Process dynamics and control, 2nd edn. John Wiley, New YorkGoogle Scholar
  37. 37.
    Ziegler JG, Nichols B (1942) Optimum settings for automatic controllers. Trans ASME 64:759–765Google Scholar
  38. 38.
    Cutler CR, Ramaker BL (1979) Dynamic matrix control—a computer control algorithm. AIChE National Meeting, Houston, TX, April 1979Google Scholar
  39. 39.
    Prett DM, Gillette RD (1980) Optimization and constrained multivariable control of a catalytic cracking unit. AIChE National Meeting, Houston, TX, April 1979Google Scholar
  40. 40.
    Wojsznis W, Gudaz J, Blevins T, Mehta A (2003) Practical approach to tuning MPC. ISA Trans 42:149–162CrossRefPubMedGoogle Scholar
  41. 41.
    Singh R, Boukouvala F, Jayjock E, Ramachandran R, Ierapetritou M, Muzzio F (2012b) Flexible multipurpose continuous processing of pharmaceutical tablet manufacturing process. GMP news, European Compliance Academic (ECE). http://www.gmpcompliance.org/ecanl_503_0_news_3268_7248_n.html. Accessed 26 Sept 2013.
  42. 42.
    Singh R, Boukouvala F, Jayjock E, Ramachandran R, Ierapetritou M, Muzzio F (2012c) Flexible multipurpose continuous processing. PharmPro Magazine, Pharmaceut Process 27(6): 22–25Google Scholar
  43. 43.
    Kawakita K, Ludde KH (1971) Some considerations on powder compression equations. Powder Technol 4:61–68CrossRefGoogle Scholar
  44. 44.
    Kuentz M, Leuenberger H (2000) A new model for the hardness of a compacted particle system, applied to tablets of pharmaceutical polymers. Powder Technol 111:143–145CrossRefGoogle Scholar
  45. 45.
    Kimber JA, Kazarian SG, Stepánek F (2011) Microstructure-based mathematical modelling and spectroscopic imaging of tablet dissolution. Comput Chem Eng 35:1328–1339CrossRefGoogle Scholar
  46. 46.
    Singh R, Gernaey KV, Gani R (2010) An ontological knowledge based system for selection of process monitoring and analysis tools. Comput Chem Eng 34(7):1137–1154CrossRefGoogle Scholar
  47. 47.
    Bristol E (1966) On a new measure of interaction for multivariable process control. IEEE Trans Autom Control 11(1):133–134CrossRefGoogle Scholar
  48. 48.
    Blevins T, Wojsznis WK, Nixon M (2013) Advanced control foundation: tools, techniques and applications. International Society of Automation, NC, USA. ISBN ISBN: 978-1-937560-55-3Google Scholar
  49. 49.
    Trierweiler JO, Farina LA (2003) RPN tuning strategy for model predictive control. J Process Control 13:591–598CrossRefGoogle Scholar
  50. 50.
    Singh R, Sahay A, Karry KM, Muzzio F, Ierapetritou M, Ramachandran R (2014) Implementation of a hybrid MPC-PID control strategy using PAT tools into a direct compaction continuous pharmaceutical tablet manufacturing pilot-plant. Int J Pharm 473:38–54CrossRefPubMedGoogle Scholar
  51. 51.
    Singh R, Barrasso D, Chaudhury A, Sen M, Ierapetritou M, Ramachandran R (2014) Closed-loop feedback control of a continuous pharmaceutical tablet manufacturing process via wet granulation. J Pharm Innov 9:16–37CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ravendra Singh
    • 1
  • Carlos Velazquez
    • 2
  • Abhishek Sahay
    • 1
  • Krizia M. Karry
    • 1
  • Fernando J. Muzzio
    • 1
  • Marianthi G. Ierapetritou
    • 1
  • Rohit Ramachandran
    • 1
  1. 1.Engineering Research Center for Structured Organic Particulate Systems (ERC-SOPS), Department of Chemical and Biochemical EngineeringRutgers, The State University of New JerseyPiscatawayUSA
  2. 2.Engineering Research Center for Structured Organic Particulate Systems (ERC-SOPS), Department of Chemical EngineeringUniversity of Puerto Rico MayaguezMayaguezUSA

Personalised recommendations