Skip to main content

Population Balance Model Development, Validation, and Prediction of CQAs of a High-Shear Wet Granulation Process: Towards QbD in Drug Product Pharmaceutical Manufacturing

Journal of Pharmaceutical Innovation volume 9, pages 53–64 (2014)Cite this article

Abstract

This paper focuses on the predictive model development for a pharmaceutically relevant model granulation process. A population balance modeling (PBM) framework has been employed for modeling purposes which is then utilized to obtain accurate predictions of the process. The model is aligned to adequately describe the high-shear mode of granulation operation in a batch process. The model is calibrated using the particle swarm algorithm (PSA) in the form of a multiobjective optimization problem. The multiobjective optimization problem was implemented based on the ε-constraint method which involves the handling of multiple cost functions in the form of constraints with the minimization of one primary objective function from the entire set of cost functions. The resultant solutions obtained from the model are Pareto optimal. The effects of the impeller speed, liquid-to-solid ratio, and wet massing time on the particle size distributions were characterized, and predicted size distributions were in agreement with experimental results. The predictive model framework lends itself to the quality by design (QbD) initiative undertaken by the US Food and Drug Administration (US FDA).

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price includes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. Barrasso D, Ramachandran R. A comparison of model order reduction techniques for a four-dimensional population balance model describing multi-component wet granulation processes. Chem Eng Sci. 2012;80:380–92.

    CAS  Article  Google Scholar 

  2. Barrasso D, Walia S, Ramachandran R. Multi-component population balance modeling of continuous granulation processes: a parametric study and comparison with experimental trends. Powder Technol. 2013;241:85–97.

    CAS  Article  Google Scholar 

  3. Boukouvala F, Chaudhury A, Sen M, Zhou R, Mioduszewski L, Ierapetritou M, Ramachandran R. Computer-aided flowsheet simulation of a continuous tablet manufacturing process incorporating wet granulation. J Pharm Innov. 2013;8(1):11–27.

    Article  Google Scholar 

  4. Braumann A, Kraft M, Wagner W. Numerical study of a stochastic particle algorithm solving a multidimensional population balance model for high shear granulation. J Comput Phys. 2010;229:7672–91.

    CAS  Article  Google Scholar 

  5. Chaudhury A, Kapadia A, Prakash AV, Barrasso D, Ramachandran R. An extended cell-average technique for a multi-dimensional population balance of granulation describing aggregation and breakage. Adv Powder Technol. 2013a;24(6):962–71.

    Article  Google Scholar 

  6. Chaudhury A, Niziolek A, Ramachandran R. Multi-dimensional mechanistic modeling of fluid bed granulation processes: an integrated approach. Adv Powder Technol. 2013b;24(1):113–31.

    Article  Google Scholar 

  7. Chaudhury A, Ramachandran R. Integrated population balance model development and validation of a granulation process. Part Sci Technol. 2013;31(4):407–18.

    CAS  Article  Google Scholar 

  8. Chaudhury A, Wu H, Khan M, Ramachandran R. A mechanistic population balance model for granulation processes: effect of process and formulation parameters. Chem Eng Sci. 2014;107:76–92.

    CAS  Article  Google Scholar 

  9. Christofides PD. Control of nonlinear distributed process systems: recent developments and challenges. AIChE J. 2001;47(3):514–8.

    CAS  Article  Google Scholar 

  10. FDA. 2004. Guidance for industry. pat-a framework for innovative pharmaceutical development, manufacturing, and quality assurance. http://www.fda.giv/cder/guidance/6419fnl.pdf. accessed on 30 May 2013.

  11. Gantt JA, Gatzke EP. A stochastic technique for multidimensional granulation modeling. AIChE J. 2006;52(9):3067–77.

    CAS  Article  Google Scholar 

  12. Gernaey KV, Gani R. A model-based systems approach to pharmaceutical product-process design and analysis. Chem Eng Sci. 2010;65(21):5757–69.

    CAS  Article  Google Scholar 

  13. Giry K, Genty M, Viana M, Wuthrich P, Chulia D. Multiphase versus single pot granulation process: influence of process and granulation parameters on granules properties. Drug Dev Ind Pharm. 2006;32(5):509–30.

    CAS  PubMed  Article  Google Scholar 

  14. Hapgood KP, Litster JD, Smith R. Nucleation regime map for liquid bound granules. AIChE J. 2003;49(2):350–61.

    CAS  Article  Google Scholar 

  15. Immanuel CD, Doyle FJ III. Computationally efficient solution of population balance models incorporating nucleation, growth and coagulation: application to emulsion polymerization. Chem Eng Sci. 2003;58(16):3681–98.

    CAS  Article  Google Scholar 

  16. Immanuel CD, Doyle FJ III. Solution technique for a multi-dimensional population balance model describing granulation processes. Powder Technol. 2005;156(2–3):213–25.

    CAS  Article  Google Scholar 

  17. Iveson S, Litster J. Fundamental studies of granule consolidation part 2: quantifying the effects of particle and binder properties. Powder Technol. 1998a;99(3):243–50.

    CAS  Article  Google Scholar 

  18. Iveson SM, Litster JD. Growth regime map for liquid-bound granules. AIChE J. 1998b;44(7):1510–18.

    CAS  Article  Google Scholar 

  19. Iveson SM, Litster JD, Hapgood K, Ennis BJ. Nucleation, growth and breakage phenomena in agitated wet granulation processes: a review. Powder Technol. 2001;117(1–2):3–39.

    CAS  Article  Google Scholar 

  20. Kayrak-Talay D, Litster JD. A priori performance prediction in pharmaceutical wet granulation: testing the applicability of the nucleation regime map to a formulation with a broad size distribution and dry binder addition. Int J Pharm. 2011;418(2):254–64.

    CAS  PubMed  Article  Google Scholar 

  21. Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, 1995. Proceedings. vol 4. 1995 p. 1942–1948.

  22. Klatt KU, Marquardt W. Perspectives of process systems engineering-personal views from academia and industry. Comput Chem Eng. 2009;33:536–50.

    CAS  Article  Google Scholar 

  23. Knight P, Instone T, Pearson JM, Hounslow M. An investigation into the kinetics of granulation using a high shear mixer. Powder Technol. 1993;77:159–69.

    CAS  Article  Google Scholar 

  24. Kristensen HG. Particle agglomeration in high shear mixers. Powder Technol. 1996;88(3):197–202.

    CAS  Article  Google Scholar 

  25. Li L, Yu X, Li X, Guo W. 2009. A modified pso algorithm for constrained multi-objective optimization. In: Third international conference on network and system security, 2009. NSS ’09. p. 462–467.

  26. Linninger AA, Chowdhry S, Bahl V, Krendl H, Pinger H. A systems approach to mathematical modeling of industrial processes. Comput Chem Eng. 2000;24:591–8.

    CAS  Article  Google Scholar 

  27. Madec L, Falk L, Plasari E. Modelling of the agglomeration in suspension process with multidimensional kernels. Powder Technol. 2003;130(1–3):147–53.

    CAS  Article  Google Scholar 

  28. Pandey P, Tao J, Chaudhury A, Ramachandran R, Gao JZ, Bindra DS. A combined experimental and modeling approach to study the effects of high-shear wet granulation process parameters on granule characteristics. Pharm Dev Technol. 2013;18(1):210–24.

    CAS  PubMed  Article  Google Scholar 

  29. Pandey P, Tao J, Gao JZ, Bindra D, Narang A, Ramachandran R, Chaudhury A. 2011. A combined experimental and computational approach to the scale-up of high-shear wet granulation. In: Proc. 2011 AIChE annual meeting (Minneapolis, USA, October 2011).

  30. Ramachandran R, Barton PI. Effective parameter estimation within a multi-dimensional population balance model framework. Chem Eng Sci. 2010;65(16):4884–93.

    CAS  Article  Google Scholar 

  31. Ramachandran R, Poon JMH, Sanders CFW, Glaser T, Immanuel CD, Doyle FJ III, Litster JD, Stepanek F, Wang FY, Cameron IT. Experimental studies on distributions on granule size, binder content and porosity in batch drum granulation: inferences on process modelling requirements and process sensitivities. Powder Technol. 2008;188:89–101.

    CAS  Article  Google Scholar 

  32. Salman AD, Hounslow MJ, Seville JPK. Granulation. Oxford: Elsevier; 2007.

    Google Scholar 

  33. Shi Y, Eberhart R. A modified particle swarm optimizer. In: Evolutionary computation proceedings, 1998. IEEE world congress on computational intelligence. (1998) p. 69–73.

  34. Soos M, Sefcik J, Morbidelli M. Investigation of aggregation, breakage and restructuring kinetics of colloidal dispersions in turbulent flows by population balance modeling and static light scattering. Chem Eng Sci. 2006;61(8):2349–63.

    CAS  Article  Google Scholar 

  35. Stepanek F, Rajniak P. Droplet morphologies on particles with macroscopic surface roughness. Langmuir. 2006;22(3):917–23.

    CAS  PubMed  Article  Google Scholar 

  36. Verkoeijen D, Pouw GA, Meesters GMH, Scarlett B. Population balances for particulate processes—a volume approach. Chem Eng Sci. 2002;57(12):2287–303.

    CAS  Article  Google Scholar 

  37. žižek K, Hraste M, Gomzi Z. High shear granulation of dolomite—I: effect of shear regime on process kinetics. Chem Eng Res Des. 2013;91(1):70–86.

    Article  Google Scholar 

Download references

Acknowledgments

We are grateful to 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 “Critical Path Manufacturing Sector Research Initiative (5U01FD004275-02).” This work was also supported by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, via Grant NSF-ECC 0540855. The authors also thank Dilbir Bindra and Jing Tao from Bristol-Myers Squibb for their contributions to this work. The support from various FDA managers including Dr. Mansoor Khan, Dr. Richard Lostritto, Dr. Vincent Vilker, and Mr. Jon E. Clark, and insightful FDA internal review by Dr. Cindy Buhse are acknowledged.

Disclaimer

The views and opinions expressed in this work are only of the authors and do not necessarily reflect the policy or statement of the US FDA.

Author information

Authors and Affiliations

  1. Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA

    Anwesha Chaudhury, Dana Barrasso & Rohit Ramachandran

  2. Drug Product Science and Technology, Bristol-Myers Squibb, New Brunswick, NJ, USA

    Preetanshu Pandey

  3. Division of Product Quality Research (HFD-940), Office of Testing and Research, Office of Pharmaceutical Science, CDER, FDA, Silver Spring, MD, USA

    Huiquan Wu

Authors
  1. Anwesha Chaudhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dana Barrasso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Preetanshu Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huiquan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rohit Ramachandran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rohit Ramachandran.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Chaudhury, A., Barrasso, D., Pandey, P. et al. Population Balance Model Development, Validation, and Prediction of CQAs of a High-Shear Wet Granulation Process: Towards QbD in Drug Product Pharmaceutical Manufacturing. J Pharm Innov 9, 53–64 (2014). https://doi.org/10.1007/s12247-014-9172-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12247-014-9172-7

Keywords

  • Granulation
  • Multidimensional population balance model
  • QbD
  • Predictive modeling
  • Multiobjective optimization
  • Particle size distribution