Advertisement

Journal of Pharmaceutical Innovation

, Volume 8, Issue 1, pp 1–10 | Cite as

Latent Variables-Based Process Modeling of a Continuous Hydrogenation Reaction in API Synthesis of Small Molecules

  • Zhenqi Shi
  • Nikolay Zaborenko
  • David E. ReedEmail author
Research Article

Abstract

Introduction

Continuous manufacturing can be benefited by the use of the Quality by Design (QbD) strategy for robust process development and by the use of Process Analytical Technology (PAT) for real-time process monitoring and control. A successful implementation of QbD and PAT for continuous processes relies on a robust and information-rich process model as a basis for process understanding, monitoring, and control. Compared to first principles and other empirical models, a latent variables-based process model is capable of decomposing multidimensional process data into a few orthogonal latent variables and of providing accessible process understanding/visualization and control within the latent variable space. This study is an extension of our group’s earlier effort (Liu et al., J Pharm Innov 6:170–180, 2011) to explore the utility of latent variables-based process modeling in pharmaceutical manufacturing processes.

Methods

The case presented here is the first application of latent variables-based modeling to a reaction process in small-molecule active pharmaceutical ingredient route synthesis, i.e., a continuous-flow hydrogenation. A particular reactor configuration and operation was used in this proof-of-concept study.

Results

It was found that time-variant profiles of pressure in the flow tube reactor served as an effective indicator of gas–liquid interaction within the reactor, thus determining process outcomes, i.e., the extent of reaction and enantiomeric excess (ee), given the importance of process set points. In addition, a design space of process parameters predicted to produce optimal outcomes, i.e., extent of reaction greater than 98 % and ee higher than 93 %, was established in order to provide a flexible operation space for performing the reaction with desired process outcomes.

Conclusions

The capabilities of latent variables-based process modeling have been well demonstrated as applied to a continuous-flow hydrogenation reaction, regarding its improved process understanding and the potential for process optimization & control as well. Future efforts will be focused on continuing understanding of the capabilities and limitations of such a methodology on a fully-automated control scheme for continuous flow reaction.

Keywords

Continuous manufacturing Latent-variable modeling Hydrogenation Design space 

Notes

Acknowledgments

The authors would like to acknowledge Dr. Martin D Johnson, Dr. Scott A May, Dr. Michael E Kopach for their indispensable contribution and constructive suggestions on the reactor setup of this manuscript, which has been very helpful in preparing this manuscript.

References

  1. 1.
    MacGregor JF, Bruwer M. A framework for the development of design and control spaces. J Pharm Innov. 2008;3:15–22.CrossRefGoogle Scholar
  2. 2.
    Seibert KD, Sethuraman S, Mitchell JD, Griffiths KL, McGarvery B. The use of routine process capability for the determination of process parameter criticality in small-molecule API synthesis. J Pharm Innov. 2008;3:105–12.CrossRefGoogle Scholar
  3. 3.
    Burt JL, Braem AD, Ramirez A, Mudryk B, Rossano L, Tummala S. Model-guided design space development for a drug substance manufacturing process. J Pharm Innov. 2011;6:181–92.CrossRefGoogle Scholar
  4. 4.
    Ende D, Bronk KS, Mustakis J, O’Connor G, Santa Maria CL, Nosal R, Watson TJN. API quality by design example from the torcetrapib manufacturing process. J Pharm Innov. 2007;2:71–86.CrossRefGoogle Scholar
  5. 5.
    Hallow DM, Mudryk BM, Braem AD, Tabora JE, Lyngberg OK, Bergum JS, Rossano LT, Tummala S. An example of utilizing mechanistic and empirical modeling in quality by design. J Pharm Innov. 2010;5:193–203.CrossRefGoogle Scholar
  6. 6.
    Castagnoli C, Yahyah M, Cimarosti Z, Peterson JJ. Application of quality by design principles for the definition of a robust crystallization process for casopitant mesylate. Org Process Res Dev. 2010;14:1407–19.CrossRefGoogle Scholar
  7. 7.
    MacGregor JF, Kourti T. Statistical process control of multivariate processes. Control Eng Pract. 1995;3:403–14.CrossRefGoogle Scholar
  8. 8.
    Kourti T, MacGregor JF. Process analysis, monitoring and diagnosis, using multivariate projection methods. Chemom Intell Lab Syst. 1995;28:3–21.Google Scholar
  9. 9.
    Kourti T, Lee J, MacGregor JF. Experiences with industrial applications of projection methods for multivariate statistical process control. Comput Chem Eng. 1996;22(Suppl):S745–50.CrossRefGoogle Scholar
  10. 10.
    Liu Z, Bruwer M, MacGregor JF, Rathore SSS, Reed DE, Champagne MJ. Modeling and optimization of a tablet manufacturing line. J Pharm Innov. 2011;6:170–80.CrossRefGoogle Scholar
  11. 11.
    Muteki K, Swaminathan V, Sekulic SS, Reid GL. De-risking pharmaceutical tablet manufacture through process understanding, latent variable modeling, and optimization technologies. AAPS PharmSciTech. 2011;12:1324–34.PubMedCrossRefGoogle Scholar
  12. 12.
    Garcia-Munoz S, Dolph S, Ward II HW. Handling uncertainty in the establishment of a design space for the manufacture of a pharmaceutical product. Comput Chem Eng. 2010;34:1098–107.CrossRefGoogle Scholar
  13. 13.
    Perry RH, Green DW. Perry’s chemical engineers’ handbook. 7th ed. New York: McGraw-Hill; 1997. p. 6–26.Google Scholar
  14. 14.
    Westerhuis JA, Coenegracht PMJ. Multivariate modelling of the pharmaceutical two-step process of wet granulation and tableting with multiblock partial least squares. J Chemom. 1997;11:379–92.CrossRefGoogle Scholar
  15. 15.
    Westerhuis JA, Kourti T, MacGregor JF. Analysis of multiblock and hierarchical PCA and PLS models. J Chemom. 1998;12:301–21.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.ASR&D, Lilly Research LaboratoriesIndianapolisUSA
  2. 2.CPR&D, Lilly Research LaboratoriesIndianapolisUSA

Personalised recommendations