Journal of Pharmaceutical Innovation

, Volume 14, Issue 4, pp 359–372 | Cite as

Advances in Continuous Active Pharmaceutical Ingredient (API) Manufacturing: Real-time Monitoring Using Multivariate Tools

  • Melanie DumareyEmail author
  • Martin Hermanto
  • Christian Airiau
  • Peter Shapland
  • Hannah Robinson
  • Peter Hamilton
  • Malcolm Berry
Original Article



The implementation of continuous processing technologies for pharmaceutical manufacturing has increased due to its potential to enhance supply chain flexibility, reduce the footprint of the manufacturing facility, and deliver more consistent quality. Additionally, it facilitates extensive, real-time monitoring by sensors and process analytical technology (PAT) tools without perturbing the process. In the presented case study, the use of multivariate tools for the real-time monitoring and retrospective review of a continuous active pharmaceutical ingredient (API) synthesis was evaluated from process development through to commercialization.


A multivariate statistical process monitoring (MSPM) approach summarizing variability in both quality critical (controlled flow rates, temperatures) and non-quality critical parameters (pressures, pump speeds, conductivity) was used to monitor three telescoped chemistry stages of a continuous API synthesis. Four different modeling strategies were presented addressing specific monitoring and analysis requirements during the pharmaceutical development lifecycle.


During development (R&D and commercial facility), the implemented multivariate monitoring resulted in the identification of potential failure modes, a deeper understanding of the natural process variability and accelerated root cause analysis for a recurrent reagent blockage. During manufacturing (commercial facility), the multivariate tool confirmed potential for predictive maintenance and early fault detection.


While the implemented control strategy based on parametric control and offline analytical testing provided the required quality assurance, the multivariate trends provided additional information on process performance. More specifically, they enabled more detailed process understanding during the development of the continuous API synthesis following quality by design (QbD) principles and demonstrated the potential for enhanced process performance during commercial manufacturing.


Multivariate statistical process monitoring MSPM Continuous manufacturing PAT Process monitoring Multivariate analysis 



We would like to thank Erik Johansson for the support on custom scaling of the process sensors. We would like to thank Ian Barylski for the informatics platform support.


  1. 1.
    Poechlauer P, Manley J, Broxterman R, Gregertsen B, Ridemark M. Continuous processing in the manufacture of active pharmaceutical ingredients and finished dosage forms: an industry perspective. Org Process Res Dev. 2012;16:1586–90.CrossRefGoogle Scholar
  2. 2.
    Anderson NG. Using continuous processes to increase production. Org Process Res Dev. 2012;16:852–69.CrossRefGoogle Scholar
  3. 3.
    Nasr MM, Krumme M, Matsuda Y, Trout BL, Badman C, Mascia S, et al. Regulatory perspectives on continuous pharmaceutical manufacturing: moving from theory to practice September 26–27, 2016, International symposium on the continuous manufacturing of pharmaceuticals. J Pharm Sci. 2017;106(11):3199–206.CrossRefGoogle Scholar
  4. 4.
    Schaber SD, Gerogiorgis DI, Ramachandran R, Evans JM, Barton PI, Trout BL. Economic analysis of integrated continuous and batch pharmaceutical manufacturing: a case study. Ind Eng Chem Res. 2011;50:10083–92.CrossRefGoogle Scholar
  5. 5.
    Allison G, Cain YT, Cooney C, Garcia T, Bizjak TG, Holte O, et al. Regulatory and quality considerations for continuous manufacturing. J Pharm Sci. 2014;104:803–12.CrossRefGoogle Scholar
  6. 6.
    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;10:191–9.CrossRefGoogle Scholar
  7. 7.
    International Council for Harmonization of Technical Requirements for Human Use (ICH): Press Releases. ICH Assembly, Kobe, Japan, June 2018 (
  8. 8.
    ICH, Pharmaceutical development Q8. 2009.Google Scholar
  9. 9.
    ICH, Development and manufacture of drug substances (chemical entities and biotechnological/biological entities) Q11. 2012.Google Scholar
  10. 10.
    Myerson AS, Krumme M, Nasr M, Thomas H, Braatz RD. Control systems engineering in continuous pharmaceutical manufacturing. J Pharm Sci. 2015;104:832–9.CrossRefGoogle Scholar
  11. 11.
    Gouveia FF, Rahbek JP, Mortensen AR, Pedersen MT, Felizardo PM, Bro R, et al. Using PAT to accelerate the transition to continuous API manufacturing. Anal Bioanal Chem. 2017;409:821–32.CrossRefGoogle Scholar
  12. 12.
    Chandi A, Daly AM, Foley DA, LaPack MA, Mukherjee S, Orr JD, et al. Industry perspectives on process analytical technology: tools and applications in API development. Org Process Res Dev. 2015;19:63–83.CrossRefGoogle Scholar
  13. 13.
    Braden TM, Johnson MD, Kopach ME, McClary Groh J, Spencer RD, Lewis J, et al. Development of a commercial flow Barbier process for a pharmaceutical intermediate. Org Process Res Dev. 2017;21:317–26.CrossRefGoogle Scholar
  14. 14.
    Ferreira AP, Tobyn M. Multivariate analysis in the pharmaceutical industry: enabling process understanding and improvement in the PAT and QbD era. Pharm Dev Technol. 2015;20(5):513–27.CrossRefGoogle Scholar
  15. 15.
    Kourti T, MacGregor JF. Process analysis, monitoring and diagnosis using multivariate projection methods. Chemom Intell Lab Syst. 1995;28:3–21.CrossRefGoogle Scholar
  16. 16.
    Albert S, Kinley RD. Multivariate statistical monitoring of batch processes: an industrial case study of fermentation supervision. Trends Biotechnol. 2001;19:53–62.CrossRefGoogle Scholar
  17. 17.
    Miletic I, Quinn S, Dudzic M, Vaculik V, Champagne M. An industrial perspective on implementing on-line applications of multivariate statistics. J Process Control. 2004;14:821–36.CrossRefGoogle Scholar
  18. 18.
    Kiran KL, Selvaraj S, Hua JLC. Application of fault monitoring and diagnostics techniques and their challenges in petrochemical industries, IFPAC Proceedings Volumes (IFPAC-Papers Online) 2012; 8: 702–707.CrossRefGoogle Scholar
  19. 19.
    Saavedra J, Cordova A. Multivariate process control by transition scheme in soft-drink process using 3-way PLS approach. Procedia Food Sci. 2011;1:1181–7.CrossRefGoogle Scholar
  20. 20.
    Kourti T, Lee J, MacGregor JF. Experiences with industrial applications of projection methods for MSPS. Comput Chem Eng. 1996;20:S745–50.CrossRefGoogle Scholar
  21. 21.
    Machin M, Liesum L, Peinado A. Implementation of modelling approaches in the QbD framework: examples from the Novartis experience. Eur Pharm Rev. 2011;16:5–8.Google Scholar
  22. 22.
    Zomer S, Zhang J, Talwar S, Chattoraj S, Hewitt C. Multivariate monitoring for the industrialization of continuous wet granulation tableting process. Int J Pharm. 2018;547(1–2):506–19.CrossRefGoogle Scholar
  23. 23.
    Esbensen KH, Geladi P. Principal component analysis: concept, geometrical interpretation, mathematical background, algorithms, history, practice. In: Brown SD, Tauler R, Walczak B, editors. Comprehensive chemometrics. Amsterdam: Elsevier; 2009. p. 211–26.CrossRefGoogle Scholar
  24. 24.
    Stork C, Kowalski B. Distinguishing between process upsets and sensor malfunction using sensor redundancy. Chemom Intell Lab Syst. 1999;46:117–31.CrossRefGoogle Scholar
  25. 25.
    Ferrer A. Multivariate statistical process control based on principal component analysis (MSPC-PCA): some reflections and a case study in an autobody assembly process. Qual Eng. 2007;19(4):311–25.CrossRefGoogle Scholar
  26. 26.
    Eriksson L, Byrne T, Johansson E, Trygg J, Wikström C, editors. Multi- and megavariate data analysis part I basic principles and applications, third revision. Umeå: Umetrics; 2013.Google Scholar
  27. 27.
    Hotelling H. Multivariate quality control – illustrated by the air tasting of sample bomb sights. In: Eisenhart C, Hastay MW, Wallis WA, editors. Techniques of statistical analysis. New York: Mc Graw Hill; 1947. p. 111–48.Google Scholar
  28. 28.
    ICH Quality IWG. Points to consider for ICH Q8/Q9/Q10 implementation. 2011.Google Scholar

Copyright information

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

Authors and Affiliations

  • Melanie Dumarey
    • 1
    Email author
  • Martin Hermanto
    • 2
  • Christian Airiau
    • 3
  • Peter Shapland
    • 1
  • Hannah Robinson
    • 1
  • Peter Hamilton
    • 1
  • Malcolm Berry
    • 1
  1. 1.Product Development and Supply, Research and DevelopmentGlaxoSmithKlineHertfordshireUK
  2. 2.Technical Development, Global Manufacturing SiteGlaxoSmithKlineJurongSingapore
  3. 3.Product Development and Supply, Research and DevelopmentGlaxoSmithKlineUpper MerionUSA

Personalised recommendations