Advertisement

AAPS PharmSciTech

, Volume 15, Issue 6, pp 1447–1453 | Cite as

Integrating Artificial and Human Intelligence into Tablet Production Process

  • Matjaž Gams
  • Matej Horvat
  • Matej Ožek
  • Mitja Luštrek
  • Anton GradišekEmail author
Research Article
  • 406 Downloads

Abstract

We developed a new machine learning-based method in order to facilitate the manufacturing processes of pharmaceutical products, such as tablets, in accordance with the Process Analytical Technology (PAT) and Quality by Design (QbD) initiatives. Our approach combines the data, available from prior production runs, with machine learning algorithms that are assisted by a human operator with expert knowledge of the production process. The process parameters encompass those that relate to the attributes of the precursor raw materials and those that relate to the manufacturing process itself. During manufacturing, our method allows production operator to inspect the impacts of various settings of process parameters within their proven acceptable range with the purpose of choosing the most promising values in advance of the actual batch manufacture. The interaction between the human operator and the artificial intelligence system provides improved performance and quality. We successfully implemented the method on data provided by a pharmaceutical company for a particular product, a tablet, under development. We tested the accuracy of the method in comparison with some other machine learning approaches. The method is especially suitable for analyzing manufacturing processes characterized by a limited amount of data.

KEY WORDS

artificial intelligence machine learning process analytical technology process optimization tablet manufacture 

Abbreviations

AI

Artificial intelligence

ML

Machine learning

PAT

Process Analytical Technology

QbD

Quality by Design

Notes

Acknowledgments

The authors thank the Slovenian Research Agency (ARRS) for the financial support.

References

  1. 1.
    Guidance for Industry: PAT—a framework for innovative pharmaceutical development, manufacturing, and quality assurance. U.S. Department of Health and Human Services, Food and Drug Administration, 2004.Google Scholar
  2. 2.
    Schneidir R. Achieving process understanding—the foundation of strategic PAT programme. Processing in Pharmaceutical manufacturing, 2006.Google Scholar
  3. 3.
    Yu LX. Pharmaceutical quality by design: product and process development. Pharm Res. 2008;25(4):781–91.PubMedCrossRefGoogle Scholar
  4. 4.
    Saerens L, Vervaet C, Remon JP, De Beer T. Process monitoring and visualization solutions for hot-melt extrusion: a review. J Pharm Pharmacol. 2014;66:180–203.PubMedCrossRefGoogle Scholar
  5. 5.
    Kenett RS, Kenett DA. Quality by design applications in biosimilar pharmaceutical products. Accred Qual Assur. 2008;13(12):681–90.CrossRefGoogle Scholar
  6. 6.
    Yu LX, Lionberger RA, Raw AS, D’Costa R, Wu H, Hussain AS. Applications of process analytical technology to crystallization process. Adv Drug Deliv Rev. 2004;56(4):349–69.PubMedCrossRefGoogle Scholar
  7. 7.
    Wang XZ. Data mining and knowledge discovery for process monitoring and control. London: Springer; 1999.CrossRefGoogle Scholar
  8. 8.
    Sadoyan H, Zakarian A, Mohanty P. Data mining algorithm for manufacturing process control. Int J Adv Manuf Technol. 2006;28:342–50.CrossRefGoogle Scholar
  9. 9.
    Cotofrei P, Stoffel K. Rule extraction from time series databases using classification trees. Proceedings of the 20th IASTED Conference on Applied Informatics, Innsbruck, 2002.Google Scholar
  10. 10.
    Quinlan JR. C4.5: Programs for machine learning. Morgan Kaufmann; 1993.Google Scholar
  11. 11.
    Witten IH, Frank E. Data mining: practical machine learning tools and techniques. 3rd ed. San Francisco: Morgan Kaufmann; 2011.Google Scholar
  12. 12.
    Gams M. Weak intelligence: through the principle and paradox of multiple knowledge. New York: Nova Science Publishers, Inc; 2001.Google Scholar
  13. 13.
    Maudes J, Rodríguez JJ, García-Osorio C, Pardo C. Random projections for linear SVM ensembles. Appl Intell. 2011;34(3):347–59.CrossRefGoogle Scholar
  14. 14.
    Mishra A, Bhatwadekar N, Mahajan P, Karode P, Banerjee S. Process Analytical Technology (PAT): boon to pharmaceutical industry. Pharm Rev. 2008; 6(6).Google Scholar
  15. 15.
    Russell SJ, Norvig P. Artificial intelligence: a modern approach. 3rd ed. Upper Saddle River: Prentice Hall; 2010.Google Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2014

Authors and Affiliations

  • Matjaž Gams
    • 1
  • Matej Horvat
    • 2
  • Matej Ožek
    • 1
  • Mitja Luštrek
    • 1
  • Anton Gradišek
    • 1
    Email author
  1. 1.Department of Intelligent SystemsJožef Stefan InstituteLjubljanaSlovenia
  2. 2.Sandoz Biopharmaceuticals MengešMengešSlovenia

Personalised recommendations