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Journal of Pharmaceutical Innovation

, Volume 12, Issue 4, pp 347–356 | Cite as

Calibration Transfer of a Quantitative Transmission Raman PLS Model: Direct Transfer vs. Global Modeling

  • Douglas Steinbach
  • Carl A. AndersonEmail author
  • Gary McGeorge
  • Benoit Igne
  • Robert W. Bondi
  • James K. DrennenIII
Original Article
  • 231 Downloads

Abstract

Global regulatory agencies have encouraged the use of process analytical technology (PAT) to assure quality in the pharmaceutical industry. A frequently cited obstacle to the implementation of spectroscopy-based PAT methods is the difficulty associated with directly transferring calibration models between PAT instruments. The goal of this study was to compare model transfer strategies for method transfer between two transmission Raman spectroscopy (TRS) instruments. The calibration and test samples were pharmaceutical compacts of acetaminophen and excipients. The experimental design was a 3 factor by 5 level circumscribed central composite design of active pharmaceutical ingredient, lactose, and microcrystalline cellulose concentrations. The calibration and test data were collected using two instruments. Quantitative models were constructed using partial least squares regression. Global calibration modeling and direct model transfer were compared to evaluate opportunities for situations involving method transfer, calibration update, and line extension. Models were compared using a t test-based method to evaluate performance statistics. Statistical analysis demonstrated equivalent performance of the global modeling and direct transfer methods. This work demonstrated that a quantitative transmission Raman model could be directly transferred across instruments, thus avoiding the challenges and resources necessary when creating global models.

Keywords

Transmission Raman Calibration transfer Quantitative modeling Partial least squares Compacts Process analytical technology 

Notes

Acknowledgments

Special thanks are extended to Shikhar Mohan and Yuxiang Zhao for their advice and assistance during the execution and evaluation of this research.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Douglas Steinbach
    • 1
  • Carl A. Anderson
    • 1
    • 2
    Email author
  • Gary McGeorge
    • 3
  • Benoit Igne
    • 2
  • Robert W. Bondi
    • 2
  • James K. DrennenIII
    • 1
    • 2
  1. 1.Duquesne University Graduate School of Pharmaceutical SciencesPittsburghUSA
  2. 2.Duquesne Center for Pharmaceutical TechnologyPittsburghUSA
  3. 3.Bristol-Myers Squibb CompanyNew BrunswickUSA

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