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Comparison Between Pure Component Modeling Approaches for Monitoring Pharmaceutical Powder Blends with Near-Infrared Spectroscopy in Continuous Manufacturing Schemes

Abstract

Near-infrared (NIR) spectroscopy has become an important process analytical technology (PAT) for monitoring and implementing control in continuous manufacturing (CM) schemes. However, NIR requires complex multivariate models to properly extract the relevant information and the traditional model of choice, partial least squares, can be unfavorable on account of its high material and time investments for generating calibrations. To account for this, pure component-based approaches have been gaining attention due to their higher flexibility and ease of development. In the present study, the application of two pure component approaches, classical least squares (CLS) models and iterative optimization technology (IOT) algorithms, to pharmaceutical powder blends in a continuous feed frame was considered. The approaches were compared from both a model performance and practical implementation perspective. IOT were found to demonstrate superior performance in predicting drug content compared to CLS. The practical implementation of each modelling approach was also given consideration.

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References

  1. Food and Drug Administration. Guidance for industry, PAT-A framework for innovative pharmaceutical development, manufacturing and quality assurance. http://www.fdagov/cder/guidance/published.html. 2004.

  2. Badman C, Cooney CL, Florence A, Konstantinov K, Krumme M, Mascia S, et al. Why we need continuous pharmaceutical manufacturing and how to make it happen. J Pharm Sci. 2019;108(11):3521–3. https://doi.org/10.1016/j.xphs.2019.07.016.

    CAS  Article  PubMed  Google Scholar 

  3. Roggo Y, Pauli V, Jelsch M, Pellegatti L, Elbaz F, Ensslin S, et al. Continuous manufacturing process monitoring of pharmaceutical solid dosage form: a case study. J Pharm Biomed Anal. 2020;179:112971. https://doi.org/10.1016/j.jpba.2019.112971.

    CAS  Article  PubMed  Google Scholar 

  4. Ward HW, Blackwood DO, Polizzi M, Clarke H. Monitoring blend potency in a tablet press feed frame using near infrared spectroscopy. J Pharm Biomed Anal. 2013;80:18–23. https://doi.org/10.1016/j.jpba.2013.02.008.

    CAS  Article  PubMed  Google Scholar 

  5. Hetrick EM, Shi Z, Barnes LE, Garrett AW, Rupard RG, Kramer TT, et al. Development of near infrared spectroscopy-based process monitoring methodology for pharmaceutical continuous manufacturing using an offline calibration approach. Anal Chem. 2017;89(17):9175–83. https://doi.org/10.1021/acs.analchem.7b01907.

    CAS  Article  PubMed  Google Scholar 

  6. Liu Y, Blackwood D. Sample presentation in rotary tablet press feed frame monitoring by near infrared spectroscopy. Am Pharm Rev. 2012;1.

  7. Šašić S, Blackwood D, Liu A, Ward HW, Clarke H. Detailed analysis of the online near-infrared spectra of pharmaceutical blend in a rotary tablet press feed frame. J Pharm Biomed Anal. 2015;103:73–9. https://doi.org/10.1016/j.jpba.2014.11.008.

    CAS  Article  PubMed  Google Scholar 

  8. De Maesschalck R, Sànchez FC, Massart D, Doherty P, Hailey P. On-line monitoring of powder blending with near-infrared spectroscopy. Appl Spectrosc. 1998;52(5):725–31. https://doi.org/10.1366/0003702981944148.

    Article  Google Scholar 

  9. Skibsted E, Boelens H, Westerhuis J, Witte D, Smilde A. Simple assessment of homogeneity in pharmaceutical mixing processes using a near-infrared reflectance probe and control charts. J Pharm Biomed Anal. 2006;41(1):26–35. https://doi.org/10.1016/j.jpba.2005.10.009.

    CAS  Article  PubMed  Google Scholar 

  10. Sekulic SS, Ward HW, Brannegan DR, Stanley ED, Evans CL, Sciavolino ST, et al. On-line monitoring of powder blend homogeneity by near-infrared spectroscopy. Anal Chem. 1996;68(3):509–13. https://doi.org/10.1021/ac950964m.

    CAS  Article  PubMed  Google Scholar 

  11. Bondi RW, Igne B, Drennen JK, Anderson CA. Effect of experimental design on the prediction performance of calibration models based on near-infrared spectroscopy for pharmaceutical applications. Appl Spectrosc. 2012;66(12):1442–53. https://doi.org/10.1366/12-06689.

    CAS  Article  PubMed  Google Scholar 

  12. Haaland DM, Easterling RG. Improved sensitivity of infrared spectroscopy by the application of least squares methods. Appl Spectrosc. 1980;34(5):539–48.

    CAS  Article  Google Scholar 

  13. Antoon M, Koenig J, Koenig J. Least-squares curve-fitting of Fourier transform infrared spectra with applications to polymer systems. Appl Spectrosc. 1977;31(6):518–24.

    CAS  Article  Google Scholar 

  14. Muteki K, Blackwood DO, Maranzano B, Zhou Y, Liu YA, Leeman KR, et al. Mixture component prediction using iterative optimization technology (calibration-free/minimum approach). Ind Eng Chem Res. 2013;52(35):12258–68. https://doi.org/10.1021/ie3034587.

    CAS  Article  Google Scholar 

  15. Diwan A, Linford MR. An introduction to classical least squares (CLS) and multivariate curve resolution (MCR) as applied to UV-VIS, FTIR, and ToF-SIMS. Vac Technol Coat. 2015.

  16. Li Q, Wang N, Zhou Q, Sun S, Yu Z. Excess infrared absorption spectroscopy and its applications in the studies of hydrogen bonds in alcohol-containing binary mixtures. Appl Spectrosc. 2008;62(2):166–70.

    CAS  Article  Google Scholar 

  17. Koga Y, Sebe F, Minami T, Otake K, Saitow K-i, Nishikawa K. Spectrum of excess partial molar absorptivity. I. Near infrared spectroscopic study of aqueous acetonitrile and acetone. J Phys Chem B. 2009;113(35):11928–35. https://doi.org/10.1021/jp901934c.

    CAS  Article  PubMed  Google Scholar 

  18. Haaland DM, Melgaard DK. New augmented classical least squares methods for improved quantitative spectral analyses. Vib Spectrosc. 2002;29(1-2):171–5. https://doi.org/10.1016/S0924-2031(01)00199-0.

    CAS  Article  Google Scholar 

  19. Shi Z, Hermiller J, Muñoz SG. Estimation of mass-based composition in powder mixtures using Extended Iterative Optimization Technology (EIOT). AIChE J. 2019;65(1):87–98. https://doi.org/10.1002/aic.16417.

    CAS  Article  Google Scholar 

  20. Jackson JE. Principal components and factor analysis: part I—principal components. J Qual Technol. 1980;12(4):201–13. https://doi.org/10.1080/00224065.1980.11980967.

    Article  Google Scholar 

  21. Alam MA, Liu YA, Dolph S, Pawliczek M, Peeters E, Palm A. Benchtop NIR method development for continuous manufacturing scale to enable efficient PAT application for solid oral dosage form. Int J Pharm. 2021;601:120581. https://doi.org/10.1016/j.ijpharm.2021.120581.

    CAS  Article  PubMed  Google Scholar 

  22. Potra FA, Wright SJ. Interior-point methods. J Comput Appl Math. 2000;124(1-2):281–302. https://doi.org/10.1016/S0377-0427(00)00433-7.

    Article  Google Scholar 

  23. Fearn T. Comparing standard deviations. NIR News. 1996;7(5):5–6.

    Article  Google Scholar 

  24. Zeaiter M, Roger J-M, Bellon-Maurel V. Robustness of models developed by multivariate calibration. Part II: the influence of pre-processing methods. TrAC Trends Anal Chem. 2005;24(5):437–45. https://doi.org/10.1016/j.trac.2004.11.023.

    CAS  Article  Google Scholar 

  25. Su Q, Ganesh S, Moreno M, Bommireddy Y, Gonzalez M, Reklaitis GV, et al. A perspective on quality-by-control (QbC) in pharmaceutical continuous manufacturing. Comput Chem Eng. 2019;125:216–31. https://doi.org/10.1016/j.compchemeng.2019.03.001.

    CAS  Article  Google Scholar 

  26. Shi Z, Anderson CA. Pharmaceutical applications of separation of absorption and scattering in near-infrared spectroscopy (NIRS). J Pharm Sci. 2010;99(12):4766–83. https://doi.org/10.1002/jps.22228.

    CAS  Article  PubMed  Google Scholar 

  27. Muñoz SG, Torres EH. Supervised extended iterative optimization technology for estimation of powder compositions in pharmaceutical applications: method and lifecycle management. Ind Eng Chem Res. 2020;59(21):10072–81. https://doi.org/10.1021/acs.iecr.0c01385.

    CAS  Article  Google Scholar 

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Acknowledgements

The authors wish to thank the Worldwide Research and Development, Pfizer Inc. for funding this work.

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Authors

Contributions

Adam J. Rish contributed to the conceptualization, methodology, programming, formal analysis, investigation, data curation, and writing the original draft. Samuel R. Henson contributed to the methodology, validation, formal analysis, investigation, data curation, and writing the original draft. Md. Anik Alam contributed to the conceptualization, resources, formal analysis, writing the original draft, and project administration. Yang Liu contributed to conceptualization, resources, writing the original draft, and project administration. James K. Drennen contributed to writing the original draft and project administration. Carl A. Anderson contributed to conceptualization, resources, formal analysis, writing the original draft, and project administration.

Corresponding author

Correspondence to Carl A. Anderson.

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The authors declare no competing interests.

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Responsible Editor: Anurag S. Rathore

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Rish, A.J., Henson, S.R., Alam, M.A. et al. Comparison Between Pure Component Modeling Approaches for Monitoring Pharmaceutical Powder Blends with Near-Infrared Spectroscopy in Continuous Manufacturing Schemes. AAPS J 24, 82 (2022). https://doi.org/10.1208/s12248-022-00725-x

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  • DOI: https://doi.org/10.1208/s12248-022-00725-x

KEY WORDS

  • classical least squares (CLS)
  • continuous manufacturing
  • iterative optimization technology (IOT)
  • near-infrared spectroscopy (NIR)
  • process analytical technology (PAT)