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The Use of a Closed Feed Frame for the Development of Near-Infrared Spectroscopic Calibration Model to Determine Drug Concentration

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Abstract

Purpose

This study evaluates the use of the closed feed frame as a material sparing approach to develop near-infrared (NIR) spectroscopic calibration models for monitoring blend uniformity. The effect of shear induced by recirculation on NIR spectra was also studied.

Methods

Calibration models were developed using NIR spectra obtained in the closed feed frame for two cases. For case 2, blends that flowed through the open feed frame were predicted with the model. The shear effect of the feed frame on the blends was assessed through the characterization of powder properties before and after recirculation.

Results

The physical characterization of the blends confirmed that the powder properties were not altered after recirculation within the closed feed frame. Both calibration models provided highly accurate predictions of the test sets with low bias (0.03% w/w and -0.06% w/w) and relative standard error of prediction (1.9% and 3.7%), respectively. The predictive performance of the calibration models was not affected by the shear effect.

Conclusion

Recirculation within the closed feed frame did not change the physical properties of the blends studied. The prediction of blends flowing through the open feed frame was possible with a calibration model developed in the closed feed frame. The closed feed frame could reduce the materials needed to develop calibration models by more than 90%.

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Data Availability

The data is available in the following data repository: https://github.com/Nmmeza07/Closed-Feed-Frame-data.

Abbreviations

API:

Active pharmaceutical ingredient

CM:

Continuous manufacturing

MgSt:

Magnesium stearate

MPE:

Minimal practical error

NIR:

Near-infrared

PLS:

Partial least squares

RMSEP:

Root mean square error of prediction

RSEP:

Relative standard error of prediction

SNV:

Standard normal variate

References

  1. Muzzio FJ, Oka S. How to design and implement powder-to-tablet continuous manufacturing systems. Elsevier; 2022. p. 1–413. https://doi.org/10.1016/C2016-0-03217-5.

  2. Sierra-Vega NO, Romañach RJ, Méndez R. Feed frame: The last processing step before the tablet compaction in pharmaceutical manufacturing. Int J Pharm. 2019;572. https://doi.org/10.1016/j.ijpharm.2019.118728.

  3. Velez NL, Drennen JK, Anderson CA. Challenges, opportunities and recent advances in near infrared spectroscopy applications for monitoring blend uniformity in the continuous manufacturing of solid oral dosage forms. Int J Pharm. 2022;615. https://doi.org/10.1016/j.ijpharm.2022.121462.

  4. Rangel-Gil RS, Sierra-Vega NO, Romañach RJ, Méndez R. Assessment of blend uniformity in a stream sampler device using Raman spectroscopy. Int J Pharm. 2023;25(639). https://doi.org/10.1016/j.ijpharm.2023.122934

  5. FDA. Guidance for Industry PAT — A Framework for Innovative Pharmaceutical. 2004. Available from: http://www.fda.gov/cvm/guidance/published.html. Accessed 17 Jul 2023.

  6. Gupta A, Peck GE, Miller RW, Morris KR. Real-time near-infrared monitoring of content uniformity, moisture content, compact density, tensile strength, and young’s modulus of roller compacted powder blends. J Pharm Sci. 2005;94(7):1589–97. https://doi.org/10.1002/jps.20375.

    Article  CAS  PubMed  Google Scholar 

  7. Pérez-Ramos JD, Findlay WP, Peck G, Morris KR. Quantitative analysis of film coating in a pan coater based on in-line sensor measurements. AAPS PharmSciTech. 6(1):E127–36. https://doi.org/10.1208/pt060120

  8. Morris KR, Stowell JG, Byrn SR, et al. Accelerated fluid bed drying using NIR monitoring and phenomenological modeling. Drug Development and Industrial Pharmacy. 2000;26:985–8. https://doi.org/10.1081/DDC-100101326.

    Article  CAS  PubMed  Google Scholar 

  9. 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.

  10. Sierra-Vega NO, Román-Ospino A, Scicolone J, Muzzio FJ, Romañach RJ, Méndez R. Assessment of blend uniformity in a continuous tablet manufacturing process. Int J Pharm. 2019;5(560):322–33. https://doi.org/10.1016/j.ijpharm.2019.01.073

  11. Sierra-Vega NO, Sánchez-Paternina A, Maldonado N, Cárdenas V, Romañach RJ, Méndez R. In line monitoring of the powder flow behavior and drug content in a Fette 3090 feed frame at different operating conditions using Near Infrared spectroscopy. J Pharm Biomed Anal. 2018;154:384–96. https://doi.org/10.1016/j.jpba.2018.03.017.

    Article  CAS  PubMed  Google Scholar 

  12. Li Y, Anderson CA, Drennen JK, Airiau C, Igne B. Development of an in-line near-infrared method for blend content uniformity assessment in a tablet feed frame. Appl Spectrosc. 2019;73(9):1028–40. https://doi.org/10.1177/0003702819842189.

    Article  CAS  PubMed  Google Scholar 

  13. Ortega-Zúñiga C, la Rosa CP, De R-O, Serrano-Vargas A, Romañach RJ, Méndez R. Development of near infrared spectroscopic calibration models for in-line determination of low drug concentration, bulk density, and relative specific void volume within a feed frame. J Pharm Biomed Anal. 2019;5(164):211–22. https://doi.org/10.1016/j.jpba.2018.10.046.

    Article  CAS  Google Scholar 

  14. 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.

    Article  CAS  Google Scholar 

  15. 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

  16. Rish AJ, Henson SR, AnikAlam M, Liu Y, Drennen JK, Anderson CA. Development of calibration-free/minimal calibration wavelength selection for iterative optimization technology algorithms toward process analytical technology application. Int J Pharm. 2022;614. https://doi.org/10.1016/j.ijpharm.2022.121463.

    Article  CAS  PubMed  Google Scholar 

  17. Román-Ospino AD, Baranwal Y, Li J, Vargas J, Igne B, Bate S, et al. Sampling optimization for blend monitoring of a low dose formulation in a tablet press feed frame using spatially resolved near-infrared spectroscopy. Int J Pharm. 2021;602. https://doi.org/10.1016/j.ijpharm.2021.120594.

  18. 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. https://doi.org/10.1016/j.ijpharm.2021.120581

  19. Mendez R, Muzzio FJ, Velazquez C. Powder hydrophobicity and flow properties: Effect of feed frame design and operating parameters. AIChE J. 2012;58(3):697–706. https://doi.org/10.1002/aic.12639

  20. Hernandez E, Pawar P, Keyvan G, Wang Y, Velez N, Callegari G, et al. Prediction of dissolution profiles by non-destructive near infrared spectroscopy in tablets subjected to different levels of strain. J Pharm Biomed Anal. 2016;117:568–76. https://doi.org/10.1016/j.ijpharm.2019.05.022.

  21. Sierra-Vega NO, Karry KM, Romañach RJ, Méndez R. Monitoring of high-load dose formulations based on co-processed and non co-processed excipients. Int J Pharm. 2021;5(606). https://doi.org/10.1016/j.ijpharm.2021.120910.

  22. USP-NF. General Chapter 〈1097〉 Bulk Powder Sampling Procedures. In: United States Pharmacopeia. 2023.

  23. Sierra-Vega NO, Martínez-Cartagena PA, Alvarado-Hernández BB, Romañach RJ, Méndez R. In-line monitoring of low drug concentration of flowing powders in a new sampler device. Int J Pharm. 2020;583. https://doi.org/10.1016/j.ijpharm.2020.119358.

    Article  CAS  PubMed  Google Scholar 

  24. Llusa M, Levin M, Snee RD, Muzzio FJ. Measuring the hydrophobicity of lubricated blends of pharmaceutical excipients. Powder Technol. 2010;198(1):101–7. https://doi.org/10.1016/j.powtec.2009.10.021.

    Article  CAS  Google Scholar 

  25. Mateo-Ortiz D, Colon Y, Romañach RJ, Méndez R. Analysis of powder phenomena inside a Fette 3090 feed frame using in-line NIR spectroscopy. J Pharm Biomed Anal. 2014;100:40–9. https://doi.org/10.1016/j.jpba.2014.07.014.

    Article  CAS  PubMed  Google Scholar 

  26. Dühlmeyer KP, Özcoban H, Leopold CS. A novel method for determination of the filling level in the feed frame of a rotary tablet press. Drug Dev Ind Pharm. 2018;44(11):1744–51. https://doi.org/10.1080/03639045.2018.1492609

  27. Li Y, Anderson CA, Drennen JK, Airiau C, Igne B. Method Development and Validation of an Inline Process Analytical Technology Method for Blend Monitoring in the Tablet Feed Frame Using Raman Spectroscopy. Anal Chem. 2018;90(14):8436–44. https://doi.org/10.1021/acs.analchem.8b01009.

    Article  CAS  PubMed  Google Scholar 

  28. Esbensen KH, Román-Ospino AD, Sanchez A, Romañach RJ. Adequacy and verifiability of pharmaceutical mixtures and dose units by variographic analysis (Theory of Sampling) - A call for a regulatory paradigm shift. Int J Pharm. 2016;499(1–2):156–74. https://doi.org/10.1016/j.ijpharm.2015.12.038.

    Article  CAS  PubMed  Google Scholar 

  29. Alvarado-Hernández BB, Sierra-Vega NO, Martínez-Cartagena P, Hormaza M, Méndez R, Romañach RJ. A sampling system for flowing powders based on the theory of sampling. Int J Pharm. 2020;574. https://doi.org/10.1016/j.ijpharm.2019.118874.

    Article  CAS  PubMed  Google Scholar 

  30. Esbensen KH, Paoletti C, Minkkinen P. Representative sampling of large kernel lots I. Theory of Sampling and variographic analysis.Trends Anal Chem. 2012;32:154–64. https://doi.org/10.1016/j.trac.2011.09.008.

    Article  CAS  Google Scholar 

  31. Esbensen KH, Julius LP. Representative sampling, data quality, validation - a necessary trinity in chemometrics. In: Brown SD, Tauler R and Walczak B (eds) Comprehensive Chemometrics. Oxford: Elsevier, 2009;1–20. https://doi.org/10.1016/B978-044452701-1.00088-0.

  32. Sánchez-Paternina A, Sierra-Vega NO, Cárdenas V, Méndez R, Esbensen KH, Romañach RJ. Variographic analysis: A new methodology for quality assurance of pharmaceutical blending processes. Comput Chem Eng. 2019;124:109–23. https://doi.org/10.1016/j.compchemeng.2019.02.010.

    Article  CAS  Google Scholar 

  33. EMA. ICH Q2 (R1): Validation of analytical procedures: text and methodology. Int Conf Harmon. 2005. Available from: https://www.ema.europa.eu/en/ich-q2r2-validation-analytical-procedures-scientific-guideline. Accessed 11 Jul 2023

  34. EMA. Guideline on the use of Near Infrared Spectroscopy (NIRS) by the pharmaceutical industry and the data requirements for new submissions and variations. Eur Med Agency. 2014. Available from: https://www.ema.europa.eu/en/use-near-infrared-spectroscopy-nirs-pharmaceutical-industry-data-requirements-new-submissions. Accessed 11 Jul 2023.

  35. FDA. Development and Submission of Near Infrared Analytical Procedures Guidance for Industry. Food Drug Adm. 2015. Available from: http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm. Accessed 08 Mar 2023.

  36. Vargas JM, Roman-Ospino AD, Sanchez E, Romañach RJ. Evaluation of Analytical and Sampling Errors in the Prediction of the Active Pharmaceutical Ingredient Concentration in Blends From a Continuous Manufacturing Process. J Pharm Innov. 2017;12(2):155–67. https://doi.org/10.1007/s12247-017-9273-1.

    Article  Google Scholar 

  37. Sierra-Vega NO, González-Rosario RA, Rangel-Gil RS, Romañach RJ, Méndez R. Quantitative analysis of blend uniformity within a Three-Chamber feed frame using simultaneously Raman and Near-Infrared spectroscopy. Int J Pharm. 2022;5(613). https://doi.org/10.1016/j.ijpharm.2021.121417.

  38. Vargas JM, Nielsen S, Cárdenas V, Gonzalez A, Aymat EY, Almodovar E, et al. Process analytical technology in continuous manufacturing of a commercial pharmaceutical product. Int J Pharm. 2018;538(1–2):167–78. https://doi.org/10.1016/j.ijpharm.2018.01.003.

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Acknowledgements

The authors greatly appreciate the assistance of Pedro Martínez-Cartagena and Raúl Rangel-Gil in the development of the experiments in the feed frame.

Funding

This research was funded by Puerto Rico Science Technology and Research Trust [grant number 2020–00116].

Author information

Authors and Affiliations

  1. Department of Chemistry, University of Puerto Rico at Mayagüez, Mayagüez, PR, USA

    Nathaly A. Movilla-Meza, Bárbara B. Alvarado-Hernández & Rodolfo J. Romañach

  2. Department of Chemical Engineering, University of Puerto Rico at Mayagüez, Mayagüez, PR, USA

    Nobel O. Sierra-Vega & Rafael Méndez

  3. Center for Structured Organic Particulate Systems (C-SOPS), Department of Chemistry, University of Puerto Rico at Mayagüez, PO Box 9000, Mayagüez, PR, 00681, USA

    Rodolfo J. Romañach

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  1. Nathaly A. Movilla-Meza
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Contributions

Nathaly A. Movilla-Meza: conceptualization, methodology, data curation, software, investigation, writing original draft. Nobel O. Sierra-Vega: formal analysis and writing (review and editing). Bárbara B. Alvarado-Hernández: formal analysis and writing (review and editing). Rafael Méndez: conceptualization, methodology, review of drafts). Rodolfo J. Romañach: funding acquisition, project administration, resources, conceptualization, and review of draft manuscripts).

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Correspondence to Rodolfo J. Romañach.

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Movilla-Meza, N.A., Sierra-Vega, N.O., Alvarado-Hernández, B.B. et al. The Use of a Closed Feed Frame for the Development of Near-Infrared Spectroscopic Calibration Model to Determine Drug Concentration. Pharm Res 40, 2903–2916 (2023). https://doi.org/10.1007/s11095-023-03601-1

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