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Tablet Quality-Prediction Model Using Quality Material Attributes: Toward Flexible Switching Between Batch and Continuous Granulation

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Abstract

Purpose

The purpose of the study was to develop a model to predict the critical quality attribute (CQA) of tablets during continuous and batch manufacturing using only critical material attributes (CMAs).

Methods

Experiments were performed using ethenzamide as the active pharmaceutical ingredient processed with batch and continuous high-shear granulators. The disintegration time of tablets was defined as the CQA, and the particle-size distribution of granules and tablet hardness were defined as the CMAs. We first investigated the influence of granulation conditions on particle-size distribution during batch and continuous granulation. We then proceeded to construct the CQA estimation model by producing tables using batch and continuous granulation.

Results

The results indicated the similarity of the granulation mechanisms, as observed by the bimodality of the distributions and the significant causal factors. Principal component analysis revealed that the CQA was influenced strongly by the particle-size distribution and that the CMA–CQA correlations were similar for both processes. Finally, a model based on partial least-squares regression could be developed that could reasonably estimate the CQA using CMAs without involving any process parameters.

Conclusion

This approach of using process-independent CQA prediction could enable flexible switching between batch and continuous manufacturing during a product life cycle, thus offering new possibilities for efficient life cycle management.

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Acknowledgments

The authors acknowledge Mr. Kazuhiro Kotaka at Powrex Corporation for conducting the experiments in batch and continuous high-shear granulation. The authors also thank the members of the Focus Group of the Academy of Pharmaceutical Science and Technology for providing great support to this research and also Mr. Kensaku Matsunami at the University of Tokyo for assistance with the literature survey.

Funding

The research was partly funded by Powrex Corporation.

Author information

Authors and Affiliations

  1. Formulation Technology Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd., 1-2-58, Hiromachi, Shinagawa, Tokyo, 140-8710, Japan

    Hiroaki Arai

  2. Research & Development Department, Technical Division, Powrex Corporation, 5-5-5, Kitagawara, Itami, Hyogo, 664-0837, Japan

    Takuya Nagato

  3. Division of Drugs, National Institute of Health Sciences, Tonomachi 3-25-26, Kawasaki, Kanagawa, 210-9501, Japan

    Tatsuo Koide

  4. Department of Pharmaceutics, Hoshi University, 2-4-41 Ebara, Shinagawa, Tokyo, 142-8501, Japan

    Etsuo Yonemochi

  5. Department of Pharmaceutical Engineering, Aichi Gakuin University, Kusumoto-cho 1-100, Chikusa, Nagoya, Aichi, 464-8650, Japan

    Hiromitsu Yamamoto

  6. Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

    Hirokazu Sugiyama

Authors
  1. Hiroaki Arai
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  2. Takuya Nagato
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  3. Tatsuo Koide
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  4. Etsuo Yonemochi
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  5. Hiromitsu Yamamoto
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  6. Hirokazu Sugiyama
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Corresponding author

Correspondence to Hiroaki Arai.

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This work presents a model that can predict a critical quality attribute (CQA) of tablets prepared by both batch and continuous manufacturing based only on their critical material attributes (CMAs), towards flexible technology switching.

Appendix

Appendix

Table 11 Physical property data for principal component analysis and partial least-squares regression modeling for the disintegration time of ethenzamide tablets
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Table 12 Regression coefficient and residual of partial least-squares regression model for disintegration time of ethenzamide tablets
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Table 13 Factor loading of partial least-squares regression model for disintegration time of ethenzamide tablets
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Arai, H., Nagato, T., Koide, T. et al. Tablet Quality-Prediction Model Using Quality Material Attributes: Toward Flexible Switching Between Batch and Continuous Granulation. J Pharm Innov 16, 588–602 (2021). https://doi.org/10.1007/s12247-020-09466-w

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  • DOI: https://doi.org/10.1007/s12247-020-09466-w

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