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<p>In the development of pharmaceutical formulations, controlling the particle characteristics of solid dosage forms, particularly the particle size of Active Pharmaceutical Ingredients, is critical for ensuring bioavailability, drug efficacy, and regulatory compliance, as emphasised in the International Council for Harmonisation Q6A guidelines. Although laser diffraction provides rapid and reproducible particle size measurements, it cannot capture particle shape data. Manual image analysis can assess size and shape, but is limited by low particle counts. Recent technological advances have enabled Automated Particle Image Analysis, which can measure tens of thousands of particles within an hour, enabling robust statistical and morphological evaluations. Furthermore, Morphology-Directed Raman Spectroscopy (MDRS) integrates morphological imaging with Raman spectroscopy to enable the chemical classification of individual particles. However, MDRS is time-consuming, restricting its applicability to process control. In this study, we propose a strategic framework for particle classification using particle morphological parameters – Classification by particle morphological information (CPMI), validated against MDRS results. The proposed CPMI method achieved a classification precision of 96.9% and reconstructed comparable particle size distributions while reducing measurement time by approximately 90%. This approach is expected to establish a rapid and statistically reliable method for particle classification based on robust morphological parameters, addressing key challenges in solid formulation development and regulatory compliance.</p>

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Strategic Development of an Image Analysis-Driven Morphology-Based Particle Classification Method in Pharmaceutical Powder Mixtures Validated by Raman Spectroscopy

  • 期刊论文

  • 发表日期：

    2026年6月18日

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  • Daisuke Sasakura,
  • Sho Kimura,
  • Shinya Nakamura

摘要

In the development of pharmaceutical formulations, controlling the particle characteristics of solid dosage forms, particularly the particle size of Active Pharmaceutical Ingredients, is critical for ensuring bioavailability, drug efficacy, and regulatory compliance, as emphasised in the International Council for Harmonisation Q6A guidelines. Although laser diffraction provides rapid and reproducible particle size measurements, it cannot capture particle shape data. Manual image analysis can assess size and shape, but is limited by low particle counts. Recent technological advances have enabled Automated Particle Image Analysis, which can measure tens of thousands of particles within an hour, enabling robust statistical and morphological evaluations. Furthermore, Morphology-Directed Raman Spectroscopy (MDRS) integrates morphological imaging with Raman spectroscopy to enable the chemical classification of individual particles. However, MDRS is time-consuming, restricting its applicability to process control. In this study, we propose a strategic framework for particle classification using particle morphological parameters – Classification by particle morphological information (CPMI), validated against MDRS results. The proposed CPMI method achieved a classification precision of 96.9% and reconstructed comparable particle size distributions while reducing measurement time by approximately 90%. This approach is expected to establish a rapid and statistically reliable method for particle classification based on robust morphological parameters, addressing key challenges in solid formulation development and regulatory compliance.