Scholarlink

<p>Quantitative, reproducible morphological characterization represents a critical quality control bottleneck in pharmaceutical nanoformulation development, particularly for poorly water-soluble drug candidates. This study aims to establish fractal dimension as a quantitative morphological fingerprint for batch consistency assessment and quality control of Loperamide-loaded β-cyclodextrin nanosponges (β-CD NS), addressing the need for objective, operator-independent metrics that replace subjective visual SEM assessment. Through integrated computational image preprocessing and box-counting fractal analysis of SEM images, surface complexity was quantified with a mean fractal dimension of 2.108 (range: 2.0857–2.1140), characteristic of moderately complex macro-scale roughness (2 ≤ D ≤ 3). Exceptionally low intra-image variance (6.683 × 10⁻⁵) across 25 analyzed regions confirmed morphological homogeneity, while statistical validation revealed perfect scale invariance (Pearson <i>r</i> = − 0.9998, <i>p</i> &lt; 0.0001; R² = 1.000; F-statistic = 2.654 × 10⁴), establishing measurement robustness across multiple spatial scales. This validated approach transforms fractal dimension from a descriptive parameter into a quantitative quality attribute suitable for real-time pharmaceutical manufacturing process monitoring, enabling early detection of morphological deviations during formulation scale-up and providing a reproducible, cost-effective alternative to conventional qualitative characterization methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

SEM Image–Derived Processing and Fractal Analysis for Morphological Quality Control of Loperamide-Loaded β-Cyclodextrin Nanosponge Drug Carriers

  • 期刊论文

  • 发表日期：

    2025年12月18日

查看全文
  • Aashish Kumar,
  • Manan Bhasin,
  • Rajni Bala,
  • Malika Gupta,
  • Mansi Chitkara

摘要

Quantitative, reproducible morphological characterization represents a critical quality control bottleneck in pharmaceutical nanoformulation development, particularly for poorly water-soluble drug candidates. This study aims to establish fractal dimension as a quantitative morphological fingerprint for batch consistency assessment and quality control of Loperamide-loaded β-cyclodextrin nanosponges (β-CD NS), addressing the need for objective, operator-independent metrics that replace subjective visual SEM assessment. Through integrated computational image preprocessing and box-counting fractal analysis of SEM images, surface complexity was quantified with a mean fractal dimension of 2.108 (range: 2.0857–2.1140), characteristic of moderately complex macro-scale roughness (2 ≤ D ≤ 3). Exceptionally low intra-image variance (6.683 × 10⁻⁵) across 25 analyzed regions confirmed morphological homogeneity, while statistical validation revealed perfect scale invariance (Pearson r = − 0.9998, p < 0.0001; R² = 1.000; F-statistic = 2.654 × 10⁴), establishing measurement robustness across multiple spatial scales. This validated approach transforms fractal dimension from a descriptive parameter into a quantitative quality attribute suitable for real-time pharmaceutical manufacturing process monitoring, enabling early detection of morphological deviations during formulation scale-up and providing a reproducible, cost-effective alternative to conventional qualitative characterization methods.