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<p>This paper demonstrates&#xa0;and validates dissoLab, a dissolution&#xa0;modeling software using microscopic imaging data. First principle dissolution models are solved for image voxels representing sample-specific particle sizes and morphologies. 2D images, such as SEM or PLM, can be utilized to predict dissolution profiles after a generative artificial intelligence method synthesizes a structurally similar 3D volume. Predictions can be performed from 3D datasets, such as X-ray micro-CT, without generative synthesis. Dissolution profiles predicted via this approach are validated against in vitro dissolution measurements and verified with theoretical models&#xa0;for particle samples.</p>

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Dissolution prediction from images: method and validation of dissoLab platform

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月8日

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  • Jonah Gautreau,
  • Mike Shen,
  • Tim Hornick,
  • Cheney Zhang,
  • Sam Lin,
  • Shawn Zhang

摘要

This paper demonstrates and validates dissoLab, a dissolution modeling software using microscopic imaging data. First principle dissolution models are solved for image voxels representing sample-specific particle sizes and morphologies. 2D images, such as SEM or PLM, can be utilized to predict dissolution profiles after a generative artificial intelligence method synthesizes a structurally similar 3D volume. Predictions can be performed from 3D datasets, such as X-ray micro-CT, without generative synthesis. Dissolution profiles predicted via this approach are validated against in vitro dissolution measurements and verified with theoretical models for particle samples.