Dissolution prediction from images: method and validation of dissoLab platform
Abstract 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 pred...
| 出版年: | AAPS Open |
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| 主要な著者: | , , , , , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
SpringerOpen
2025-09-01
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| オンライン･アクセス: | https://doi.org/10.1186/s41120-025-00122-6 |
| 要約: | Abstract 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. |
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| ISSN: | 2364-9534 |