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Predicting intratumoral fluid pressure and liposome accumulation using physics informed deep learning
Liposome-based anticancer agents take advantage of the increased vascular permeability and transvascular pressure gradients for selective accumulation in tumors, a phenomenon known as the enhanced permeability and retention(EPR) effect. The EPR effect has motivated the clinical use of nano-therapeut...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667280/ https://www.ncbi.nlm.nih.gov/pubmed/37996509 http://dx.doi.org/10.1038/s41598-023-47988-8 |
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author | Meaney, Cameron Stapleton, Shawn Kohandel, Mohammad |
author_facet | Meaney, Cameron Stapleton, Shawn Kohandel, Mohammad |
author_sort | Meaney, Cameron |
collection | PubMed |
description | Liposome-based anticancer agents take advantage of the increased vascular permeability and transvascular pressure gradients for selective accumulation in tumors, a phenomenon known as the enhanced permeability and retention(EPR) effect. The EPR effect has motivated the clinical use of nano-therapeutics, with mixed results on treatment outcome. High interstitial fluid pressure (IFP) has been shown to limit liposome drug delivery to central tumour regions. Furthermore, high IFP is an independent prognostic biomarker for treatment efficacy in radiation therapy and chemotherapy for some solid cancers. Therefore, accurately measuring spatial liposome accumulation and IFP distribution within a solid tumour is crucial for optimal treatment planning. In this paper, we develop a model capable of predicting voxel-by-voxel intratumoral liposome accumulation and IFP using pre and post administration imaging. Our approach is based on physics informed machine learning, a novel technique combining machine learning and partial differential equations. through application to a set of mouse data and a set of synthetically-generated tumours, we show that our approach accurately predicts the spatial liposome accumulation and IFP for an individual tumour while relying on minimal information. This is an important result with applications for forecasting tumour progression and designing treatment. |
format | Online Article Text |
id | pubmed-10667280 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106672802023-11-23 Predicting intratumoral fluid pressure and liposome accumulation using physics informed deep learning Meaney, Cameron Stapleton, Shawn Kohandel, Mohammad Sci Rep Article Liposome-based anticancer agents take advantage of the increased vascular permeability and transvascular pressure gradients for selective accumulation in tumors, a phenomenon known as the enhanced permeability and retention(EPR) effect. The EPR effect has motivated the clinical use of nano-therapeutics, with mixed results on treatment outcome. High interstitial fluid pressure (IFP) has been shown to limit liposome drug delivery to central tumour regions. Furthermore, high IFP is an independent prognostic biomarker for treatment efficacy in radiation therapy and chemotherapy for some solid cancers. Therefore, accurately measuring spatial liposome accumulation and IFP distribution within a solid tumour is crucial for optimal treatment planning. In this paper, we develop a model capable of predicting voxel-by-voxel intratumoral liposome accumulation and IFP using pre and post administration imaging. Our approach is based on physics informed machine learning, a novel technique combining machine learning and partial differential equations. through application to a set of mouse data and a set of synthetically-generated tumours, we show that our approach accurately predicts the spatial liposome accumulation and IFP for an individual tumour while relying on minimal information. This is an important result with applications for forecasting tumour progression and designing treatment. Nature Publishing Group UK 2023-11-23 /pmc/articles/PMC10667280/ /pubmed/37996509 http://dx.doi.org/10.1038/s41598-023-47988-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Meaney, Cameron Stapleton, Shawn Kohandel, Mohammad Predicting intratumoral fluid pressure and liposome accumulation using physics informed deep learning |
title | Predicting intratumoral fluid pressure and liposome accumulation using physics informed deep learning |
title_full | Predicting intratumoral fluid pressure and liposome accumulation using physics informed deep learning |
title_fullStr | Predicting intratumoral fluid pressure and liposome accumulation using physics informed deep learning |
title_full_unstemmed | Predicting intratumoral fluid pressure and liposome accumulation using physics informed deep learning |
title_short | Predicting intratumoral fluid pressure and liposome accumulation using physics informed deep learning |
title_sort | predicting intratumoral fluid pressure and liposome accumulation using physics informed deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667280/ https://www.ncbi.nlm.nih.gov/pubmed/37996509 http://dx.doi.org/10.1038/s41598-023-47988-8 |
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