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Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach
The development of an in silico approach that evaluates and identifies appropriate treatment protocols for individuals could help grow personalized treatment and increase cancer patient lifespans. With this motivation, the present study introduces a novel approach for sequential treatment cycles bas...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563754/ https://www.ncbi.nlm.nih.gov/pubmed/34728726 http://dx.doi.org/10.1038/s41598-021-00989-x |
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author | Moradi Kashkooli, Farshad Soltani, M. |
author_facet | Moradi Kashkooli, Farshad Soltani, M. |
author_sort | Moradi Kashkooli, Farshad |
collection | PubMed |
description | The development of an in silico approach that evaluates and identifies appropriate treatment protocols for individuals could help grow personalized treatment and increase cancer patient lifespans. With this motivation, the present study introduces a novel approach for sequential treatment cycles based on simultaneously examining drug delivery, tumor growth, and chemotherapy efficacy. This model incorporates the physical conditions of tumor geometry, including tumor, capillary network, and normal tissue assuming real circumstances, as well as the intravascular and interstitial fluid flow, drug concentration, chemotherapy efficacy, and tumor recurrence. Three treatment approaches—maximum tolerated dose (MTD), metronomic chemotherapy (MC), and chemo-switching (CS)—as well as different chemotherapy schedules are investigated on a real tumor geometry extracted from image. Additionally, a sensitivity analysis of effective parameters of drug is carried out to evaluate the potential of using different other drugs in cancer treatment. The main findings are: (i) CS, MC, and MTD have the best performance in reducing tumor cells, respectively; (ii) multiple doses raise the efficacy of drugs that have slower clearance, higher diffusivity, and lower to medium binding affinities; (iii) the suggested approach to eradicating tumors is to reduce their cells to a predetermined rate through chemotherapy and then apply adjunct therapy. |
format | Online Article Text |
id | pubmed-8563754 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-85637542021-11-03 Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach Moradi Kashkooli, Farshad Soltani, M. Sci Rep Article The development of an in silico approach that evaluates and identifies appropriate treatment protocols for individuals could help grow personalized treatment and increase cancer patient lifespans. With this motivation, the present study introduces a novel approach for sequential treatment cycles based on simultaneously examining drug delivery, tumor growth, and chemotherapy efficacy. This model incorporates the physical conditions of tumor geometry, including tumor, capillary network, and normal tissue assuming real circumstances, as well as the intravascular and interstitial fluid flow, drug concentration, chemotherapy efficacy, and tumor recurrence. Three treatment approaches—maximum tolerated dose (MTD), metronomic chemotherapy (MC), and chemo-switching (CS)—as well as different chemotherapy schedules are investigated on a real tumor geometry extracted from image. Additionally, a sensitivity analysis of effective parameters of drug is carried out to evaluate the potential of using different other drugs in cancer treatment. The main findings are: (i) CS, MC, and MTD have the best performance in reducing tumor cells, respectively; (ii) multiple doses raise the efficacy of drugs that have slower clearance, higher diffusivity, and lower to medium binding affinities; (iii) the suggested approach to eradicating tumors is to reduce their cells to a predetermined rate through chemotherapy and then apply adjunct therapy. Nature Publishing Group UK 2021-11-02 /pmc/articles/PMC8563754/ /pubmed/34728726 http://dx.doi.org/10.1038/s41598-021-00989-x Text en © The Author(s) 2021 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 Moradi Kashkooli, Farshad Soltani, M. Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach |
title | Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach |
title_full | Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach |
title_fullStr | Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach |
title_full_unstemmed | Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach |
title_short | Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach |
title_sort | evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563754/ https://www.ncbi.nlm.nih.gov/pubmed/34728726 http://dx.doi.org/10.1038/s41598-021-00989-x |
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