On a data-driven mathematical model for prostate cancer bone metastasis
Prostate cancer bone metastasis poses significant health challenges, affecting countless individuals. While treatment with the radioactive isotope radium-223 ($ ^{223} $Ra) has shown promising results, there remains room for therapy optimization. In vivo studies are crucial for optimizing radium the...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-12-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.20241656 |
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| author | Zholaman Bektemessov Laurence Cherfils Cyrille Allery Julien Berger Elisa Serafini Eleonora Dondossola Stefano Casarin |
| author_facet | Zholaman Bektemessov Laurence Cherfils Cyrille Allery Julien Berger Elisa Serafini Eleonora Dondossola Stefano Casarin |
| author_sort | Zholaman Bektemessov |
| collection | DOAJ |
| description | Prostate cancer bone metastasis poses significant health challenges, affecting countless individuals. While treatment with the radioactive isotope radium-223 ($ ^{223} $Ra) has shown promising results, there remains room for therapy optimization. In vivo studies are crucial for optimizing radium therapy; however, they face several roadblocks that limit their effectiveness. By integrating in vivo studies with in silico models, these obstacles can be potentially overcome. Existing computational models of tumor response to $ ^{223} $Ra are often computationally intensive. Accordingly, we here present a versatile and computationally efficient alternative solution. We developed a PDE mathematical model to simulate the effects of $ ^{223} $Ra on prostate cancer bone metastasis, analyzing mitosis and apoptosis rates based on experimental data from both control and treated groups. To build a robust and validated model, our research explored three therapeutic scenarios: no treatment, constant $ ^{223} $Ra exposure, and decay-accounting therapy, with tumor growth simulations for each case. Our findings align well with experimental evidence, demonstrating that our model effectively captures the therapeutic potential of $ ^{223} $Ra, yielding promising results that support our model as a powerful infrastructure to optimize bone metastasis treatment. |
| format | Article |
| id | doaj-art-ea8438190e214254b2b8717f4f0417bf |
| institution | Kabale University |
| issn | 2473-6988 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj-art-ea8438190e214254b2b8717f4f0417bf2025-01-23T07:53:25ZengAIMS PressAIMS Mathematics2473-69882024-12-01912347853480510.3934/math.20241656On a data-driven mathematical model for prostate cancer bone metastasisZholaman Bektemessov0Laurence Cherfils1Cyrille Allery2Julien Berger3Elisa Serafini4Eleonora Dondossola5Stefano Casarin6Department of Mathematical and Computer Modeling, Al-Farabi Kazakh National University, Al-Farabi Ave. 71, Almaty, 050060, KazakhstanLaboratoire des Sciences de l'Ingénieur pour l'Environnement, UMR CNRS 7356, La Rochelle Université, La Rochelle Cedex 1, F-17042, FranceLaboratoire des Sciences de l'Ingénieur pour l'Environnement, UMR CNRS 7356, La Rochelle Université, La Rochelle Cedex 1, F-17042, FranceLaboratoire des Sciences de l'Ingénieur pour l'Environnement, UMR CNRS 7356, La Rochelle Université, La Rochelle Cedex 1, F-17042, FranceLaboratoire des Sciences de l'Ingénieur pour l'Environnement, UMR CNRS 7356, La Rochelle Université, La Rochelle Cedex 1, F-17042, FranceDavid H. Koch Center for Applied Research of Genitourinary Cancers, University of Texas MD Anderson Cancer Center, Houston, TX, United StatesLaboratoire des Sciences de l'Ingénieur pour l'Environnement, UMR CNRS 7356, La Rochelle Université, La Rochelle Cedex 1, F-17042, FranceProstate cancer bone metastasis poses significant health challenges, affecting countless individuals. While treatment with the radioactive isotope radium-223 ($ ^{223} $Ra) has shown promising results, there remains room for therapy optimization. In vivo studies are crucial for optimizing radium therapy; however, they face several roadblocks that limit their effectiveness. By integrating in vivo studies with in silico models, these obstacles can be potentially overcome. Existing computational models of tumor response to $ ^{223} $Ra are often computationally intensive. Accordingly, we here present a versatile and computationally efficient alternative solution. We developed a PDE mathematical model to simulate the effects of $ ^{223} $Ra on prostate cancer bone metastasis, analyzing mitosis and apoptosis rates based on experimental data from both control and treated groups. To build a robust and validated model, our research explored three therapeutic scenarios: no treatment, constant $ ^{223} $Ra exposure, and decay-accounting therapy, with tumor growth simulations for each case. Our findings align well with experimental evidence, demonstrating that our model effectively captures the therapeutic potential of $ ^{223} $Ra, yielding promising results that support our model as a powerful infrastructure to optimize bone metastasis treatment.https://www.aimspress.com/article/doi/10.3934/math.20241656prostate cancerbone metastasistumor growthpde modelsimulationin vivo-in silico modelingparameter estimationinverse problems |
| spellingShingle | Zholaman Bektemessov Laurence Cherfils Cyrille Allery Julien Berger Elisa Serafini Eleonora Dondossola Stefano Casarin On a data-driven mathematical model for prostate cancer bone metastasis AIMS Mathematics prostate cancer bone metastasis tumor growth pde model simulation in vivo-in silico modeling parameter estimation inverse problems |
| title | On a data-driven mathematical model for prostate cancer bone metastasis |
| title_full | On a data-driven mathematical model for prostate cancer bone metastasis |
| title_fullStr | On a data-driven mathematical model for prostate cancer bone metastasis |
| title_full_unstemmed | On a data-driven mathematical model for prostate cancer bone metastasis |
| title_short | On a data-driven mathematical model for prostate cancer bone metastasis |
| title_sort | on a data driven mathematical model for prostate cancer bone metastasis |
| topic | prostate cancer bone metastasis tumor growth pde model simulation in vivo-in silico modeling parameter estimation inverse problems |
| url | https://www.aimspress.com/article/doi/10.3934/math.20241656 |
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