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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.17481 |
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| _version_ | 1866910917998936064 |
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| author | Perales-Patón, Miguel Italia, Matteo Castelló-Pons, María Gómez-Soria, Irene Belmonte-Beitia, Juan Sánchez-Gómez, Pilar Pérez-García, Víctor M. |
| author_facet | Perales-Patón, Miguel Italia, Matteo Castelló-Pons, María Gómez-Soria, Irene Belmonte-Beitia, Juan Sánchez-Gómez, Pilar Pérez-García, Víctor M. |
| contents | Malignant gliomas (MGs), particularly glioblastoma, are among the most aggressive brain tumors, with limited treatment options and a poor prognosis. Maximal safe resection and the so-called Stupp protocol are the standard first-line therapies. Despite combining radiotherapy and chemotherapy in an intensive manner, it provides limited survival benefits over radiation therapy alone, underscoring the need for innovative therapeutic strategies. Emerging evidence suggests that alternative dosing schedules, such as less aggressive regimens with extended intervals between consecutive treatment applications, may improve outcomes, enhancing survival, delaying the emergence of resistance, and minimizing side effects. In this study, we develop, calibrate, and validate in animal models a novel ordinary differential equation-based mathematical model, using in vivo data to describe MG dynamics under combined chemoradiotherapy. The proposed model incorporates key biological processes, including cancer cell dormancy, phenotypic switching, drug resistance through persister cells, and treatment-induced effects. Through in silico trials, we identified optimized combination treatment protocols that may outperform the standard Stupp protocol. Finally, we computationally extrapolated the results obtained from the in vivo animal model to humans, showing up to a four-fold increase in median survival with protracted administration protocols in silico. Although further experimental and clinical validation is required, our framework provides a computational foundation to optimize and personalize treatment strategies for MG and potentially other cancers with similar biological mechanisms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17481 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing chemoradiotherapy for malignant gliomas: a validated mathematical approach Perales-Patón, Miguel Italia, Matteo Castelló-Pons, María Gómez-Soria, Irene Belmonte-Beitia, Juan Sánchez-Gómez, Pilar Pérez-García, Víctor M. Populations and Evolution Malignant gliomas (MGs), particularly glioblastoma, are among the most aggressive brain tumors, with limited treatment options and a poor prognosis. Maximal safe resection and the so-called Stupp protocol are the standard first-line therapies. Despite combining radiotherapy and chemotherapy in an intensive manner, it provides limited survival benefits over radiation therapy alone, underscoring the need for innovative therapeutic strategies. Emerging evidence suggests that alternative dosing schedules, such as less aggressive regimens with extended intervals between consecutive treatment applications, may improve outcomes, enhancing survival, delaying the emergence of resistance, and minimizing side effects. In this study, we develop, calibrate, and validate in animal models a novel ordinary differential equation-based mathematical model, using in vivo data to describe MG dynamics under combined chemoradiotherapy. The proposed model incorporates key biological processes, including cancer cell dormancy, phenotypic switching, drug resistance through persister cells, and treatment-induced effects. Through in silico trials, we identified optimized combination treatment protocols that may outperform the standard Stupp protocol. Finally, we computationally extrapolated the results obtained from the in vivo animal model to humans, showing up to a four-fold increase in median survival with protracted administration protocols in silico. Although further experimental and clinical validation is required, our framework provides a computational foundation to optimize and personalize treatment strategies for MG and potentially other cancers with similar biological mechanisms. |
| title | Optimizing chemoradiotherapy for malignant gliomas: a validated mathematical approach |
| topic | Populations and Evolution |
| url | https://arxiv.org/abs/2504.17481 |