A mathematical justification for metronomic chemotherapy in oncology

Ver/Abrir
Identificadores
URI: https://hdl.handle.net/10902/32759
ISSN: 1760-6101
ISSN: 0973-5348
Compartir
Estadísticas
Ver Estadísticas
Google Scholar
Registro completo
Mostrar el registro completo DC
Autoría
Fernández Fernández, Luis AlbertoAutoridad Unican; Pola Méndez, CeciliaAutoridad Unican; Sáinz-Pardo Díaz, JudithAutoridad Unican
Fecha
2022
Derechos
© The authors. Published by EDP Sciences, 2022. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publicado en
Mathematical Modelling of Natural Phenomena, 2022, 17, 2022010
Editorial
EDP Sciences
Enlace a la publicación
Palabras clave
Metronomic chemotherapy
Mixed-integer nonlinear optimization
Norton-Simon hypothesis
Emax model
Resumen/Abstract
We mathematically justify metronomic chemotherapy as the best strategy to apply many cytotoxic drugs in oncology for both curative and palliative approaches, assuming the classical pharmacokinetic model together with the Emax pharmacodynamic and the Norton-Simon hypothesis. From the mathematical point of view, we will consider two mixed-integer nonlinear optimization problems, where the unknowns are the number of the doses and the quantity of each one, adjusting the administration times a posteriori.
Colecciones a las que pertenece
© The authors. Published by EDP Sciences, 2022. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Excepto si se señala otra cosa, la licencia del ítem se describe como © The authors. Published by EDP Sciences, 2022. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.