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Mathematical Details on a Cancer Resistance Model

One of the most important factors limiting the success of chemotherapy in cancer treatment is the phenomenon of drug resistance. We have recently introduced a framework for quantifying the effects of induced and non-induced resistance to cancer chemotherapy (Greene et al., 2018a, 2019). In this work...

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Autores principales: Greene, James M., Sanchez-Tapia, Cynthia, Sontag, Eduardo D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325889/
https://www.ncbi.nlm.nih.gov/pubmed/32656186
http://dx.doi.org/10.3389/fbioe.2020.00501
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author Greene, James M.
Sanchez-Tapia, Cynthia
Sontag, Eduardo D.
author_facet Greene, James M.
Sanchez-Tapia, Cynthia
Sontag, Eduardo D.
author_sort Greene, James M.
collection PubMed
description One of the most important factors limiting the success of chemotherapy in cancer treatment is the phenomenon of drug resistance. We have recently introduced a framework for quantifying the effects of induced and non-induced resistance to cancer chemotherapy (Greene et al., 2018a, 2019). In this work, we expound on the details relating to an optimal control problem outlined in Greene et al. (2018a). The control structure is precisely characterized as a concatenation of bang-bang and path-constrained arcs via the Pontryagin Maximum Principle and differential Lie algebraic techniques. A structural identifiability analysis is also presented, demonstrating that patient-specific parameters may be measured and thus utilized in the design of optimal therapies prior to the commencement of therapy. For completeness, a detailed analysis of existence results is also included.
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spelling pubmed-73258892020-07-09 Mathematical Details on a Cancer Resistance Model Greene, James M. Sanchez-Tapia, Cynthia Sontag, Eduardo D. Front Bioeng Biotechnol Bioengineering and Biotechnology One of the most important factors limiting the success of chemotherapy in cancer treatment is the phenomenon of drug resistance. We have recently introduced a framework for quantifying the effects of induced and non-induced resistance to cancer chemotherapy (Greene et al., 2018a, 2019). In this work, we expound on the details relating to an optimal control problem outlined in Greene et al. (2018a). The control structure is precisely characterized as a concatenation of bang-bang and path-constrained arcs via the Pontryagin Maximum Principle and differential Lie algebraic techniques. A structural identifiability analysis is also presented, demonstrating that patient-specific parameters may be measured and thus utilized in the design of optimal therapies prior to the commencement of therapy. For completeness, a detailed analysis of existence results is also included. Frontiers Media S.A. 2020-06-17 /pmc/articles/PMC7325889/ /pubmed/32656186 http://dx.doi.org/10.3389/fbioe.2020.00501 Text en Copyright © 2020 Greene, Sanchez-Tapia and Sontag. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Greene, James M.
Sanchez-Tapia, Cynthia
Sontag, Eduardo D.
Mathematical Details on a Cancer Resistance Model
title Mathematical Details on a Cancer Resistance Model
title_full Mathematical Details on a Cancer Resistance Model
title_fullStr Mathematical Details on a Cancer Resistance Model
title_full_unstemmed Mathematical Details on a Cancer Resistance Model
title_short Mathematical Details on a Cancer Resistance Model
title_sort mathematical details on a cancer resistance model
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325889/
https://www.ncbi.nlm.nih.gov/pubmed/32656186
http://dx.doi.org/10.3389/fbioe.2020.00501
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