Evolution of cancer cell populations under cytotoxic therapy and treatment optimisation : insight from a phenotype-structured model
| Autor: | Patrizia Bagnerini, Tommaso Lorenzi, Luis Almeida, Barry D. Hughes, Giulia Fabrini |
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| Přispěvatelé: | University of St Andrews. Applied Mathematics, Sorbonne Universités, Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Modelling and Analysis for Medical and Biological Applications (MAMBA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Ingegneria della produzione, termoenergetica e modelli matematici (DIPTEM), Universita degli studi di Genova, University of Melbourne, University of St Andrews [Scotland], Barry D. Hughes acknowledges support from the Australian Research Council (DP140100339). Luıs Almeida and Tommaso Lorenzi gratefully acknowledge supportof the project PICS-CNRS no. 07688 and the French 'ANR blanche' project Kibord: ANR-13-BS01-0004, ANR-13-BS01-0004,KIBORD,Modèles cinétiques en biologie et domaines connexes(2013), Sorbonne Université (SU), Università degli studi di Genova = University of Genoa (UniGe) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Oncology
medicine.medical_specialty [SDV]Life Sciences [q-bio] Cancer modelling [SDV.CAN]Life Sciences [q-bio]/Cancer Drug resistance Somatic evolution in cancer 3rd-NDAS RC0254 03 medical and health sciences 0302 clinical medicine SDG 3 - Good Health and Well-being Internal medicine medicine [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP] [MATH]Mathematics [math] Structured model Cytotoxic Therapy Exact solutions 030304 developmental biology Mathematics 0303 health sciences Numerical Analysis RC0254 Neoplasms. Tumors. Oncology (including Cancer) Applied Mathematics Nonlocal parabolic equations Metronomic Chemotherapy Phenotype 3. Good health Computational Mathematics Research council Therapy optimisation Numerical optimal control 030220 oncology & carcinogenesis Modeling and Simulation Cancer cell [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] Analysis |
| Zdroj: | ESAIM: Mathematical Modelling and Numerical Analysis ESAIM: Mathematical Modelling and Numerical Analysis, EDP Sciences, 2019, 53 (4), pp.1157-1190. ⟨10.1051/m2an/2019010⟩ ESAIM: Mathematical Modelling and Numerical Analysis, 2019, 53 (4), pp.1157-1190. ⟨10.1051/m2an/2019010⟩ |
| ISSN: | 0764-583X 1290-3841 |
| Popis: | BDH acknowledges support from the Australian Research Council (DP140100339). LA and TL gratefully acknowledge support of the project PICS-CNRS no. 07688 and the French "ANR blanche" project Kibord: ANR-13-BS01-0004. We consider a phenotype-structured model of evolutionary dynamics in a population of cancer cells exposed to the action of a cytotoxic drug. The model consists of a nonlocal parabolic equation governing the evolution of the cell population density function. We develop a novel method for constructing exact solutions to the model equation, which allows for a systematic investigation of the way in which the size and the phenotypic composition of the cell population change in response to variations of the drug dose and other evolutionary parameters. Moreover, we address numerical optimal control for a calibrated version of the model based on biological data from the existing literature, in order to identify the drug delivery schedule that makes it possible to minimise either the population size at the end of the treatment or the average population size during the course of treatment. The results obtained challenge the notion that traditional high-dose therapy represents a 'one-fits-all solution' in anticancer therapy by showing that the continuous administration of a relatively low dose of the cytotoxic drug performs more closely to the optimal dosing regimen to minimise the average size of the cancer cell population during the course of treatment. Postprint |
| Databáze: | OpenAIRE |
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