Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy

Abstract The in-silico development of a chemotherapeutic dosing schedule for treating cancer relies upon a parameterization of a particular tumour growth model to describe the dynamics of the cancer in response to the dose of the drug. In practice, it is often prohibitively difficult to ensure the v...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Brydon Eastman, Michelle Przedborski, Mohammad Kohandel
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/346e4b6869474fd3982e56778d98ce5a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:346e4b6869474fd3982e56778d98ce5a
record_format	dspace
spelling	oai:doaj.org-article:346e4b6869474fd3982e56778d98ce5a2021-12-02T14:58:46ZReinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy10.1038/s41598-021-97028-62045-2322https://doaj.org/article/346e4b6869474fd3982e56778d98ce5a2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97028-6https://doaj.org/toc/2045-2322Abstract The in-silico development of a chemotherapeutic dosing schedule for treating cancer relies upon a parameterization of a particular tumour growth model to describe the dynamics of the cancer in response to the dose of the drug. In practice, it is often prohibitively difficult to ensure the validity of patient-specific parameterizations of these models for any particular patient. As a result, sensitivities to these particular parameters can result in therapeutic dosing schedules that are optimal in principle not performing well on particular patients. In this study, we demonstrate that chemotherapeutic dosing strategies learned via reinforcement learning methods are more robust to perturbations in patient-specific parameter values than those learned via classical optimal control methods. By training a reinforcement learning agent on mean-value parameters and allowing the agent periodic access to a more easily measurable metric, relative bone marrow density, for the purpose of optimizing dose schedule while reducing drug toxicity, we are able to develop drug dosing schedules that outperform schedules learned via classical optimal control methods, even when such methods are allowed to leverage the same bone marrow measurements.Brydon EastmanMichelle PrzedborskiMohammad KohandelNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Brydon Eastman Michelle Przedborski Mohammad Kohandel Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy
description	Abstract The in-silico development of a chemotherapeutic dosing schedule for treating cancer relies upon a parameterization of a particular tumour growth model to describe the dynamics of the cancer in response to the dose of the drug. In practice, it is often prohibitively difficult to ensure the validity of patient-specific parameterizations of these models for any particular patient. As a result, sensitivities to these particular parameters can result in therapeutic dosing schedules that are optimal in principle not performing well on particular patients. In this study, we demonstrate that chemotherapeutic dosing strategies learned via reinforcement learning methods are more robust to perturbations in patient-specific parameter values than those learned via classical optimal control methods. By training a reinforcement learning agent on mean-value parameters and allowing the agent periodic access to a more easily measurable metric, relative bone marrow density, for the purpose of optimizing dose schedule while reducing drug toxicity, we are able to develop drug dosing schedules that outperform schedules learned via classical optimal control methods, even when such methods are allowed to leverage the same bone marrow measurements.
format	article
author	Brydon Eastman Michelle Przedborski Mohammad Kohandel
author_facet	Brydon Eastman Michelle Przedborski Mohammad Kohandel
author_sort	Brydon Eastman
title	Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy
title_short	Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy
title_full	Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy
title_fullStr	Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy
title_full_unstemmed	Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy
title_sort	reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/346e4b6869474fd3982e56778d98ce5a
work_keys_str_mv	AT brydoneastman reinforcementlearningderivedchemotherapeuticschedulesforrobustpatientspecifictherapy AT michelleprzedborski reinforcementlearningderivedchemotherapeuticschedulesforrobustpatientspecifictherapy AT mohammadkohandel reinforcementlearningderivedchemotherapeuticschedulesforrobustpatientspecifictherapy
_version_	1718389257138077696

Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy

Ejemplares similares