Cargando…

An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments

One of the key challenges in current cancer research is the development of computational strategies to support clinicians in the identification of successful personalized treatments. Control theory might be an effective approach to this end, as proven by the long-established application to therapy d...

Descripción completa

Detalles Bibliográficos
Autores principales: Angaroni, Fabrizio, Graudenzi, Alex, Rossignolo, Marco, Maspero, Davide, Calarco, Tommaso, Piazza, Rocco, Montangero, Simone, Antoniotti, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7270334/
https://www.ncbi.nlm.nih.gov/pubmed/32548108
http://dx.doi.org/10.3389/fbioe.2020.00523
_version_ 1783541885205217280
author Angaroni, Fabrizio
Graudenzi, Alex
Rossignolo, Marco
Maspero, Davide
Calarco, Tommaso
Piazza, Rocco
Montangero, Simone
Antoniotti, Marco
author_facet Angaroni, Fabrizio
Graudenzi, Alex
Rossignolo, Marco
Maspero, Davide
Calarco, Tommaso
Piazza, Rocco
Montangero, Simone
Antoniotti, Marco
author_sort Angaroni, Fabrizio
collection PubMed
description One of the key challenges in current cancer research is the development of computational strategies to support clinicians in the identification of successful personalized treatments. Control theory might be an effective approach to this end, as proven by the long-established application to therapy design and testing. In this respect, we here introduce the Control Theory for Therapy Design (CT4TD) framework, which employs optimal control theory on patient-specific pharmacokinetics (PK) and pharmacodynamics (PD) models, to deliver optimized therapeutic strategies. The definition of personalized PK/PD models allows to explicitly consider the physiological heterogeneity of individuals and to adapt the therapy accordingly, as opposed to standard clinical practices. CT4TD can be used in two distinct scenarios. At the time of the diagnosis, CT4TD allows to set optimized personalized administration strategies, aimed at reaching selected target drug concentrations, while minimizing the costs in terms of toxicity and adverse effects. Moreover, if longitudinal data on patients under treatment are available, our approach allows to adjust the ongoing therapy, by relying on simplified models of cancer population dynamics, with the goal of minimizing or controlling the tumor burden. CT4TD is highly scalable, as it employs the efficient dCRAB/RedCRAB optimization algorithm, and the results are robust, as proven by extensive tests on synthetic data. Furthermore, the theoretical framework is general, and it might be applied to any therapy for which a PK/PD model can be estimated, and for any kind of administration and cost. As a proof of principle, we present the application of CT4TD to Imatinib administration in Chronic Myeloid leukemia, in which we adopt a simplified model of cancer population dynamics. In particular, we show that the optimized therapeutic strategies are diversified among patients, and display improvements with respect to the current standard regime.
format Online
Article
Text
id pubmed-7270334
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-72703342020-06-15 An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments Angaroni, Fabrizio Graudenzi, Alex Rossignolo, Marco Maspero, Davide Calarco, Tommaso Piazza, Rocco Montangero, Simone Antoniotti, Marco Front Bioeng Biotechnol Bioengineering and Biotechnology One of the key challenges in current cancer research is the development of computational strategies to support clinicians in the identification of successful personalized treatments. Control theory might be an effective approach to this end, as proven by the long-established application to therapy design and testing. In this respect, we here introduce the Control Theory for Therapy Design (CT4TD) framework, which employs optimal control theory on patient-specific pharmacokinetics (PK) and pharmacodynamics (PD) models, to deliver optimized therapeutic strategies. The definition of personalized PK/PD models allows to explicitly consider the physiological heterogeneity of individuals and to adapt the therapy accordingly, as opposed to standard clinical practices. CT4TD can be used in two distinct scenarios. At the time of the diagnosis, CT4TD allows to set optimized personalized administration strategies, aimed at reaching selected target drug concentrations, while minimizing the costs in terms of toxicity and adverse effects. Moreover, if longitudinal data on patients under treatment are available, our approach allows to adjust the ongoing therapy, by relying on simplified models of cancer population dynamics, with the goal of minimizing or controlling the tumor burden. CT4TD is highly scalable, as it employs the efficient dCRAB/RedCRAB optimization algorithm, and the results are robust, as proven by extensive tests on synthetic data. Furthermore, the theoretical framework is general, and it might be applied to any therapy for which a PK/PD model can be estimated, and for any kind of administration and cost. As a proof of principle, we present the application of CT4TD to Imatinib administration in Chronic Myeloid leukemia, in which we adopt a simplified model of cancer population dynamics. In particular, we show that the optimized therapeutic strategies are diversified among patients, and display improvements with respect to the current standard regime. Frontiers Media S.A. 2020-05-28 /pmc/articles/PMC7270334/ /pubmed/32548108 http://dx.doi.org/10.3389/fbioe.2020.00523 Text en Copyright © 2020 Angaroni, Graudenzi, Rossignolo, Maspero, Calarco, Piazza, Montangero and Antoniotti. 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
Angaroni, Fabrizio
Graudenzi, Alex
Rossignolo, Marco
Maspero, Davide
Calarco, Tommaso
Piazza, Rocco
Montangero, Simone
Antoniotti, Marco
An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments
title An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments
title_full An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments
title_fullStr An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments
title_full_unstemmed An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments
title_short An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments
title_sort optimal control framework for the automated design of personalized cancer treatments
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7270334/
https://www.ncbi.nlm.nih.gov/pubmed/32548108
http://dx.doi.org/10.3389/fbioe.2020.00523
work_keys_str_mv AT angaronifabrizio anoptimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT graudenzialex anoptimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT rossignolomarco anoptimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT masperodavide anoptimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT calarcotommaso anoptimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT piazzarocco anoptimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT montangerosimone anoptimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT antoniottimarco anoptimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT angaronifabrizio optimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT graudenzialex optimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT rossignolomarco optimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT masperodavide optimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT calarcotommaso optimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT piazzarocco optimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT montangerosimone optimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments
AT antoniottimarco optimalcontrolframeworkfortheautomateddesignofpersonalizedcancertreatments