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Model-based optimized steering and focusing of local magnetic particle concentrations for targeted drug delivery

Magnetic drug targeting (MDT) is an application in the field of targeted drug delivery in which magnetic (nano)particles act as drug carriers. The particles can be steered toward specific regions in the human body by adapting the currents of external (electro)magnets. Accurate models of particle mov...

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Autores principales: Van Durme, Rikkert, Crevecoeur, Guillaume, Dupré, Luc, Coene, Annelies
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7751415/
https://www.ncbi.nlm.nih.gov/pubmed/33342319
http://dx.doi.org/10.1080/10717544.2020.1853281
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author Van Durme, Rikkert
Crevecoeur, Guillaume
Dupré, Luc
Coene, Annelies
author_facet Van Durme, Rikkert
Crevecoeur, Guillaume
Dupré, Luc
Coene, Annelies
author_sort Van Durme, Rikkert
collection PubMed
description Magnetic drug targeting (MDT) is an application in the field of targeted drug delivery in which magnetic (nano)particles act as drug carriers. The particles can be steered toward specific regions in the human body by adapting the currents of external (electro)magnets. Accurate models of particle movement and control algorithms for the electromagnet currents are two of the many requirements to ensure effective drug targeting. In this work, a control approach for the currents is presented, based on an underlying physical model that describes the dynamics of particles in a liquid in terms of their concentration in each point in space. Using this model, the control algorithm determines the currents generating the magnetic fields that maximize the particle concentration in spots of interest over a period of time. Such an approach is computationally only feasible thanks to our innovative combination of model order reduction with the method of direct multiple shooting. Simulation results of an in-vitro targeting setup demonstrated that a particle collection can be successfully guided toward the targeted spot with limited dispersion through a surrounding liquid. As now present and future particle behavior can be taken into account, and non-stationary surrounding liquids can be dealt with, a more precise and flexible targeting is achieved compared to existing MDT methods. This proves that the presented methodology can bring MDT closer to its clinical application. Moreover, the developed model is compatible with state-of-the-art imaging methods, paving the way for theranostic platforms that combine both therapy as well as diagnostics.
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spelling pubmed-77514152021-01-14 Model-based optimized steering and focusing of local magnetic particle concentrations for targeted drug delivery Van Durme, Rikkert Crevecoeur, Guillaume Dupré, Luc Coene, Annelies Drug Deliv Research Article Magnetic drug targeting (MDT) is an application in the field of targeted drug delivery in which magnetic (nano)particles act as drug carriers. The particles can be steered toward specific regions in the human body by adapting the currents of external (electro)magnets. Accurate models of particle movement and control algorithms for the electromagnet currents are two of the many requirements to ensure effective drug targeting. In this work, a control approach for the currents is presented, based on an underlying physical model that describes the dynamics of particles in a liquid in terms of their concentration in each point in space. Using this model, the control algorithm determines the currents generating the magnetic fields that maximize the particle concentration in spots of interest over a period of time. Such an approach is computationally only feasible thanks to our innovative combination of model order reduction with the method of direct multiple shooting. Simulation results of an in-vitro targeting setup demonstrated that a particle collection can be successfully guided toward the targeted spot with limited dispersion through a surrounding liquid. As now present and future particle behavior can be taken into account, and non-stationary surrounding liquids can be dealt with, a more precise and flexible targeting is achieved compared to existing MDT methods. This proves that the presented methodology can bring MDT closer to its clinical application. Moreover, the developed model is compatible with state-of-the-art imaging methods, paving the way for theranostic platforms that combine both therapy as well as diagnostics. Taylor & Francis 2020-12-21 /pmc/articles/PMC7751415/ /pubmed/33342319 http://dx.doi.org/10.1080/10717544.2020.1853281 Text en © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Van Durme, Rikkert
Crevecoeur, Guillaume
Dupré, Luc
Coene, Annelies
Model-based optimized steering and focusing of local magnetic particle concentrations for targeted drug delivery
title Model-based optimized steering and focusing of local magnetic particle concentrations for targeted drug delivery
title_full Model-based optimized steering and focusing of local magnetic particle concentrations for targeted drug delivery
title_fullStr Model-based optimized steering and focusing of local magnetic particle concentrations for targeted drug delivery
title_full_unstemmed Model-based optimized steering and focusing of local magnetic particle concentrations for targeted drug delivery
title_short Model-based optimized steering and focusing of local magnetic particle concentrations for targeted drug delivery
title_sort model-based optimized steering and focusing of local magnetic particle concentrations for targeted drug delivery
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7751415/
https://www.ncbi.nlm.nih.gov/pubmed/33342319
http://dx.doi.org/10.1080/10717544.2020.1853281
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