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Modeling Patient-Specific Magnetic Drug Targeting Within the Intracranial Vasculature.
Patronis, Alexander; Richardson, Robin A; Schmieschek, Sebastian; Wylie, Brian J N; Nash, Rupert W; Coveney, Peter V.
Afiliação
  • Patronis A; Centre for Computational Science, University College London, London, United Kingdom.
  • Richardson RA; Centre for Computational Science, University College London, London, United Kingdom.
  • Schmieschek S; Centre for Computational Science, University College London, London, United Kingdom.
  • Wylie BJN; Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany.
  • Nash RW; Edinburgh Parallel Computing Centre, University of Edinburgh, Edinburgh, United Kingdom.
  • Coveney PV; Centre for Computational Science, University College London, London, United Kingdom.
Front Physiol ; 9: 331, 2018.
Article em En | MEDLINE | ID: mdl-29725303
ABSTRACT
Drug targeting promises to substantially enhance future therapies, for example through the focussing of chemotherapeutic drugs at the site of a tumor, thus reducing the exposure of healthy tissue to unwanted damage. Promising work on the steering of medication in the human body employs magnetic fields acting on nanoparticles made of paramagnetic materials. We develop a computational tool to aid in the optimization of the physical parameters of these particles and the magnetic configuration, estimating the fraction of particles reaching a given target site in a large patient-specific vascular system for different physiological states (heart rate, cardiac output, etc.). We demonstrate the excellent computational performance of our model by its application to the simulation of paramagnetic-nanoparticle-laden flows in a circle of Willis geometry obtained from an MRI scan. The results suggest a strong dependence of the particle density at the target site on the strength of the magnetic forcing and the velocity of the background fluid flow.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2018 Tipo de documento: Article