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Multimodality imaging and mathematical modelling of drug delivery to glioblastomas
Author(s) -
Ahmed Hamdi Boujelben,
Michael G. Watson,
Steven Robert McDougall,
YiFen Yen,
Elizabeth R. Gerstner,
Ciprian Catana,
Thomas S. Deisboeck,
Tracy T. Batchelor,
David A. Boas,
Bruce R. Rosen,
Jayashree KalpathyCramer,
Mark A. J. Chaplain
Publication year - 2016
Publication title -
interface focus
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 49
eISSN - 2042-8901
pISSN - 2042-8898
DOI - 10.1098/rsfs.2016.0039
Subject(s) - multimodality , computer science , drug delivery , data science , medicine , world wide web , nanotechnology , materials science
Patients diagnosed with glioblastoma, an aggressive brain tumour, have a poor prognosis, with a median overall survival of less than 15 months. Vasculature within these tumours is typically abnormal, with increased tortuosity, dilation and disorganization, and they typically exhibit a disrupted blood–brain barrier (BBB). Although it has been hypothesized that the ‘normalization’ of the vasculature resulting from anti-angiogenic therapies could improve drug delivery through improved blood flow, there is also evidence that suggests that the restoration of BBB integrity might limit the delivery of therapeutic agents and hence their effectiveness. In this paper, we apply mathematical models of blood flow, vascular permeability and diffusion within the tumour microenvironment to investigate the effect of these competing factors on drug delivery. Preliminary results from the modelling indicate that all three physiological parameters investigated—flow rate, vessel permeability and tissue diffusion coefficient—interact nonlinearly to produce the observed average drug concentration in the microenvironment.

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