Multimodality imaging and mathematical modelling of drug delivery to glioblastomas
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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. 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 blood brain barrier 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.
Boujelben , A , Watson , M , McDougall , S , Yen , Y-F , Gerstner , E , Catana , C , Deisboeck , T , Batchelor , T , Boas , D , Rosen , B , Kalpathy-Cramer , J & Chaplain , M A J 2016 , ' Multimodality imaging and mathematical modelling of drug delivery to glioblastomas ' , Interface Focus , vol. 6 , no. 5 , 20160039 . https://doi.org/10.1098/rsfs.2016.0039
© 2016, the Author(s). This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at rsfs.royalsocietypublishing.org / https://dx.doi.org/10.1098/rsfs.2016.0039
DescriptionMAJC would like to thank the Isaac Newton Institute for Mathematical Sciences for its hospitality during the programme “Coupling Geometric PDEs with Physics for Cell Morphology, Motility and Pattern Formation” supported by EPSRC Grant Number EP/K032208/1.
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