Multimodality imaging and mathematical modelling of drug delivery to glioblastomas

Abstract

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.

Computational fluid dynamics with imaging of cleared tissue and of in vivo perfusion predicts drug uptake and treatment responses in tumours

“…Numerous studies have subsequently incorporated more realistic vasculature into models stemming from Baxter and Jain's work, such as synthetically-generated vascular networks, 49-subnetworks from tumors, derived from imaging data. [55][56][57] Synthetically-generated vasculature, using angiogenesis models, have been used to formulate hypotheses on the delivery of chemotherapeutics 58,59 , investigate the impact of tumor size on chemotherapeutic efficacy and to investigate the effect of dynamic vasculature 61 A key advantage of REANIMATE is its ability to compare model predictions with experimental measurements from the same tumors. We found a good correspondence between our predictions of vascular perfusion and delivery of Gd-DTPA, and those from in vivo imaging, both in their magnitude and spatial distribution.…”

The need for mathematical modelling of spatial drug distribution within the brain

“…These networks consist of a multitude of blood vessel segments connected by nodes, where parameters defining the network (such as blood vessel radius, volume and length) are based on images, experimental data or random distribution. These brain vascular networks can be applied to drug delivery [116, 125]. In a model on drug delivery to brain tumours, an image-based brain capillary network is coupled to a cubic mesh representation of the brain tissue [116].…”

Principles of Simultaneous PET/MR Imaging

“…Using mathematical models to combine the multimodal quantitative information obtained in an integrated PET/MRI scanner could help us better understand the tumor biology and the mechanisms of action of promising therapeutic agents (103). The potential uses of multimodal PET/MRI data in radiation therapy for improved target delineation as well as for the evaluation of tumor response are discussed in Tong Zhu, Shiva Das and Terence Wong’s article, “Integration of PET/MR Hybrid Imaging into Radiation Therapy Treatment,” in this issue.…”