Modeling Patient-Specific Magnetic Drug Targeting Within the Intracranial Vasculature

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.

Introducing VECMAtk - Verification, Validation and Uncertainty Quantification for Multiscale and HPC Simulations

“…Through MRI and CT scans, patient specific models may be used, with clinical applications such as predicting aneurysm rupture or treatment. We use a 3D lattice-Boltzmann solver, HemeLB [27], to simulate the continuum dynamics of bloodflow through large and highly sparse vascular systems efficiently [28]. A recent validation study focused on HemeLB simulations of a real patient Middle Cerebral Artery (MCA), using transcranial Doppler measurements of the blood velocity profile for comparison, as well as exploring the effects of a change in rheology model or inlet flow rate on the results [29].…”

Suitability of lattice Boltzmann inlet and outlet boundary conditions for simulating flow in image‐derived vasculature

“…For researchers looking to simulate blood flow in realistic vascular geometries, the lattice Boltzmann method (LBM) is an attractive approach because it easily handles complex geometries and efficiently scales across many processors . These advantages have led to biomedical applications of the LBM such as modeling clot formation and deposition, hemodynamic patterns in stented cerebral aneurysms, and patient‐specific drug targeting . For example, HemeLB, a large‐scale LBM solver, has been extensively applied to study vascular modeling and angiogenesis .…”

Heterogeneous System-on-Chip-Based Lattice-Boltzmann Visual Simulation System

“…The computational approach must not only model the native flows in the ICA, but also the perturbations introduced by insertion of flow-diverting stents, as well as the effect on the pattern of clotting within coil-filled aneurysms. Most usefully, simulation input data should be specific to the patient in question, thus taking into account the variability of vascular geometries, vessel wall mechanics and flow-phase specific pulsatile changes in pressures and shear stresses across patients [7], or differences in physiological states for a given patient (such as heart rate or blood pressure) [8].…”

“…Previous studies showed a comparable accuracy between LB and FEM [9], [10]. In this study, we used HemeLB, a massively parallel LB solver optimized for sparse and complex systems on large supercomputing resources [8], [11], [12]. It has been designed with the ultimate intention of allowing doctors to investigate cerebral blood flow behaviour in the human body and demonstrated its potential in hospital environment [13], [14].…”

“…HemeLB has been successfully deployed on open academic high-performance computing (HPC) platforms such as HEC-ToR, ARCHER, SuperMUC and Blue Waters [8], [12], [13], among others. In order to allow such hemodynamic simulations to be run as part of routine clinical practice, the workload will need to be deployed on a dedicated computational infrastructure.…”