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Register a new userValidation of Sinus Drug Delivery Computational Fluid Dynamics (CFD) Modeling with In Vitro Gamma Scintigraphy
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Background: Chronic rhinosinusitis (CRS) is a prevalent and disruptive disease. Medical management including nasal steroid sprays is the primary treatment modality. Computational fluid dynamics (CFD) has been used to characterize sinonasal airflow and intranasal drug delivery; however, variation in simulation methods indicates a need for large scale CFD model validation. Methods: Anatomic reconstructions of pre and post-operative CT scans of 3 functional endoscopic sinus surgery patients were created in Mimics(TM). Fluid analysis and drug particle deposition modeling were conducted using CFD methods with Fluent(TM) in 18 cases. Models were 3D printed and in vitro studies were performed using Tc99-labeled Nasacort(TM). Gamma scintigraphy signals and CFD-modeled spray mass were post-processed in a superimposed grid and compared. Statistical analysis using overlap coefficients (OCs) evaluated similarities between computational and experimental distributions and Kendalls tau rank correlation coefficient was employed to test independence. Results: OCs revealed strong agreement in percent deposition and grid profiles between CFD models and experimental results (mean [range] for sagittal, axial, and coronal grids were 0.69 [0.57], 0.61 [0.49], and 0.78 [0.44], respectively). Kendalls tau values showed strong agreement (average 0.73) between distributions, which were statistically significant (p < 0.05) apart from a single coronal grid in one model and two sagittal grids of another. Conclusions: CFD modeling demonstrates statistical agreement with in vitro experimental results. This validation study is one of the largest of its kind and supports the applicability of CFD in accurately modeling nasal spray drug delivery and using computational methods to investigate means of improving clinical drug delivery.
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The human aorta is a high-risk area for vascular diseases, which are commonly restored by thoracic endovascular aortic repair. In this paper, we report a promising shear-activated targeted nanoparticle drug delivery strategy to assist in the treatment of coarctation of the aorta and aortic aneurysm. Idealized three-dimensional geometric models of coarctation of the aorta and aortic aneurysm are designed, respectively. The unique hemodynamic environment of the diseased aorta is used to improve nanoparticle drug delivery. Micro-carriers with nanoparticle drugs would be targeting activated to release nanoparticle drugs by local abnormal shear stress rate (SSR). Coarctation of the aorta provides a high SSR hemodynamic environment, while the aortic aneurysm is exposed to low SSR. Results show that the upstream near-wall area of the diseased location is an ideal injection point for the micro-carriers, which could be activated by the abnormal SSR. Released nanoparticle drugs would be successfully targeted delivered to the aortic diseased wall. Besides, coarctation of the aorta would prevent blood flow to the descending aorta, while the effect of the aortic aneurysm on the blood flow distribution is negligible. This study preliminary demonstrates the feasibility of shear-activated targeted nanoparticle drug delivery in the treatment of aortic diseases and provides a theoretical basis for developing novel therapy.
Drug delivery systems represent a promising strategy to treat cancer and to overcome the side effects of chemotherapy. In particular, polymeric nanocontainers have attracted major interest because of their structural and morphological advantages and the variety of polymers that can be used, allowing the synthesis of materials capable of responding to the biochemical alterations of the tumour microenvironment. While experimental methodologies can provide much insight, the generation of experimental data across a wide parameter space is usually prohibitively time consuming and/or expensive. To better understand the influence of varying design parameters on the drug release profile and drug kinetics involved, appropriately-designed mathematical models are of great benefit. Here, we developed a novel mathematical model to describe drug transport within, and release from, a hollow nanocontainer consisting of a core and a pH-responsive polymeric shell. The two-layer mathematical model fully accounts for drug dissolution, diffusion and interaction with polymer. We generated experimental drug release profiles using daunorubicin and [Cu(TPMA)(Phenantroline)](ClO_4)_2 as model drugs, for which the nanocontainers exhibited excellent encapsulation ability. The in vitro drug release behaviour was studied under different conditions, where the system proved capable of responding to the selected pH stimuli by releasing a larger amount of drug in an acidic than in the physiological environments. By comparing the results of the mathematical model with our experimental data, we were able to identify the model parameter values that best-fit the data and demonstrate that the model is capable of describing the phenomena at hand. The proposed methodology can be used to describe and predict the release profiles for a variety of drug delivery systems.
Tracking and characterizing the blood uptake process within solid pancreatic tumors and the subsequent spatio-temporal distribution of red blood cells are critical to the clinical diagnosis of the cancer. This systematic computational study of physical factors, affecting the percolation and penetration of blood into a solid tumor, can assist in the development of a new objective clinical diagnosis approach and a framework for personalized targeted drugs.
In vitro cell proliferation assays are widely used in pharmacology, molecular biology, and drug discovery. Using theoretical modeling and experimentation, we show that current antiproliferative drug effect metrics suffer from time-dependent bias, leading to inaccurate assessments of parameters such as drug potency and efficacy. We propose the drug-induced proliferation (DIP) rate, the slope of the line on a plot of cell population doublings versus time, as an alternative, time-independent metric.
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics. Fluids are well described by the Navier-Stokes equations, but solving these equations at scale remains daunting, limited by the computational cost of resolving the smallest spatiotemporal features. This leads to unfavorable trade-offs between accuracy and tractability. Here we use end-to-end deep learning to improve approximations inside computational fluid dynamics for modeling two-dimensional turbulent flows. For both direct numerical simulation of turbulence and large eddy simulation, our results are as accurate as baseline solvers with 8-10x finer resolution in each spatial dimension, resulting in 40-80x fold computational speedups. Our method remains stable during long simulations, and generalizes to forcing functions and Reynolds numbers outside of the flows where it is trained, in contrast to black box machine learning approaches. Our approach exemplifies how scientific computing can leverage machine learning and hardware accelerators to improve simulations without sacrificing accuracy or generalization.
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