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Method Article
This manuscript reviews the modeling and simulations of different protocols to deliver medications to the olfactory region in image-based nasal airway models. Multiple software modules are used to develop the anatomically accurate nose model, generate computational mesh, simulate nasal airflows, and predict particle deposition at the olfactory region.
There are many advantages of direct nose-to-brain drug delivery in the treatment of neurological disorders. However, its application is limited by the extremely low delivery efficiency (< 1%) to the olfactory mucosa that directly connects the brain. It is crucial to develop novel techniques to deliver neurological medications more effectively to the olfactory region. The objective of this study is to develop a numerical platform to simulate and improve intranasal olfactory drug delivery. A coupled image-CFD method was presented that synthetized the image-based model development, quality meshing, fluid simulation, and magnetic particle tracking. With this method, performances of three intranasal delivery protocols were numerically assessed and compared. Influences of breathing maneuvers, magnet layout, magnetic field strength, drug release position, and particle size on the olfactory dosage were also numerically studied.
From the simulations, we found that clinically significant olfactory dosage (up to 45%) were feasible using the combination of magnet layout and selective drug release. A 64 -fold higher delivery of dosage was predicted in the case with magnetophoretic guidance compared to the case without it. However, precise guidance of nasally inhaled aerosols to the olfactory region remains challenging due to the unstable nature of magnetophoresis, as well as the high sensitivity of olfactory dosage to patient-, device-, and particle-related factors.
Drugs delivered to the olfactory region can bypass the blood-brain-barrier and directly enter the brain, leading to an efficient uptake and quick action onset of the drugs1,2. However, conventional nasal devices such as nasal pumps and sprays deliver extremely low doses to the olfactory region (< 1%) via the nasal route3,4. It is primarily due to the complicated structure of the human nose which is composed of narrow, convoluted passageways (Figure 1). The olfactory region locates above the superior meatus, where only a very small fraction of inhaled air can reach5,6. Furthermore, conventional inhalation devices depend on aerodynamic forces to transport therapeutic agents to the target area7. There is no further control over the motions of particles after their release. Therefore, the transport and deposition of these particles predominately depend on their initial speeds and release positions. Due to the convoluted nasal passage as well as the lack of particle control, the majority of drug particles are trapped in the anterior nose and cannot reach the olfactory region8.
While there are many choices of nasal devices, those designed specifically for targeted olfactory delivery have rarely been reported7,9. One exception is Hoekman and Ho10 who developed an olfactory-preferential delivery device and demonstrated higher cortex-to-blood drug levels in rats as opposed to using a nose drop. However, scaling the deposition results in rats to humans is not straightforward, considering the vast anatomical and physiological differences between these two species11. Many limitations exist when using adapted versions of standard nasal devices for olfactory deliveries. One primary setback is that only a very small portion of medications can be delivered to the olfactory mucosa, through which the medications may enter the brain. Numerical modeling predicted that less than 0.5% of intranasally administered nanoparticles can deposit in the olfactory region3,5. The deposition rate is even lower (0.007%) for micrometer particles12. In order to make the nose-to-brain delivery clinically feasible, the olfactory deposition rate has to be significantly improved.
There exist several possible approaches to improve the olfactory delivery. One approach is the smart inhaler idea proposed by Kleinstreuer et al.13 As particles depositing in one region are mainly from one specific area at the inlet, it is possible to deliver particles to the target site by releasing them only from certain areas at the inlet. The smart delivery technique has been shown to generate a much more efficient lung delivery than conventional methods.13,14 It is hypothesized that this smart delivery idea can also be applied in intranasal drug delivery to improve dosages to the olfactory mucosa. By releasing particles into different positions at the nostril opening and from different depths within the nasal cavity, improved olfactory delivery efficiencies and reduced drug waste in the anterior nose are possible.
Another possible method is to actively control the particle motion within the nasal cavity using a variety of field forces, such as electric or magnetic force. Electric control of charged particles has been suggested for targeted drug delivery to the human nose and lungs15-17. Xi et al.18 numerically tested the performance of electric guidance of charged particles and predicted significantly improved olfactory doses. Similarly, guidance of ferromagnetic drug particles with an appropriate magnetic field also has the potential to target particles to the olfactory mucosa. Behaviors of inhaled agents, if ferromagnetic, can be altered by imposing appropriate magnetic forces19. Dames et al.20 demonstrated that it is practical to target ferromagnetic particles to specific areas in mouse lungs. By packaging therapeutic agents with superparamagnetic iron oxide nanoparticles, the deposition in one lung of a mouse under the influence of a strong magnetic field was significantly increased compared to the other lung20.
Particles were assumed to be spherical and ranged from 150 nm to 30 µm in diameter. The governing equation is21:
(1)
The above equation describes the motion of a particle governed by drag force, gravitational force, Saffman lift force 22, Brownian force for nanoparticles, and magnetophoretic force if placed in a magnetic field. Here, vi is the particle velocity, ui is the flow velocity, τp is the particle response time, Cc is the Cunningham correction factor, and α is the air/particle density ratio. To effectively guide the intranasally administered drugs to the olfactory region, it is necessary for the applied magnetophoretic forces to overcome both the particle inertia and gravitational force. In this study, a composite of 20% maghemite (γ-Fe2O3, 4.9 g/cm3) and 80% active agent was assumed, which give a density of approximate 1.78 g/cm3 and a relative permeability of 50. The selection of γ-Fe2O3 was due to its low cytotoxic. Iron (3+) ions are widely found in human body and a slightly higher ion concentration will not cause significant side-effects23.
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The MRI images were provided by the Hamner Institutes for Health Sciences and the usage of these images was approved by the Virginia Commonwealth University institutional review board.
1. Image-Based Nasal Airway Preparation
2. Passive Control of Particles
3. Active Control: Magnetophoretic Guidance
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Control Case:
Figure 3 displays the airflow field and particle deposition in the nasal airway with standard nasal devices. It clearly shows that airflow from the front nostril is ventilated to the upper passage and airflow from the back nostril is directed towards the nasal floor (Figure 3A). Aerosol particles are observed to move faster in the median passages and slower near the walls, forming an aerosol front in the mean flow dire...
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A coupled image-CFD method was presented in this study that incorporated the image-based model development, quality meshing, airflow simulation, and magnetic particle tracking. Multiple software modules were implemented to this aim, which included functions of segmentation of medical images, reconstruction/meshing of anatomically accurate airway models, and flow-particle simulations. Using this numerical method, performances of three intranasal delivery protocols were tested and compared. Compared to in vitro ex...
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The authors report no conflicts of interest in this work.
This study was funded by Central Michigan University Innovative Research Grant P421071 and Early Career Grant P622911.
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Name | Company | Catalog Number | Comments |
MIMICS 13 | Materialise Inc, Ann Arbor, MI | MR image segmentation | |
Gambit | ANSYS Inc, Canonsburg, PA | Model development | |
ANSYS ICEMCFD | ANSYS Inc, Canonsburg, PA | Meshing | |
ANSYS Fluent | ANSYS Inc, Canonsburg, PA | Fluid and particle simulation | |
COMSOL Multiphsics | COMSOL Inc, Burlington, MA | Magnetic particle tracing |
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