Abstract
This study focuses on the transport, deposition, and triggered immune response of intranasal vaccine droplets to the angiotensin-converting-enzyme-2-rich region, i.e., the olfactory region (OR), in the nasal cavity of a 6-year-old female to possibly prevent corona virus disease 19 (COVID-19). To investigate how administration strategy can influence nasal vaccine efficiency, a validated multi-scale model, i.e., computational fluid-particle dynamics (CFPD) and host-cell dynamics (HCD) model, was employed. Droplet deposition fraction, size change, residence time, and the area percentage of OR covered by the vaccine droplets, and triggered immune system response were predicted with different spray cone angles, initial droplet velocities, and compositions. Numerical results indicate that droplet initial velocity and composition have negligible influences on the vaccine delivery efficiency to OR. In contrast, the spray cone angle can significantly impact the vaccine delivery efficiency. The triggered immunity was not significantly influenced by the administration investigated in this study due to the low percentage of OR area covered by the droplets. To enhance the effectiveness of the intranasal vaccine to prevent COVID-19 infection, it is necessary to optimize the vaccine formulation and administration strategy so that the vaccine droplets can cover more epithelial cells in OR to minimize the number of available receptors for SARS-CoV-2.
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Abbreviations
- A d :
-
droplet surface area (m2)
- C m :
-
correction factor for Fuchs-Knudsen number
- c p :
-
specific heat of humid air (J/(kg·K))
- c p,d :
-
specific heat of droplet (J/(kg·K))
- D w :
-
water mass diffusivity (m2/s)
- d d :
-
droplet diameter (m)
- d d,i :
-
droplet initial diameter (m)
- d d,f :
-
droplet final diameter (m)
- E 0 :
-
initial number of epithelial cells
- \({\overrightarrow F ^{{\rm{BM}}}}\) :
-
Brownian motion induced force (N)
- \({\overrightarrow F ^{\rm{D}}}\) :
-
drag force (N)
- \({\overrightarrow F ^{\rm{G}}}\) :
-
gravity (N)
- \({\overrightarrow F ^{\rm{L}}}\) :
-
Saffman lift force (N)
- \(\overrightarrow g \) :
-
gravitational acceleration (m/s2)
- H lat :
-
latent heat (J/kg)
- K w :
-
Kelvin effect factor
- Kn :
-
Knudsen number
- k :
-
turbulence kinetic energy (J/kg)
- k c :
-
thermal conductivity (W/(m·K))
- k c,t :
-
turbulent thermal conductivity (W/ (m·K))
- k hc :
-
modified thermal conductivity (W/(m2·K))
- k mc :
-
mass transfer coefficient (m/s)
- M w :
-
water molecular weight (kmol/kg)
- m d :
-
droplet mass (kg)
- N :
-
number of fragments between max and min temperature
- Nu :
-
Nusselt number
- P eq :
-
equilibrium vapor pressure (Pa)
- P sat :
-
saturation vapor pressure (Pa)
- Pr :
-
Prandtl number
- R :
-
gas constant (J/(mol·K))
- Re d :
-
droplet Reynolds number
- r d :
-
droplet radius (m)
- \(S_{\rm{w}}^{\rm{m}}\) :
-
mass source term (kg/(m3·s))
- Sc :
-
Schmidt number
- Sh :
-
Sherwood number
- T :
-
humid airflow temperature (K)
- T d :
-
droplet temperature (K)
- t :
-
time (s)
- \(\overrightarrow u \) :
-
flow velocity (m/s)
- \({\overrightarrow u _{\rm{d}}}\) :
-
droplet velocity (m/s)
- V :
-
breathing velocity (m/s)
- V d,i :
-
droplet initial velocity (m/s)
- \({\overline V _{\rm{w}}}\) :
-
water molar volume (m3/kmol)
- y w,surf :
-
water mass fraction at droplet surface
- y w,∞ :
-
water mass fraction in humid air mixture
- α m :
-
mass accommodation factor
- λ :
-
gas mixture mean free path (m)
- μ :
-
viscosity of humid air (kg/(m·s))
- μ t :
-
turbulent viscosity of humid air (kg/(m·s))
- μ d,surf :
-
viscosity of humid air at droplet surface (kg/(m·s))
- ρ :
-
density of humid airflow (kg/m3)
- ρw:
-
density of water (kg/m3)
- σ :
-
droplet surface tension (N/m)
- τ :
-
breathing cycle time (s)
- Φ exp :
-
experimental values of reported immune response
- Φ HCD :
-
computational values obtained for immune response
- ω :
-
specific rate of turbulence kinetic energy dissipation (J/(kg·s))
- λ-C:
-
λ-carrageenan
- ACE-2:
-
angiotensin-converting enzyme 2
- CFPD:
-
computational fluid-particle dynamics
- COVID-19:
-
corona virus disease 2019
- DF:
-
deposition fraction
- G:
-
generation
- GG:
-
gellan gum
- HCD:
-
host-cell dynamics
- IgA:
-
immunoglobulin A
- IgG:
-
immunoglobulin G
- MRI:
-
magnetic resonance imaging
- NK:
-
natural killer cells
- ODEs:
-
ordinary differential equations
- OR:
-
olfactory region
- PBS:
-
phosphate-buffered saline
- RH:
-
relative humidity
- RMSE:
-
root mean squared error
- S:
-
spike protein
- SARS-CoV-2:
-
severe acute respiratory syndrome corona virus 2
- SST:
-
shear stress transport
- TB:
-
tracheobronchial
- UDFs:
-
user-defined functions
- VC:
-
vaccine coverage
- VT0 :
-
initial viral titer
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Acknowledgements
The research was made possible by funding through an award from the Oklahoma Center for the Advancement of Science and Technology (OCAST) (HR19-106). The research is also partially supported by the National Science Foundation (CBET 2120688) and the National Institutes of Health (NIH) Center of Biomedical Research Excellence (COBRE) (P20 GM103648). The use of Ansys software (Ansys Inc., Canonsburg, PA, USA) as part of the Ansys-CBBL academic partnership coordinated by Dr. Thierry Marchal is gratefully acknowledged.
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Conceptualization, H.H., Y.F.; methodology, H.H., Y.F., X.C.; calibration and validation, H.H.; geometry preparation, E.K., C.F., Y.F., H.H.; simulations, H.H.; data curation, H.H.; data analysis, H.H.; writing, original draft, H.H.; writing, review and editing, H.H., E.K., X.C., Y.F.
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It is not the intention of authors to provide specific medical advice but rather to provide readers with computational modeling details to better understand the fundamentals of fluid dynamics and host cell dynamics in COVID-19 nasal vaccine development. No in vivo/in vitro studies were conducted. Hence, no specific medical advice will be provided, and the authors urge you to consult with a qualified physician for answers to your personal medical concerns.
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Hayati, H., Feng, Y., Chen, X. et al. Prediction of transport, deposition, and resultant immune response of nasal spray vaccine droplets using a CFPD-HCD model in a 6-year-old upper airway geometry to potentially prevent COVID-19. Exp. Comput. Multiph. Flow 5, 272–289 (2023). https://doi.org/10.1007/s42757-022-0145-7
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DOI: https://doi.org/10.1007/s42757-022-0145-7