Large eddy simulations of airflow and particle deposition in pulsating bi-directional nasal drug delivery

Abstract

Chronic rhinosinusitis is a common disease worldwide, and the frequently prescribed nasal sprays do not sufficiently deliver the topical medications to the target sites so that the final treatment in severe cases is surgery. Therefore, there is a huge demand to improve drug delivery devices that could target the maxillary sinuses more effectively. In the present study, different particle diameters and device pulsation flow rates, mainly used in pulsating aerosol delivery devices such as the PARI SINUS®, are considered to evaluate optimal maxillary sinus deposition efficiency (DE). Numerical simulations of the particle-laden flow using a large eddy simulation with a local dynamic k-equation sub-grid scale model are performed in a patient-specific nasal cavity. By increasing the pulsation flow rate from 4 l/min to 15 l/min, nasal DE increases from 37% to 68%. Similarly, by increasing the particle size from 1 µm to 5 µm, nasal DE increases from 34% to 43% for a pulsation flow rate of 4 l/min. Moreover, normalized velocity, vorticities, and particle deposition pattern in different regions of the main nasal cavity and maxillary sinuses are visualized and quantified. Due to the nosepiece placement in the right nostril, more particles penetrate into the right maxillary sinus than into the left maxillary sinus despite the maxillary ostium being larger in the left cavity. Lower pulsation flow rates such as 4 l/min improve the DE in the left maxillary sinus. The use of 3 µm particles enhances the DE in the right maxillary sinus as well as the overall total maxillary drug delivery.

Pulsatile Bi-Directional Aerosol Flow Affects Aerosol Delivery to the Intranasal Olfactory Region: A Patient-Specific Computational Study

“…The validity of the present numerical solver was assessed in two steps. In our previous publications ( Farnoud et al, 2017 ; Farnoud et al, 2020a ; Farnoud et al, 2020b ), the DE in 90° bends was compared to the experimental data of Pui et al (1987) and other research in the literature ( Breuer et al, 2006 ; Nicolaou and Zaki, 2016 ; Inthavong, 2019 ) to ensure that the solver is able to capture the DE of particles correctly. This benchmark is a popular approach to validate the capability of a numerical solver to predict particle deposition in curvature geometries.…”

“…A three-dimensional geometry of the nasal cavity and paranasal sinuses was reconstructed from CT images of a male, 82-year-old patient, as previously studied by Farnoud et al (2017) , Farnoud et al (2020a) , and Farnoud et al (2020b) . The CT images were segmented using the open source software package 3DSlicer.…”

Numerical and Machine Learning Analysis of the Parameters Affecting the Regionally Delivered Nasal Dose of Nano- and Micro-Sized Aerosolized Drugs

“…Moreover, Basu et al [ 9 ] suggested new methods to enhance the deposition efficiency of micro-aerosols and used 3D printed replicas to validate the numerical results. Several researchers have studied the micro-aerosols delivery into different regions of the nasal cavity such as maxillary sinuses, olfactory regions and turbinates [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. In recent years, different methods such as imposing conditions of pulsatile airflow [ 12 , 13 , 19 ], acoustic waves superposition [ 10 , 11 , 20 ], and swirling flow [ 14 ] at the nostrils have been studied.…”

“…Their results showed that drug delivery with pulsating inlet airflow resulted in a more uniform deposition pattern compared to non-pulsating flow in the nasal airways. In another study, Farnoud et al [ 13 ] studied the effect of flow rate and micro-aerosol size on deposition efficiencies in the nasal cavity, specifically the maxillary sinuses. They observed enhancement in deposition efficiency by applying a bi-directional delivery method.…”

Liquid volume and squeeze force effects on nasal irrigation using Volume of Fluid modelling

“…Computational fluid dynamics (CFD) provides a technique to predict fluid flows (both air and liquid phases) through the rigid sinonasal cavities that is quantifiable and reproducible under different conditions (Lee et al, 2016;Patki and Frank-Ito, 2016;Tong et al, 2016;Calmet et al, 2019;Frank-Ito et al, 2019;Inthavong, 2020). Recent work has demonstrated the applicability of CFD for investigating different nasal therapies, including thermal water delivery (Covello et al, 2018); bidirectional pulsating nasal aerosol dispersion (Farnoud et al, 2020a(Farnoud et al, , 2020b; nebulisers and sprays (Farzal et al, 2019); and also the effects of nasal dilation on olfactory deposition during unilateral and bi-directional delivery (Xi et al, 2018). Furthermore, models of nasal irrigation have been generated using CFD (Zhao et al, 2016;Craig et al, 2017;de Gabory et al, 2020;Inthavong et al, 2020;Salati et al, 2020) which has increased understanding of nasal irrigation and provided insight to how its effectiveness can be improved.…”