Validation of Sinus Drug Delivery Computational Fluid Dynamics (CFD) Modeling with In Vitro Gamma Scintigraphy

TL;DR

This study validates the accuracy of CFD models in predicting nasal spray drug delivery by comparing simulations with in vitro gamma scintigraphy results, supporting CFD's use in optimizing intranasal treatments.

Contribution

It provides one of the largest validation datasets confirming CFD's reliability for sinonasal drug delivery modeling against experimental data.

Findings

Strong agreement between CFD and experimental deposition patterns.

High correlation coefficients indicating model accuracy.

Statistical validation supports CFD's clinical applicability.

Abstract

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 Kendall's 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). Kendall's 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.