Numerical optimization of targeted delivery of charged nanoparticles to the ostiomeatal complex for treatment of rhinosinusitis

Abstract

Background

Despite the prevalence of rhinosinusitis that affects 10%–15% of the population, current inhalation therapy shows limited efficacy. Standard devices deliver <5% of the drugs to the sinuses due to the complexity of nose structure, secluded location of the sinus, poor ventilation, and lack of control of particle motions inside the nasal cavity.

Methods

An electric-guided delivery system was developed to guide charged particles to the ostiomeatal complex (OMC). Its performance was numerically assessed in an MRI-based nose–sinus model. Key design variables related to the delivery device, drug particles, and patient breathing were determined using sensitivity analysis. A two-stage optimization of design variables was conducted to obtain the best performance of the delivery system using the Nelder-Mead algorithm.

Results and discussion

The OMC delivery system exhibited high sensitivity to the applied electric field and electrostatic charges carried by the particles. Through the synthesis of electric guidance and point drug release, the new delivery system eliminated particle deposition in the nasal valve and turbinate regions and significantly enhanced the OMC doses. An OMC delivery efficiency of 72.4% was obtained with the optimized design, which is one order of magnitude higher than the standard nasal devices. Moreover, optimization is imperative to achieve a sound delivery protocol because of the large number of design variables. The OMC dose increased from 45.0% in the baseline model to 72.4% in the optimized system. The optimization framework developed in this study can be easily adapted for the delivery of drugs to other sites in the nose such as the ethmoid sinus and olfactory region.

Keywords:

• maxillary sinus
• rhinosinusitis
• intranasal aerosol drug delivery
• charged particles
• electric guidance
• ostiomeatal complex

Acknowledgments

This study was funded by Central Michigan University Innovative Research Grant P421071 and Early Career Award P622911. Jensen Xi, Zachary Firlit, and Alyssa Soltis are gratefully acknowledged for reviewing the paper. We also thank Ze Zhang for technical supports in modeling.

Disclosure

The authors report no conflicts of interest in this work.