A validated theoretical framework to predict the spray velocity issued from a long nozzle nasal metered dose inhaler, for intra-nasal drug delivery

 

Barzin Gavtash1, Benjamin Myatt1, Henk Versteeg2, Andy Cooper1, Ed Long2 & Christopher Blatchford1

1Kindeva Drug Delivery Limited, Derby Road, Loughborough, LE11 5SF, United Kingdom

2Loughborough University, Wolfson School of MEME, Epinal Way, Loughborough, LE11 3TU, United Kingdom

 

Intra-nasal drug delivery via nasal metered-dose inhalers (MDI) has been successful to treat conditions such as allergies and congestions by delivering the therapeutic dose to anterior regions of human nasal cavity. Intra-nasal drug delivery has been also postulated as a potential technique to treat central nervous system (CNS) diseases by delivering CNS-active drugs to the olfactory region – positioned at the roof of the nasal cavity. Our in-vitro data suggest that such localised deposition can be more accurately achieved by extending the nasal MDI nozzle length sufficiently to position the spray source closer to the olfactory region. Such nozzle length extension can impact on the spray characteristics such as velocity which directly determines the deposition efficiency. In this work we developed a modelling tool validated by particle image velocimetry (PIV) measurements, to predict the velocity of long nozzle MDIs.

Alternative two-phase propellant flashing models namely (i) homogenous frozen model (HFM), (ii) slip equilibrium model (SEM) and (iii) homogenous equilibrium model (HEM) have been assessed. Time-dependant spray velocity trend, spray velocity order of magnitude and spray duration seem to be captured most accurately by HEM. When using HFM/SEM model, spray velocity and spray duration are underpredicted by around 15%. Preliminary PIV data suggest the spray issued from the long nozzle MDI has generally a narrow cone angle of approximately 10 degrees. Such narrow cone angle can improve the accuracy of localised dose delivery to the olfactory regions. This paper demonstrates how modelling augmented by focused experiments can be used to rapidly screen potential device concepts.

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