A digital platform to assess targeted regional drug delivery inside respiratory airways

Abstract

An interactive modeling system based on experimentally validated computational fluid dynamics (CFD) simulations of particle transport inside anatomic reconstructions of human respiratory cavities has been disclosed. The system includes an interactive interface that displays CFD output and the projected drug delivery within anatomical respiratory airways using an interpolation / extrapolation algorithm. The projected drug delivery is displayed for different regions of the respiratory airway based on specific drug features, e.g., particle sizes, spray plume geometry, particle speeds, formulation density, and viscosity.

Description

Attorney Docket No. 11214-008WO1 T-00555 A DIGITAL PLATFORM TO ASSESS TARGETED REGIONAL DRUG DELIVERY INSIDE RESPIRATORY AIRWAYS Related Application

[0001] This PCT application claims priority to, and the benefit of, U.S. Provisional Patent Application No. 63 / 554,549, filed February 16, 2024, entitled “Digital Platform to Assess Targeted Regional Drug Delivery Inside Respiratory Airways,” which is incorporated by reference herein in its entirety Technical Field

[0002] This disclosure is generally related to an interactive simulation system for drug delivery inside respiratory airways. Background

[0003] Performance from nasal / oral sprays, inhalers, and nebulizers is tested on an individual basis, i.e., on a person-by-person, for different formulations and delivery devices through computational modeling, experimental testing in 3D printed anatomic casts, and with appropriate approval in small cohorts of human subjects. However, the process is time-consuming and often cost-prohibitive, with complete testing of a product happening over a timescale of months, and in some cases, years. Summary

[0004] An interactive modeling system is provided based on experimentally validated computational fluid dynamics (CFD) simulations of particle transport inside anatomic reconstructions of human respiratory cavities. The system includes a physics-backed interactive interface that displays CFD output and the projected drug delivery within anatomical respiratory airways using an interpolation / extrapolation algorithm. The projected drug delivery may be displayed for different regions of the respiratory airway, based on specific drug features, e.g., particle sizes, spray plume geometry, particle speeds, formulation density and viscosity.

[0005] The digital interface promptly generates the regional drug deposition numbers for clinical target sites along the upper respiratory pathway, for a wide range of nasal spray and oral inhaler / nebulizer products using CFD data and interpolation / extrapolation algorithms for the input parameters. The physics-backed interactive system is configured for airway drugs for a wide variety of clinical conditions, e.g., chronic rhinosinusitis, allergic rhinitis, ciliated epithelial infections such as those from the SARS-family viruses, infectionsAttorney Docket No. 11214-008WO1 T-00555 resulting from respiratory syncytial viruses, and throat complications such as laryngeal granulomas.

[0006] The interactive system eliminates the need for modeling / experimentation on a case-by-case basis for different drug delivery products because the CFD model is built around a plurality of different anatomic reconstructions of nasal passages from multiple demographics and the interpolation / extrapolation algorithm utilizes the CFD outputs to cover the exhaustive feasible range of drug formulation and delivery device features.

[0007] The interface integrates mechanics modeling frameworks and may be further configured to account for mucociliary transport while assessing the targeted delivery along the airway. A first embodiment of the system discounts the effect of surface-based mucociliary transport (along the airway walls) that can translocate anteriorly deposited drug particles to the clinical target sites which typically lie further downstream along the airway. It is contemplated that pharmacologic modeling can be incorporated to assess therapeutic efficacy.

[0008] By building upon previous research and utilizing state-of-the-art and experimentally validated computational fluid dynamics modeling, the CFD model includes parameters from a wide array of nasal sprays and oral inhalers. The outputs from the system estimates targeted drug delivery for a wide variety of respiratory diseases.

[0009] In an aspect, a system to model drug delivery in respiratory airspace is disclosed comprising a processor; a memory having instructions sorted thereon; and a means for input and output, wherein at least one set of input data are provided by the input means, wherein execution of the instructions by the processor cause the processor to execute one or more models of fluid dynamics in respiratory airspace, and wherein the system is configured to display outputs of the one or more models associated with a first set of input data.

[0010] In some embodiments, the one or more models of fluid dynamics in respiratory airspace are varied by associated (three-dimensional) 3D anatomic airway structural models.

[0011] In some embodiments, the one or more models of fluid dynamics in respiratory airspaces are configured to receive as input parameters related to drug administration modalities.

[0012] In some embodiments, the one or more models of fluid dynamics in respiratory airspace are validated by in vitro testing.

[0013] In some embodiments, the outputs of the one or more models are interpolated for the associated first set of input data.Attorney Docket No. 11214-008WO1 T-00555

[0014] In some embodiments, the outputs of the one or more models are surface maps associated with the respiratory airspace.

[0015] In some embodiments, the outputs include quantification of drug delivery within the respiratory airspace.

[0016] In some embodiments, the set of input data includes drug particle size distribution, administered weight and density, angle of delivery, plume geometry for sprays, and speeds of administered particles at a nozzle exit.

[0017] In another aspect, a method is disclosed comprising receiving, via a processor, at least one set of input data, including a first set of input data; executing, via the processor, one or more models of fluid dynamics in respiratory airspace; and displaying, via the processor, outputs of the one or more models associated with the first set of input data.

[0018] In some embodiments, the one or more models of fluid dynamics in respiratory airspace are varied by associated (three-dimensional) 3D anatomic airway structural models.

[0019] In some embodiments, the one or more models of fluid dynamics in respiratory airspace are configured to receive as input parameters related to drug administration modalities.

[0020] In some embodiments, the one or more models of fluid dynamics in respiratory airspace are validated by in vitro testing.

[0021] In some embodiments, the outputs of the one or more models are interpolated for the associated first set of input data.

[0022] In some embodiments, the outputs of the one or more models are surface maps associated with the respiratory airspace.

[0023] In some embodiments, the outputs include quantification of drug delivery within the respiratory airspace.

[0024] In some embodiments, the set of input data includes drug particle size distribution, administered weight and density, angle of delivery, plume geometry for sprays, and speeds of administered particles at a nozzle exit.

[0025] In another aspect, a non-transitory computer-readable medium is disclosed having instructions stored thereon, wherein execution of the instructions by a processor causes the processor to receive, via the processor, at least one set of input data, including a first of input data; execute one or more models of fluid dynamics in respiratory airspace; and display, via the processor, outputs of the one or more models associated with the first set of input data.Attorney Docket No. 11214-008WO1 T-00555

[0026] In some embodiments, the one or more models of fluid dynamics in respiratory airspace are varied by associated (three-dimensional) 3D anatomic airway structural models.

[0027] In some embodiments, the one or more models of fluid dynamics in respiratory airspace are configured to receive as input parameters related to drug administration modalities.

[0028] In some embodiments, the one or more models of fluid dynamics in respiratory airspace are validated by in vitro testing.

[0029] In some embodiments, the outputs of the one or more models are interpolated for the associated first set of input data.

[0030] In some embodiments, the outputs of the one or more models are surface maps associated with the respiratory airspace.

[0031] In some embodiments, the outputs include quantification of drug delivery within the respiratory airspace.

[0032] In some embodiments, the set of input data includes drug particle size distribution, administered weight and density, angle of delivery, plume geometry for sprays, and speeds of administered particles at a nozzle exit. Brief Description of Drawings

[0033] The skilled person in the art will understand that the drawings described below are for illustration purposes only.

[0034] Fig. 1 shows an illustrative embodiment of the interactive system.

[0035] Fig. 2 shows an illustrative embodiment of the user interface.

[0036] Figs. 3A-3L show the axial, sagittal, and coronal views of two representative computed tomography (CT) based upper airway reconstruction (namely Subjects 1 and 2).

[0037] Fig. 4 shows two tested nasal spray usage protocols, viz. “Current Use” (or CU, represented by the dashed line) and “Improved Use” (or IU, represented by the solid line and directed at the clinical target site of interest, in this specific case the nasopharynx – which is the initial infection onset region for SARS viruses and RSV).

[0038] Figs. 5A-5D show spatial differences between the Current Use (CU) and Improved Use (IU) spray placement protocols, as visible sagittally in Subject 1, wherein the nasopharynx is shaded, g points in the direction of gravity.Attorney Docket No. 11214-008WO1 T-00555

[0039] Figs. 6A-6D show spatial differences between the Current Use (CU) and Improved Use (IU) spray placement protocols, as visible sagittally in Subject 2, wherein the nasopharynx is shaded, g points in the direction of gravity.

[0040] Fig. 7 show the observed count distribution of droplet sizes in 1-mg sprayed mass from over-the-counter FlonaseTM(Fluticasone Propionate) and NasacortTM(Triamcinolone Acetonide) spray products, over the test size range of ∼ 1 – 24 μm used for in silico tracking.

[0041] Figs. 8A-8L show the comparison of the regional deposition trends at the nasopharynx of Subjects 1 and 2 for monodispersed spray aerosols / droplets. 8E-8F are zoomed-in visuals from 8A-8D; 8K-8L are zoomed-in visuals from 8G-8J.

[0042] Figs. 9A-9K show results of CFD model simulations wherein the nasal spray is administered using the “Improved Use” protocol, with the different line plots showing the deposition data when the spray direction is perturbed systematically from the IU direction.

[0043] Figs. 10A-10D provide a theoretical validation of the CFD framework by visually depicting the Stokes number (St)-based projections of droplet size ranges for at least 2% targeted deposition at the nasopharynx.

[0044] Figs. 11A-11B compare the CFD predictions with experimental data and show the order-of-magnitude of IU-induced improvement in drug mass deposits at the nasopharynx compared to the CU delivery numbers, when considering the droplet size distribution in each administered shot of two common over-the-counter spray products: FlonaseTMand NasacortTM. Detailed Specification

[0045] To facilitate an understanding of the principles and features of various embodiments of the present invention, they are explained hereinafter with reference to their implementation in illustrative embodiments.

[0046] Evaluation of efficacy of aerosol nasal sprays can be approximated by the ability of the aerosol particles to reach the diseased areas of the nasal passages. In state-of- the-art testing, testing is conducted on human subjects and may include testing of multiple nasal spray parameters (e.g. particle size distribution, spray means, etc.). The application of nasal spray into a cavity can be approximated by fluid dynamics models in a computational simulation. The present disclosure describes a computational fluid dynamics (CFD) model of one or more anatomically relevant nasal passages in a user-interactive computational system for modeling multiple nasal spray parameters in such physiologically relevant environments.Attorney Docket No. 11214-008WO1 T-00555 The system outputs the extent of spray coverage on a regional level in the model anatomically relevant nasal passages. The computational fluid dynamics model that is used in the system is pre-validated using reconstructed anatomic nasal passages of real patients from a variety of demographic backgrounds.

[0047] The exemplary system includes (per Fig. 1) a processing circuit 102 that executes fluid dynamics models 112 (e.g. CFD models) of particulate transport in a nasal cavity, in particular, representing medicated particles administered via nasal spray medium. The CFD models include parameterizations of anatomic nasal passages and a refined mesh along the nasal cavity. The CFD models take as input parameters specific to the administered nasal spray, for example, particle size distribution, type of application (spray) numerical description, and others. The CFD models are validated against experimental results of a plurality of anatomically varied nasal passages. The CFD models output medicinal particle deposition profiles along a nasal passage by interpolating and / or extrapolating (e.g. interpolation / extrapolation module 113) the input parameters to the validated CFD model.

[0048] The system returns the regional particle deposition profiles, which is a proxy to the potential efficacy of a tested nasal spray medicine. The goal of the nasal spray administration is to provide medicinal particles to affected areas (i.e. diseased) along the nasal passage. The system output provides resulting CFD analysis at discretized points along the nasal passage cavity, for instance, to determine if a tested nasal spray (per user input of nasal spray parameters) reaches a target region of the nasal passage (e.g. nasopharynx).

[0049] The input parameters may describe the recommended dosage and direction of nasal spray administration. The CFD model is configured to simulate realistic airflow from inhalation / exhalation through the nasal passages. Together with the input parameters of a test nasal spray, a realistic simulation of the effect of the test nasal spray along the nasal passage is computed. The output of the simulation computed in the system is displayed to a user device at discretized points along the nasal passage, which provides information to the user about the effectiveness of the test naval spray. It is understood in the field, that contact of the nasal spray particles with affected / diseased tissue is a key indicator of pharmacological efficacy. The described system allows a user to simulate a plurality of nasal spray parameterizations without conducting time-consuming, expensive clinical tests.

[0050] The generality of the CFD model is defined by the number of geometries of scanned nasal passages that are used in the refinement and validation of the CFD model. During simulation of a test nasal spray, the environmental parameters can be interpolated toAttorney Docket No. 11214-008WO1 T-00555 provide a wide range of applicable, anatomically relevant results as to the potential efficacy of the nasal spray and / or recommended usage.

[0051] An interactive modeling system based on experimentally validated computational fluid dynamics (CFD) simulations of particle transport inside anatomic reconstructions of human respiratory cavities is disclosed. The system 100 includes a user device 130 configured to execute an interactive interface 200 (per Fig. 2) that displays the output of the CFD and the projected drug delivery within the anatomical respiratory airway using an interpolation / extrapolation algorithm. The interactive interface receives input features 210, for example, formulation density 211, formulation viscosity 212, plume half- cone angle 213, and particle size distribution 214, which may be defined by mass median diameter 215 and geometric standard deviation 216. It should be understood that the input features may be any relevant features of the drug delivery for the projected clinical application, although it is contemplated that a minimum number of input features should be provided to obtain useful results. The relevant input features should be those that have some physical and mathematical relationship to the underlying CFD.

[0052] In an example shown in Fig. 2, the projected targeting efficiency 220 is displayed for different regions of the respiratory airway, based on input drug features 210, e.g., particle sizes, spray plume geometry, particle speeds, formulation density and viscosity.

[0053] The projected targeting efficiency is calculated based on the percentage / ratio between the mass deposited at the clinical target tissue regions along the airway cavity divided by the total administered drug mass, per shot. The system quantifies the efficacy of the drug administration modality, in terms of what percentage mass of the administered drug ends up depositing at the desired site. The clinical condition the drug is being administered for determines the desired site of drug application.

[0054] The backend simulations (i.e. fluid dynamics models 112) form the data architecture of the interface and cover the feasible range of drug particle and delivery device features. The user defined input features are extrapolated / interpolated from the backend simulations in module 113, which can be stored and retrieved from the database 116.

[0055] The fluid dynamics models 112 compute targeted delivery for a discrete selection (over a wide range) of input values for the device plume angle, the formulation density and viscosity, and the drug aerosol / droplet sizes. The resulting output (deposition efficiency) will have a set of discrete values (corresponding to the discrete inputs). The interpolation / extrapolation algorithm will enable estimate of delivery efficiency for inputAttorney Docket No. 11214-008WO1 T-00555 values that are in between the inputs used while running the CFD simulations. For example, the CFD simulation generates numbers for 30 degree and 31-degree spray plume angle, while a real device may come with a random angle, say 30.65 degree. The algorithm will interpolate between the 30-degree and 31-degree numbers, to get the delivery estimate for 30.65 degree.

[0056] The digital interface 200 may be hosted on a server and designed for external access, wherein the users (drug formulation and device manufacturers) can input drug device (e.g. spray apparatus) and formulation parameters to more quickly (e.g. quicker than standard human-based testing) assess whether their product will be able to achieve therapeutically effective delivery of pharmaceutics at the clinical targets.

[0057] The digital interface generates the regional drug deposition numbers for clinical target sites along the upper respiratory pathway using CFD data and interpolation / extrapolation algorithms for the input parameters for a wide range of nasal spray and oral inhaler / nebulizer products. For example, in Fig. 2, output is separately displayed for the anterior airway 222, ostiomeatal complex 223, nasopharynx 224, and area towards the trachea 225. It should be understood that the choice of output display regions of the nasal pathway in Fig. 2 is one example. The display of projected target efficiency can be shown for one or more clinically relevant target sites along the upper respiratory pathway shown in the digital interface. The choice of target site to display may be configured to the relevant sites for treatment of a specific clinical condition.

[0058] The physics-backed interactive system is configured for airway drugs for a wide variety of clinical conditions, e.g., chronic rhinosinusitis, allergic rhinitis, ciliated epithelial infections such as those from the SARS-family viruses, infections resulting from respiratory syncytial viruses, and throat complications such as laryngeal granulomas.

[0059] The interactive system eliminates the need for modeling / experimentation on a case-by-case basis for different drug delivery products because the CFD model is built around a plurality of different anatomic reconstructions of nasal passages from multiple demographics. The CFD model generalizes results of each anatomic reconstruction geometry and outputs predictions of deposition along a generalized nasal passage.

[0060] The interface integrates mechanical modeling frameworks, and it is contemplated that the interface may be further configured to account for mucociliary transport while assessing the targeted delivery along the airway. A first embodiment of the system discounts the effect of surface-based mucociliary transport (along the airway walls) that can translocate anteriorly deposited drug particles to the clinical target sites whichAttorney Docket No. 11214-008WO1 T-00555 typically lie further downstream along the airway. It is contemplated that pharmacologic modeling can be incorporated to assess therapeutic efficacy is also another open end.

[0061] The interactive system calculates the efficiency of nasal sprays or oral inhalers for drug delivery to clinical target sites along the nose, nasal cavity, mouth and throat and predicts respiratory drug delivery for a wide range of drug formulations and delivery devices.

[0062] The following examples provide use-cases of the described interactive system. In Example 1, the use-case studies the recommended usage of nasal spray application using the interactive system. The specific information output from the interactive system from a plurality of input parameters provided a detailed analysis to improve the recommended usage of the test nasal spray. Similarly, the usage of the described interactive system can be applied to test multiple parameters that can be varied in the development of nasal medicines. Examples

[0063] Example 1: A model-based approach to improve intranasal spray targeting for respiratory viral infections

[0064] Additional examples and description can be found in Akash, M.M.H., et. al. (2023), “On a model-based approach to improve intranasal spray targeting for respiratory viral infections”. Front. Drug Deliv., which is incorporated herein in its entirety.

[0065] A computational fluid dynamics (CFD) model of the respiratory transport process in computed tomography (CT)-based anatomically realistic upper airway geometries is presented and verified. The related simulations replicate sprayed drug transmission against two different ambient inhalation rates, viz. 15 and 30 L / min; corresponding to steady relaxed and moderately heavy breathing conditions, respectively (Garcia et al., 2009).

[0066] Anatomic upper airway reconstruction: The upper airway geometries reconstructed in silico in this study were digitally reconstructed from de-identified medical- grade CT imaging data derived from two healthy test subjects. Subject 1 was a 61 year-old female and Subject 2 was a 37 year-old female. For subsequent experimental verification of the in silico findings, a 3D-printed solid anatomic replica of a 41 year-old male subject’s nasal cavity was used. The use of the archived and anonymized medical records was approved with exempt status by the Institutional Review Board (IRB) of the University of North Carolina (UNC) at Chapel Hill, with the requirement of informed consent waived for retrospective use in computational research.

[0067] The CT slices of the airway cavities were extracted at coronal depth increments of 0.348 mm in Subject 1’s scans and 0.391 mm in Subject 2’s scans.Attorney Docket No. 11214-008WO1 T-00555 Digitization of the anatomic airspaces was carried out on the image processing software Mimics Research v18.0 (Materialise, Plymouth, Michigan), using a radio-density delineation range of -1024 to -300 Hounsfield units and was complemented by clinically monitored hand- editing of the selected pixels to ensure anatomic accuracy. The output STL (stereolithography) geometries were then spatially meshed on ICEM-CFD 2019 R3 (ANSYS Inc., Canonsburg, Pennsylvania) with minute volume elements. Therein, to confirm grid-independent solutions, established mesh-refinement protocols (Frank-Ito et al., 2016; Basu et al., 2017b) were followed such that each computational grid contained more than 4 million unstructured, graded, tetrahedral elements. To enable accurate tracking near tissue surfaces, further mesh refinement involved adding three prism layers at the cavity walls, with 0.1 mm thickness and a height ratio of 1.

[0068] Simulation of breathing transport and drug delivery: Inhalation parameters for gentle-to-moderate breathing conditions were numerically replicated at 15 and 30 L / min (Garcia et al., 2009). The lower flow rate commensurate with resting breathing is dominated by viscous-laminar steady-state flow physics (Basu et al., 2020b; Inthavong et al., 2019; Zhang et al., 2019; Basu et al., 2018; Farzal et al., 2019; Kimbell et al., 2019). The higher flow rate for moderately heavy breathing (e.g., during sniffs), however, triggers shear- induced (Brown and Stewartson, 1969; Smith, 1986; Basu and Stremler, 2017, 2015; Stremler and Basu, 2014; Stremler et al., 2020) flow separation from the tortuous cavity walls, resulting in turbulence (Longest and Vinchurkar, 2007; Perkins et al., 2018; Hosseini et al., 2020; Doorly et al., 2008). The latter was tracked through Large Eddy Simulation (LES) with a sub-grid scale kinetic energy transport model (Baghernezhad and Abouali, 2010; Farnoud et al., 2020) accounting for the small-scale fluctuations. The computational scheme on ANSYS Fluent 2019 R3 employed a segregated solver, with SIMPLEC pressure-velocity coupling and second-order upwind spatial discretization. Solution convergence was monitored by minimizing mass continuity and velocity component residuals, and through stabilizing mass flow rate and static pressure at airflow outlets (see the nasopharyngeal outlet location in Figs. 3B and C). For the pressure gradient-driven laminar airflow solutions, the typical execution time for 5000 iterations was 2–3 hours with 4- processor based parallel computations operating at 3.1 GHz speed on Xeon nodes. Additionally, the LES computations each required a run-time of 1–2 days, for a pressure-driven simulated flow interval of 0.25 s, with a time-step of 0.0001 s. To realistically capture the continuum properties for inhaled warmed-up air transport inside the respiratory pathway, theAttorney Docket No. 11214-008WO1 T-00555 air density and dynamic viscosity were set at 1.204 kg / m3and 1.825x10−5kg / m‧s, respectively. However, the simulations did not incorporate any heat transfer effects.

[0069] Figs. 3D-3F depict representative CT slices for the same subject, whereas Figs. 3G-3I, respectively show the axial, sagittal, and coronal views of the CT-based upper airway reconstruction in Subject 2

[0070] Spray dynamics against the ambient airflow was tracked via Lagrangian- based inert discrete phase simulations with a Runge-Kutta solver, with localized droplet clustering along intranasal tissues obtained through numerically integrating the transport equations that consider airflow drag, gravity, and other body forces relevant for small particulates, e.g., the Saffman lift force, and by implementing a no-slip trap boundary condition on the cavity walls. Note that Brownian effects were neglected in view of the tracked droplet sizes. The drug formulation density was set to 1.5 g / mL, as a realistic estimate (Alfadhel et al., 2011; Michael et al., 2001). All simulations released monodispersed inert drug droplets ranging in diameters from 1 – 24 µm, with 3000 monodispersed inert droplets being released during each iteration. The droplets were introduced into the airspace as a solid-cone injection emanating from a single source point where the spray nozzle is located, mimicking the action of a nasal spray. Aptar Pharma’s VP7, a commercially produced pharmaceutical nasal spray pump, with its accompanying dimension properties, such as plume angle and initial spray velocity, was used as an initial point of reference for the cone injections (Pharma, accessed 18-January-2022). The droplets were given a starting velocity of 10 m / s (Liu et al., 2011) and a total non-zero mass flow rate of 1x10−20kg / s for the streams in the spray cone. The plume angle (i.e., the half-angle at the spray cone vertex) and the intranasal nozzle insertion depth were selected (Basu et al., 2020b) to be 27.93° and 5 mm, respectively. Subsequently, by varying the spray direction – a new usage condition that would significantly augment droplet deposition at the target site was detected. Additional details on the numerical setup are found in Basu, S., Holbrook, L. T., Kudlaty, K.,Fasanmade, O., Wu, J., Burke, A., et al. ( 2020), Scientific Reports 10, 1–18, and Basu, S.(2021), Scientific Reports 11, 1–13, which are incorporated herein in entirety.

[0071] Spray bottle orientation: A parameter for targeted delivery is the direction of the nasal spray axis, as the sprayed droplet trajectories are often inertia-dominated (Basu et al., 2018, 2017a, 2020b; Finlay, 2001). Instructional ambiguities (Benninger et al., 2004; Kundoor and Dalby, 2011) point toward a lack of definitive knowledge on the best ways to use a nasal spray device, with package inserts accompanying different commercial sprayAttorney Docket No. 11214-008WO1 T-00555 products often offering somewhat contrasting recommendations. There is, however, a consensus that the patient should tilt her / his head slightly forward, while holding the spray bottle upright (NIH, 2013; Benninger et al., 2004). There is an additional clinical recommendation (Flonase, accessed 11-February-2022) to avoid pointing the spray directly at the septum, which is the separating cartilaginous wall between the two sides of the nasal cavity. These suggestions were adopted in the standardization (Kimbell et al., 2018; Basu et al., 2020b) of “Current Use” (CU) protocol for topical sprays. The digital airway models were inclined forward by an angle of 22.5°, and the vertically placed upright (Benninger et al., 2004) spray axis was aligned closer to the lateral nasal wall, at one-third of the distance between the lateral side and septal wall. Finally, the spray bottle was placed at the nostril to penetrate 5-mm into the airspace, to conform with the package recommendations of commercial sprayers (NIH, 2013) for a “shallow” intranasal nozzle placement.

[0072] While the CU protocol is the accepted state-of-art technique for targeted drug delivery with nasal sprays, the CFD model simulated the angle(s) at which the spray is administered relative to the nasal geometry (“spray direction”) to test alternate protocols that bear the promise to improve delivery of drugs at the nasopharyngeal infection site. Earlier findings (Basu et al., 2020b) showed that to target the clinical site of the ostiomeatal complex, or OMC (a key target site for corticosteroid-based topical therapeutic management for chronic rhinosinusitis (Basu et al., 2020b; Farzal et al., 2019) and allergic rhinitis (Treat et al., 2020)), the spray axis should be oriented to pass through the OMC itself. The inertial motion of the sprayed particulates assists such a transport mechanism. Accordingly, to optimize the spray administration protocol in the current study, the nozzle was oriented such that the spray axis passes through the nasopharynx, wherein this strategy is hereto referred as “Improved Use”, or IU protocol. When determining the IU direction, three conditions needed to be satisfied as a way of ensuring the optimal placement of a nasal spray for drug release: (i) the extended spray axis for the IU protocol must intersect the nasopharynx; (ii) the spray axis must not cut through the septal wall to conform with clinical safety; and (iii) the axis should intersect the lateral wall in the posterior part of the nasal cavity. Fig. 4 depicts broad-spectrum visual difference between the recommended CU and the IU protocols. Additionally, Figs. 5A-D and 6A-D depict the spatial distinctions in spray placement between the IU and CU protocols, in the two test subjects, as visible from the sagittal perspective.Attorney Docket No. 11214-008WO1 T-00555

[0073] Tolerance sensitivity analysis: Once the IU for an airway reconstruction was determined, an axis perturbation-based tolerance sensitivity study was performed to assess how far the user could deviate from the determined IU spray direction and still get comparable regional drug deposition results, or in other words how robust (or, on the contrary, user-sensitive) the chosen IU direction really is.

[0074] To generate the new perturbed axes in the in silico space, a 1-mm radius circle was created perpendicular to the perturbed direction either 5-mm or 10-mm away from the central point on the nostril plane of each model. The two different distances were chosen in order to test the sensitivity of the results at different perturbation levels. The 5-mm method was performed on the left nostril of the subjects, while the 10-mm method was performed on the right nostril. Five peripheral points equidistant from each other were then selected on the circle created. The axis formed between the centroid point on the nostril plane and the peripheral point on the circle determined the new perturbed direction (PD). In all, five additional perturbed spray axis directions were created for each nostril, henceforth referred to as PD 1 – PD 5. For each new perturbed direction, the injection point was selected by measuring 5-mm from the centroid on the nostril plane, toward the nasopharynx. Each newly identified PD axis satisfied the criteria developed to identify the IU direction, and drug delivery simulations were performed. The results of the tolerance simulations were analyzed for congruity using Pearson’s correlation coefficient.

[0075] Experimental verification of computationally predicted spray performance: To extrapolate to real-world spray performance that could be projected from the in silico framework, the computationally predicted nasopharyngeal droplet deposition efficiencies were linked with the size distribution of droplets (per Fig. 7) in two existing over-the- counter spray products, thus assessing the expected deposition at the nasopharynx with a typical nasal spray. Specifically, measured distributions for FlonaseTM (Fluticasone Propionate) and NasacortTM (Triamcinolone Acetonide), both of which are commonly prescribed medications that are commercially available, were used. Four units of each product were tested at Next Breath, an Aptar Pharma company (Baltimore, MD, USA). Note that rigorous numerical testing for droplets > 24 μm clearly show (Basu, 2021; Basu et al., 2020b) that they would mostly deposit along the anterior nasal cavity and will largely miss the posterior target site of the nasopharynx.

[0076] The plume geometry was measured through a SprayVIEWR NOSP, which is a non-impaction laser sheet-based instrument. With the droplet sizes in a spray shot following aAttorney Docket No. 11214-008WO1 T-00555 log-normal distribution, the droplet size distribution (where droplet diameters are represented by x) can be framed as a probability density function (Cheng et al., 2001) per Equation 1.

[0077] In Equation1, the mass median diameters (Finlay, 2001) for FlonaseTMand NasacortTMwere respectively: x50 = 37.16 µm and 43.81 µm; the corresponding geometric standard deviations were respectively: σg = 2.080 and 1.994. The latter statistically quantifies the measured range of the droplet size data, while the x50marks the diameter such that 50% of the spray mass is in droplets smaller than x50. Note that the measurements were also collected with and without a saline additive in the sprayer, with the tests returning similar droplet size distributions. Additional details are found in Basu, et al. International Journal for Numerical Methods in Biomedical Engineering 34(4), (2018) and Basu et al., Medical Research Archives 10, 2020, which are incorporated herein in entirety.

[0078] To test the extensibility of the computational predictions derived for actual sprays, the instant study subsequently conducted twenty runs of physical spray experiments with 10-ml boluses (for measurable posterior deposits) of dyed water- based solutions injected through a 3D- printed anatomically realistic airway cavity of a different subject, Subject 3 (a 41-year-old male; the corresponding imaging data had a CT- slice resolution of 0.352 mm). Printing of the related anterior soft plastic part on a Connex3TM3D printer was carried out using a polymer ink- jetting process on Tangogray FLX950 material, approximately mimicking the material properties of the external nares and the internal tissues and cartilages. The 3D-printed cavity extent terminated just before the nasopharynx, thereby allowing us to measure the outflow volume of administered solution that would reach the nasopharyngeal walls. During the experiments, the 3D cast was clamped and an adjustable angle hinge connector of diameter 0.75 in was used to precisely fix the injector device to replicate the CU protocol. To recreate the IU products protocol, the injection was administered horizontally (as much as possible) with the spray nozzle inserted at a shallow depth of 5 mm inside the airspace. Any discharge from the front of the nose was collected separately to ensure that it did not contaminate the measurement of the penetrating solution. Additionally, for a review of prior work on the use of 3D-printed anatomic casts for nasal drug delivery studies. Results

[0079] Improved orientation of the spray axis for effective targeting: Airflow and droplet transport have been simulated for spray nozzle placement at the left and right nostrilsAttorney Docket No. 11214-008WO1 T-00555 of Subjects 1 and 2, under two standard inhalation rates (15 and 30 L / min), for drug droplet diameters 1 – 24 µm, and for spray directions as per the “Current Use” (or, CU) and “Improved Use” (or, IU) protocols. See Fig.4 for the respective spray usage protocol visuals. In all eight cases, the IU direction of the spray axis results in higher deposition at the nasopharynx in comparison to the CU protocol (see Figs. 8A-8D and Figs. 8G-8J). For instance, if the deposition trends for spray administration through the right nostril of Subject 2 are examined for the laminar regime inhalation (i.e., at 15 L / min), the peak nasopharyngeal deposition for IU is 46.5% for 13 µm drug droplets (Fig.8H), while the peak deposition for CU is only 0.53% for 14 µm drug droplets (see again Fig. 8H) and the corresponding zoomed-in visual for the CU delivery trends in Fig.8K. The nearly hundred- fold increase in targeted deposition was remarkable and was achievable simply by re- orienting the spray axis from CU to IU.

[0080] The IU and CU protocols with monodispersed conical injections Shown in Figs. 8A-8D for the IU and CU protocols with monodispersed conical injections. The row comprising Figs. 8A and 8B are for 15 L / min inhalation; the row with Figs. 8C and 8D are for 30 L / min inhalation. Figs. 8E and 8F depict the representative zoomed-in trends for nasopharyngeal deposition with the CU protocol, on administering the spray through the right nostril of Subject 1. Similarly, Figs. 8G and 8J show the comparison of the regional deposition trends at the nasopharynx of Subject 2, for the IU and CU protocols. The row comprising Figs. 8G-8H are for 15 L / min inhalation; the row with Figs. 8I and 8J are for 30 L / min inhalation. Figs. 8K and 8L depict the representative zoomed-in trends for nasopharyngeal deposition with the CU protocol, on administering the spray through the right nostril of Subject 2. The IU trend lines are marked in red; the CU trend lines are in blue. The reader should note the abbreviated vertical range on Figs. 8E, 8F, 8K, and 8L are prompted by the 2 orders-of-magnitude smaller deposition efficiency with CU.

[0081] Assessing sensitivity to IU perturbations: The variation of the nasopharyngeal deposition percentages over the assessed droplet size range (1 – 24 µm) was compared between that of the IU protocol and each of the perturbed direction (PD) data, viz. PD 1 – PD 5. The PD spray orientations were obtained by slightly perturbing the IU direction. Pearson’s correlation coefficient was comfortably greater than 0.5 for nearly every such comparison (see Figs. 9A-9J), showing a high degree of linearity between the perturbed directions and the IU protocol in terms of the ranked order of the nasopharyngeal deposition efficiencies exemplified by the tested spray droplet sizes. Moreover, the p-value associated with each correlation was much lower than the significance level of 0.05. This indicates that there is aAttorney Docket No. 11214-008WO1 T-00555 statistically significant correlation between the simulation results on the targeted nasopharyngeal drug delivery for the IU and the perturbed directions. Physically, the satisfactory correlation between IU and PD 1 – PD 5 establishes the robustness of the IU spray protocol to user subjectivities.

[0082] Figs. 9A and 9B illustrate the in silico detection of the perturbed spray directions (PD), deviating slightly from the IU axis. The direction vectors are from the centroid of the nostril plane to the points lying on a 1-mm circle that is 5 mm and 10 mm (respectively for the left and right nostril placement) from the nostril plane centroid (see Section 2.4 for related details). Figs. 9C–9F for Subject 1 and Figs. 9G - 9J for Subject 2 compare the respective nasopharyngeal deposition trends for PD 1 through PD 5 directions, with respect to that of the “Improved Use”(IU) protocol. The top row shows 15 L / min inhalation; the bottom row shows 30 L / min inhalation rate. Clustering of the plots signifies robustness of the IU usage parameters; in other words, the IU protocol is satisfactorily insensitive to user subjectivities. Fig. 9K shows tabulated values of statistical test performed to check the correlation between regional deposition efficiencies (for the discrete drug droplet sizes 1–24 μm) at the nasopharynx for the perturbed spray directions (i.e., PD 1 thru PD 5), when compared to the nasopharyngeal deposition efficiencies for the same droplet sizes with the IU protocol. The tabulated data includes the Pearson’s Correlation Coefficients (and associated p-values, with α = 0.05).

[0083] Verification of optimal droplet sizes through scaling analysis: The droplet size ranges that registered peak nasopharyngeal deposition under each inhalation condition were further analyzed and validated for reliability, through a Stokes number-based scaling analysis (Balachandar, 2009). The Stokes number (St), a ratio of the local transient inertia over viscosity, is mathematically defined as (Finlay, 2001)

[0084] where U for the present system is the airflow rate divided by flux area, ρD is the material density of the inhaled droplets, D is the droplet diameter, Cc is the Cunningham slip correction factor, µ is the dynamic viscosity of the ambient medium (i.e., air), and d represents the characteristic diameter of the flux cross-section. Now, with all other flow and morphological parameters staying invariant, Equation 2 directly leads to the following scaling law:Attorney Docket No. 11214-008WO1 T-00555

[0085] Herein (Qi, Di) are different inhaled airflow rate and sprayed droplet size pairings. Let us now consider a representative example, say the right nostril spray administration in Subject 2. For at least 2% nasopharyngeal deposition, the computationally predicted ideal droplet size range during 30 L / min inhalation is [Dmin, Dmax] = [5, 11] µm. Equation 3 can be used to project the corresponding ideal size range at the lower inhalation rate of 15 L / min. If the to-be-projected droplet size range that would generate peak nasopharyngeal deposition during the 15 L / min inhalation is represented by [D′max, D’min] in µm, then

[0086] This results in ′*+^= 7.07 µm and ′*-^= 15.56 µm. Despite the simplicity of this scaling analysis, the computationally identified range 9 – 24 µm for the same breathing conditions hence followed the same trend on the number scale, in terms of the respective directional variations from the extremal limits defined by*-^]. Figure 10D visually illustrates this specific example. The directional change of the extremal limits for the St-projected ideal droplet size ranges remarkably agreed with the corresponding CFD- based size ranges in all cases, except in one trivial outlier: e.g. Fig. 10B for Subject 1’s right nostril, there the droplet size limits for at least 2% nasopharyngeal deposition with both 15 and 30 L / min inhalation rates were 24 µm (an artifact resulting from the numerically tested droplet size range of 1 – 24 µm); the St-projected maximum ideal droplet size for 30 L / min, however, came out to be 33.94 µm.

[0087] Generic ideal droplet size range for targeted nasopharyngeal delivery: Droplet diameter range of 7.375 — 16.625 µm, or more practically 7 – 17 µm, was found most conducive for targeted nasopharyngeal delivery with the IU spray protocol, considering a 2% cut-off for deposition efficiency of the tracked monodispersed droplet cluster of each size. The limits of the generic ideal size range were obtained by respectively calculating the mean of the CFD-predicted minimum and maximum droplet diameters plotted in solid black and dark blue in Figs. 10A-10D; the averaging incorporated the droplet size data from all the eight test cases.

[0088] Figs. 10A and 10B (Subject 1) and Figs. 10C and 10D (Subject 2) visually depict the Stokes number (St)-based projections of droplet size ranges for at least 2% targetedAttorney Docket No. 11214-008WO1 T-00555 deposition at the nasopharynx. The directional change of the St-projected ranges along the number scale agreed with the corresponding CFD-based “ideal” droplet size ranges in all the test cases, except in one trivial outlier: see Fig. 10B, where the maximum ideal size limits at both 15 and 30 L / min are 24 μm; the St-projected maximum ideal droplet size for 30 L / min is, however, 33.94 μm.

[0089] It is, however, important to note that a dominant proportion of droplets (or, aerosols) that are smaller than 10 µm can bypass the nose and deposit in the lungs (Chakravarty et al., 2022; Crowder et al., 2002; Darquenne et al., 2022). From a regulatory standpoint, this may constitute a risk and the US Food and Drug Administration (FDA) accordingly monitors the percentage of droplets smaller than 10 µm for safety reasons.

[0090] Comparison of the in silico findings to physical experiments: Fig. 11A portrays the order-of-magnitude improvement in targeted drug deposition at the nasopharynx (with the IU protocol over the CU protocol), when taking into the account the droplet size distributions (Basu et al., 2018, 2020b) in actual over-the-counter spray products, viz. FlonaseTMand NasacortTM, in an administered shot. Considering all the test cases, the average IU-over-CU improvement for the two chosen spray products, as projected from the CFD simulations, was 2.117 orders-of-magnitude, with a standard deviation of 0.506. The physical experiments in a new Subject 3 reveal a comparable mean improvement in nasopharyngeal delivery, by 2.215 orders-of-magnitude, with a standard deviation of 0.016. Fig. 11B shows plots of the experimental measurements. The conformity on targeted delivery improvement between the computations and the representative physical experiments lends support to the implemented in silico framework.

[0091] Estimation for active pharmaceutical ingredient (API) delivery for sample over-the-counter spray products: The averaged estimates for the spray weight administered from each pump of a spray were 104.51 mg for FlonaseTM(Fluticasone Propionate) and 97.64 mg for NasacortTM(Triamcinolone Acetonide). Based on the simulation data and by imposing the droplet size distribution measured for FlonaseTM, the mean nasopharyngeal delivery during each spray pump was 1.9187 mg for the IU protocol and 0.0495 mg for the CU protocol. Subsequently, assuming an API concentration of 50 mcg / 100 mg of formulation (DailyMed, 2023) resulted in 0.96 mcg API delivery at the nasopharynx during the IU protocol, with direct inhalation. The corresponding number for the CU protocol with FlonaseTMwas 0.025 mcg, hence remarkably lower than the IU performance. Subsequently, with the droplet size distribution for NasacortTM, the simulations resulted in 1.8450 mg mean nasopharyngeal delivery during each spray pump with the IU protocol. In comparison, theAttorney Docket No. 11214-008WO1 T-00555 corresponding number with the CU protocol was 0.0482 mg. Consequently, for NasacortTMwhich presented an API concentration of 55 mcg / 110 mg of formulation (Drugs.com, 2023), the mean API mass delivered at the nasopharynx through direct inhalation was 0.92 mcg for IU and 0.024 mcg for CU. Discussion

[0092] On inputs to targeted drug and device design – With targeted delivery of pharmaceutical agents to the viral infection hot-spots in the posterior upper airway (e.g., at the nasopharynx) a clear challenge (Suman, 2021; Shah et al., 2014; Basu et al., 2020b), the experimentally- validated findings from this study point to the droplet size range of 7 – 17 µm as being the most effective at maximizing the sprayed and inhaled percentage deposition at the clinical upper airway target site for SARS-like infections. While it is admittedly challenging for today’s spray devices to consistently generate droplets / aerosols that small, the information from the current study can readily be used to inform the design of next-generation intranasal drug formulations, along with novel spray devices and atomizers. Such devices could be designed to maximize spray deposition within the ‘sweet spot’ described above. The iterative design of these devices is likely to involve engineering physical attributes of the spray device (for example, through adjusting nozzle sizes and pressure drops, adding baffles, and in general, by modifying the pump, actuator or formulation) to generate the required droplet sizes. Notably, the work described here not only provides a practical set of guidelines for device developers, but also provides an in silico platform for rapid iteration of device design (as the experimentally measured distribution of particle sizes generated by device prototypes can be rapidly evaluated for their deposition performance at the desired target site). In this context, the reader should also note that droplets and aerosols smaller than 10 µm tend to often bypass the nose and deposit in the lungs. The process is dictated by the low inertia of the particulates which are consequently efficiently swept downstream into the lower airway by the inhaled airflow streamlines on which they are embedded. From a clinical translation perspective, this may warrant requests for safety studies from the regulatory bodies for toxicological assessment if significant lung deposition via the nose is demonstrated.

[0093] On inputs for effective spray usage strategies – The significant two orders-of- magnitude improvement in nasopharyngeal delivery of intranasally sprayed drugs with the new IU protocol, over the typically recommended CU protocol, clearly warrants a revisit of the standard usage instructions for existing nasal spray products. The user can replicate the IUAttorney Docket No. 11214-008WO1 T-00555 protocol by holding the spray nozzle as horizontally as possible at the nostril, with a slight tilt toward the cheeks. The results also hint at the utility potential of a device fitted with a bent nozzle.

[0094] On caveats regarding the tested droplet size range – The tested droplet diameter range was 1 – 24 µm. While extracting the droplet sizes that would correspond to at least 2% nasopharyngeal deposition from a cluster of 3000 monodispersed droplets of each size, three of the eight test cases (i.e., the IU protocol data for left and right nostril administration in two subjects under two inhalation rates) led to 24 µm as the maximum limit of such sizes; see Figs. 8A-8L and 10A-10D. While that may justifiably raise the question on what happens if droplets that are sized bigger than 24 µm were considered, the focus of this study has been to determine a common droplet size range that would be generically robust to inhaled airflow conditions and user subjectivities. Consequently, the bigger droplets were not tracked, which tend to deposit mostly anteriorly, owing to the inertia-dominated initial phase of their trajectories when injected out of the nozzle; see publication (Basu et al., 2020b) for an extensive related discussion. Also, as a side-note to this, it is relevant to consider that administered droplets, under nebulized conditions in the same two test subjects and with comparable material density (1.3 g / mL), had resulted in an ideal size range of 2.5 – 19 µm (i.e., comfortably smaller than 24 µm) for at least 5% targeted nasopharyngeal deposition; see publications (Basu, 2021) for details.

[0095] On the limitations of respiratory flow modeling – Realistic modeling of mucociliary transport along the morphologically complex airway cavity constitutes a significant open question in the domain of respiratory transport mechanics (Ford et al., 2021; Sekaran, 2021; Rajendran and Banerjee, 2021). In this study, an algorithm was adopted to identify the droplet sizes that are efficient at direct nasopharyngeal delivery, under the impact of inhaled airflow when sprayed into the intranasal space. However, a substantial caveat lies in what happens to the larger droplets that deposit along the anterior parts of the airway. Quantifying their mucus-driven downstream transport mechanics and correlating that with the therapeutic efficacy of the drug solutes when they reach the posterior clinical target sites poses a vital translational challenge, which needs to be addressed by the interdisciplinary scientific community in future.

[0096] On caveats related to the droplet transport modeling – The Lagrangian particle transport scheme used to track the sprayed droplets is one-way momentum coupled with the continuous ambient airflow field. Additionally, the droplet tracking model ignores evaporation effects on the droplet constituents and any impact from the liquidAttorney Docket No. 11214-008WO1 T-00555 wall films. First momentum transfer from the nasal spray droplets to the surrounding fluid phase may indeed affect droplet motion and influence the nasal deposition patterns (Kolanjiyil et al., 2022). However, the one-way coupling approach for regional deposition prediction, apart from being computationally inexpensive, has been validated experimentally through multiple studies, both by us (Basu et al., 2020b) and others (Inthavong et al., 2008; Feng et al., 2017; Zhao et al., 2021). Secondly, the droplet evaporation effects, while important for tracking the slow drug delivery process inside the lower airway along the branched bronchial pathways, could be considered negligible for drug delivery to sites in the upper respiratory tract, such as the nasopharynx. The time scale for sprayed droplet transport for direct nasopharyngeal deposition is merely on the order of (10−1) s (Basu et al., 2018). With the scale at least 2 – 3 orders smaller than the evaporation time scale for a small droplet (Zang et al., 2019; Nguyen et al., 2012; Chatterjee et al., 2021), it may be argued that the inclusion of evaporative effects in the numerical scheme will have trivial impact on the direct deposition predictions at the nasopharynx. Finally, the non-consideration of the airway surface liquid film is a key limitation and a long-standing challenge, given the complex non-Newtonian rheology of the mucosal substrate (Lai et al., 2009). Several simulation features are contemplated, including (a) how long does a drug droplet stay at the target site before being swept downstream? (b) is the time scale from (a) sufficiently long for pharmaceutically effective tissue-level penetration of the drug solutes? and (c) what is the realistic nature of droplet dispersion and surface coverage over the liquid wall film?

[0097] On the constraints posed by the reconstructed in silico geometries – The CT-based anatomically realistic reconstructions, while accurately replicating the topological convolutions implicit in a real tortuous respiratory cavity (Yuk et al., 2022, 2023), still come with the caveat of containing structurally rigid airway walls. However, though the rigidity of the walls (intended to mimic the internal tissue surfaces and cartilages) is somewhat unrealistic, the timescale of inhaled transport is on the order of 10−1s (Basu et al., 2018) and the idealization could be considered a mechanistically feasible assumption that is sufficient to extract the fundamental nuances underlying such physiologically complex transport processes.

[0098] On the usability of the findings despite the small test cohort – The study was designed to test an improved protocol for administration of nasal sprays that is robust to person-to-person variation. Notably, within the geometries tested, the effect size observed by us (improved deposition efficiency) was two orders of magnitude. A key limitation of theAttorney Docket No. 11214-008WO1 T-00555 findings was the restricted sample size of only two main test subjects (i.e., Subjects 1 and 2). However, the congruity in targeted delivery improvement (see Fig.11B) in a randomly selected different subject (named as Subject 3) bodes well for the general extensibility of the essential findings to a wider cohort. The large observed effect size represents an encouraging preliminary finding, as the approach was tested for generalizability and robustness to inter- individual variability.

[0099] On toxicity evaluation – Any new formulation or drug delivery device that might attempt to replicate the improved targeted deposition at intranasal sites, based on the current findings, will essentially constitute a surface contacting mechanism with limited duration contact. For determination of the usage safety levels, such a development will also require biocompatibility testing of the device, including a check of three basic biocompatibility endpoints (viz. cytotoxicity, irritation (Basu et al., 2022b), sensitization) per the FDA guidance (ISO, 2009; FDA, 2022), by providing test data and / or relevant justification (e.g., history of clinical use for the same device).

[0100] Patient comfort and practicality of the IU protocol – A parallel study (Basu et al., 2022b) was ran for assessing the human factors, e.g., the comfort levels, associated with spray placement protocols that are similar to the IU protocol proposed here and while using an open-angle swirling jet atomizer (GentleMist®; Dr. Ferrer Biopharma, Hallandale Beach, Florida). Evaluation feedback collected from a cohort of thirteen healthy volunteers shows that the IU-like protocol offered a more gentle and soothing delivery experience, with less impact pressure. Also, 60% of participants reported that the CU technique caused painful irritation. In context to the practicality of the IU spray placement, the reader should additionally note that the physical experiments, results of which are outlined in Fig.11B, were performed in a soft solid 3D-printed anatomic cast that replicated the pliability characteristics of real nasal tissues. Simulation System

[0101] It should be appreciated that the logical operations described above can be implemented (1) as a sequence of computer-implemented acts or program modules running on a computing system and / or (2) as interconnected machine logic circuits or circuit modules within the computing system. The implementation is a matter of choice dependent on the performance and other requirements of the computing system. Accordingly, the logical operations described herein are referred to variously as state operations, acts, or modules. These operations, acts and / or modules can be implemented in software, in firmware, in special purpose digital logic, in hardware, and any combination thereof. It should also beAttorney Docket No. 11214-008WO1 T-00555 appreciated that more or fewer operations can be performed than shown in the figures and described herein. These operations can also be performed in a different order than those described herein.

[0102] Figure 1 shows an illustrative computer architecture for a computer system 100 capable of executing the software components described herein for executing the machine-readable code of the exemplary ML-based method in the manner presented above. The computer architecture shown in Fig. 1 illustrates an example computer system configuration, and the computer 100 can be utilized to execute any aspects of the components and / or modules presented herein described as executing on the Automated Related Question Recommender machine learning system or any components in communication therewith.

[0103] In an embodiment, the computing device 100 may comprise two or more computers in communication with each other that collaborate to perform a task. For example, but not by way of limitation, an application may be partitioned in such a way as to permit concurrent and / or parallel processing of the instructions of the application. Alternatively, the data processed by the application may be partitioned in such a way as to permit concurrent and / or parallel processing of different portions of a data set by the two or more computers. In an embodiment, virtualization software may be employed by the computing device 100 to provide the functionality of a number of servers that is not directly bound to the number of computers in the computing device 100. For example, virtualization software may provide twenty virtual servers on four physical computers. In an embodiment, the functionality disclosed above may be provided by executing the application and / or applications in a cloud computing environment. Cloud computing may comprise providing computing services via a network connection using dynamically scalable computing resources. Cloud computing may be supported, at least in part, by virtualization software. A cloud computing environment may be established by an enterprise and / or may be hired on an as-needed basis from a third- party provider. Some cloud computing environments may comprise cloud computing resources owned and operated by the enterprise as well as cloud computing resources hired and / or leased from a third-party provider.

[0104] In its most basic configuration, computing device 100 typically includes at least one processing unit 104 and system memory. Depending on the exact configuration and type of computing device, system memory may be volatile (such as random-access memory (RAM)), non-volatile (such as read-only memory (ROM), flash memory, etc.), or some combination of the two. The processing unit 102 may be a standard programmable processor that performs arithmetic and logic operations necessary for operation of the computing deviceAttorney Docket No. 11214-008WO1 T-00555 100. While only one processing unit 102 is shown, multiple processors may be present. As used herein, processing unit and processor refers to a physical hardware device that executes encoded instructions for performing functions on inputs and creating outputs, including, for example, but not limited to, microprocessors (MCUs), microcontrollers, graphical processing units (GPUs), and application specific circuits (ASICs). Thus, while instructions may be discussed as executed by a processor, the instructions may be executed simultaneously, serially, or otherwise executed by one or multiple processors. The computing device 100 may also include a bus or other communication mechanism for communicating information among various components of the computing device 100.

[0105] Computing device 100 may have additional features / functionality. For example, computing device 100 may include additional storage such as removable storage and non-removable storage including, but not limited to, magnetic or optical disks or tapes. Computing device 100 may also contain network connection(s) via a communication interface 120 that allow the device to communicate with other devices such as over the communication pathways described herein. The network connection(s) 120 may take the form of modems, modem banks, Ethernet cards, universal serial bus (USB) interface cards, serial interfaces, token ring cards, fiber distributed data interface (FDDI) cards, wireless local area network (WLAN) cards, radio transceiver cards such as code division multiple access (CDMA), global system for mobile communications (GSM), long-term evolution (LTE), worldwide interoperability for microwave access (WiMAX), and / or other air interface protocol radio transceiver cards, and other well-known network devices. Computing device 100 may also have input device(s) such as keyboards, keypads, switches, dials, mice, track balls, touch screens, voice recognizers, card readers, paper tape readers, or other well-known input devices. Output device(s) such as printers, video monitors, liquid crystal displays (LCDs), touch screen displays, displays, speakers, etc. may also be included. The additional devices may be connected to the bus in order to facilitate communication of data among the components of the computing device 100. All these devices are well known in the art and need not be discussed at length here.

[0106] The processing unit 104 may be configured to execute program code encoded in tangible, computer-readable media. Tangible, computer-readable media refers to any media that is capable of providing data that causes the computing device 100 (i.e., a machine) to operate in a particular fashion. Various computer-readable media may be utilized to provide instructions to the processing unit 104 for execution. Example tangible, computer- readable media may include, but is not limited to, volatile media, non-volatile media,Attorney Docket No. 11214-008WO1 T-00555 removable media and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. System memory 110, removable storage, and non-removable storage are all examples of tangible, computer storage media. Example tangible, computer-readable recording media include, but are not limited to, an integrated circuit (e.g., field- programmable gate array or application-specific IC), a hard disk, an optical disk, a magneto- optical disk, a floppy disk, a magnetic tape, a holographic storage medium, a solid-state device, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices.

[0107] In light of the above, it should be appreciated that many types of physical transformations take place in the computer architecture 100 in order to store and execute the software components presented herein. It also should be appreciated that the computer architecture 100 may include other types of computing devices, including hand-held computers, embedded computer systems, personal digital assistants, and other types of computing devices known to those skilled in the art. It is also contemplated that the computer architecture 100 may not include all of the components shown in Fig. 1, may include other components that are not explicitly shown in Fig.1, or may utilize an architecture different than that shown in Fig. 1.

[0108] In an example implementation, the processing unit 104 may execute program code stored in the system memory 110. For example, the bus may carry data to the system memory 110, from which the processing unit 104 receives and executes instructions. The data received by the system memory may optionally be stored on the removable storage or the non-removable storage before or after execution by the processing unit 104.

[0109] It should be understood that the various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination thereof. Thus, the methods and apparatuses of the presently disclosed subject matter, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a computing device, the machine becomes an apparatus for practicing the presently disclosed subject matter. In the case of program code execution on programmable computers, the computing device generally includes a processor, a storageAttorney Docket No. 11214-008WO1 T-00555 medium readable by the processor (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. One or more programs may implement or utilize the processes described in connection with the presently disclosed subject matter, e.g., through the use of an application programming interface (API), reusable controls, or the like. Such programs may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language and it may be combined with hardware implementations.

[0110] Some references, which may include various patents, patent applications, and publications, are cited in a reference list and discussed in the disclosure provided herein. The citation and / or discussion of such references is provided merely to clarify the description of the present disclosure and is not an admission that any such reference is “prior art” to any aspects of the present disclosure described herein. In terms of notation, “[n]” corresponds to the nthreference in the list. All references cited and discussed in this specification are incorporated herein by reference in their entireties and to the same extent as if each reference was individually incorporated by reference.

[0111] Although example embodiments of the present disclosure are explained in some instances in detail herein, it is to be understood that other embodiments are contemplated. Accordingly, it is not intended that the present disclosure be limited in its scope to the details of construction and arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or carried out in various ways.

[0112] It must also be noted that, as used in the specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” or “5 approximately” one particular value and / or to “about” or “approximately” another particular value. When such a range is expressed, other exemplary embodiments include from the one particular value and / or to the other particular value.

[0113] By “comprising” or “containing” or “including” is meant that at least the name compound, element, particle, or method step is present in the composition or article or method, but does not exclude the presence of other compounds, materials, particles, method steps, even if the other such compounds, material, particles, method steps have the same function as what is named.Attorney Docket No. 11214-008WO1 T-00555

[0114] In describing example embodiments, terminology will be resorted to for the sake of clarity. It is intended that each term contemplates its broadest meaning as understood by those skilled in the art and includes all technical equivalents that operate in a similar manner to accomplish a similar purpose. It is also to be understood that the mention of one or more steps of a method does not preclude the presence of additional method steps or intervening method steps between those steps expressly identified. Steps of a method may be performed in a different order than those described herein without departing from the scope of the present disclosure. Similarly, it is also to be understood that the mention of one or more components in a device or system does not preclude the presence of additional components or intervening components between those components expressly identified.

[0115] The term “about,” as used herein, means approximately, in the region of, roughly, or around. When the term “about” is used in conjunction with a numerical range, it modifies that range by extending the boundaries above and below the numerical values set forth. In general, the term “about” is used herein to modify a numerical value above and below the stated value by a variance of 10%. In one aspect, the term “about” means plus or minus 10% of the numerical value of the number with which it is being used. Therefore, about 50% means in the range of 45%-55%. Numerical ranges recited herein by endpoints include all numbers and fractions subsumed within that range (e.g., 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.90, 4, 4.24, and 5).

[0116] Similarly, numerical ranges recited herein by endpoints include subranges subsumed within that range (e.g., 1 to 5 includes 1-1.5, 1.5-2, 2-2.75, 2.75-3, 3-3.90, 3.90-4, 4-4.24, 4.24-5, 2-5, 3-5, 1-4, and 2-4). It is also to be understood that all numbers and fractions thereof are presumed to be modified by the term “about.”

[0117] The following patents, applications, and publications as listed below and throughout this document are hereby incorporated by reference in their entirety herein. [1] Afkhami, S., D’Agostino, M. R., Zhang, A., Stacey, H. D., Marzok, A., Kang, A., et al. (2022). Respiratory mucosal delivery of next-generation COVID-19 vaccine provides robust protection against both ancestral and variant strains of SARS-CoV-2. Cell [2] Akash, M.M.H., Lao, Y., Balivada, P.A., Ato, P., Ka, N.K., Mituniewicz, A., Silfen, Z., Suman, J., Chakravarty, A., Joseph-McCarthy, D., and Basu, S. (2023). On a model-based approach to improve intranasal spray targeting for respiratory viral infections. Frontiers in Drug Delivery 3:1164671.Attorney Docket No. 11214-008WO1 T-00555 [3] Akash, M. M. H., Mituniewicz, A., Lao, Y., Balivada, P., Ato, P., Ka, N., et al. (2021). A better way to spray?–a model-based optimization of nasal spray use protocols. Bulletin of the American Physical Society [4] Alfadhel, M., Puapermpoonsiri, U., Ford, S. J., McInnes, F. J., and van der Walle, C. F. (2011). Lyophilized inserts for nasal administration harboring bacteriophage selective for Staphylococcus aureus: in vitro evaluation. International Journal of Pharmaceutics 416, 280–287 [5] [Dataset] Axe, D. (accessed 9-February-2022). Will COVID-19 Vaccine Nasal Sprays Be the Pandemic Game-Changer We Need? News link [6] Baghernezhad, N. and Abouali, O. (2010). Different SGS models in Large Eddy Simulation of 90 degree square cross-section bends. Journal of Turbulence , N50 [7] Balachandar, S. (2009). A scaling analysis for point–particle approaches to turbulent multiphase flows. International Journal of Multiphase Flow 35, 801–810 [8] Basu, A., Sarkar, A., and Maulik, U. (2020a). Molecular docking study of potential phytochemicals and their effects on the complex of SARS-CoV2 spike protein and human ACE2. Scientific Reports 10, 17699 [9] Basu, S. (2021). Computational characterization of inhaled droplet transport to the nasopharynx. Scientific Reports 11, 1–13

[0010] Basu, S., Akash, M. M. H., Hochberg, N. S., Senior, B. A., Joseph-McCarthy, D., and Chakravarty, A. (2022a). From SARS-CoV-2 infection to COVID-19 morbidity: an in silico projection of virion flow rates to the lower airway via nasopharyngeal fluid boluses. Rhinology Online 5

[0011] Basu, S., Farzal, Z., and Kimbell, J. S. (2017a). “Magical” fluid pathways: inspired airflow corridors for optimal drug delivery to human sinuses. In APS Division of Fluid Dynamics Meeting Abstracts. L4–004

[0012] Basu, S., Frank-Ito, D. O., and Kimbell, J. S. (2018). On computational fluid dynamics models for sinonasal drug transport: Relevance of nozzle subtraction and nasal vestibular dilation. International Journal for Numerical Methods in Biomedical Engineering 34, e2946

[0013] Basu, S., Holbrook, L. T., Kudlaty, K., Fasanmade, O., Wu, J., Burke, A., et al. (2020b). Numerical evaluation of spray position for improved nasal drug delivery.Scientific Reports 10, 1–18

[0014] Basu, S., Khawaja, U. A., Rizvi, S. A. A., Sanchez-Gonzalez, M. A., and Ferrer, G. (2022b). Evaluation of patient experience for a computationally-guided intranasalAttorney Docket No. 11214-008WO1 T-00555 spray protocol to augment therapeutic penetration: Implications for effective treatments for COVID-19, Rhinitis, and Sinusitis. Medical Research Archives 10

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[0047] ISO (2009). Biological evaluation of medical devices – Part 1: Evaluation and testing within a risk management process. ISO 10993-1: 2009

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[0057] Michael, Y., Snowden, M. J., Chowdhry, B. Z., Ashurst, I. C., Davies-Cutting, C. J., and Riley, T. (2001). Characterisation of the aggregation behaviour in a salmeterol and fluticasone propionate inhalation aerosol system. International Journal of Pharmaceutics 221, 165–174

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[0062] Perkins, E. L., Basu, S., Garcia, G. J. M., Buckmire, R. A., Shah, R. N., and Kimbell, J. S. (2018). Ideal particle sizes for inhaled steroids targeting vocal granulomas: preliminary study using computational fluid dynamics. Otolaryngology–Head and Neck Surgery 158, 511–519

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Claims

Attorney Docket No. 11214-008WO1 T-00555 What is claimed is:

1. A system to model drug delivery in respiratory airspace, the system comprising: a processor; a memory having instructions sorted thereon; and a means for input and output, wherein at least one set of input data are provided by the input means, wherein execution of the instructions by the processor cause the processor to execute one or more models of fluid dynamics in respiratory airspace, and wherein the system is configured to display outputs of the one or more models associated with a first set of input data.

2. The system of claim 1, wherein the one or more models of fluid dynamics in respiratory airspace are varied by associated 3D anatomic airway structural models.

3. The system of claim 1, wherein the one or more models of fluid dynamics in respiratory airspaces are configured to receive as input parameters related to drug administration modalities.

4. The system of claim 1, wherein the one or more models of fluid dynamics in respiratory airspace are validated by in vitro testing.

5. The system of claim 1, wherein the outputs of the one or more models are interpolated for the associated first set of input data.

6. The system of claim 1, wherein the outputs of the one or more models are surface maps associated with the respiratory airspace.

7. The system of claim 1, wherein the outputs comprise quantification of drug delivery within the respiratory airspace.Attorney Docket No. 11214-008WO1 T-00555 8. The system of claim 1, wherein the set of input data includes drug particle size distribution, administered weight and density, angle of delivery, plume geometry for sprays, and speeds of administered particles at a nozzle exit.

9. A method comprising: receiving, via a processor, at least one set of input data, including a first set of input data; executing, via the processor, one or more models of fluid dynamics in respiratory airspace; and displaying, via the processor, outputs of the one or more models associated with the first set of input data.

10. The method of claim 9, wherein the one or more models of fluid dynamics in respiratory airspace are varied by associated (three-dimensional) 3D anatomic airway structural models.

11. The method of claim 9, wherein the one or more models of fluid dynamics in respiratory airspace are configured to receive as input parameters related to drug administration modalities.

12. The method of claim 9, wherein the one or more models of fluid dynamics in respiratory airspace are validated by in vitro testing.

13. The method of claim 9, wherein the outputs of the one or more models are interpolated for the associated first set of input data.

14. The method of claim 9, wherein the outputs of the one or more models are surface maps associated with the respiratory airspace.

15. The method of claim 9, wherein the outputs comprise quantification of drug delivery within the respiratory airspace.Attorney Docket No. 11214-008WO1 T-00555 16. The method of claim 9, wherein the set of input data includes drug particle size distribution, administered weight and density, angle of delivery, plume geometry for sprays, and speeds of administered particles at a nozzle exit.

17. A non-transitory computer-readable medium having instructions stored thereon, wherein execution of the instructions by a processor causes the processor to: receive, via the processor, at least one set of input data, including a first of input data; execute one or more models of fluid dynamics in respiratory airspace; and display, via the processor, outputs of the one or more models associated with the first set of input data.

18. The non-transitory computer-readable medium of claim 17, wherein the one or more models of fluid dynamics in respiratory airspace are varied by associated (three- dimensional) 3D anatomic airway structural models.

19. The non-transitory computer-readable medium of claim 17, wherein the one or more models of fluid dynamics in respiratory airspace are configured to receive as input parameters related to drug administration modalities.

20. The non-transitory computer-readable medium of claim 17, wherein the one or more models of fluid dynamics in respiratory airspace are validated by in vitro testing.

21. The non-transitory computer-readable medium of claim 17, wherein the outputs of the one or more models are interpolated for the associated first set of input data.

22. The non-transitory computer-readable medium of claim 17, wherein the outputs of the one or more models are surface maps associated with the respiratory airspace.

23. The non-transitory computer-readable medium of claim 17, wherein the outputs comprise quantification of drug delivery within the respiratory airspace.

24. The non-transitory computer-readable medium of claim 17, wherein the set of input data includes drug particle size distribution, administered weight and density, angleAttorney Docket No. 11214-008WO1 T-00555 of delivery, plume geometry for sprays, and speeds of administered particles at a nozzle exit.

Images