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14 June 2022 Inhalation assumption that the droplet deposition location is the initial point of wall contact. For the nasal spray pump chosen for this study, the baseline spray metrics that are required for set- ting up the initial and boundary conditions of the CFD model were measured using in-house in vitro spray experiments [14]. Mean values of the measure- ments were used as the baseline case spray simulation parameters for the specific spray, which consisted of an average spray injection velocity = 14.4 m/s, aver- age cone angle = 35 0 , ovality ~1, shot weight = 56.3 mg, formulation density = 960 kg/m 3 , spray duration = 50 ms and polydisperse droplet size distribution. To calculate the relative efficiency of nasal spray delivery to the intended site of action, which is the posterior nasal region, the nasal model was split into anterior and posterior regions based on the nasal valve loca- tion [36]. To investigate the effects of changes in spray cone angle, simulations were conducted while vary- ing the cone angle metric by ~30% from the base- line value and simulated the spray deposition in the anterior and posterior nasal regions while holding all other parameters (including the insertion conditions) at baseline. A new relative deposition parameter, rel- ative difference in the posterior delivery (RD_PD) with respect to the baseline case, was introduced to compare the sensitivity of drug delivery to change in a spray metric value (e.g., change in cone angle or spray velocity). e use of a relative parameter helped to avoid the need to simulate spray deposition for a large number of spray metric values while enabling extrap- olation to wider ranges of spray metric values [6]. the shot weight of the spray. Furthermore, for captur- ing an approximate spray droplet injection condition and near-nozzle spray momentum conditions, spray droplets can be injected from the nozzle orifice with a conical injection and a turbulent velocity profile with an average velocity matching the experimentally mea- sured spray velocity at 3 cm from the spray tip, as shown in Figure 1 [1, 5]. is approach of incorpo- rating the in vitro measured spray parameters is a time and resource efficient computational spray modeling framework that can accurately predict spray trans- port in nasal airways and nasal deposition upon ini- tial wall contact, while avoiding the need to simulate the highly complex spray atomization process [1, 5]. e CFD spray modeling framework can be further simplified by added assumptions on the quasi steady nature of the momentum exchange ("quasi two-way coupled" approach), while still maintaining reliability and accuracy [5]. Simulations may also be performed in conjunction with a nasal inhalation flow to study the influence of the subject's inhalation on spray drop- let transport [5]. Furthermore, in simplified CFD models of nasal spray droplet transport and depo- sition, a deposit-on-touch boundary condition can be employed. However, depending on spray pump administration conditions and insertion angles, nasal sprays may interact with the nasal surface/mucus layer in a way that creates complex droplet-wall inter- actions followed by significant liquid motion after initial wall contact [35]. In order to model these phe- nomena, additional physics based sub-models need to be added to the CFD modeling framework [35]. Use of CFD for spray metric sensitivity analysis/bioequivalence evaluation In vitro spray metrics, such as spray plume geomet- rical features, are currently used to characterize nasal sprays. Most commercial nasal sprays are hand-actu- ated; therefore, a change in spray usage conditions or any change in formulation characteristics and/ or actuation conditions could result in changes in spray metrics, which could lead to variability in the amount of drug delivered. Currently, there is only limited information on the sensitivity of nasal depo- sition to changes in a specific spray metric. When comparing generic nasal inhalation drug products to reference listed product performance, it may be advantageous to know how variability in an in vitro test metric of a specific product impacts nasal delivery in comparison to the reference product. is section of the article illustrates the applicability of CFD sim- ulations in determining the sensitivity of nasal spray drug delivery to a given spray metric. Specifically, the simulations show the effect of changes in cone angle on spray transport and its impact on posterior nasal deposition in a human nasal airway model [5]. e model estimates spray droplet deposition with the Figure 2 CFD simulation results showing the impact of spray cone angle on nasal delivery (image modified from Kolanjiyil, et al. [6] with permission from Respiratory Drug Delivery 2021, RDD Online). 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 Slope = -0.73 RD_PD Linear trendline Spray pump inserted into nostril Posterior region Velocity [m s^-1] Anterior region Turbinates RD_PD Relative difference in cone angle cone angle 55° cone angle 15° cone angle 25° cone angle 35° cone angle 45° 16.1 14.3 12.5 10.7 8.9 7.1 5.4 3.6 1.8 0.0