Computational fluid dynamics (CFD) and discrete element modeling (DEM) approach for predictions of dry powder inhaler (DPI) drug delivery (U01)

Description

Dry powder inhalers (DPIs) are typically formulated as a combination of the active pharmaceutical ingredient (API) and significantly larger carrier particles [1-2]. The device is actuated via patient effort, where the flow rate induced by inhalation produces shear fluidization of the particles within the device [3]. Carrier particles are typically a necessary component of the formulation because expected flow rates are not sufficient to entrain the smaller API particles in the inhaled airflow [3]. After entrainment, the API particles are subsequently deagglomerated from the carrier particles via several processes, where impaction of the carrier-API combination against the interior of the device is expected to be the dominant mechanism [3]. This deagglomeration process is important because carrier particle diameters are typically on the order of magnitude of 100 µm [3] and are too large to penetrate the mouth-throat region [4], whereas deagglomerated API particles are ideally in the range of 1-5 µm [5-9]. Thus, the performance of a given drug-device combination DPI will depend on the ability of the product to entrain the carrier-API combination particles as well as the time and thoroughness of the deagglomeration of the individual API particles from the carrier particles.To gain approval in the United States, a generic version of a brand name DPI must show bioequivalence (BE) based on the "weight of evidence" approach where an applicant is recommended to conduct a combination of in vitro, pharmacokinetic (PK), and a pharmacodynamic (PD) or clinical endpoint (CE) studies [5]. The in vitro experiments are expected to establish similar product performance, the PK study ensures similar systemic exposure, while the PD or CE study evaluates local drug exposure [5]. Currently, product specific BE guidance's for DPIs recommend single actuation content and aerodynamic particle size distribution (APSD) in vitro experiments [10-13]. Though it would be advantageous if single actuation content and APSD were in vivo predictive metrics, there is no current evidence to support an in vitro-in vivo correlation (IVIVC) for these or any other in vitro metrics for any DPI drug product.The primary challenge for developing an IVIVC for a locally-acting DPI based on a given in vitro metric is the inability of current techniques to directly measure local tissue concentration at the site of action without altering the formulation. Gamma scintigraphy is a technique which uses a radiolabel, typically technetium 99m, to produce two-dimensional images from a gamma camera and has been used to characterize a wide variety of oral inhalation drug products (OIDPs) [14]. Though gamma scintigraphy can provide information related to local exposure of an OIDP, this may not be appropriate for BE evaluation due to the need to alter the formulation by including a radiolabel. However, gamma scintigraphy data may be useful for validating computational fluid dynamics (CFD) models, which are physics-based models capable of predicting aerosol deposition from an OIDP. The combination of CFD predictions and gamma scintigraphy data for validation of these predictions may be useful for developing an IVIVC.An obstacle for using CFD to model regional lung deposition from a DPI is that, unlike with metered dose inhalers where the aerosol may be treated as individual spherical particles without any particle interaction forces [15], DPI particle transport is significantly affected by particle interaction forces and individual particle shapes [3]. Discrete element modeling (DEM) is a particle trajectory modeling strategy that can be used with CFD to predict particle interaction forces and their effects on the agglomeration and deagglomeration of carrier and API particles from a DPI [16]. Numerous studies have developed and investigated new CFD-DEM methods for spherical and irregular shaped particles which include aggregation effects such as van der Waals and electrostatic forces, and deagglomeration forces from air flow and impact [16]. These new CFD-DEM methods may be useful when APSD is known for a given sample, where the impact of different amounts of carrier and API particles on regional lung deposition may be investigated. This would be helpful for determining whether single actuation content and APSD alone would be sufficient for developing an IVIVC, or if other metrics are needed.ObjectivesThe objective is to develop a combined computational fluid dynamics (CFD) and discrete element modeling (DEM) method for the prediction of agglomeration and deagglomeration of dry powder inhaler (DPI) carrier and active pharmaceutical ingredient (API) particles. The desired outcome is a set of DEM codes for the prediction of DPI particle transport and deposition. The expectation is that the codes developed in execution of this objective will at a minimum be shared with the Office of Generic Drugs (OGD) in CDER, and preferably be made available to the public as well. Additionally, a sensitivity analysis of various formulation properties will be used to understand the impact of formulation differences on drug delivery.Detailed DescriptionA set of DEM codes will be developed for use with CFD that may be used to predict agglomeration and deagglomeration of carrier and API particles. A set of preexisting codes may serve as a firm basis for further development. These codes should be capable of modeling agglomeration effects from van der Waals and electrostatic forces, as well as deagglomeration effects from air flow and impact, and should be capable of handling non-spherical particle shapes. Once code development is complete, the DEM code will be used with CFD to simulate a series of test cases based on data from the literature and/or in vitro data generated for this study. These test cases will verify the ability of the developed code to capture the various forces of interest. It is preferable that the test cases encompass several drug products to ensure broad applicability to this class of drug products. The test cases may focus on device performance and/or drug delivery to the human upper airways, where the choice of either region of interest may depend on available data and/or relevance to the force of interest. Of interest is the ability of the model to predict APSD where in vitro APSD data from the exit of a USP induction port may be used for comparison. As mentioned in the Objectives, it is expected that the developed codes will be shared with OGD and potentially to the public as well. If possible, it would be preferable for the set of DEM codes to be applicable to multiple CFD platforms, but it is recognized that this may be challenging. Moreover, to facilitate the sharing of information, it would be preferable if the DEM codes were tailored for use with an open-source CFD platform, though it is not a requirement.Once development and validation of the CFD-DEM codes are complete, a sensitivity analysis of various formulation properties on drug delivery to the lungs will be conducted. Examples of formulation properties will include carrier mass, API mass, carrier particle size, and API particle size. A key metric of interest is APSD, where the location of the APSD characterization may be at the device mouthpiece and/or after the mouth-throat and/or USP induction port. Though not required, if possible an exploration of the effects of formulation properties on regional lung deposition would be useful as well.The study should consist of four phases:Phase 1: Development of DEM computational algorithms that describe relevant agglomeration and deagglomeration forces as comprehensively as possible.Phase 2: Validation of CFD and DEM models with available data from literature and/or from generated in vitro data.Phase 3: Conduct a sensitivity analysis of various formulation properties and their effects on APSD and preferably also on regional deposition fraction within the lung.Phase 4: Prepare and publish manuscripts. This is intended to be done in the third year of the grant, which is the reason for the reduced budget for that year. This phase may also begin earlier than the third year.References[1] Hickey AJ, editor. Pharmaceutical inhalation aerosol technology. CRC Press; 2003.[2] Donovan MJ, Kim SH, Raman V, Smyth HD. Dry powder inhaler device influence on carrier particle performance. J Pharm Sci. 2011;101(3):1097-1107.[3] Finlay WH. The mechanics of inhaled pharmaceutical aerosols: an introduction. Academic Press; 2001.[4] Byron PR. Prediction of drug residence times in regions of the human respiratory tract following aerosol inhalation. J Pharm Sci. 1986;75(5):433-438.[5] Lee SL, Adams WP, Li BV, Conner DP, Chowdhury BA, Yu LX. In vitro considerations to support bioequivalence of locally acting drugs in dry powder inhalers for lung diseases. AAPS J. 2009;11(3):414-423.[6] Zanen P, Go LT, Lammers JW. Optimal particle size for beta 2 agonist and anticholinergic aerosols in patients with severe airflow obstruction. Thorax. 1996;51(10):977–80.[7] Zanen P, Go LT, Lammers J-W. The optimal particle size for β-adrenergic aerosols in mild asthmatics. Int J Pharm. 1994;107(3):211–7.[8] Zanen P, Go LT, Lammers J-WJ. The optimal particle size for parasympathicolytic aerosols in mild asthmatics. Int J Pharm. 1995;114(1):111–5.[9] Usmani OS, Biddiscombe MF, Nightingale JA, Underwood SR, Barnes PJ. Effects of bronchodilator particle size in asthmatic patients using monodisperse aerosols. J Appl Physiol. 2003;95(5):2106–12.[10] Draft guidance on fluticasone propionate. Center for Drug Evaluation and Research; 2017.[11] Draft guidance on mometasone furoate. Center for Drug Evaluation and Research; 2017.[12] Draft guidance on salmeterol xinafoate. Center for Drug Evaluation and Research; 2017.[13] Draft guidance on tiotropium bromide. Center for Drug Evaluation and Research; 2017.[14] Newman SP. Use of gamma scintigraphy to evaluate performa

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