Recent developments in the computational simulation of dry powder inhalers

Engineering nanoliposomal tiotropium bromide embedded in a lactose-arginine carrier forming Trojan-particle dry powders for efficient pulmonary drug delivery: A combined approach of in vitro-3D printing and in silico-CFD modeling

Nanocarrier-based dry powders for lung disease treatment are crucial, with in vitro and in silico research being pivotal to their success. This study introduces a method for creating Tiotropium-bromide liposomal inhalation dry powder, termed "Trojan-particles," utilizing thin-film hydration and spray-drying with lactose-arginine carriers. Encapsulating tiotropium-bromide in nanoliposomes enhances lung treatment via liposomes' unique features. This formulation was examined through in vitro-3D-printing and in silico-CFD analysis. Nanoliposomes and powder were evaluated for physicochemical attributes, aerosolization, encapsulation-efficiency (EE%), and release. Both liposomes (90 nm) and powder particles (3 µm) were spherical. Liposomes had an EE% over 95 % and a zeta-potential of -28.3 mV. The optimal formulation was tested in vitro at 30, 60, and 90 L/min using a 3D-printed airway replica. CFD analysis evaluated particle deposition in steady and realistic inhalation with monodisperse and polydisperse particles. Based on realistic airway geometry, model utilized k-ω-SST turbulence model for the continuous phase and Lagrangian-DEM for the discrete phase, analyzed through ANSYS Fluent. The 20 %-arginine nanoliposomal-tiotropium formulation outperformed others due to arginine's dispersibility and therapeutic benefits, including nitric oxide conversion. The formulation competes with commercial dry powders due to its chemical, biochemical advantages, and Trojan-based physical traits, reducing exhalation risk. Simulation data aligned with experimental findings, showing that higher inhalation flows increase particle deposition in airways due to greater inertia and turbulence. At 60 L/min, the polydisperse model matched experimental data better than the monodisperse model. Alongside improving dry powder performance via a nanoliposomal formulation, this research highlights the development of a novel CFD method for their assessment.

Leveraging Numerical Simulation Technology to Advance Drug Preparation: A Comprehensive Review of Application Scenarios and Cases

BACKGROUND/OBJECTIVES

Numerical simulation plays an important role in pharmaceutical preparation recently. Mechanistic models, as a type of numerical model, are widely used in the study of pharmaceutical preparations. Mechanistic models are based on a priori knowledge, i.e., laws of physics, chemistry, and biology. However, due to interdisciplinary reasons, pharmacy researchers have greater difficulties in using computer models.

METHODS

In this paper, we highlight the application scenarios and examples of mechanistic modelling in pharmacy research and provide a reference for drug researchers to get started.

RESULTS

By establishing a suitable model and inputting preparation parameters, researchers can analyze the drug preparation process. Therefore, mechanistic models are effective tools to optimize the preparation parameters and predict potential quality problems of the product. With product quality parameters as the ultimate goal, the experiment design is optimized by mechanistic models. This process emphasizes the concept of quality by design.

CONCLUSIONS

The use of numerical simulation saves experimental cost and time, and speeds up the experimental process. In pharmacy experiments, part of the physical information and the change processes are difficult to obtain, such as the mechanical phenomena during tablet compression and the airflow details in the nasal cavity. Therefore, it is necessary to predict the information and guide the formulation with the help of mechanistic models.

Understanding the role of swirling flow in dry powder inhalers: Implications for design considerations and pulmonary delivery

Dry powder inhalers (DPIs) are widely employed to treat respiratory diseases, offering numerous advantages such as high dose capacity and stable formulations. However, they usually face challenges in achieving sufficient pulmonary drug delivery and minimizing excessive oropharyngeal deposition. This review provides a new viewpoint to address these challenges by focusing on the role of swirling flow, a crucial yet under-researched aspect that induces strong turbulence. In the review, we comprehensively discuss both key classic designs (tangential inlet, swirling chamber, grid mesh, and mouthpiece) and innovative designs in inhalers, exploring how the induced swirling flow initiates powder dispersion and promotes delivery efficiency. Valuable design considerations to effectively coordinate inhalers with formulations and patients are also provided. It is highlighted that the delicate manipulation of swirling flow is essential to maximize benefits. By emphasizing the role of swirling flow and its potential application, this review offers promising insights for advancing DPI technology and optimizing therapeutic outcomes in inhaled therapy.

Development of an effective two-equation turbulence modeling approach for simulating aerosol deposition across a range of turbulence levels

Pharmaceutical aerosol systems present a significant challenge to computational fluid dynamics (CFD) modeling based on the need to capture multiple levels of turbulence, frequent transition between laminar and turbulent flows, anisotropic turbulent particle dispersion, and near-wall particle transport phenomena often within geometrically complex systems over multiple time scales. Two-equation turbulence models, such as the k - ω family of approximations, offer a computationally efficient solution approach, but are known to require the use of near-wall (NW) corrections and eddy interaction model (EIM) modifications for accurate predictions of aerosol deposition. The objective of this study was to develop an efficient and effective two-equation turbulence modeling approach that enables accurate predictions of pharmaceutical aerosol deposition across a range of turbulence levels. Key systems considered were the traditional aerosol deposition benchmark cases of a 90-degree bend (R e = 6,000 ) and a vertical straight section of pipe (R e = 10,000 ), as well as a highly complex case of direct-to-infant (D2I) nose-to-lung pharmaceutical aerosol delivery from an air-jet dry powder inhaler (DPI) including a patient interface and infant nasal geometry through mid-trachea (500 < R e < 7,000 ). Of the k - ω family of models, the low Reynolds number (LRN) shear stress transport (SST) approach was determined to provide the best agreement with experimental aerosol deposition data in the D2I system, based on an improved simulation of turbulent jet flow that frequently occurs in DPIs. Considering NW corrections, a new correlation was developed to quantitatively predict best regional values of the y + l i m i t , within which anisotropic NW turbulence is approximated. Considering EIM modifications, a previously described drift correction approach was implemented in pharmaceutical aerosol simulations for the first time. Considering all model corrections and modifications applied to the D2I system, regional relative errors in deposition fractions between CFD predictions and new experimental data were improved from 19-207% (no modifications) to 2-15% (all modifications) with a notable decrease in computational time (up to ∼15%). In conclusion, the highly efficient two-equation k - ω models with physically realistic corrections and modifications provided a viable, efficient and accurate approach to simulate the transport and deposition of pharmaceutical aerosols in complex airway systems that include laminar, turbulent and transitional flows.

Discrete Element Method Evaluation of Triboelectric Charging Due to Powder Handling in the Capsule of a DPI

The generation and accumulation of an electrostatic charge from handling pharmaceutical powders is a well-known phenomenon, given the insulating nature of most APIs (Active Pharmaceutical Ingredients) and excipients. In capsule-based DPIs (Dry Powder Inhalers), the formulation is stored in a gelatine capsule placed in the inhaler just before inhalation. The action of capsule filling, as well as tumbling or vibration effects during the capsule life cycle, implies a consistent amount of particle-particle and particle-wall contacts. A significant contact-induced electrostatic charging can then take place, potentially affecting the inhaler's efficiency. DEM (Discrete Element Method) simulations were performed on a carrier-based DPI formulation (salbutamol-lactose) to evaluate such effects. After performing a comparison with the experimental data on a carrier-only system under similar conditions, a detailed analysis was conducted on two carrier-API configurations with different API loadings per carrier particle. The charge acquired by the two solid phases was tracked in both the initial particle settling and the capsule shaking process. Alternating positive-negative charging was observed. Particle charging was then investigated in relation to the collision statistics, tracking the particle-particle and particle-wall events for the carrier and API. Finally, an analysis of the relative importance of electrostatic, cohesive/adhesive, and inertial forces allowed the importance of each term in determining the trajectory of the powder particles to be estimated.

Numerical and Machine Learning Analysis of the Parameters Affecting the Regionally Delivered Nasal Dose of Nano- and Micro-Sized Aerosolized Drugs

The nasal epithelium is an important target for drug delivery to the nose and secondary organs such as the brain via the olfactory bulb. For both topical and brain delivery, the targeting of specific nasal regions such as the olfactory epithelium (brain) is essential, yet challenging. In this study, a numerical model was developed to predict the regional dose as mass per surface area (for an inhaled mass of 2.5 mg), which is the biologically most relevant dose metric for drug delivery in the respiratory system. The role of aerosol diameter (particle diameter: 1 nm to 30 µm) and inhalation flow rate (4, 15 and 30 L/min) in optimal drug delivery to the vestibule, nasal valve, olfactory and nasopharynx is assessed. To obtain the highest doses in the olfactory region, we suggest aerosols with a diameter of 20 µm and a medium inlet air flow rate of 15 L/min. High deposition on the olfactory epithelium was also observed for nanoparticles below 1 nm, as was high residence time (slow flow rate of 4 L/min), but the very low mass of 1 nm nanoparticles is prohibitive for most therapeutic applications. Moreover, high flow rates (30 L/min) and larger micro-aerosols lead to highest doses in the vestibule and nasal valve regions. On the other hand, the highest drug doses in the nasopharynx are observed for nano-aerosol (1 nm) and fine microparticles (1-20 µm) with a relatively weak dependence on flow rate. Furthermore, using the 45 different inhalation scenarios generated by numerical models, different machine learning models with five-fold cross-validation are trained to predict the delivered dose and avoid partial differential equation solvers for future predictions. Random forest and gradient boosting models resulted in R² scores of 0.89 and 0.96, respectively. The aerosol diameter and region of interest are the most important features affecting delivered dose, with an approximate importance of 42% and 47%, respectively.