Comparative assessment of in vitro and in silico methods for aerodynamic characterization of powders for inhalation

In vitro assessment of dry powders for inhalation (DPIs) aerodynamic performance is an inevitable test in DPI development. However, contemporary trends in drug development also implicate the use of in silico methods, e.g., computational fluid dynamics (CFD) coupled with discrete phase modeling (DPM). The aim of this study was to compare the designed CFD-DPM outcomes with the results of three in vitro methods for aerodynamic assessment of solid lipid microparticle DPIs. The model was able to simulate particle-to-wall sticking and estimate fractions of particles that stick or bounce off the inhaler’s wall; however, we observed notable differences between the in silico and in vitro results. The predicted emitted fractions (EFs) were comparable to the in vitro determined EFs, whereas the predicted fine particle fractions (FPFs) were generally lower than the corresponding in vitro values. In addition, CFD-DPM predicted higher mass median aerodynamic diameter (MMAD) in comparison to the in vitro values. The outcomes of different in vitro methods also diverged, implying that these methods are not interchangeable. Overall, our results support the utility of CFD-DPM in the DPI development, but highlight the need for additional improvements in these models to capture all the key processes influencing aerodynamic performance of specific DPIs

Optimization and prediction of ibuprofen release from 3D DLP printlets using artificial neural networks

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. The aim of this work was to investigate effects of the formulation factors on tablet printability as well as to optimize and predict extended drug release from cross-linked polymeric ibuprofen printlets using an artificial neural network (ANN). Printlets were printed using digital light processing (DLP) technology from formulations containing polyethylene glycol diacrylate, polyethylene glycol, and water in concentrations according to D-optimal mixture design and 0.1% w/w riboflavin and 5% w/w ibuprofen. It was observed that with higher water content longer exposure time was required for successful printing. For understanding the effects of excipients and printing parameters on drug dissolution rate in DLP printlets two different neural networks were developed with using two commercially available softwares. After comparison of experimental and predicted values of in vitro dissolution at the corresponding time points for optimized formulation, the R2 experimental vs. predicted value was 0.9811 (neural network 1) and 0.9960 (neural network 2). According to difference f1 and similarity factor f2 (f1 = 14.30 and f2 = 52.15) neural network 1 with supervised multilayer perceptron, backpropagation algorithm, and linear activation function gave a similar dissolution profile to obtained experimental results, indicating that adequate ANN is able to set out an input–output relationship in DLP printing of pharmaceutics

Development of solid lipid microparticles by melt-emulsification/spray-drying processes as carriers for pulmonary drug delivery

The aim of this study was to optimize the parameters of the complex melt-emulsification process coupled with the spray-drying, in order to maintain the balance between solid lipid microparticles (SLMs) powders aerodynamic performance and salbutamol sulfate release rate. Quality target product profile was identified and risk management and principal component analysis were used to guide formulation development. Obtained dry powders for inhalation (DPIs) were evaluated in terms of SLMs size distribution, morphology, true density, drug content, solid state characterization studies, in vitro aerosol performance and in vitro drug release. SLMs micrographs indicated spherical, porous particles. Selected powders showed satisfactory aerosol performance with a mean mass aerodynamic diameter of around 3 μm and acceptable fine particle fraction (FPF). Addition of trehalose positively affected SLMs aerodynamic properties. The results of in vitro dissolution testing indicated that salbutamol sulfate release from the tested SLMs formulations was modified, in comparison to the raw drug release. In conclusion, SLMs in a form of DPIs were successfully developed and numerous factors that affects SLMs properties were identified in this study. Further research is required for full understanding of each factor's influence on SLMs properties and optimization of DPIs with maximized FPFs

Comparative assessment of in vitro and in silico methods for aerodynamic characterization of powders for inhalation

In vitro assessment of dry powders for inhalation (DPIs) aerodynamic performance is an inevitable test in DPI development. However, contemporary trends in drug development also implicate the use of in silico methods, e.g., computational fluid dynamics (CFD) coupled with discrete phase modeling (DPM). The aim of this study was to compare the designed CFD-DPM outcomes with the results of three in vitro methods for aerodynamic assessment of solid lipid microparticle DPIs. The model was able to simulate particle-to-wall sticking and estimate fractions of particles that stick or bounce off the inhaler’s wall; however, we observed notable differences between the in silico and in vitro results. The predicted emitted fractions (EFs) were comparable to the in vitro determined EFs, whereas the predicted fine particle fractions (FPFs) were generally lower than the corresponding in vitro values. In addition, CFD-DPM predicted higher mass median aerodynamic diameter (MMAD) in comparison to the in vitro values. The outcomes of different in vitro methods also diverged, implying that these methods are not interchangeable. Overall, our results support the utility of CFD-DPM in the DPI development, but highlight the need for additional improvements in these models to capture all the key processes influencing aerodynamic performance of specific DPIs