TY  - JOUR
AU  - Šušteršič, Tijana
AU  - Bodić, Aleksandar
AU  - Ignjatović, Jelisaveta
AU  - Cvijić, Sandra
AU  - Ibrić, Svetlana
AU  - Filipović, Nenad
PY  - 2022
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/4367
AB  - The development of novel dry powders for dry powder inhalers (DPIs) requires the in vitro assessment of DPI aerodynamic performance. As a potential complementary method, in silico numerical simulations can provide additional information about the mechanisms that guide the particles and their behavior inside DPIs. The aim of this study was to apply computational fluid dynamics (CFDs) coupled with a discrete phase model (DPM) to describe the forces and particle trajectories inside the RS01® as a model DPI device. The methodology included standard fluid flow equations but also additional equations for the particle sticking mechanism, as well as particle behavior after contacting the DPI wall surface, including the particle detachment process. The results show that the coefficient of restitution between the particle and the impact surface does not have a high impact on the results, meaning that all tested combinations gave similar output efficiencies and particle behaviors. No sliding or rolling mechanisms were observed for the particle detachment process, meaning that simple bouncing off or deposition particle behavior is present inside DPIs. The developed methodology can serve as a basis for the additional understanding of the particles’ behavior inside DPIs, which is not possible using only in vitro experiments; this implies the possibility of increasing the efficiency of DPIs.
PB  - MDPI
T2  - Pharmaceutics
T1  - Numerical Modeling of Particle Dynamics Inside a Dry Powder Inhaler
VL  - 14
IS  - 12
DO  - 10.3390/pharmaceutics14122591
ER  - 

@article{
author = "Šušteršič, Tijana and Bodić, Aleksandar and Ignjatović, Jelisaveta and Cvijić, Sandra and Ibrić, Svetlana and Filipović, Nenad",
year = "2022",
abstract = "The development of novel dry powders for dry powder inhalers (DPIs) requires the in vitro assessment of DPI aerodynamic performance. As a potential complementary method, in silico numerical simulations can provide additional information about the mechanisms that guide the particles and their behavior inside DPIs. The aim of this study was to apply computational fluid dynamics (CFDs) coupled with a discrete phase model (DPM) to describe the forces and particle trajectories inside the RS01® as a model DPI device. The methodology included standard fluid flow equations but also additional equations for the particle sticking mechanism, as well as particle behavior after contacting the DPI wall surface, including the particle detachment process. The results show that the coefficient of restitution between the particle and the impact surface does not have a high impact on the results, meaning that all tested combinations gave similar output efficiencies and particle behaviors. No sliding or rolling mechanisms were observed for the particle detachment process, meaning that simple bouncing off or deposition particle behavior is present inside DPIs. The developed methodology can serve as a basis for the additional understanding of the particles’ behavior inside DPIs, which is not possible using only in vitro experiments; this implies the possibility of increasing the efficiency of DPIs.",
publisher = "MDPI",
journal = "Pharmaceutics",
title = "Numerical Modeling of Particle Dynamics Inside a Dry Powder Inhaler",
volume = "14",
number = "12",
doi = "10.3390/pharmaceutics14122591"
}

Šušteršič, T., Bodić, A., Ignjatović, J., Cvijić, S., Ibrić, S.,& Filipović, N.. (2022). Numerical Modeling of Particle Dynamics Inside a Dry Powder Inhaler. in Pharmaceutics
MDPI., 14(12).
https://doi.org/10.3390/pharmaceutics14122591

Šušteršič T, Bodić A, Ignjatović J, Cvijić S, Ibrić S, Filipović N. Numerical Modeling of Particle Dynamics Inside a Dry Powder Inhaler. in Pharmaceutics. 2022;14(12).
doi:10.3390/pharmaceutics14122591 .

Šušteršič, Tijana, Bodić, Aleksandar, Ignjatović, Jelisaveta, Cvijić, Sandra, Ibrić, Svetlana, Filipović, Nenad, "Numerical Modeling of Particle Dynamics Inside a Dry Powder Inhaler" in Pharmaceutics, 14, no. 12 (2022),
https://doi.org/10.3390/pharmaceutics14122591 . .

TY  - JOUR
AU  - Ignjatović, Jelisaveta
AU  - Šušteršič, Tijana
AU  - Bodić, Aleksandar
AU  - Cvijić, Sandra
AU  - Ðuriš, Jelena
AU  - Rossi, Alessandra
AU  - Dobričić, Vladimir
AU  - Ibrić, Svetlana
AU  - Filipović, Nenad
PY  - 2021
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3993
AB  - 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.
PB  - MDPI
T2  - Pharmaceutics
T1  - Comparative assessment of in vitro and in silico methods for aerodynamic characterization of powders for inhalation
VL  - 13
IS  - 11
DO  - 10.3390/pharmaceutics13111831
ER  - 

@article{
author = "Ignjatović, Jelisaveta and Šušteršič, Tijana and Bodić, Aleksandar and Cvijić, Sandra and Ðuriš, Jelena and Rossi, Alessandra and Dobričić, Vladimir and Ibrić, Svetlana and Filipović, Nenad",
year = "2021",
abstract = "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.",
publisher = "MDPI",
journal = "Pharmaceutics",
title = "Comparative assessment of in vitro and in silico methods for aerodynamic characterization of powders for inhalation",
volume = "13",
number = "11",
doi = "10.3390/pharmaceutics13111831"
}

Ignjatović, J., Šušteršič, T., Bodić, A., Cvijić, S., Ðuriš, J., Rossi, A., Dobričić, V., Ibrić, S.,& Filipović, N.. (2021). Comparative assessment of in vitro and in silico methods for aerodynamic characterization of powders for inhalation. in Pharmaceutics
MDPI., 13(11).
https://doi.org/10.3390/pharmaceutics13111831

Ignjatović J, Šušteršič T, Bodić A, Cvijić S, Ðuriš J, Rossi A, Dobričić V, Ibrić S, Filipović N. Comparative assessment of in vitro and in silico methods for aerodynamic characterization of powders for inhalation. in Pharmaceutics. 2021;13(11).
doi:10.3390/pharmaceutics13111831 .

Ignjatović, Jelisaveta, Šušteršič, Tijana, Bodić, Aleksandar, Cvijić, Sandra, Ðuriš, Jelena, Rossi, Alessandra, Dobričić, Vladimir, Ibrić, Svetlana, Filipović, Nenad, "Comparative assessment of in vitro and in silico methods for aerodynamic characterization of powders for inhalation" in Pharmaceutics, 13, no. 11 (2021),
https://doi.org/10.3390/pharmaceutics13111831 . .

TY  - JOUR
AU  - Madžarević, Marijana
AU  - Medarević, Đorđe
AU  - Vulović, Aleksandra
AU  - Šušteršič, Tijana
AU  - Đuriš, Jelena
AU  - Filipović, Nenad
AU  - Ibrić, Svetlana
PY  - 2019
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3466
AB  - The aim of this work was to investigate eects 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 eects of excipients and printing parameters on drug dissolution rate in DLP printlets two dierent 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 dierence 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.
PB  - MDPI
T2  - Pharmaceutics
T1  - Optimization and prediction of ibuprofen release from 3D DLP printlets using artificial neural networks
VL  - 11
IS  - 10
SP  - 1
EP  - 16
DO  - 10.3390/pharmaceutics11100544
ER  - 

@article{
author = "Madžarević, Marijana and Medarević, Đorđe and Vulović, Aleksandra and Šušteršič, Tijana and Đuriš, Jelena and Filipović, Nenad and Ibrić, Svetlana",
year = "2019",
abstract = "The aim of this work was to investigate eects 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 eects of excipients and printing parameters on drug dissolution rate in DLP printlets two dierent 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 dierence 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.",
publisher = "MDPI",
journal = "Pharmaceutics",
title = "Optimization and prediction of ibuprofen release from 3D DLP printlets using artificial neural networks",
volume = "11",
number = "10",
pages = "1-16",
doi = "10.3390/pharmaceutics11100544"
}

Madžarević, M., Medarević, Đ., Vulović, A., Šušteršič, T., Đuriš, J., Filipović, N.,& Ibrić, S.. (2019). Optimization and prediction of ibuprofen release from 3D DLP printlets using artificial neural networks. in Pharmaceutics
MDPI., 11(10), 1-16.
https://doi.org/10.3390/pharmaceutics11100544

Madžarević M, Medarević Đ, Vulović A, Šušteršič T, Đuriš J, Filipović N, Ibrić S. Optimization and prediction of ibuprofen release from 3D DLP printlets using artificial neural networks. in Pharmaceutics. 2019;11(10):1-16.
doi:10.3390/pharmaceutics11100544 .

Madžarević, Marijana, Medarević, Đorđe, Vulović, Aleksandra, Šušteršič, Tijana, Đuriš, Jelena, Filipović, Nenad, Ibrić, Svetlana, "Optimization and prediction of ibuprofen release from 3D DLP printlets using artificial neural networks" in Pharmaceutics, 11, no. 10 (2019):1-16,
https://doi.org/10.3390/pharmaceutics11100544 . .