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  - Smole, Tim
AU  - Žunkovič, Bojan
AU  - Pičulin, Matej
AU  - Kokalj, Enja
AU  - Robnik-Šikonja, Marko
AU  - Kukar, Matjaž
AU  - Fotiadis, Dimitrios I.
AU  - Pezoulas, Vasileios C.
AU  - Tachos, Nikolaos S.
AU  - Barlocco, Fausto
AU  - Mazzarotto, Francesco
AU  - Popović, Dejana
AU  - Maier, Lars
AU  - Velicki, Lazar
AU  - MacGowan, Guy A.
AU  - Olivotto, Iacopo
AU  - Filipović, Nenad
AU  - Jakovljević, Đorđe G.
AU  - Bosnić, Zoran
PY  - 2021
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3928
AB  - Background: Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools. Method: Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death. Results: The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively. Conclusions: The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general.
PB  - Elsevier Ltd
T2  - Computers in Biology and Medicine
T1  - A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy
VL  - 135
DO  - 10.1016/j.compbiomed.2021.104648
ER  - 

@article{
author = "Smole, Tim and Žunkovič, Bojan and Pičulin, Matej and Kokalj, Enja and Robnik-Šikonja, Marko and Kukar, Matjaž and Fotiadis, Dimitrios I. and Pezoulas, Vasileios C. and Tachos, Nikolaos S. and Barlocco, Fausto and Mazzarotto, Francesco and Popović, Dejana and Maier, Lars and Velicki, Lazar and MacGowan, Guy A. and Olivotto, Iacopo and Filipović, Nenad and Jakovljević, Đorđe G. and Bosnić, Zoran",
year = "2021",
abstract = "Background: Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools. Method: Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death. Results: The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively. Conclusions: The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general.",
publisher = "Elsevier Ltd",
journal = "Computers in Biology and Medicine",
title = "A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy",
volume = "135",
doi = "10.1016/j.compbiomed.2021.104648"
}

Smole, T., Žunkovič, B., Pičulin, M., Kokalj, E., Robnik-Šikonja, M., Kukar, M., Fotiadis, D. I., Pezoulas, V. C., Tachos, N. S., Barlocco, F., Mazzarotto, F., Popović, D., Maier, L., Velicki, L., MacGowan, G. A., Olivotto, I., Filipović, N., Jakovljević, Đ. G.,& Bosnić, Z.. (2021). A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy. in Computers in Biology and Medicine
Elsevier Ltd., 135.
https://doi.org/10.1016/j.compbiomed.2021.104648

Smole T, Žunkovič B, Pičulin M, Kokalj E, Robnik-Šikonja M, Kukar M, Fotiadis DI, Pezoulas VC, Tachos NS, Barlocco F, Mazzarotto F, Popović D, Maier L, Velicki L, MacGowan GA, Olivotto I, Filipović N, Jakovljević ĐG, Bosnić Z. A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy. in Computers in Biology and Medicine. 2021;135.
doi:10.1016/j.compbiomed.2021.104648 .

Smole, Tim, Žunkovič, Bojan, Pičulin, Matej, Kokalj, Enja, Robnik-Šikonja, Marko, Kukar, Matjaž, Fotiadis, Dimitrios I., Pezoulas, Vasileios C., Tachos, Nikolaos S., Barlocco, Fausto, Mazzarotto, Francesco, Popović, Dejana, Maier, Lars, Velicki, Lazar, MacGowan, Guy A., Olivotto, Iacopo, Filipović, Nenad, Jakovljević, Đorđe G., Bosnić, Zoran, "A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy" in Computers in Biology and Medicine, 135 (2021),
https://doi.org/10.1016/j.compbiomed.2021.104648 . .

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 . .

TY  - JOUR
AU  - Vulović, Aleksandra
AU  - Sustersić, Tijana
AU  - Cvijić, Sandra
AU  - Ibrić, Svetlana
AU  - Filipović, Nenad
PY  - 2018
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3186
AB  - One of the critical components of the respiratory drug delivery is the manner in which the inhaled aerosol is deposited in respiratory tract compartments. Depending on formulation properties, device characteristics and breathing pattern, only a certain fraction of the dose will reach the target site in the lungs, while the rest of the drug will deposit in the inhalation device or in the mouth-throat region. The aim of this study was to link the Computational fluid dynamics (CFD) with physiologically-based pharmacokinetic (PBPK) modelling in order to predict aerolisolization of different dry powder formulations, and estimate concomitant in vivo deposition and absorption of amiloride hydrochloride. Drug physicochemical properties were experimentally determined and used as inputs for the CFD simulations of particle flow in the generated 3D geometric model of Aerolizer (R) dry powder inhaler (DPI). CFD simulations were used to simulate air flow through Aerolizer (R) inhaler and Discrete Phase Method (DPM) was used to simulate aerosol particles deposition within the fluid domain. The simulated values for the percent emitted dose were comparable to the values obtained using Andersen cascade impactor (ACI). However, CFD predictions indicated that aerosolized DPI have smaller particle size and narrower size distribution than assumed based on ACI measurements. Comparison with the literature in vivo data revealed that the constructed drug-specific PBPK model was able to capture amiloride absorption pattern following oral and inhalation administration. The PBPK simulation results, based on the CFD generated particle distribution data as input, illustrated the influence of formulation properties on the expected drug plasma concentration profiles. The model also predicted the influence of potential changes in physiological parameters on the extent of inhaled amiloride absorption. Overall, this study demonstrated the potential of the combined CFD-PBPK approach to model inhaled drug bioperformance, and suggested that CFD generated results might serve as input for the prediction of drug deposition pattern in vivo.
PB  - Elsevier Science BV, Amsterdam
T2  - European Journal of Pharmaceutical Sciences
T1  - Coupled in silico platform: Computational fluid dynamics (CFD) and physiologically-based pharmacokinetic (PBPK) modelling
VL  - 113
SP  - 171
EP  - 184
DO  - 10.1016/j.ejps.2017.10.022
ER  - 

@article{
author = "Vulović, Aleksandra and Sustersić, Tijana and Cvijić, Sandra and Ibrić, Svetlana and Filipović, Nenad",
year = "2018",
abstract = "One of the critical components of the respiratory drug delivery is the manner in which the inhaled aerosol is deposited in respiratory tract compartments. Depending on formulation properties, device characteristics and breathing pattern, only a certain fraction of the dose will reach the target site in the lungs, while the rest of the drug will deposit in the inhalation device or in the mouth-throat region. The aim of this study was to link the Computational fluid dynamics (CFD) with physiologically-based pharmacokinetic (PBPK) modelling in order to predict aerolisolization of different dry powder formulations, and estimate concomitant in vivo deposition and absorption of amiloride hydrochloride. Drug physicochemical properties were experimentally determined and used as inputs for the CFD simulations of particle flow in the generated 3D geometric model of Aerolizer (R) dry powder inhaler (DPI). CFD simulations were used to simulate air flow through Aerolizer (R) inhaler and Discrete Phase Method (DPM) was used to simulate aerosol particles deposition within the fluid domain. The simulated values for the percent emitted dose were comparable to the values obtained using Andersen cascade impactor (ACI). However, CFD predictions indicated that aerosolized DPI have smaller particle size and narrower size distribution than assumed based on ACI measurements. Comparison with the literature in vivo data revealed that the constructed drug-specific PBPK model was able to capture amiloride absorption pattern following oral and inhalation administration. The PBPK simulation results, based on the CFD generated particle distribution data as input, illustrated the influence of formulation properties on the expected drug plasma concentration profiles. The model also predicted the influence of potential changes in physiological parameters on the extent of inhaled amiloride absorption. Overall, this study demonstrated the potential of the combined CFD-PBPK approach to model inhaled drug bioperformance, and suggested that CFD generated results might serve as input for the prediction of drug deposition pattern in vivo.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "European Journal of Pharmaceutical Sciences",
title = "Coupled in silico platform: Computational fluid dynamics (CFD) and physiologically-based pharmacokinetic (PBPK) modelling",
volume = "113",
pages = "171-184",
doi = "10.1016/j.ejps.2017.10.022"
}

Vulović, A., Sustersić, T., Cvijić, S., Ibrić, S.,& Filipović, N.. (2018). Coupled in silico platform: Computational fluid dynamics (CFD) and physiologically-based pharmacokinetic (PBPK) modelling. in European Journal of Pharmaceutical Sciences
Elsevier Science BV, Amsterdam., 113, 171-184.
https://doi.org/10.1016/j.ejps.2017.10.022

Vulović A, Sustersić T, Cvijić S, Ibrić S, Filipović N. Coupled in silico platform: Computational fluid dynamics (CFD) and physiologically-based pharmacokinetic (PBPK) modelling. in European Journal of Pharmaceutical Sciences. 2018;113:171-184.
doi:10.1016/j.ejps.2017.10.022 .

Vulović, Aleksandra, Sustersić, Tijana, Cvijić, Sandra, Ibrić, Svetlana, Filipović, Nenad, "Coupled in silico platform: Computational fluid dynamics (CFD) and physiologically-based pharmacokinetic (PBPK) modelling" in European Journal of Pharmaceutical Sciences, 113 (2018):171-184,
https://doi.org/10.1016/j.ejps.2017.10.022 . .