Various three-dimensional printing (3DP) technologies have been investigated so far in relation to their potential to produce customizable medicines and medical devices. The aim of this study was to examine the possibility of tailoring drug release rates from immediate to prolonged release by varying the tablet thickness and the drug loading, as well as to develop artificial neural network (ANN) predictive models for atomoxetine (ATH) release rate from DLP 3D-printed tablets. Photoreactive mixtures were comprised of poly(ethylene glycol) diacrylate (PEGDA) and poly(ethylene glycol) 400 in a constant ratio of 3:1, water, photoinitiator and ATH as a model drug whose content was varied from 5% to 20% (w/w). Designed 3D models of cylindrical shape tablets were of constant diameter, but different thickness. A series of tablets with doses ranging from 2.06 mg to 37.48 mg, exhibiting immediate- and modified-release profiles were successfully fabricated, confirming the potential of this techno...logy in manufacturing dosage forms on demand, with the possibility to adjust the dose and release behavior by varying drug loading and dimensions of tablets. DSC (differential scanning calorimetry), XRPD (X-ray powder diffraction) and microscopic analysis showed that ATH remained in a crystalline form in tablets, while FTIR spectroscopy confirmed that no interactions occurred between ATH and polymers.
TY - JOUR
AU - Stanojević, Gordana
AU - Medarević, Đorđe
AU - Adamov, Ivana
AU - Pešić, Nikola
AU - Kovačević, Jovana
AU - Ibrić, Svetlana
PY - 2020
UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3771
AB - Various three-dimensional printing (3DP) technologies have been investigated so far in relation to their potential to produce customizable medicines and medical devices. The aim of this study was to examine the possibility of tailoring drug release rates from immediate to prolonged release by varying the tablet thickness and the drug loading, as well as to develop artificial neural network (ANN) predictive models for atomoxetine (ATH) release rate from DLP 3D-printed tablets. Photoreactive mixtures were comprised of poly(ethylene glycol) diacrylate (PEGDA) and poly(ethylene glycol) 400 in a constant ratio of 3:1, water, photoinitiator and ATH as a model drug whose content was varied from 5% to 20% (w/w). Designed 3D models of cylindrical shape tablets were of constant diameter, but different thickness. A series of tablets with doses ranging from 2.06 mg to 37.48 mg, exhibiting immediate- and modified-release profiles were successfully fabricated, confirming the potential of this technology in manufacturing dosage forms on demand, with the possibility to adjust the dose and release behavior by varying drug loading and dimensions of tablets. DSC (differential scanning calorimetry), XRPD (X-ray powder diffraction) and microscopic analysis showed that ATH remained in a crystalline form in tablets, while FTIR spectroscopy confirmed that no interactions occurred between ATH and polymers.
PB - MDPI
T2 - Molecules (Basel, Switzerland)
T1 - Tailoring Atomoxetine Release Rate from DLP 3D-Printed Tablets Using Artificial Neural Networks: Influence of Tablet Thickness and Drug Loading
VL - 26
IS - 1
DO - 10.3390/molecules26010111
ER -
@article{
author = "Stanojević, Gordana and Medarević, Đorđe and Adamov, Ivana and Pešić, Nikola and Kovačević, Jovana and Ibrić, Svetlana",
year = "2020",
abstract = "Various three-dimensional printing (3DP) technologies have been investigated so far in relation to their potential to produce customizable medicines and medical devices. The aim of this study was to examine the possibility of tailoring drug release rates from immediate to prolonged release by varying the tablet thickness and the drug loading, as well as to develop artificial neural network (ANN) predictive models for atomoxetine (ATH) release rate from DLP 3D-printed tablets. Photoreactive mixtures were comprised of poly(ethylene glycol) diacrylate (PEGDA) and poly(ethylene glycol) 400 in a constant ratio of 3:1, water, photoinitiator and ATH as a model drug whose content was varied from 5% to 20% (w/w). Designed 3D models of cylindrical shape tablets were of constant diameter, but different thickness. A series of tablets with doses ranging from 2.06 mg to 37.48 mg, exhibiting immediate- and modified-release profiles were successfully fabricated, confirming the potential of this technology in manufacturing dosage forms on demand, with the possibility to adjust the dose and release behavior by varying drug loading and dimensions of tablets. DSC (differential scanning calorimetry), XRPD (X-ray powder diffraction) and microscopic analysis showed that ATH remained in a crystalline form in tablets, while FTIR spectroscopy confirmed that no interactions occurred between ATH and polymers.",
publisher = "MDPI",
journal = "Molecules (Basel, Switzerland)",
title = "Tailoring Atomoxetine Release Rate from DLP 3D-Printed Tablets Using Artificial Neural Networks: Influence of Tablet Thickness and Drug Loading",
volume = "26",
number = "1",
doi = "10.3390/molecules26010111"
}
Stanojević, G., Medarević, Đ., Adamov, I., Pešić, N., Kovačević, J.,& Ibrić, S.. (2020). Tailoring Atomoxetine Release Rate from DLP 3D-Printed Tablets Using Artificial Neural Networks: Influence of Tablet Thickness and Drug Loading. in Molecules (Basel, Switzerland)
MDPI., 26(1).
https://doi.org/10.3390/molecules26010111
Stanojević G, Medarević Đ, Adamov I, Pešić N, Kovačević J, Ibrić S. Tailoring Atomoxetine Release Rate from DLP 3D-Printed Tablets Using Artificial Neural Networks: Influence of Tablet Thickness and Drug Loading. in Molecules (Basel, Switzerland). 2020;26(1).
doi:10.3390/molecules26010111 .
Stanojević, Gordana, Medarević, Đorđe, Adamov, Ivana, Pešić, Nikola, Kovačević, Jovana, Ibrić, Svetlana, "Tailoring Atomoxetine Release Rate from DLP 3D-Printed Tablets Using Artificial Neural Networks: Influence of Tablet Thickness and Drug Loading" in Molecules (Basel, Switzerland), 26, no. 1 (2020),
https://doi.org/10.3390/molecules26010111 . .
