TY  - JOUR
AU  - Ibrić, Svetlana
AU  - Jovanović, Milica
AU  - Đurić, Zorica
AU  - Parojčić, Jelena
AU  - Solomun, Ljiljana
AU  - Lucić, Branka
PY  - 2007
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/922
AB  - This study had two aims. Firstly, we wanted to model the effects of the percentage of Eudragit RS PO and compression pressure as the most important process and formulation variables on the time course of drug release from extended-release matrix aspirin tablets. Secondly, we investigated the possibility of predicting drug stability and shelf-life using an artificial neural network (ANN). Ten types of matrix aspirin tablets were prepared as model formulations and were stored in stability chambers at 60 degrees C, 50 degrees C, 40 degrees C and 30 degrees C and controlled humidity. Samples were removed at predefined time points and analysed for acetylsalicylic acid (ASA) and salicylic acid (SA) content using stability-indicating HPLC. The decrease in aspirin content followed apparent zero-order kinetics. The amount of Eudragit RS PO and compression pressure were selected as causal factors. The apparent zero-order rate constants for each temperature were chosen as output variables for the ANN. A set of output parameters and causal factors were used as training data for the generalized regression neural network (GRNN). For two additional test formulations, Arrhenius plots were constructed from the experimentally observed and GRNN-predicted results. The slopes of experimentally observed and predicted Arrhenius plots were tested for significance using Student's t-test. For test formulations, the shelf life (t(95%)) was then calculated from experimentally observed values (t(95%) 82.90 weeks), as well as from GRNN-predicted values (t(95%) 81.88 weeks). These results demonstrate that GRNN networks can be used to predict ASA content and shelf life without stability testing for formulations in which the amount of polymer and tablet hardness are within the investigated range.
PB  - Pharmaceutical Press-Royal Pharmaceutical Soc Great Britian, London
T2  - Journal of Pharmacy and Pharmacology
T1  - Generalized regression neural networks in prediction of drug stability
VL  - 59
IS  - 5
SP  - 745
EP  - 750
DO  - 10.1211/jpp.59.5.0017
ER  - 

@article{
author = "Ibrić, Svetlana and Jovanović, Milica and Đurić, Zorica and Parojčić, Jelena and Solomun, Ljiljana and Lucić, Branka",
year = "2007",
abstract = "This study had two aims. Firstly, we wanted to model the effects of the percentage of Eudragit RS PO and compression pressure as the most important process and formulation variables on the time course of drug release from extended-release matrix aspirin tablets. Secondly, we investigated the possibility of predicting drug stability and shelf-life using an artificial neural network (ANN). Ten types of matrix aspirin tablets were prepared as model formulations and were stored in stability chambers at 60 degrees C, 50 degrees C, 40 degrees C and 30 degrees C and controlled humidity. Samples were removed at predefined time points and analysed for acetylsalicylic acid (ASA) and salicylic acid (SA) content using stability-indicating HPLC. The decrease in aspirin content followed apparent zero-order kinetics. The amount of Eudragit RS PO and compression pressure were selected as causal factors. The apparent zero-order rate constants for each temperature were chosen as output variables for the ANN. A set of output parameters and causal factors were used as training data for the generalized regression neural network (GRNN). For two additional test formulations, Arrhenius plots were constructed from the experimentally observed and GRNN-predicted results. The slopes of experimentally observed and predicted Arrhenius plots were tested for significance using Student's t-test. For test formulations, the shelf life (t(95%)) was then calculated from experimentally observed values (t(95%) 82.90 weeks), as well as from GRNN-predicted values (t(95%) 81.88 weeks). These results demonstrate that GRNN networks can be used to predict ASA content and shelf life without stability testing for formulations in which the amount of polymer and tablet hardness are within the investigated range.",
publisher = "Pharmaceutical Press-Royal Pharmaceutical Soc Great Britian, London",
journal = "Journal of Pharmacy and Pharmacology",
title = "Generalized regression neural networks in prediction of drug stability",
volume = "59",
number = "5",
pages = "745-750",
doi = "10.1211/jpp.59.5.0017"
}

Ibrić, S., Jovanović, M., Đurić, Z., Parojčić, J., Solomun, L.,& Lucić, B.. (2007). Generalized regression neural networks in prediction of drug stability. in Journal of Pharmacy and Pharmacology
Pharmaceutical Press-Royal Pharmaceutical Soc Great Britian, London., 59(5), 745-750.
https://doi.org/10.1211/jpp.59.5.0017

Ibrić S, Jovanović M, Đurić Z, Parojčić J, Solomun L, Lucić B. Generalized regression neural networks in prediction of drug stability. in Journal of Pharmacy and Pharmacology. 2007;59(5):745-750.
doi:10.1211/jpp.59.5.0017 .

Ibrić, Svetlana, Jovanović, Milica, Đurić, Zorica, Parojčić, Jelena, Solomun, Ljiljana, Lucić, Branka, "Generalized regression neural networks in prediction of drug stability" in Journal of Pharmacy and Pharmacology, 59, no. 5 (2007):745-750,
https://doi.org/10.1211/jpp.59.5.0017 . .