TY  - JOUR
AU  - Golubović, Jelena
AU  - Protić, Ana
AU  - Zečević, Mira
AU  - Otašević, Biljana
AU  - Mikić, Marija
PY  - 2014
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2094
AB  - The study of experimental design in conjunction with artificial neural networks for optimization of isocratic ultra performance liquid chromatography method for separation of mycophenolate mofetil and its degradation products has been reported. Experimental design showed to be suitable for selection of experimental scheme, while Kennard-Stone algorithm was used for selection of training data set. The input variables were column temperature and composition of mobile phase including percentage of acetonitrile, concentration of ammonium acetate in buffer, and its pH value. The retention factor of the most retentive component and selectivity factors were used as the dependent variables (outputs). In this way, artificial neural network has been applied as a predictable tool in solving a method optimization problem using small number of experiments. Network architecture and training parameters were optimized to the lowest root-mean-square error values, and the network with 5-4-4-4 topology has been selected as the most predictable one. Predicted data were in good agreement with experimental data, and regression statistics confirmed good ability of trained network to predict compounds retention. The optimal chromatographic conditions included column temperature of 40 degrees C, flow rate of 700 mu l min(-1), 26% of acetonitrile and 9 mM ammonium acetate in mobile phase, and buffer pH of 5.87. The chromatographic analysis has been achieved within 5.2 min. The validation of the proposed method was also performed considering selectivity, linearity, accuracy, precision, limit of detection, and limit of quantification, and the results indicated that the method fulfilled all required criteria. The method was successfully applied to the analysis of commercial dosage form. Copyright
PB  - Wiley-Blackwell, Hoboken
T2  - Journal of Chemometrics
T1  - Artificial neural networks modeling in ultra performance liquid chromatography method optimization of mycophenolate mofetil and its degradation products
VL  - 28
IS  - 7
SP  - 567
EP  - 574
DO  - 10.1002/cem.2616
ER  - 

@article{
author = "Golubović, Jelena and Protić, Ana and Zečević, Mira and Otašević, Biljana and Mikić, Marija",
year = "2014",
abstract = "The study of experimental design in conjunction with artificial neural networks for optimization of isocratic ultra performance liquid chromatography method for separation of mycophenolate mofetil and its degradation products has been reported. Experimental design showed to be suitable for selection of experimental scheme, while Kennard-Stone algorithm was used for selection of training data set. The input variables were column temperature and composition of mobile phase including percentage of acetonitrile, concentration of ammonium acetate in buffer, and its pH value. The retention factor of the most retentive component and selectivity factors were used as the dependent variables (outputs). In this way, artificial neural network has been applied as a predictable tool in solving a method optimization problem using small number of experiments. Network architecture and training parameters were optimized to the lowest root-mean-square error values, and the network with 5-4-4-4 topology has been selected as the most predictable one. Predicted data were in good agreement with experimental data, and regression statistics confirmed good ability of trained network to predict compounds retention. The optimal chromatographic conditions included column temperature of 40 degrees C, flow rate of 700 mu l min(-1), 26% of acetonitrile and 9 mM ammonium acetate in mobile phase, and buffer pH of 5.87. The chromatographic analysis has been achieved within 5.2 min. The validation of the proposed method was also performed considering selectivity, linearity, accuracy, precision, limit of detection, and limit of quantification, and the results indicated that the method fulfilled all required criteria. The method was successfully applied to the analysis of commercial dosage form. Copyright",
publisher = "Wiley-Blackwell, Hoboken",
journal = "Journal of Chemometrics",
title = "Artificial neural networks modeling in ultra performance liquid chromatography method optimization of mycophenolate mofetil and its degradation products",
volume = "28",
number = "7",
pages = "567-574",
doi = "10.1002/cem.2616"
}

Golubović, J., Protić, A., Zečević, M., Otašević, B.,& Mikić, M.. (2014). Artificial neural networks modeling in ultra performance liquid chromatography method optimization of mycophenolate mofetil and its degradation products. in Journal of Chemometrics
Wiley-Blackwell, Hoboken., 28(7), 567-574.
https://doi.org/10.1002/cem.2616

Golubović J, Protić A, Zečević M, Otašević B, Mikić M. Artificial neural networks modeling in ultra performance liquid chromatography method optimization of mycophenolate mofetil and its degradation products. in Journal of Chemometrics. 2014;28(7):567-574.
doi:10.1002/cem.2616 .

Golubović, Jelena, Protić, Ana, Zečević, Mira, Otašević, Biljana, Mikić, Marija, "Artificial neural networks modeling in ultra performance liquid chromatography method optimization of mycophenolate mofetil and its degradation products" in Journal of Chemometrics, 28, no. 7 (2014):567-574,
https://doi.org/10.1002/cem.2616 . .

TY  - JOUR
AU  - Golubović, Jelena
AU  - Protić, Ana
AU  - Zečević, Mira
AU  - Otašević, Biljana
AU  - Mikić, Marija
AU  - Živanović, Ljiljana
PY  - 2012
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1748
AB  - Artificial neural network (ANN) is a learning system based on a computational technique which can simulate the neurological processing ability of the human brain. It was employed for building of the quantitative structure-retention relationships (QSRRs) model of antifungal agents-imidazoles or triazoles by structure. Computed molecular descriptors together with the percentage of acetonitrile in mobile phase (v/v) and buffer pH, being the most influential HPLC factors, were used as network inputs, giving the retention factor as model output. The multilayer perceptron network with a 9-5-1 topology was trained by using the back propagation algorithm. Good correlation between experimentally obtained data and ones predicted by using QSRR-ANN on previously unseen data sets indicates good predictive ability of the model.
PB  - Elsevier Science BV, Amsterdam
T2  - Talanta
T1  - Quantitative structure retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography
VL  - 100
SP  - 329
EP  - 337
DO  - 10.1016/j.talanta.2012.07.071
ER  - 

@article{
author = "Golubović, Jelena and Protić, Ana and Zečević, Mira and Otašević, Biljana and Mikić, Marija and Živanović, Ljiljana",
year = "2012",
abstract = "Artificial neural network (ANN) is a learning system based on a computational technique which can simulate the neurological processing ability of the human brain. It was employed for building of the quantitative structure-retention relationships (QSRRs) model of antifungal agents-imidazoles or triazoles by structure. Computed molecular descriptors together with the percentage of acetonitrile in mobile phase (v/v) and buffer pH, being the most influential HPLC factors, were used as network inputs, giving the retention factor as model output. The multilayer perceptron network with a 9-5-1 topology was trained by using the back propagation algorithm. Good correlation between experimentally obtained data and ones predicted by using QSRR-ANN on previously unseen data sets indicates good predictive ability of the model.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "Talanta",
title = "Quantitative structure retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography",
volume = "100",
pages = "329-337",
doi = "10.1016/j.talanta.2012.07.071"
}

Golubović, J., Protić, A., Zečević, M., Otašević, B., Mikić, M.,& Živanović, L.. (2012). Quantitative structure retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography. in Talanta
Elsevier Science BV, Amsterdam., 100, 329-337.
https://doi.org/10.1016/j.talanta.2012.07.071

Golubović J, Protić A, Zečević M, Otašević B, Mikić M, Živanović L. Quantitative structure retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography. in Talanta. 2012;100:329-337.
doi:10.1016/j.talanta.2012.07.071 .

Golubović, Jelena, Protić, Ana, Zečević, Mira, Otašević, Biljana, Mikić, Marija, Živanović, Ljiljana, "Quantitative structure retention relationships of azole antifungal agents in reversed-phase high performance liquid chromatography" in Talanta, 100 (2012):329-337,
https://doi.org/10.1016/j.talanta.2012.07.071 . .