TY  - CONF
AU  - Jovanović, Marija
AU  - Sokić, Dragoslav
AU  - Grabnar, Iztok
AU  - Vovk, Tomaz
AU  - Prostran, Milica
AU  - Erić, Slavica
AU  - Kuzmanovski, Igor
AU  - Vučićević, Katarina
AU  - Miljković, Branislava
PY  - 2015
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2453
PB  - Wiley-Blackwell, Hoboken
C3  - Pharmacotherapy
T1  - Prediction of topiramate serum levels according to variability factors using artificial neural networks.
VL  - 35
IS  - 5
SP  - e75
EP  - e76
DO  - 10.1002/phar.1606
ER  - 

@conference{
author = "Jovanović, Marija and Sokić, Dragoslav and Grabnar, Iztok and Vovk, Tomaz and Prostran, Milica and Erić, Slavica and Kuzmanovski, Igor and Vučićević, Katarina and Miljković, Branislava",
year = "2015",
publisher = "Wiley-Blackwell, Hoboken",
journal = "Pharmacotherapy",
title = "Prediction of topiramate serum levels according to variability factors using artificial neural networks.",
volume = "35",
number = "5",
pages = "e75-e76",
doi = "10.1002/phar.1606"
}

Jovanović, M., Sokić, D., Grabnar, I., Vovk, T., Prostran, M., Erić, S., Kuzmanovski, I., Vučićević, K.,& Miljković, B.. (2015). Prediction of topiramate serum levels according to variability factors using artificial neural networks.. in Pharmacotherapy
Wiley-Blackwell, Hoboken., 35(5), e75-e76.
https://doi.org/10.1002/phar.1606

Jovanović M, Sokić D, Grabnar I, Vovk T, Prostran M, Erić S, Kuzmanovski I, Vučićević K, Miljković B. Prediction of topiramate serum levels according to variability factors using artificial neural networks.. in Pharmacotherapy. 2015;35(5):e75-e76.
doi:10.1002/phar.1606 .

Jovanović, Marija, Sokić, Dragoslav, Grabnar, Iztok, Vovk, Tomaz, Prostran, Milica, Erić, Slavica, Kuzmanovski, Igor, Vučićević, Katarina, Miljković, Branislava, "Prediction of topiramate serum levels according to variability factors using artificial neural networks." in Pharmacotherapy, 35, no. 5 (2015):e75-e76,
https://doi.org/10.1002/phar.1606 . .

TY  - JOUR
AU  - Jovanović, Marija
AU  - Sokić, Dragoslav
AU  - Grabnar, Iztok
AU  - Vovk, Tomaz
AU  - Prostran, Milica
AU  - Erić, Slavica
AU  - Kuzmanovski, Igor
AU  - Vučićević, Katarina
AU  - Miljković, Branislava
PY  - 2015
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2366
AB  - Purpose: The application of artificial neural networks in the pharmaceutical sciences is broad, ranging from drug discovery to clinical pharmacy. In this study, we explored the applicability of counter-propagation artificial neural networks (CPANNs), combined with genetic algorithm (GA) for prediction of topiramate (TPM) serum levels based on identified factors important for its prediction. Methods: The study was performed on 118 TPM measurements obtained from 78 adult epileptic patients. Patients were on stable TPM dosing regimen for at least 7 days; therefore, steady-state was assumed. TPM serum concentration was determined by high performance liquid chromatography with fluorescence detection. The influence of demographic, biochemical parameters and therapy characteristics of the patients on TPM levels were tested. Data analysis was performed by CPANNs. GA was used for optimal CPANN parameters, variable selection and adjustment of relative importance. Results: Data for training included 88 measured TPM concentrations, while remaining were used for validation. Among all factors tested, TPM dose, renal function (eGFR) and carbamazepine dose significantly influenced TPM level and their relative importance were 0.7500, 0.2813, 0.0625, respectively. Relative error and root mean squared relative error (%) and their corresponding 95% confidence intervals for training set were 2.14 [(-2.41) - 6.70] and 21.5 [18.5 - 24.1]; and for test set were 6.21 [(-21.2) - 8.77] and 39.9 [31.7 - 46.7], respectively. Conclusions: Statistical parameters showed acceptable predictive performance. Results indicate the feasibility of CPANNs combined with GA to predict TPM concentrations and to adjust relative importance of identified variability factors in population of adult epileptic patients.
PB  - Canadian Soc Pharmaceutical Sciences, Edmonton
T2  - Journal of Pharmacy and Pharmaceutical Sciences
T1  - Application of Counter-propagation Artificial Neural Networks in Prediction of Topiramate Concentration in Patients with Epilepsy
VL  - 18
IS  - 5
SP  - 856
EP  - 862
DO  - 10.18433/J33031
ER  - 

@article{
author = "Jovanović, Marija and Sokić, Dragoslav and Grabnar, Iztok and Vovk, Tomaz and Prostran, Milica and Erić, Slavica and Kuzmanovski, Igor and Vučićević, Katarina and Miljković, Branislava",
year = "2015",
abstract = "Purpose: The application of artificial neural networks in the pharmaceutical sciences is broad, ranging from drug discovery to clinical pharmacy. In this study, we explored the applicability of counter-propagation artificial neural networks (CPANNs), combined with genetic algorithm (GA) for prediction of topiramate (TPM) serum levels based on identified factors important for its prediction. Methods: The study was performed on 118 TPM measurements obtained from 78 adult epileptic patients. Patients were on stable TPM dosing regimen for at least 7 days; therefore, steady-state was assumed. TPM serum concentration was determined by high performance liquid chromatography with fluorescence detection. The influence of demographic, biochemical parameters and therapy characteristics of the patients on TPM levels were tested. Data analysis was performed by CPANNs. GA was used for optimal CPANN parameters, variable selection and adjustment of relative importance. Results: Data for training included 88 measured TPM concentrations, while remaining were used for validation. Among all factors tested, TPM dose, renal function (eGFR) and carbamazepine dose significantly influenced TPM level and their relative importance were 0.7500, 0.2813, 0.0625, respectively. Relative error and root mean squared relative error (%) and their corresponding 95% confidence intervals for training set were 2.14 [(-2.41) - 6.70] and 21.5 [18.5 - 24.1]; and for test set were 6.21 [(-21.2) - 8.77] and 39.9 [31.7 - 46.7], respectively. Conclusions: Statistical parameters showed acceptable predictive performance. Results indicate the feasibility of CPANNs combined with GA to predict TPM concentrations and to adjust relative importance of identified variability factors in population of adult epileptic patients.",
publisher = "Canadian Soc Pharmaceutical Sciences, Edmonton",
journal = "Journal of Pharmacy and Pharmaceutical Sciences",
title = "Application of Counter-propagation Artificial Neural Networks in Prediction of Topiramate Concentration in Patients with Epilepsy",
volume = "18",
number = "5",
pages = "856-862",
doi = "10.18433/J33031"
}

Jovanović, M., Sokić, D., Grabnar, I., Vovk, T., Prostran, M., Erić, S., Kuzmanovski, I., Vučićević, K.,& Miljković, B.. (2015). Application of Counter-propagation Artificial Neural Networks in Prediction of Topiramate Concentration in Patients with Epilepsy. in Journal of Pharmacy and Pharmaceutical Sciences
Canadian Soc Pharmaceutical Sciences, Edmonton., 18(5), 856-862.
https://doi.org/10.18433/J33031

Jovanović M, Sokić D, Grabnar I, Vovk T, Prostran M, Erić S, Kuzmanovski I, Vučićević K, Miljković B. Application of Counter-propagation Artificial Neural Networks in Prediction of Topiramate Concentration in Patients with Epilepsy. in Journal of Pharmacy and Pharmaceutical Sciences. 2015;18(5):856-862.
doi:10.18433/J33031 .

Jovanović, Marija, Sokić, Dragoslav, Grabnar, Iztok, Vovk, Tomaz, Prostran, Milica, Erić, Slavica, Kuzmanovski, Igor, Vučićević, Katarina, Miljković, Branislava, "Application of Counter-propagation Artificial Neural Networks in Prediction of Topiramate Concentration in Patients with Epilepsy" in Journal of Pharmacy and Pharmaceutical Sciences, 18, no. 5 (2015):856-862,
https://doi.org/10.18433/J33031 . .

TY  - JOUR
AU  - Erić, Slavica
AU  - Kalinić, Marko
AU  - Popović, Aleksandar
AU  - Zloh, Mire
AU  - Kuzmanovski, Igor
PY  - 2012
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1644
AB  - In this work, we present a novel approach for the development of models for prediction of aqueous solubility, based on the implementation of an algorithm for the automatic adjustment of descriptor's relative importance (AARI) in counter-propagation artificial neural networks (CPANN). Using this approach, the interpretability of the models based on artificial neural networks, which are traditionally considered as "black box" models, was significantly improved. For the development of the model, a data set consisting of 374 diverse drug-like molecules, divided into training (n = 280) and test (n = 94) sets using self-organizing maps, was used. Heuristic method was applied in preselecting a small number of the most significant descriptors to serve as inputs for CPANN training. The performances of the final model based on 7 descriptors for prediction of solubility were satisfactory for both training (RMSEPtrain = 0.668) and test set (RMSEPtest = 0.679). The model was found to be a highly interpretable in terms of solubility, as well as rationalizing structural features that could have an impact on the solubility of the compounds investigated. Therefore, the proposed approach can significantly enhance model usability by giving guidance for structural modifications of compounds with the aim of improving solubility in the early phase of drug discovery.
PB  - Elsevier Science BV, Amsterdam
T2  - International Journal of Pharmaceutics
T1  - Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks
VL  - 437
IS  - 1-2
SP  - 232
EP  - 241
DO  - 10.1016/j.ijpharm.2012.08.022
ER  - 

@article{
author = "Erić, Slavica and Kalinić, Marko and Popović, Aleksandar and Zloh, Mire and Kuzmanovski, Igor",
year = "2012",
abstract = "In this work, we present a novel approach for the development of models for prediction of aqueous solubility, based on the implementation of an algorithm for the automatic adjustment of descriptor's relative importance (AARI) in counter-propagation artificial neural networks (CPANN). Using this approach, the interpretability of the models based on artificial neural networks, which are traditionally considered as "black box" models, was significantly improved. For the development of the model, a data set consisting of 374 diverse drug-like molecules, divided into training (n = 280) and test (n = 94) sets using self-organizing maps, was used. Heuristic method was applied in preselecting a small number of the most significant descriptors to serve as inputs for CPANN training. The performances of the final model based on 7 descriptors for prediction of solubility were satisfactory for both training (RMSEPtrain = 0.668) and test set (RMSEPtest = 0.679). The model was found to be a highly interpretable in terms of solubility, as well as rationalizing structural features that could have an impact on the solubility of the compounds investigated. Therefore, the proposed approach can significantly enhance model usability by giving guidance for structural modifications of compounds with the aim of improving solubility in the early phase of drug discovery.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "International Journal of Pharmaceutics",
title = "Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks",
volume = "437",
number = "1-2",
pages = "232-241",
doi = "10.1016/j.ijpharm.2012.08.022"
}

Erić, S., Kalinić, M., Popović, A., Zloh, M.,& Kuzmanovski, I.. (2012). Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks. in International Journal of Pharmaceutics
Elsevier Science BV, Amsterdam., 437(1-2), 232-241.
https://doi.org/10.1016/j.ijpharm.2012.08.022

Erić S, Kalinić M, Popović A, Zloh M, Kuzmanovski I. Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks. in International Journal of Pharmaceutics. 2012;437(1-2):232-241.
doi:10.1016/j.ijpharm.2012.08.022 .

Erić, Slavica, Kalinić, Marko, Popović, Aleksandar, Zloh, Mire, Kuzmanovski, Igor, "Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks" in International Journal of Pharmaceutics, 437, no. 1-2 (2012):232-241,
https://doi.org/10.1016/j.ijpharm.2012.08.022 . .

TY  - JOUR
AU  - Stojić, Nataša
AU  - Erić, Slavica
AU  - Kuzmanovski, Igor
PY  - 2010
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1357
AB  - In this work, a novel algorithm for optimization of counter-propagation artificial neural networks has been used for development of quantitative structure-activity relationships model for prediction of the estrogenic activity of endocrine-disrupting chemicals. The search for the best model was performed using genetic algorithms. Genetic algorithms were used not only for selection of the most suitable descriptors for modeling, but also for automatic adjustment of their relative importance. Using our recently developed algorithm for automatic adjustment of the relative importance of the input variables, we have developed simple models with very good generalization performances using only few interpretable descriptors. One of the developed models is in details discussed in this article. The simplicity of the chosen descriptors and their relative importance for this model helped us in performing a detailed data exploratory analysis which gave us an insight in the structural features required for the activity of the estrogenic endocrine-disrupting chemicals.
PB  - Elsevier Science Inc, New York
T2  - Journal of Molecular Graphics & Modelling
T1  - Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks
VL  - 29
IS  - 3
SP  - 450
EP  - 460
DO  - 10.1016/j.jmgm.2010.09.001
ER  - 

@article{
author = "Stojić, Nataša and Erić, Slavica and Kuzmanovski, Igor",
year = "2010",
abstract = "In this work, a novel algorithm for optimization of counter-propagation artificial neural networks has been used for development of quantitative structure-activity relationships model for prediction of the estrogenic activity of endocrine-disrupting chemicals. The search for the best model was performed using genetic algorithms. Genetic algorithms were used not only for selection of the most suitable descriptors for modeling, but also for automatic adjustment of their relative importance. Using our recently developed algorithm for automatic adjustment of the relative importance of the input variables, we have developed simple models with very good generalization performances using only few interpretable descriptors. One of the developed models is in details discussed in this article. The simplicity of the chosen descriptors and their relative importance for this model helped us in performing a detailed data exploratory analysis which gave us an insight in the structural features required for the activity of the estrogenic endocrine-disrupting chemicals.",
publisher = "Elsevier Science Inc, New York",
journal = "Journal of Molecular Graphics & Modelling",
title = "Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks",
volume = "29",
number = "3",
pages = "450-460",
doi = "10.1016/j.jmgm.2010.09.001"
}

Stojić, N., Erić, S.,& Kuzmanovski, I.. (2010). Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks. in Journal of Molecular Graphics & Modelling
Elsevier Science Inc, New York., 29(3), 450-460.
https://doi.org/10.1016/j.jmgm.2010.09.001

Stojić N, Erić S, Kuzmanovski I. Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks. in Journal of Molecular Graphics & Modelling. 2010;29(3):450-460.
doi:10.1016/j.jmgm.2010.09.001 .

Stojić, Nataša, Erić, Slavica, Kuzmanovski, Igor, "Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks" in Journal of Molecular Graphics & Modelling, 29, no. 3 (2010):450-460,
https://doi.org/10.1016/j.jmgm.2010.09.001 . .