Kuzmanovski, Igor

Preferred name

Kuzmanovski, Igor

Official Name

Kuzmanovski, Igor

Main Affiliation

shigor@pmf.ukim.mk

Now showing 1 - 10 of 18

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Development of models for prediction of the antioxidant activity of derivatives of natural compounds
(Elsevier BV, 2015-04-08)
Martinčič, Rok
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Wagner, Alain
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Novič, Marjana
Antioxidants are important for maintaining the appropriate balance between oxidizing and reducing species in the body and thus preventing oxidative stress. Many natural compounds are being screened for their possible antioxidant activity. It was found that a mushroom pigment Norbadione A, which is a pulvinic acid derivative, shows an antioxidant activity; the same was found for other pulvinic acid derivatives and structurally related coumarines. Based on the results of in vitro studies performed on these compounds as a part of this study quantitative structure-activity relationship (QSAR) predictive models were constructed using multiple linear regression, counter-propagation artificial neural networks and support vector regression (SVR). The models have been developed in accordance with current QSAR guidelines, including the assessment of the models applicability domains. A new approach for the graphical evaluation of the applicability domain for SVR models is suggested. The developed models show sufficient predictive abilities for the screening of virtual libraries for new potential antioxidants.
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Examination of the influence of different variables on prediction of unit cell parameters in perovskites using counter-propagation artificial neural networks
(Wiley, 2012-01)
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Dimitrovska-Lazova, Sandra
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Application of Counter-propagation Artificial Neural Networks in Prediction of Topiramate Concentration in Patients with Epilepsy
(2015)
Jovanović, Marija
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Sokić, Dragoslav
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Grabnar, Iztok
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Vovk, Tomaž
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Prostran, Milica
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.
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Optimization of supervised self-organizing maps with genetic algorithms for classification electrophoretic profiles
(Society of Chemists and Technologists of Macedonia, 2014-05-02)
Tomovska, Natalia
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<jats:p><p>Standard electrophoresis methods were used in the classification of analyzed proteins in cerebrospinal fluid from patients with multiple sclerosis. Disc electrophoresis was carried out for detection of oligoclonal IgG bands in cerebrospinal fluid on polyacrylamide gel, mainly with multiple sclerosis and other central nervous system dysfunctions. ImageMaster 1D Elite and GelPro specialized software packages were used for fast accurate image and gel analysis. The classification model was based on supervised self-organizing maps. In order to perform the modeling in automated manner genetic algorithms were used. Using this approach and a data set composed of 69 samples we were able to develop models based on supervised self-organizing maps which were able to correctly classify 83 % of the samples in the data set used for external validation.</p></jats:p>
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Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks
(Elsevier BV, 2010-11)
Stojić, Nataša
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Erić, Slavica
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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.
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Incidence, risk factors and management practices in post-viral encephalitis epilepsy: A long-term, nationwide population-based study and review of literature
(Elsevier BV, 2026-03)
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Adjami, Bekim
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Boshkovski, Bojan
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Babunovska, Marija
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Objective: We investigated the incidence, risk factors, and management of post-viral encephalitis epilepsy (PEE) in a nationwide cohort in North Macedonia. Additionally, we conducted a comprehensive literature search on PEE. Methods: Data were obtained from the electronic National Health System (eNHS), encompassing all patients diagnosed with viral encephalitis (VE) in 2016. Patients with pre-existing epilepsy diagnoses were excluded. Clinical, neuroimaging, and EEG data were collected and analyzed, and participants were followed for seven years. Results: Of 1660,584 individuals registered in the eNHS in 2016, 68 were confirmed to have VE (incidence: 4.1/100,000). Among these, six patients died during hospitalization, and the remaining 62 were included in the study cohort. Acute symptomatic seizures (ASyS) occurred in 39 % of patients, with focal to bilateral tonic-clonic seizures (FBTCS) being the most common seizure type. Over the seven-year follow-up period, 11 patients (18 %) developed PEE, with 73 % of cases diagnosed within the first year. Significant risk factors for PEE included ASyS, younger age, and epileptiform abnormalities on EEG. By the end of the follow-up, seven patients with PEE (64 %) remained on antiseizure medications (ASMs). Conclusions: Our results confirm ASyS and highlight acute electro-clinical findings and young age as risk factors for PEE. There is a need for evidence-based clinical pathways and care protocols for patients at risk.
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Clinical genetic study in juvenile myoclonic epilepsy
(2014-11)
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To evaluate clinical features of probands with juvenile myoclonic epilepsy (JME) and affected members of their families in order to study clinical genetics of JME.
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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
(Elsevier BV, 2012-11-01)
Erić, Slavica
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Kalinić, Marko
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Popović, Aleksandar
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Zloh, Mire
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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 (RMSEP(train)=0.668) and test set (RMSEP(test)=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.
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Design and synthesis of new antioxidants predicted by the model developed on a set of pulvinic acid derivatives
(American Chemical Society (ACS), 2011-12-27)
Le Roux, Antoine
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Habrant, Damien
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Meunier, Stéphane
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Bischoff, Pierre
Antioxidative activity expressed as protection of thymidine has been investigated for a set of 30 pulvinic acid derivatives. A combination of in vitro testing and in silico modeling was used for synthesis of new potential antioxidants. Experimental data obtained from a primary screening test based on oxidation under Fenton conditions and by an UV exposure followed by back-titration of the amount of thymidine remaining intact have been used to develop a computer model for prediction of antioxidant activity. Structural descriptors of 30 compounds tested for their thymidine protection activity were calculated in order to define the structure-property relationship and to construct predictive models. Due to the potential nonlinearity, the counter-propagation artificial neural networks were assessed for modeling of the antioxidant activity of these compounds. The optimized model was challenged with 80 new molecules not present in the initial training set. The compounds with the highest predicted antioxidant activity were considered for synthesis. Among the predicted structures, some coumarine derivatives appeared to be especially interesting. One of them was synthesized and tested on in vitro assays and showed some antioxidant and radioprotective activities, which turned out as a promising lead toward more potent antioxidants.
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Levetiracetam-Induced Seizure Aggravation in Patients With Focal Cortical Dysplasia
(Lippincott, Williams & Wilkins, 2018)
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Babunovska, Marija
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Boshkovski, Bojan
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Aleksovska, Katina
The choice of antiepileptic drug is typically based on seizure type, and there is no evidence for superior effectiveness or potential deterioration of particular antiepileptic drug in specific etiologic subgroups. The aim of the study was to identify etiological factor(s) associated with increased risk of seizure aggravation with levetiracetam (LEV).

Kuzmanovski, Igor

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