Time filter

Source Type

Kleandrova V.V.,Moscow State University of Food Production | Speck-Planche A.,University of the East of Cuba
Frontiers in Bioscience - Elite | Year: 2013

The chemical risk assessment is determinant for the approval of any kind of chemical. Each aspect of chemical is taken into consideration for the new chemical legislation registration, evaluation, and authorization of chemicals (REACH). However, some improvements can be made in order to select and authorize a chemical. QSAR techniques have been used for the study of several kind of toxicological properties in order to realize a deeper study concerning to risk assessment. For this reason, this work is focused into present a review of chemical legislation policies in the European Union (EU) and in Russia, and changes in chemicals regulations to meet the requirement of REACH. Also, we reported the used of several approaches and chemo-bioinformatics tools applied to QSAR methodologies for the several parameters relative to toxicity and how they can be used for regulatory purposes in risk assessment. Source

Speck-Planche A.,University of the East of Cuba | Kleandrova V.V.,Moscow State University of Food Production | Rojas-Vargas J.A.,University of the East of Cuba
Molecular Diversity | Year: 2011

The increasing resistance of several phytopathogenic fungal species to the existing agrochemical fungicides has alarmed to the worldwide scientific community. There is no available methodology to predict in an efficient way if a new fungicide will have resistance risk due to fungal species which cause considerable crop losses. In an attempt to overcome this problem, a multi-resistance risk QSAR model, based on substructural descriptors was developed from a heterogeneous database of compounds. The purpose of this model is the classification, design, and prediction of agrochemical fungicides according to resistance risk categories. The QSAR model classified correctly 85.11% of the fungicides and the 85.07% of the inactive compounds in the training series, for an accuracy of 85.08%. In the prediction series, the percentages of correct classification were 85.71 and 86.55% for fungicides and inactive compounds, respectively, with an accuracy of 86.39%. Some fragments were extracted and their quantitative contributions to the fungicidal activity were calculated taking into consideration the different resistance risk categories for agrochemical fungicides. In the same way, some fragments present in molecules with fungicidal activity and with negative contributions were analyzed like structural alerts responsible of resistance risk. © 2011 Springer Science+Business Media B.V. Source

Speck-Planche A.,University of Porto | Kleandrova V.V.,Moscow State University of Food Production | Luan F.,University of Porto | Cordeiro M.N.D.S.,University of Porto
Anti-Cancer Agents in Medicinal Chemistry | Year: 2012

A brain tumor (BT) constitutes a neoplasm located in the brain or the central spinal canal. The number of new diagnosed cases with BT increases with the pass of the time. Understanding the biology of BT is essential for the development of novel therapeutic strategies, in order to prevent or deal with this disease. An active area for the search of new anti-BT therapies is the use of Chemoinformatics and/or Bioinformatics toward the design of new and potent anti-BT agents. The principal limitation of all these approaches is that they consider small series of structurally related compounds and/or the studies are realized for only one target like protein. The present work is an effort to overcome this problem. We introduce here the first Chemoinformatics multi-target approach for the in silico design and prediction of anti-BT agents against several cell lines. Here, a fragment-based QSAR model was developed. The model correctly classified 89.63% and 90.93% of active and inactive compounds respectively, in training series. The validation of the model was carried out by using prediction series which showed 88.00% of correct classification for active and 88.59% for inactive compounds. Some fragments were extracted from the molecules and their contributions to anti-BT activity were calculated. Several fragments were identified as potential substructural features responsible of anti-BT activity and new molecular entities designed from fragments with positive contributions were suggested as possible anti-BT agents. © 2012 Bentham Science Publishers. Source

Speck-Planche A.,University of Porto | Kleandrova V.V.,University of Porto | Kleandrova V.V.,Moscow State University of Food Production | Cordeiro M.N.D.S.,University of Porto
Bioorganic and Medicinal Chemistry | Year: 2013

Streptococci are a group of Gram-positive bacteria which are responsible for causing many diverse diseases in humans and other animals worldwide. The high prevalence of resistance of these bacteria to current antibacterial drugs is an alarming problem for the scientific community. The battle against streptococci by using antimicrobial chemotherapies will depend on the design of new chemicals with high inhibitory activity, having also as low toxicity as possible. Multi-target approaches based on quantitative-structure activity relationships (mt-QSAR) have played a very important role, providing a better knowledge about the molecular patterns related with the appearance of different pharmacological profiles including antimicrobial activity. Until now, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. In the present study, we develop by the first time, a unified multitasking (mtk) QSAR model for the simultaneous prediction of anti-streptococci activity and toxic effects against biological models like Mus musculus and Rattus norvegicus. The mtk-QSAR model was created by using artificial neural networks (ANN) analysis for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under diverse sets of experimental conditions. Our mtk-QSAR model, correctly classified more than 97% of the cases in the whole database (more than 11,500 cases), serving as a promising tool for the virtual screening of potent and safe anti-streptococci drugs. © 2013 Elsevier Ltd.All rights reserved. Source

Ugrozov V.V.,Moscow State University of Food Production
Colloid Journal | Year: 2010

A cluster model of the kinetics of the reversible sorption of vapor by amorphous polymers is proposed. The effect of the equilibrium and kinetic parameters of a membrane system on the kinetics of vapor sorption is studied by mathematical modeling. An analytical equation of vapor sorption isotherm is derived. It is shown that this equation gives an adequate description of the process of vapor sorption by amorphous polymers in both glassy and rubber-like states. © Pleiades Publishing, Ltd., 2010. Source

Discover hidden collaborations