Moscow State University of Food Production

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Speck-Planche A.,University of the East of Cuba | Kleandrova V.V.,Moscow State University of Food Production
Molecular Diversity | Year: 2012

Rational design of entry inhibitors is an active area for the discovery of new and effective anti-HIV agents. C-C Chemokine receptors represent key targets for the HIV entry process. Several of these proteins with features to be HIV co-receptors have not been sufficiently studied or used for the design of novel entry inhibitors. With the purpose to overcome this problem, we develop here a fragment-based approach for the design of multi-target inhibitors against four C-C chemokine receptors. This approach was focused on the construction of a multi-target QSAR discriminant model using a large and heterogeneous database of compounds and substructural descriptors for the classification and prediction of inhibitors for C-C chemokine receptors. The model correctly classified more than 89% of active and inactive compounds in both: training and prediction series. As principal advantage, this model permitted the automatic and fast extraction of fragments responsible for the inhibitory activity against the different C-C chemokine receptors under study and new molecular entities were suggested as possible versatile inhibitors for these proteins. © 2011 Springer Science+Business Media B.V.


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.


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.


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.


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.


Vasin S.I.,Moscow State University of Food Production
Colloid Journal | Year: 2010

The Happel-Brenner cell model is employed to calculate the hydrodynamic permeability of membranes composed of impenetrable spherical particles coated with nonuniform porous layers. All of the boundary conditions corresponding to the Happel, Kuwabara, Kvashnin, and Cunningham models are considered at the cell surface. Liquid flows in the porous layers are described by the Brinkman equation. The force applied to a composite particle flowed around by a uniform stream is calculated. © Pleiades Publishing, Ltd., 2010.


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.


Speck-Planche A.,University of Porto | Kleandrova V.V.,Moscow State University of Food Production | Luan F.,University of Porto | Luan F.,Yantai University | Cordeiro M.N.D.S.,University of Porto
Molecular BioSystems | Year: 2012

Acquired immunodeficiency syndrome (AIDS) is a dangerous disease, which damages the immune system cells to the point that the immune system can no longer fight against other infections that it would usually be able to prevent. The causal agent is the human immunodeficiency virus (HIV), and for this reason, the search for more effective chemotherapies against HIV is a challenge for the scientific community. Chemoinformatics and Quantitative Structure-Activity Relationship (QSAR) studies have played an essential role in the design of potent inhibitors for proteins associated with the HIV infection. However, all previous studies took into consideration the discovery of future drug candidates using homogeneous series of compounds against only one protein. This fact limits the use of more efficient anti-HIV chemotherapies. In this work, we develop the first ligand-based approach for the in silico design of multi-target (mt) inhibitors for seven key proteins associated with the HIV infection. Two mt-QSAR models were constructed from a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors. The second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted and their contributions to anti-HIV activity through inhibition of the different proteins were calculated using the mt-QSAR-LDA model. New molecules designed from fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-HIV agents. © 2012 The Royal Society of Chemistry.


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
Bioorganic and Medicinal Chemistry | Year: 2011

Prostate cancer (PCa) is the second-leading cause of cancer deaths among men in the around the world. Understanding the biology of PCa is essential to the development of novel therapeutic strategies, in order to prevent this disease. However, after PCa make metastases, chemotherapy plays an extremely important role. With the pass of the time, PCa cell lines become resistant to the current anti-PCa drugs. For this reason, there is a necessity to develop new anti-PCa agents with the ability to be active against several PCa cell lines. The present work is an effort to overcome this problem. We introduce here the first multi-target approach for the design and prediction of anti-PCa agents against several cell lines. Here, a fragment-based QSAR model was developed. The model had a sensitivity of 88.36% and specificity 89.81% in training series. Also, the model showed 94.06% and 92.92% for sensitivity and specificity, respectively. Some fragments were extracted from the molecules and their contributions to anti-PCa activity were calculated. Several fragments were identified as potential substructural features responsible of anti-PCa activity and new molecular entities designed from fragments with positive contributions were suggested as possible anti-PCa agents. © 2011 Elsevier Ltd. All rights reserved.


Speck-Planche A.,University of Porto | Kleandrova V.V.,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
Current Alzheimer Research | Year: 2013

Alzheimer disease (AD) is one of the most common and serious neurodegenerative disorders in humans. For this reason, the search for new anti-AD treatments is a very active area. Only few biological receptors associated with AD have been well studied. The efficacy of the current drugs is limited by the fact that they inhibit only one target like protein. Thus, the rational design of new drug candidates as versatile inhibitors for different proteins associated with AD, constitutes a major goal. With the aim to overcome this problem, we developed here the first fragment-based approach by exploring quantitative-structure-activity relationships (QSAR). The principal purpose was the in silico design of multitarget (mt) inhibitors against five proteins associated with AD. Our approach was focused on the construction of an mt-QSAR discriminant model using a large and heterogeneous database of compounds and substructural descriptors, which permitted the simultaneous classification and prediction of inhibitors against five proteins associated with AD. The model correctly classified more than 90% of active and inactive compounds in both, training and prediction series. As principal advantage, this mt-QSAR discriminant model was used for the automatic and fast extraction of fragments responsible for the inhibitory activity against the five proteins under study, and new molecular entities were suggested as possible versatile inhibitors for these proteins. © 2013 Bentham Science Publishers.

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