Moscow State University of Food Production

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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
European Journal of Pharmaceutical Sciences | Year: 2013

Tuberculosis (TB) constitutes one of the most dangerous and serious health problems around the world. It is a very lethal disease caused by microorganisms of the genus mycobacterium, principally Mycobacterium tuberculosis (MTB) which affects humans. A very active field for the search of more efficient anti-TB chemotherapies is the use in silico methodologies for the discovery of potent anti-TB agents. The battle against MTB by using antimicrobial chemotherapies will depend on the design of new chemicals with high anti-TB activity and low toxicity as possible. Multi-target methodologies focused on quantitative- structure activity relationships (mt-QSAR) have played a very important role for the rationalization of drug design, providing a better understanding about the molecular patterns related with diverse pharmacological profiles including antimicrobial activity. Nowadays, 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 model based on quantitative-structure biological effect relationships (mtk-QSBER) for the simultaneous prediction of anti-TB activity and toxicity against Mus musculus and Rattus norvegicus. The mtk-QSBER model was created by using linear discriminant analysis (LDA) for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under many experimental conditions. Our mtk-QSBER model, correctly classified more than 90% of the case in the whole database (more than 12,000 cases), serving as a powerful tool for the computer-assisted screening of potent and safe anti-TB drugs. © 2013 Elsevier B.V. All rights reserved.


Speck-Planche A.,University of Porto | Kleandrova V.V.,Moscow State University of Food Production
Current Topics in Medicinal Chemistry | Year: 2012

The search for new therapies against neurodegenerative disorders (NDs) such as Alzheimer (AD) and Parkinson (PD) constitutes a very active area. Although the scientific community has realized great efforts for the study of AD and PD from the most diverse points of view, these diseases remain incurable. Consequently, the design of new and more potent compounds for proteins associated with AD and PD represents nowadays, an objective of major importance. In this sense, the protein known as monoamine oxidase B (MAO-B) constitutes one of the key targets for the search of new drug candidates which could be employed as neuroprotective agents in both anti-AD and anti-PD chemotherapies. The present work is focused on the role of the Quantitative-Structure Activity Relationship (QSAR) analysis and molecular docking (MDock) techniques which have been applied for the discovery of new and promising molecular entities with high inhibitory activity against MAO-B. We also give a brief overview about one of the most potent MAO-B inhibitor drugs: rasagiline. Finally, as contribution to the field, we constructed a QSAR model using artificial neural network (ANN) analysis for the virtual screening of potent MAO-B inhibitors. By realizing a careful inspection of the meaning of the variables in the QSAR-ANN model, new rasagiline bioisosteres were suggested as possible potent MAO-B inhibitors. © 2012 Bentham Science Publishers.


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
European Journal of Pharmaceutical Sciences | Year: 2012

The discovery of new and more efficient anti-cancer chemotherapies is a field of research in expansion and growth. Breast cancer (BC) is one of the most studied cancers because it is the principal cause of cancer deaths in women. In the active area for the search of more potent anti-BC drugs, the use of approaches based on Chemoinformatics has played a very important role. However, until now there is no methodology able to predict anti-BC activity of compounds against more than one BC cell line, which should constitute a greater interest. In this study we introduce the first chemoinformatic multi-target (mt) approach for the in silico design and virtual screening of anti-BC agents against 13 cell lines. Here, an mt-QSAR discriminant model was developed using a large and heterogeneous database of compounds. The model correctly classified 88.47% and 92.75% of active and inactive compounds respectively, in training set. The validation of the model was carried out by using a prediction set which showed 89.79% of correct classification for active and 92.49% for inactive compounds. Some fragments were extracted from the molecules and their contributions to anti-BC activity were calculated. Several fragments were identified as potential substructural features responsible for anti-BC activity and new molecules designed from those fragments with positive contributions were suggested as possible potent and versatile anti-BC agents. © 2012 Elsevier B.V. All rights reserved.


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.


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.,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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