Browsing by Author "Dosenko, Victor"

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    Genotype Associations with the Different Phenotypes of Atopic Dermatitis in Children
    (Acta Medica (Hradec Králové), 2021-07-30) Dosenko, Victor; Dytiatkovskyi, Volodymyr; Drevytska, Tetiana
    This study deals with detecting the associations of atopic dermatitis' (AD) phenotypes in children: alone or combined with seasonal allergic rhino-conjunctivitis (SARC) and/or perennial allergic rhinitis (PAR), and/or with bronchial asthma (BA) with single nucleotide polymorphisms (SNP) of filaggrin (FLG), thymic stromal lymphopoietin (TSLP) and orsomucoid-like-1 protein 3 (ORMDL3) genes. Male and female pediatric patients aged from 3 to 18 years old were recruited into the main (AD in different combinations with SARC, PAR, BA) and control groups (disorders of digestives system, neither clinical nor laboratory signs of atopy). Patients were genotyped for SNP of rs_7927894 FLG, rs_11466749 TSLP, rs_7216389 ORMDL3 variants. Statistically significant associations of the increased risk were detected of AD combined with SARC and/or PAR and AD combined with BA (possibly, SARC and/or PAR) with C/T rs_7927894 FLG and T/T rs_7216389 ORMDL3 genotypes. Genotype C/C rs_7927894 FLG significantly decreases the risk of AD combined with SARC and/or PAR by 2.56 fold. Several genotypes' associations had a trend to significance: C/C rs_7216389 ORMDL3 decreases and C/T rs_7216389 ORMDL3 increases the risk for developing AD alone phenotype; A/G rs_11466749 TSLP decreases the risk of AD combined with BA (possibly, SARC and/or PAR) phenotype development.
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    Pharmacophore modeling, docking and molecular dynamics simulation for identification of novel human protein kinase C beta (PKCβ) inhibitors.
    (Structural Chemistry, 2022-10-11) Pashevin, Denis; Starosyla, Sergiy; Dosenko, Victor
    Protein kinase Cβ (PKCβ) is considered as an attractive molecular target for the treatment of COVID-19-related acute respiratory distress syndrome (ARDS). Several classes of inhibitors have been already identified. In this article, we developed and validated ligand-based PKCβ pharmacophore models based on the chemical structures of the known inhibitors. The most accurate pharmacophore model, which correctly predicted more than 70% active compounds of test set, included three aromatic pharmacophore features without vectors, one hydrogen bond acceptor pharmacophore feature, one hydrophobic pharmacophore feature and 158 excluded volumes. This pharmacophore model was used for virtual screening of compound collection in order to identify novel potent PKCβ inhibitors. Also, molecular docking of compound collection was performed and 28 compounds which were selected simultaneously by two approaches as top-scored were proposed for further biological research.