Biochemistry (Moscow), Supplement Series B: Biomedical Chemistry最新文献

The priority direction of the pharmaceutical composition Phytoladaptogene (PLA) development based on the prediction of biological activity spectra for several secondary metabolites of medicinal plants using the PASS computer program and validation in vitro of the prediction result was determined. PLA is a complex of structurally diverse small organic compounds including biologically active substances of phytoadaptogenes (ginsenosides from Panax ginseng, rhodionin from Rhodiola rosea and others) compiled considering previously developed pharmaceutical compositions. Two variants of the pharmaceutical composition were studied—major (22 compounds) and minor (13 compounds) ones. The probability of activity exceeds the probability of inactivity for 1400 out of 1945 pharmacological effects and mechanisms predicted by PASS for the major version of PLA. The wide range of predicted activities is mainly due to the compounds' low structural similarity to each other. An in silico prediction indicates the possibilities of antitumor properties against bladder, stomach, colon, ovarian and cervical cancers both for minor and major PLA compositions. It was found that the highest probability values of activity are predicted for three mechanisms: Apoptosis agonist, Caspase 3 stimulant, and Transcription factor NF kappa B inhibitor. According to the PharmaExpert program, they are associated with the antitumor effect against bladder cancer. Experimental validation was performed in vitro on the human bladder cancer cell line RT-112. As a result of the MTT test the cytotoxicity of the PLA major variant was found to be higher than that of the minor one. In the in vitro experiments using two methods—double staining with annexin V and propidium iodide as well as detection of active caspase 3 in cells—the death of bladder cancer cells by the apoptosis mechanism was also confirmed. The data obtained correspond to the results of the prediction and indicate the major PLA composition advantages. Phytoladaptogene can become the basis for the development of a drug with antitumor activity against bladder cancer. Antitumor activity predicted by PASS for other cancers may be the subject of further studies.

RAGE signal transduction via the RAGE-NF-κB signaling pathway is one of the mechanisms of inflammatory reactions that cause severe diabetic complications. RAGE inhibitors are promising pharmacological compounds; their development requires creation of new predictive models. Based on the methodology of artificial neural networks, a consensus ensemble neural network multitarget model has been constructed. This model describes the dependence of the RAGE inhibitory activity on the affinity of compounds for 34 target proteins of the RAGE-NF-κB signal pathway. For this purpose an expanded database of valid three-dimensional models of target proteins of the RAGE-NF-κB signal chain has been created using the database of three-dimensional models of relevant biotargets. Ensemble molecular docking of known RAGE inhibitors from the verified database into the sites of added models of target proteins was performed, and the minimum docking energies for each compound in relation to each target were determined. An extended training set for neural network modeling was formed. Using seven variants of learning by the method of artificial multilayer perceptron neural networks, three ensembles of classification decision rules were constructed to predict three level of the RAGE-inhibitory activity based on the calculated affinity of compounds for significant target proteins of the RAGE-NF-κB signaling pathway. Using a simple consensus of the second level, the predictive ability of the created model was assessed and its high accuracy and statistical significance were demonstrated. The resultant consensus ensemble neural network multitarget model has been used for virtual screening of new derivatives of different chemical classes. The most promising substances have been synthesized and sent for experimental studies.

Chronic obstructive pulmonary disease (COPD) is characterized by reduced sensitivity of cells to the anti-inflammatory effects of glucocorticoids (GCs). Azithromycin and a low dose theophylline have a significant impact on molecular mechanisms leading to corticosteroid resistance. The aim of this study was to evaluate the ability of azithromycin and theophylline to enhance the anti-inflammatory effects of GCs on the production of cytokines by NK and NKT-like blood cells of COPD patients. Whole blood cells from COPD patients (n = 21) were incubated in the presence of budesonide (10 nM), azithromycin (10 μg/mL), theophylline (1 μM), or their combinations and stimulated with phorbol myristate acetate (50 ng/mL). Intracellular production of proinflammatory cytokines in NK (CD3-CD56+) and NKT-like (CD3+CD56+) blood cells was analyzed by flow cytometry. Budesonide reduced synthesis of interleukin 4 (IL-4), CXCL8, tumor necrosis factor α (TNFα) by NK and NKT-like cells, as well as production of interferon γ (IFNγ) by NK cells. Azithromycin suppressed production of IL-4 and CXCL8 by NK and NKT-like cells, and theo-phylline inhibited IL-4 synthesis by these lymphocytes. The combination of azithromycin and budesonide had a more pronounced inhibitory effect on the production of IL-4 and CXCL8 by NK and NKT-like cells than the effect of these drugs alone. The combination of theophylline and budesonide suppressed synthesis of IL-4 and CXCL8 by NK and NKT-like cells, as well as the production of TNFα and IFNγ by NK cells stronger than budesonide alone. The obtained results demonstrate the benefits for the combined use of GCs with theophylline at a low dose or azithromycin to suppress the inflammatory process in patients with COPD.

Metabolic stability determines the susceptibility of compounds to biotransformation on the body. It is characterized by such parameters as half-life (T_1/2) and clearance (CL). In vitro systems based on cells or subcellular fractions (mainly liver microsomal enzymes) are consider as models of processes occurring in a living organism and are used for metabolic stability assessment. The data obtained from the experiments are used to build QSAR models. Based on the freely available database ChEMBL v.27, we collected more than 8000 records containing the structures of compounds and their clearance and/or half-life time values obtained on human liver microsomes. GUSAR (General Unrestricted Structure-Activity Relationships) and PASS (Prediction of Activity Spectra for Substances) software were used to create quantitative and qualitative models based on the collected data. A 5-fold cross-validation procedure was used to the model assessments. Thresholds T_1/2 = 30 min and CL = 20 mL/min/kg were chosen to distinguish between stable and unstable molecules. The accuracy of the models changes from 0.5 (calculated using 5-fold cross-validation on quantitative models for predicting the half-life) to 0.96 (calculated using 5-fold cross-validation on classification models for predicting the clearance).