reza aalizadeh
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Objective
Given the apparent life-threatening nature of COVID-19, finding an effective treatment is under investigation.
Materials and MethodsWe assessed effect of shallomin oral syrup (co IranAmin®) as a complementary treatment to improve the clinical outcomes in COVID-19 patients. Patients in the control group received the approved treatment protocol (lopinavir/ritonavir), while those in the intervention group were treated with the oral syrup shallomin in addition to the approved treatment. Clinical status of treated patients was recorded and compared.
ResultsThere were meaningful differences between the two groups regarding shortened length of hospital stay and the recovery time for cough, myalgia, sore throat, and shortness of breath. No side effect occurred in the intervention group compared to the control group in terms of biochemical and hematological factors.
ConclusionIt seems that the treatment with shallomin syrup showed remarkable contribution to the recovery of COVID-19 induced symptoms in the patients under lopinavir/ritonavir therapy.
Keywords: SARS-Cov-2, COVID-19, Lopinavir, Ritonavir, Shallomin Syrup, Drug Safety -
CoMFA and CoMSIA methods were used to perform 3D quantitative structure-activity relationship (3D-QSAR) evaluation and molecular docking, of 5-HT6 receptor inhibitors. The CoMFA model performed on training set in biases of alignment with suitable statistical parameters (q2= 0.556, r2 = 0.836, F= 26.334, SEE=0.171). The best prediction for 5-HT6 receptor inhibitors was obtained by CoMFA (after focusing region) model with highest predictive ability (q2= 0.599, r2 = 0.857, F= 30.853, SEE=0.160) in biases of the same alignment. Using the same alignment, a consistent CoMSIA model was obtained (q2= 0.580, r2 = 0.752, F= 34.361, SEE=0.201) from the three combinations. To evaluate the prediction capability of the CoMFA and CoMSIA models, a test set of 9 compounds was used so that they could show the good predictive r2 values for CoMFA, CoMFA (after focusing region), and CoMSIA models, 0.554, 0.473, and 0.670, respectively. The obtained contour maps form models were used to identify the structural features responsible for the biological activity to design potent 5-HT6 receptor inhibitors. Molecular docking analysis along with the CoMSIA model could reveal the significant role of hydrophobic characteristics in increasing the inhibitors potency. Using the results, some new compounds were designed which showed the higher inhibitory activities as 5-HT6 receptor inhibitors.
Keywords: 3D-QSAR, Molecular docking, CoMFA, CoMSIA, 5-HT6 receptor -
The 17β-HSD3 enzyme plays a key role in treatment of prostate cancer and small inhibitors can be used to efficiently target it. In the present study, the multiple linear regression (MLR), and support vector machine (SVM) methods were used to interpret the chemical structural functionality against the inhibition activity of some 17β-HSD3inhibitors. Chemical structural information were described through various types of molecular descriptors and genetic algorithm (GA) was applied to decrease the complexity of inhibition pathway to a few relevant molecular descriptors. Non-linear method (GA-SVM) showed to be better than the linear (GA-MLR) method in terms of the internal and the external prediction accuracy. The SVM model, with high statistical significance (R2 train = 0.938; R2 test = 0.870), was found to be useful for estimating the inhibition activity of 17β-HSD3 inhibitors. The models were validated rigorously through leave-one-out cross-validation and several compounds as external test set. Furthermore, the external predictive power of the proposed model was examined by considering modified 2 and concordance correlation coefficient values, Golbraikh and Tropsha acceptable model criteriaʹs, and an extra evaluation set from an external data set. Applicability domain of the linear model was carefully defined using Williams plot. Moreover, Euclidean based applicability domain was applied to define the chemical structural diversity of the evaluation set and training set.Keywords: QSAR, Genetic algorithms, Support vector machine, Multiple linear regressions, 17?-HSD3
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