tahereh momeni-isfahani

A quantitative structure- activity relationship (QSAR) has been widely used to investigation a correlation between chemical structures of molecules to their activities. In the present study, QSAR models have been carried out on 76 camptothecin (CPT) derivatives as anticancer drugs to determine the 14N nucleus quadrupole coupling constants (QCC). These quantum chemical properties have been calculated using Density Functional Theory (DFT) and B3LYP/6-311G (d, p) method in the gas phase. A training set of 60 CPT derivatives were used to construct QSAR models and a test set of 16 compounds were used to evaluate the build models that were made using multiple linear regression (MLR) analysis. Molecular descriptors were calculated by Dragon software, and the stepwise multiple linear regression and the Genetic algorithm (GA) techniques were used to select the best descriptors and build QSAR models respectively.  QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and external validation methods. The multicollinearity of the descriptors contributed in the models was tested by calculating the variance inflation factor (VIF) and the Durbin–Watson (DW) statistics. The predictive ability of the models was found to be satisfactory. The results of QSAR study show that quantum parameters, 2D autocorrelations and Walk and path counts  descriptors contains important structural information sufficient to develop useful predictive models for the studied activities.

In this research, predicting the logP of 34 types different pyrethroid derivatives was studied using quantitative structure-property relationship. The logP of studied pyrethroids was modeled using the genetic algorithm based on linear regression method (GA-MLR). It was found that the three effective descriptors GATS4P, PW3 and ZM1V have a reasonable correlation with logP, and led to the creation of a model with the highest regression coefficient and the lower error. The evaluation of GA-MLR model predictive ability for test set was performed by statistical parameters such as R2= 0.862, R2adj = 0.848, F=62.296 and MSE = 0.503. Also, the value of Q2LOO= 0.861 using the cross-validation method, and the values of R2 =0.880 and 0.929 for the training and test sets respectively, in the external validation method showed a very good correlation between experimental and prediction values. It was specified that the MLR model was reliable for predicting the logP of pyrethroid insecticides, and had sufficient accuracy with the lowest error.

Quinoline alkaloids and their derivatives have wide medical and agricultural applications. In this research, a quantitative structure- property relationship (QSPR) has been employed to predict the octanol-water partition coefficient (logP) of 76 quinoline alkaloid camptothecin (CPT) derivatives as antitumor potencies using GA-MLR method and molecular descriptors. The Gauss View 05 software was used for drawing chemical structure of the studied compounds. The geometry optimizations of the studied compounds were done by the Gaussian 09W software at B3YLP density functional theory (DFT) with 6-311G (d,p) basis set. Molecular descriptors for each of optimized structures were calculated by Dragon software in different category. In order to reduce and select the best descriptors, the Genetic Algorithm technique and stepwise multiple linear regression method was used. The pearson coefficient correlation (PCC) and the variance inflation factor (VIF) statistics were used to test the multicollinearity of descriptors in the best model. The different types of internal and external validations were used to evaluate predictive model performance. The best QSPR model is obtained with R2 value of 0.901, Q2LOO =0.919, and RMSE=0.706.     The results of statistical parameters and validations of the GA-MLR model generated were found to be satisfactory. The model revealed that octanol-water partition coefficient of CPT derivatives is influence by ATS8e (2D-autocorrelation) descriptor. This information could be used to design novel quinoline alkaloid camptothecin (CPT) derivatives as insecticide agents.

In this study, we performed quantum mechanics computation at density function theory level with 6-31G* basis set to construct a quantitative structure-toxicity relationship (QSTR) model for predicting lethal dose (LD50) pesticide carbamates derivatives. The best molecular descriptors were selected using genetic algorithm (GA) by MATLAB software. Then, we studied the relationship between the selected descriptors and the logLD50 of carbamate derivatives using backward-stepwise multiple linear regression (BW-MLR) and backpropagation artificial neural network (BP-ANN) models. The RDF010e, WW, and R3e descriptors were applied for modeling the GA-BWMLR and GA-BPANN models. The comparison of results illustrated that the R2 and Q2 of GA-BPANN model for all set were significantly higher than the GA-BWMLR model. The GA-BPANN model was more accurate with lower mean square error (MSE), root-mean-square error (RMSE), standard error of prediction (SEP), and absolute average deviation (ADD) values of data set for predicting the LD50 of studied carbamates.

In this research, removal of Rhodamine B (RB) dye from aqueous samples was investigated by a new Ni (II) based metal-organic framework (MOF) synthesized named [Ni(II) L]n [where L= 4, 4′-diamino diphenyl sulfone] (Ni- MOF). These materials were fully characterized using Fourier transform infrared (FT-IR), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), TGA/DTG and X-ray diffraction (XRD). WE used response surface methodology (RSM) based on the central composite design (CCD) to study the effects of four various parameters including dye concentration, MOF dosage, contact time and pH on the process of adsorption. The optimal condition for removal of RB was achieved for pH=9, 0.063 g of MOF for 25 mL of 16.06 mg/lit dye concentration and contact time of 39 minutes. Adsorption equilibrium and kinetic data were fitted with the Langmuir monolayerisotherm model (qmax=163.66 mg/g and R2= 0.9827) and like pseudo second-order kinetics mechanism (R2=0.992). 
isotherm model (qmax=163.66 mg/g and R2= 0.9827) and like pseudo second-order kinetics mechanism (R2=0.992).

In the present study, an environmentally friendly microextraction of nickel based on solid-phase method coupled to flame atomic absorption spectrophotometry was investigated. Due to elimination of organic solvents in this method, it can be used as a green technique to extract and reduce the detection limit of nickel in water samples.

By adding 0.06 g sodium borohydride (NaBH4) to 100 ml of the solution containing Fe (II) ions with concentration of 12 mg/l and nickel, these ions were converted to zero-valent particles. The nickel revived particles were trapped in the iron microparticles and deposited with them. After that, the solid phase produced was dissolved in 200 µl  of hydrochloric acid (HCl) 6N and the nickel concentration was measured using atomic absorption method. To optimize the process, effect (1 to 8), the amount of potassium hydrogen phthalate (KHP) (0.02-0.30), Fe (II) concentration (2.20-5.00 mg/l), NaBH4 (0.20-0.01 g), time (0.5-15.00 minutes), and temperature (20° -80°  C) were investigated. In addition, the effect of interfering ions was evaluated. Finally, the figures of merit were calculated, and the function of the method was investigated in the real water samples.

According to the performed optimization, pH = 4.5, NaBH4 = 0.06 g, Fe (II) = 2.5 mg/l, KHP = 0.08 g, time= 4 minutes, temperature = 50° C , and sample size = 100 ml were the optimum conditions. Besides, the concentration factor of 410, the detection limit of 0.3 ng/ml, and relative standard deviation (RSD) of 2.7% were obtained.

Due to the low detection limit and the elimination of organic solvents in nickel microextraction, this method can be a suitable method for determining trace amount of nickel with high efficiency. The low detection limit is due to the high concentration factor, which is significantly higher than the other microextraction methods.

In this work, we used ZnS-Ni/coco Ac nanocomposite as a cheap adsorbent for the elimination of Crystal Violet (CV) dye from an aqueous medium by an ultrasound-based adsorption method. First ZnS-Ni nanoparticles were synthesized by using the microwave-assisted co-precipitation method. These nanoparticles were then stabilized on activated carbons derived from coconut shells. The ZnS-Ni/coco AC composite was characterized using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmitted electron microscopy (TEM), and energy-dispersive X-ray spectrometry (EDX). WE used response surface methodology (RSM) based on the central composite design (CCD) to study the effects of four various parameters including dye concentration, the amount of adsorbent, sonication time and pH on the process of adsorption. The optimal condition for removal of CV up to 98.96% was achieved for pH=6.31, 0.023 g of adsorbent for 16.06 mg/L dye concentration and sonication time of 5 minutes. Adsorption equilibrium and kinetic data were fitted with the Langmuir monolayer isotherm model (qmax=178.57 mg/g and R2= 0.9925) and like pseudo-second-order kinetics mechanism (R2=0.9994)

A Quantitative Structure–Activity Relationship (QSAR) study based on Genetic Algorithm  Multiple Linear Regressions (GA-MLR) were carried out for the prediction of the toxicity (logIC50) and the logarithm of octanol-water partition coefficient (logPow) of some carbamate derivatives as insecticides. The optimized conformation of compounds were obtained at HF/6-31G* level with Gaussian 98 software. Dragon software is used to calculate molecular descriptors. A data set of these compounds was randomly divided into 2 groups: training and test sets. The QSAR models were optimized using multiple linear regressions (MLR).The most relevant molecular descriptors were collected by Genetic Algorithm (GA) and backward regression. The best GA-MLR models are obtained using statistical parameters, such as squared correlation coefficient (R2), adjusted squared correlation coefficient (R2adj), root mean square error (RMSE) values for training and test sets. The best QSAR models are obtained based on the statistical parameters Leave-one-out (LOO) cross-validation, external test set, external validation parameters (Q2F1, Q2F2, Q2F3) and the concordance correlation coefficient (CCC) were used to quantify the predictive ability of GA-MLR models. The results showed that GA-MLR models could be used to predict the activities of carbamate derivatives.

Imidazole is compound with a wide range of biological activities and imidazole derivatives are the basis of several groups of drugs.In this study the relationship between molecular descriptors and the thermal energy (Eth kJ/mol), and heat capacity (Cv J/mol) of imidazole derivatives is studied. The chemical structures of 85 Imidazole derivatives were optimized at HF/6-311G* level with Gaussian 98 software.Molecular descriptors were calculated for selected compound by using the Dragon software.The Genetic algorithm- multiple linear regression (GA-MLR) and backward methods were used to select the suitable descriptors and also for predicting the thermodynamic properties of imidazole derivatives.The obtained models were evaluated by statistical parameters, such as correlation coefficient (R2adj), Fisher ratio (F), Root Mean Square Error (RMSE), Durbin-Watson statistic (D) and significance (Sig).The predictive powers of the GA- MLR models are studied using leave-one-out (LOO) cross-validation and external test set. The predictive ability of the GA-MLR models with two-three selected molecular descriptors was found to be satisfactory. The developed QSPR models can be used to predict the property of compounds not yet synthesized.

Phytomedicine or herbal medicine, refers to the use of plants to treat diseases and promote good health. Anti-oxidative and anti-inflammatory characteristics of the medicinal herbs make them logical adjuvant to improve wound healing. The aim of this study was to evaluate the wound healing potential of Biarum straussiis’ (B. straussii) rhizome extract on cutaneous wounds in rats.

Experimental: 

Adult male rats (n=18) were divided into three groups (n=6), as group A, B and C. Then, full-thickness, square shape  cutaneous wounds were created on the skin. In group A, as negative control, the wound area was only washed using normal saline solution; in group B as positive control, the wound was treated using phenytoin and the wound treatment using B. straussii rhizome extract was done in group C, as experimental subject. The progressive changes in wounds of each group were evaluated for the contraction degree on days 4,7,10 and 14. The tissue samples of the wound area were removed from each group on day 14, fixed in 10% formalin and finally stained with H&E for histological examination. Data analysis was carried out using one-way ANOVA test, followed by Tukey-Kramer test (p <0.05).

The wound contraction was higher in group treated with B. straussii extractthan in control group at 7th, 10th and 14th days (p <0.0001). The histological analysis showed a significant accelerated wound contraction, complete re-epithelialization, and tissue recovery due to the topical application of B. straussii rhizome extract.

Recommended applications/ industries:

 It can be concluded that the rhizome extract of B. straussii is favorable for cutaneous wound healing in rats and would be considered as a medicinal plant, but further studies are required to reach more definitive results.

Essential Oils are highly concentrated substances the subtle, aromatic and volatile liquids. The use of essential oils is largely widespread in foods, deodorants, pharmaceuticals, drinks, cosmetics, medicine and embalming antiseptics especially with aromatherapy becoming increasingly popular. The lipophilicity of an organic compound can be described by a partition coefficient, logP, which plays a significant role in drug discovery and compound design. A data set of 40 compounds in the essential oil of kesum was randomly divided into 3 groups: training, test and validation sets consisting of 70%, 15% and 15% of data point, respectively. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm - Multiple Linear Regressions (GA-MLR) and genetic algorithm -artificial neural network (GA-ANN) were employed to design the Quantitative Structure-Property Relationship (QSPR) models. The predictive powers of the QSPR model was discussed using Coefficient of determination (R2), Absolute Average Deviation (AAD) and the Mean Squared Error (MSE). The R2 and MSE values of the MLR model were calculated to be 0.734 and 0.194 respectively. The R2 and MSE values for the training set of the ANN model were calculated to be 0.9905 and 2×10-4 respectively. Comparison of the results revealed that the application the GA-ANN method gave better results than GA-MLR method

A QSPR study on a series of 2-Phenylindole derivatives as anticancer agents was performed to explore the important molecular descriptor which is responsible for their thermodynamic properties such as heat capacity (Cv) and entropy(S).Molecular descriptors were calculated using DRAGON software and the Genetic Algorithm (GA) and backward selection procedure were used to reduce and select the suitable descriptors. Multiple Linear Regression (MLR) analysis was carried out to derive QSPR models, which were further evaluated for statistical significance such as squared correlation coefficient (R2) root mean square error (RMSE), adjusted correlation coefficient (R2adj) and fisher index of quality (F).The multicollinearity of the descriptors selected in the models were tested by calculating the variance inflation factor (VIF), Pearson correlation coefficient (PCC) and the Durbin–Watson (DW) statistics. The predictive powers of the MLR models were discussed using Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The best QSPR models for prediction the Cv(J/molK) and S(J/molK), having squared correlation coefficient R2 =0.907 and 0.901, root mean squared error RMSE=2.019 and RMSE= 2.505, and cross-validated squared correlation coefficient R2 cv = 0.902 and 0.889, respectively. The statistical outcomes derived from the present study demonstrate good predictability and may be useful in the design of new 2-Phenylindole derivatives.

An analytical methodology based on spectrophotometric and partial least squares (PLS) algorithm for the simultaneous determination of ampicillin and penicillin in human plasma was developed and validated. The multivariate model was developed as a binary calibration model and it was built and validated with an independent set of synthesis and real samples in presence of matrix. It is shown how a developed technique for signal filtering, orthogonal signal correction (OSC), can be applied in multivariate calibration to enhance predictive power. The experimental calibration matrix was constructed with 25 samples. The concentration ranges considered were 1.0-40.0 and 0.5-20.0 μg mL-1 for ampicillin and penicillin, respectively. This procedure allows the simultaneous determination of ampicillin and penicillin in synthetic and human plasma good reliability of the determination was proved. The results obtained by the OSC-PLS and HPLC were statistically compared. Very similar values were found by two methods. No time consuming pretreatment was needed and this method also provides rapid, accurate and economical analysis of these drugs.