Eslam Pourbasheer

In this study, in order to model the prediction of potentiometric selectivity (Mg ²⁺ /Ca ²⁺ ) of several amide ligands, the quantitative structure-property relationship (QSPR) approach was used along with two stepwise (SW) and genetic algorithm (GA) methods as variable selection techniques. Experimental data and molecular structures were entered into the models after calculating and screening the descriptors. The SW method identified five descriptors and GA five descriptors (with two common items) as key parameters. Using these variable selection methods and machine learning modeling methods of multiple linear regrassions (MLR) and support vector machine (SVM), various models including SW-MLR, GA-MLR, GA-SVM were created, and also, by identifying an outlier and removing it, GA-MLR and GA-SVM models were re-developed. The model obtained by GA-SVM (with one outlier removed) had R ² _train =0.893, R ² _test =0.757, RMSE _train =0.322, and RMSE _test =0.705, indicating the high predictive power and fit of the models. These values were a significant improvement compared to the reference paper (R ² = 0.66 and RMSE = 0.53 even after removing 11 outliers). In addition, findings provide important mechanistic insights into the role of molecular features in potentiometric selectivity (Mg ²⁺ /Ca ²⁺ ) of amide ligands.

The 3D-QSAR models were developed using CoMFA and CoMSIA techniques to investigate essential molecular fields, optimization strategies, and structure-activity relationships for utrophin-modulating compounds. The data set (71 molecules) was divided into two training and test sets using the hierarchical clustering approach. The training set was aligned based on the most active compound. The built and optimized models based on the PLS approach provided acceptable results. The results were q(2) = 0.528 and r(2) = 0.776 for CoMFA and q(2) = 0.600 and r(2) = 0.811 for CoMSIA models. According to the statistical results, it was found that both the CoMFA models with and without regional focusing and also the CoMSIA model have good estimation ability. Molecular docking was also performed with high-activity compounds (as ligands) and target receptors (protein), and its results, together with the results of 3D-QSAR, give new insights for the design of compounds with higher biological activity. Finally, based on the overall results, the design of new compounds with higher utrophin modulation activity was carried out.

The mixed hemimicelle-based solid phase extraction method using the coated sodium dodecyl sulfate by magnetic iron oxide nanoparticles as adsorbent was developed for extraction and determination of Sunitinib malate in real samples prior to determination by UV–Visible spectrophotometry. For the characterization of synthesized nanoparticles, Fourier transform infrared spectroscopy, and scanning electron microscopy was used. The influences of different factors affecting the extraction efficiency of Sunitinib malate, including the pH, the adsorbent amount, the volume and eluent type, the amount of the surfactant, the ionic strength, extraction, and desorption time, were investigated. At the optimized conditions, a good linearity with correlation coefficients of 0.998 and 0.999 was obtained over the concentration ranges of 1–22 and 1–19 µg/mL for water and urine samples, in order. The good recoveries of 97% and 99% and also, the limits of detection equal with 0.9, and 0.8 µg/mL for water and urine samples were enhanced, respectively. These results demonstrate that mixed hemimicelle solid phase extraction is a fast, efficient, economical and selective sample preparation method for the extraction and determination of Sunitinib malate in different water and urine sample solutions.

Chitosan magnetic nanoparticles (CTS-Fe₃O₄) were synthesized and used for the adsorption and removal of Everzol Black ED-2R dye from wastewater samples prior to spectrophotometric determination. High adsorption capacity with high surface area after surface modification of this new sorbent by chitosan can result in a satisfactory adsorption recovery. The main factors affecting the adsorption recovery of the analyte that were studied and optimized included pH, ionic strength, adsorbent amount, adsorption and desorption times, and desorption solvent volume. The calibration curve was linear over the range of 1–50 mg/L with determination coefficients >0.991. The detection and quantification limits were found to be 0.04 and 0.15 mg/L, respectively. To evaluate the method accuracy, the recoveries of the spiked samples were measured, excellent precisions (RSD < 1.7%) and good recoveries were obtained for wastewater and lake water samples.

Background: Copper is one of several heavy metals. A low concentration of copper is vital for animals and plants, whereas it is highly toxic to aquatic plants and bacteria in a high concentration. Therefore, copper ions in water and food must be controlled, and as a result, the development of novel methods for the determination of copper in water samples is of interest. Objective: Different techniques have been proposed for copper ions extraction and determination. The magnetic solid-phase extraction method is considered superior to the other method for simplicity, its higher enrichment, and the need for lower quantities of solvents. The novel modified magnetite nanoparticles as the sorbent, along with the atomic absorption spectrometry analysis, can be a low-cost, simple and rapid method for this propose. Methods: Traces of Cu(II) in environmental samples were preconcentrated using a novel magnetic adsorbent developed based on 2,2´-((1E,1´E)-hydrazine-1,2-diylidenebis(methanylylidene)) diphenol coated magnetite nanoparticles. The influence of ligand concentration, amount of adsorbent, pH, type of eluent, sample volume, and effects of interfering ions were optimized. The adsorbed species were eluted for analysis through atomic absorption spectrometry. Results: A linear calibration curve was recorded from 2 to 40 μg ml-1 (r2= 0.999) under optimal conditions, and the detection limit of the method was as low as 1.6 μg ml-1. Also, good recoveries were obtained for the real sample analysis. Conclusion: The developed procedure constituted a rapid extraction, a low-cost and efficient method, and was used for the analysis of copper ions in the tap, river, and lake water.

The aim of this study is to apply the magnetic solid phase extraction and high performance liquid chromatography equipped with an ultraviolet detector for the simultaneous extraction of four mostly utilized kinds of drugs, Benzodiazepines, as acidic and basic drugs. Fe ₃ O ₄ /octanoic acid nanocomposite as adsorbent was synthesized during the facile one‐pot co‐precipitation method. The morphological, structural and magnetic characterization of the synthesized nanomaterial was carried out by different techniques. Main parameters, influencing the magnetic solid‐phase extraction efficiency, such as pH, the amount of Hexadecyltrimethylammonium bromide (HTAB), adsorbent amount, ionic strength, extraction time, and desorption conditions were optimized. To quantify the trace amounts of drugs, least amount of methanol was used for the proper elution and better preconcentration, while batch technique of extraction was used too. Under the optimized extraction conditions, the method proved to be effective due to the good linearity (10–5000 ng mL ⁻¹ ), low limits of detection (1.5–12.0 ng mL ⁻¹ ), and relatively high enrichment factors (118–132). The method was applied for monitoring of the benzodiazepines in urine, hair and water samples. In addition, the relative standard deviation was determined around 1.7–6.5, and the intra‐ and inter‐day precisions for five replications found lower 5 and 7 %, respectively. All attained results bring the suggested method in comparison with other before utilized methods.

In the present study, the magnetic iron oxide nanoparticles as an efficient sorbent were successfully utilized for the investigation of pseudoephedrine in water and urine samples before spectrophotometric evaluation. Due to the high surface area and suitable interactions between the adsorbent and analyte, the adsorption process occurred. The optimization process was applied to the parameters that might have an impact on the extraction efficiencies, including the pH, sorbent quantity, salt effect, type and volume of eluent, and the contact time affecting the adsorption and desorption of the analyte. By exerting the optimum procedure, the proper linearities ranging from 1 to 30 and 1 to 24 μg/mL with good correlation coefficients of 0.999 and 0.998 enhanced for water and urine samples. This method is proved to be as accurate and precise as it is necessary to be executed for the analytical measurements, since the measured recoveries were 99 and 96.12%, with the detection limits of 0.88 and 1.003 μg/mL for water and urine samples, respectively. The obtained results indicate that the proposed method is simple, effective, and inexpensive for the measurements of pseudoephedrine in water and urine samples.

In this work, the quantitative structure–activity relationship models were developed for predicting activity of a series of compounds such as CK2 inhibitors using multiple linear regressions and support vector machine methods. The data set consisted of 48 compounds was divided into two subsets of training and test set, randomly. The most relevant molecular descriptors were selected using the genetic algorithm as a feature selection tool. The predictive ability of the models was evaluated using Y-randomization test, cross-validation and external test set. The genetic algorithm-multiple linear regression model with six selected molecular descriptors was obtained and showed high statistical parameters (R2train=0.893, R2test=0.921, Q2LOO=0.844, F=43.17, RMSE=0.287). Comparison of the results between GA-MLR and GA-SVM demonstrates that GA-SVM provided better results for the training set compounds; however, the predictive quality for both models is acceptable. The results suggest that atomic mass and polarizabilities and also number of heteroatom in molecules are the main independent factors contributing to the CK2 inhibition activity. The predicted results of this study can be used to design new and potent CK2 inhibitors