QSAR analysis of pyrimidine derivatives as VEGFR-2 receptor inhibitors to inhibit cancer using multiple linear regression and artificial neural network.

BACKGROUND AND PURPOSE: In this study, the pharmacological activity of 33 compounds of furopyrimidine and thienopyrimidine as vascular endothelial growth factor receptor 2 (VEGFR-2) inhibitors to inhibit cancer was investigated. The most important angiogenesis inducer is VEGF endothelial growth factor, which exerts its activity by binding to two tyrosine kinase receptors called VEGFR-1 and VEGFR-2. Due to the critical role of VEGF in the pathological angiogenesis of this molecule, it is a valuable therapeutic target for anti-angiogenesis therapies. EXPERIMENTAL APPROACH: After calculating descriptors using SPSS software and stepwise selection method, 5 descriptors were used for modeling in multiple linear regression (MLR) and artificial neural network (ANN). The calibration series and the test series in this study included 26 and 7 combinations, respectively. FINDINGS/RESULTS: The performance evaluation of models was determined by the R2, RMSE, and Q2 statistic parameters. The R2 values of MLR and ANN models were 0.889 and 0.998, respectively. Also, the value of RMSE in the ANN model was lower and its Q2 value was higher than the MLR model. CONCLUSION AND IMPLICATIONS: The results were evaluated by different statistical methods and it was concluded that the nonlinear neural network method is powerful to predict the pharmacological activity of similar compounds, and because of the complex and nonlinear relationships, the MLR was not capable of establishing a good model with high predictive power.

Synthesis of ion-imprinted polymer-decorated SBA-15 as a selective and efficient system for the removal and extraction of Cu(ii) with focus on optimization by response surface methodology.

Ion-imprinted polymer-decorated SBA-15 (SBA-15-IIP) for the adsorption of copper was synthesized and characterized using different techniques, including FT-IR, XRD, TG/DTA, SEM, BET, and TEM. It was used as a green, efficient and rapid sorbent for the removal of Cu(ii) from aqueous solution. The effect of several parameters on the removal percentage of copper was studied using the central composite design. The equilibrium data were investigated using the Langmuir and Freundlich isotherm models, and found to be well-fitted by the Langmuir model with the maximum adsorption capacity of 322.58 mg g-1. The adsorption kinetics was investigated using four models, and found to be well-fitted by the pseudo-second-order model. Also, the recyclability of SBA-15-IIP was studied, and the results after 6 cycles demonstrated that SBA-15-IIP was a promising sorbent for the removal of Cu(ii) from aqueous solution. Subsequently, SBA-15-IIP was used as an efficient and selective sorbent for the extraction of Cu(ii). Various parameters affecting the extraction efficiency of the analyte were investigated and optimized using the Box-Behnken design. The optimized methodology presented good linearity between 0.2 and 100 µg L-1 (R2 > 0.9946) and a detection limit of 0.05 µg L-1. The method had an enhancement factor of 220 and relative standard deviation (RSD) of 3.1% and 4.5% for intra-day and inter-day, respectively.