Anticancer evaluations of iodoquinazoline substituted with allyl and/or benzyl as dual inhibitors of EGFRWT and EGFRT790M: design, synthesis, ADMET and molecular docking.

Fifteen new iodoquinazoline derivatives, 5a,b to 18, are reported in this study and their anticancer evaluation as dual inhibitors of EGFRWT and EGFRT790M. The new derivatives were designed according to the target of structural requirements of receptors. Cytotoxicity of our compounds was evaluated against MCF-7, A549, HCT116 and HepG2 cell lines using MTT assay. Compounds 18, 17 and 14b showed the highest anticancer effects with IC50 = 5.25, 6.46, 5.68 and 5.24 µM, 5.55, 6.85, 5.40 and 5.11 µM and 5.86, 7.03, 6.15 and 5.77 µM against HepG2, MCF-7, HCT116 and A549 cell lines, respectively. The eight highly effective compounds 10, 13, 14a, 14b, 15, 16, 17 and 18 were inspected against VERO normal cell lines to evaluate their cytotoxicity. Our conclusion was that compounds 10, 13, 14a, 14b, 15, 16, 17 and 18 possessed low toxicity against VERO normal cells with IC50 increasing from 43.44 to 52.11 µM. All compounds were additionally assessed for their EGFRWT and EGFRT790M inhibitory activities. Additionally, their ability to bind with EGFRWT and EGFR receptors was confirmed by molecular docking. Compound 17 exhibited the same inhibitory activity as erlotinib. Compounds 10, 13, 14b, 16 and 18 excellently inhibited VEGFR-2 activity with IC50 ranging from 0.17 to 0.50 µM. Moreover, compounds 18, 17, 14b and 16 remarkably inhibited EGFRT790M activity with IC50 = 0.25, 0.30, 0.36 and 0.40 µM respectively. As planned, compounds 18, 17 and 14b showed excellent dual EGFRWT/EGFRT790M inhibitory activities. Finally, our compounds 18, 17 and 14b displayed good in silico ADMET calculated profiles.

Soils with more clay and dense vegetation were rich in soil carbon along Wadi Al-Sharaea, Makkah, Saudi Arabia.

In arid ecosystems, lack of vegetation and nutrients can negatively impact soil carbon (C) content. In the current study, our goals were to assess soil C stocks to a depth of 50 cm in an arid ecosystem (Wadi Al-Sharaea, Saudi Arabia) and determine their relation to different vegetation cover. To address our research objective, a total of 102 quadrate (randomly selected) were established along the desert wadi. Soil samples were collected to a depth of 50 cm with 5 cm interval, then Soil Bulk Density (SBD, g/cm3), Soil Organic C Content (SOC, g C/kg), and stocks (kg C/m2) were estimated. Both soil mechanical and chemical analyses were conducted for a composite soil sample. Study sites were categorized based on their visual vegetation cover (VC) percentage (%) into three major groups: 1) scarce vegetation cover (VC less than 25%); 2) medium vegetation cover (VC is higher than 25% and less than 75%); and lastly 3) dense vegetation cover (VC is higher than 75%). Soils were characterized by higher sand content (48.2%, both fine and coarse compiled) than silt (36.7 ± 1.64%) or clay (10.1 ± 1.28%). There were significant differences among soil Calcium (Ca) and Potassium (K) content (p < 0.05), while those plant communities with medium vegetation cover showed the highest soil content of Ca and K (1.7 ± 0.24 and 0.2 ± 0.03 meq/l, respectively). Plant communities with dense vegetation cover had the lowest SBD (1.96 ± 0.03 g/cm3) and the highest SOC stocks (14.9 ± 2.1 kg C/m2). Moreover, our data analyses indicated that SBD and SOC content had strong and negative correlation, where soils with dense vegetation cover had the most significant correlation (R2 = 0.95). Our results recommend that soil carbon stocks to a depth of 50 cm based on different vegetation cover of arid ecosystems should be implemented on global soil carbon budget to better elucidate factors controlling SOC content at the regional and global scales.

Phytoremediation of nitrogen and phosphorus pollutants from glass industry effluent by using water hyacinth (Eichhornia crassipes (Mart.) Solms): Application of RSM and ANN techniques for experimental optimization.

The present study aimed to assess the efficiency of the water hyacinth (Eichhornia crassipes (Mart.) Solms) plant for the reduction of nitrogen and phosphorus pollutants from glass industry effluent (GIE) as batch mode phytoremediation experiments. For this, response surface methodology (RSM) and artificial neural networks (ANN) methods were adopted to evidence the optimization and prediction performances of E. crassipes for total Kjeldahl's nitrogen (TKN) and total phosphorus (TP) removal. The control parameters, i.e., GIE concentration (0, 50, and 100%) and plant density (1, 3, and 5 numbers) were used to optimize the best reduction conditions of TKN and TP. A quadratic model of RSM and feed-forward backpropagation algorithm-based logistic model (input layer: 2 neurons, hidden layer: 10 neurons, and output layer: 1 neuron) of ANN showed good fitness results for experimental optimization. Optimization results showed that maximum reduction of TKN (93.86%) and TP (87.43%) was achieved by using 60% of GIE concentration and nearly five plants. However, coefficient of determination (R2) values showed that ANN models (TKN: 0.9980; TP: 0.9899) were superior in terms of prediction performance as compared to RSM (TKN: 0.9888; TP: 0.9868). Therefore, the findings of this study concluded that E. crassipes can be effectively used to remediate nitrogen and phosphorus loads of GIE and minimize environmental hazards caused by its unsafe disposal.