Search

Background Cancer of the breast is known to be among the top spreading diseases on the globe. Triple-negative breast cancer is painstaking the most destructive type of mammary tumor because it spreads faster to other parts of the body, with high chances of early relapse and mortality. This research would aim at utilizing computational methods like quantitative structure–activity relationship (QSAR), performing molecular docking studies and again to further design new effective molecules using the QSAR model parameters and to analyze the pharmacokinetics “drug-likeliness” properties of the new compounds before they could proceed to pre-clinical trials. Results The QSAR model of the derivatives was highly robust as it also conforms to the least minimum requirement for QSAR model from the statistical assessments of (R2) = 0.6715, (R2adj) = 0.61920, (Q2) = 0.5460 and (R2pred) of 0.5304, and the model parameters (AATS6i and VR1_Dze) were used in designing new derivative compounds with higher potency. The molecular docking studies between the derivative compounds and Maternal Embryonic Leucine Zipper Kinase (MELK) protein target revealed that ligand 2, 9 and 17 had the highest binding affinities of − 9.3, − 9.3 and − 8.9 kcal/mol which was found to be higher than the standard drug adriamycin with − 7.8 kcal/mol. The pharmacokinetics analysis carried out on the newly designed compounds revealed that all the compounds passed the drug-likeness test and also the Lipinski rule of five. Conclusions The results obtained from the QSAR mathematical model of parthenolide derivatives were used in designing new derivatives compounds that were more effective and potent. The molecular docking result of parthenolide derivatives showed that compounds 2, 9 and 17 had higher docking scores than the standard drug adriamycin. The compounds would serve as the most promising inhibitors (MELK). Furthermore, the pharmacokinetics analysis carried out on the newly designed compounds revealed that all the compounds passed the drug-likeness test (ADME and other physicochemical properties) and they also adhered to the Lipinski rule of five. This gives a great breakthrough in medicine in finding the cure to triple-negative breast cancer (MBA-MD-231 cell line).

The perovskite nanoparticles, Sr1-XMXFeO3(M=Na and K, x=0.1, 0.2) were prepared using metal precursors; Fe(NO3)2.9H2O and Sr(NO3)2 by co-precipitation method in the presence of sodium dodecylsulphate. The produced powder was annealed at 500°C, 700°C, and 800°C in a Muffle furnace. The obtained powders were characterised using the powder X-ray diffraction (PXRD) which indicated that the samples crystallised in a distorted hexagonal structure. The prominent peak in each of the perovskite sample, increased with increasing annealing temperature. Thermogravimetric analysis (TGA) of the samples showed a weight loss between the temperature range of 300–500°C due to K2O decomposition. The differential thermal analysis (DTA) showed the powders to possess endothermic activities at the positive derivative weight lost, with desorption and vapourisation up to 400°C. The oxidative and thermal stabilities determined at 500°C and above for both the TGA and DTA were reported in this work. Scanning electron microscopy (SEM) of the powdered nanoparticles showed the morphology of the particles starting from the nucleation stage, to formation and distribution. The photocatalytic activity of all powders were tested against methylene blue pollutant as a mordant dye, both in the presence and absence of hydrogen peroxide at pH=6.8. Powders at lower annealing temperatures were not very active but all powders at 800°C showed the highest degradation efficiency for the Na and K doped SrFeO3, this was also confirmed at the pH of 12 for K-doped nanaoparticle on the MB photodegradation.

Background Colorectal cancer is common to both sexes; third in terms of morbidity and second in terms of mortality, accounting for 10% and 9.2% of cancer cases in men and women globally. Although drugs such as bevacizumab, Camptosar, and cetuximab are being used to manage colorectal cancer, the efficacy of the drugs has been reported to vary from patient to patient. These drugs have also been reported to have varying degrees of side effects; thus, the need for novel drug therapies with better efficacy and lesser side effects. In silico drugs design methods provide a faster and cost-effect method for lead identification and optimization. The aim of this study, therefore, was to design novel imidazol-5-ones via in silico design methods. Results A QSAR model was built using the genetic function algorithm method to model the cytotoxicity of the compounds against the HCT116 colorectal cancer cell line. The built model had statistical parameters; R2 = 0.7397, R2adj = 0.6712, Q2cv = 0.5547, and R2ext. = 0.7202 and revealed the cytotoxic activity of the compounds to be dependent on the molecular descriptors nS, GATS5s, VR1_Dze, ETA_dBetaP, and L3i. These molecular descriptors were poorly correlated (VIF < 4.0) and made unique contributions to the built model. The model was used to design a novel set of derivatives via the ligand-based drug design approach. Compounds e, h, j, and l showed significantly better cytotoxicity (IC50 < 5.0 μM) compared to the template. The interaction of the compounds with the CDK2 enzyme (PDB ID: 6GUE) was investigated via molecular docking study. The compounds were potent inhibitors of the enzyme having binding affinity of range −10.8 to −11.0 kcal/mol and primarily formed hydrogen bond interaction with lysine, aspartic acid, leucine, and histidine amino acid residues of the enzyme. Conclusion The QSAR model built was stable, robust, and had a good predicting ability. Thus, predictions made by the model were reliably employed in further in silico studies. The compounds designed were more active than the template and showed better inhibition of the CDK2 enzyme compared to the standard drugs sorafenib and kenpaullone.

Additional file 1: Table S1. External validation of built model. Table S2. Experimental, predicted and residual cytotoxic activity of training set compounds. Table S3. Experimental, predicted and residual cytotoxic activity of test set compounds. Table S4. Molecular descriptors and predicted cytotoxic activity of designed compounds