Paul Mamza

Ethanol extract of Terminalia catappa as a green inhibitor for the corrosion of mild steel in H2SO4

Abstract The inhibitive and adsorption properties of ethanol extract of Terminalia catappa for the corrosion of mild steel in H2SO4 were investigated using weight loss, hydrogen evolution, and infra red methods of monitoring corrosion. Ethanol extract of T. catappa is a good adsorption inhibitor for the corrosion of mild steel in H2SO4. The inhibition efficiency of the inhibitor increases with increasing concentration but decreases with increasing temperature. The inhibition potential of ethanol extract of T. catappa is attributed to the presence of saponnin, tannin, phlobatin, anthraquinone, cardiac glycosides, flavanoid, terpene, and alkaloid in the extract. The adsorption of the inhibitor on mild steel surface is exothermic, spontaneous and is best described by Langmuir adsorption model. From the calculated values of activation energy, free energy of adsorption and the trend in the variation of inhibition efficiency with temperature, the mechanism of adsorption of the inhibitor is physical adsorption.

In silico modelling of quantitative structure–activity relationship of multi-target anticancer compounds on k-562 cell line

The pGI50 cytotoxicity values of 112 compounds on K-562 cancer cell line were modeled to illustrate the quantitative structure–activity relationship (QSAR) of the compounds. The dataset were divided into training and test set through Kennard-stone algorithm, while the pool of molecular descriptors calculated with paDEL descriptor metric program was subjected to the genetic functional algorithm (GFA) for selection of descriptor to be modeled. The best QSAR model developed was then subjected to a rigorous statistical test. The statistical significance of the model was verified by calculating the values of Q2LOO (0.845), Q2F1 (0.9397), Q2F2 (0.6862) and R2pred (0.6862) needed to evaluate the strength and robustness of the model. The result of the internal and external validation of the model indicates that the model is good and could be used to predict the GI50 of anticancer compounds on K-562 leukemia cell line. The model developed was used in designing new anticancer drugs with higher activity or more potent and less toxic in nature when compared to the lead compound. These compounds significant activities were found to correlate to with some of the molecular descriptors such as the number of hydrogen bond acceptors present in the surface of the molecule.

Insilico Modelling of Quantitative Structure-Activity Relationship of Pgi50 Anticancer Compounds on k-562 Cell Line

Abstract The pGI50 cytotoxicity values of 112 compounds on K-562 cancer cell line were modelled in order to illustrate the quantitative structure–activity relationship of the compounds. The data set were divided into training and test set through Kennard-stone algorithm, while the pool of molecular descriptors calculated with paDEL descriptor metric program was subjected to genetic functional algorithm for selection of descriptor to be modeled. The statistical significance of the model was verified by calculating the values of Q2LOO (0.845), Q2F1 (0.9397), Q2F2 (0.6862) and R2pred (0.6862) needed to evaluate the strength and robustness of the model. The result of the internal and external validation of the model indicates that the model is good and could be used to predict the GI50 of anticancer compounds on K-562 leukemia cell line.