Graph Theoretical Analysis, Insilico Modeling and Formulation of Pyrimidine Nanoparticles as p38α MAP Kinases inhibitors: A Quantitative Proteomics Approach - Theivendren, Panneerselvam; Kunjiappan, Selvaraj; Govindraj, Saravanan; Chandrasekarn, Jaikanth; Pavadai, Parasuraman; Saraswathy, Ganesan Rajalekshmi; Murugan, Indhumathy (Drug Research, 2019)
In this study, the optimized 4-(4-hydroxybenzyl)-2-amino-6-hydroxypyrimidine-5-carboxamide derivative was formulated as nanoparticles to evaluate for their anticancer activity. The response surface methodology (RSM) was performed with utilization of Box-Behnken statistical design (BBSD) to optimize the experimental conditions for identification of significant synthetic methodology. To explore the stability of the derivative was done by density functional theory (DFT). Graph theoretical analysis was introduced to identify the drug target p38α MAP Kinases and then insilico modeling was performed to provide straightforward information for further structural optimization. The experimental results under optimal experimental conditions obtained 74.55–76% yield of 4-(4-hydroxybenzyl)-2-amino-6-hydroxypyrimidine-5-carboxamide, 127oC melting point and Rf value 0.59 were well matched with the predicted results and this was gaining 95% of confidence level and suitability of RSM. The spectral data were reliable with the assigned structures of synthetic yields. The formulated nanoparticles were exhibited a good anticancer activity against used cancer cell line MCF7. Amusingly the observed docking scores and in-vitro anticancer activity was proving the compound significance and potential as a potent p38α inhibitor. Further, we have elucidated the mechanism of action at its functional level using label-free quantitative proteomics. Interestingly the observed results were indicating that the derived proteomics data involving in the alteration process in cancer-related regulatory pathways.
Keywords: Graph Theoretical Analysis; Insilico Modeling; Response Surface Methodology; Pyrimidine Nanoparticles; Anticancer activity; Quantitative Proteomics