QSAR, Molecular Dynamics, and Biological Evaluation of Novel Myeloperoxidase Inhibitors Via Ligand-Based Pharmacophore Modeling As Potential Anticancer Agents

Myeloperoxidase (MPO) has shown promise as a therapeutic target due to its critical role in inflammatory mechanisms and cancer progression. Despite extensive research on MPO inhibitors, the lack of integrated computational–experimental workflows constrains the efficient identification and validation of biologically relevant candidates. Hence, in this study, a ligand-based pharmacophore model of MPO inhibitors was developed to identify crucial molecular features required for inhibition. A quantitative structure–activity relationship (QSAR) model was built using the Genetic Function Approximation (GFA) algorithm, and the model statistics were found to be statistically significant (R2 = 0.765, R2_adj = 0.733, R2_Pred = 0.672, LOF = 1.2). The generated and validated pharmacophore model was used virtually to screen 53,352 compounds, yielding five structurally distinct hits. These hits were subjected to in vitro cytotoxicity analysis using two cancer cell lines. Among all the tested compounds, BTB11556 showed the strongest cytotoxic activity, with an IC50 of 12.5 μM against the Kasumi-1 leukemia cell line, and was therefore considered the lead compound. A one-way ANOVA of the IC50 values for the active compounds in Kasumi-1 cells showed a statistically significant difference in cytotoxic potency (p < 0.001). This integrated computational and experimental approach highlights the importance of pharmacophore-guided virtual screening, combined with QSAR modeling, in the development of MPO inhibitors. The findings from molecular docking, 500-ns molecular dynamics simulations, MM-GBSA calculations, and alanine scanning analyses collectively corroborate a stable binding mode of BTB11556 within the MPO active site. These results support further investigation of BTB11556 as a candidate compound associated with MPO-targeted therapeutic strategies.