Structure-guided computational design of novel polycyclic aromatic compounds as telomerase inhibitors for the treatment of cancer

Telomerase, a ribonucleoprotein (RNP) that maintains telomere length and chromosomal integrity, is overexpressed in most cancer cells but absent in normal cells, making it an ideal target for selective cancer therapy. This study used a structure-guided computational approach to design novel tricyclic acridine derivatives as telomerase inhibitors. Pharmacophore-based virtual screening generated a five-point hypothesis (AHRRR_1) with a survival score of 4.5283. A 3D-QSAR model was developed using ethene-sulfonyl fluoride derivatives, demonstrating strong predictive accuracy with R2 values of 0.9753 (field-based) and 0.9059 (atom-based). QSAR contour maps guided the rational design of acridine derivatives with various substitutions. Molecular docking revealed significant interactions with telomerase, outperforming the co-crystal ligand and BRACO19, with a docking score of -5.732 kcal/mol. Molecular dynamics simulations (100 ns) confirmed the stability of the HB_01/telomerase complex, with RMSD values between 2 & Aring; and 3 & Aring;, supported by RMSF and radius of gyration analyses. This integrated computational strategy underscores the potential of acridine derivatives as promising telomerase inhibitors and provides a framework for developing targeted anticancer therapies.