From in-silico QSAR modeling to in-vitro MTT assay: experimental validation of novel uPAR leads for triple-negative breast cancer (TNBC) and skin cancer.

The urokinase plasminogen activator receptor (uPAR) is crucial in processes such as tumor invasion, epithelial-mesenchymal transition, and the metastatic spread of aggressive cancers, including triple-negative breast cancer (MDA-MB-231) and skin cancer (A431). Due to its overexpression and key role in regulating extracellular matrix degradation and cell migration, uPAR stands out as a promising but underutilized therapeutic target. This research combines computational modeling with experimental validation to discover new small-molecule inhibitors of uPAR. A comprehensive QSAR model was developed utilizing 816 structurally diverse uPAR antagonists, resulting in high internal predictivity (R² = 0.84) and external validation accuracy (R²_ext = 0.8014). Key molecular descriptors that influence inhibition, such as com_Nminus_2A, lipo_S_1Ac, fHC3B, and fdonringC7A, have been identified as critical factors in determining electrostatic and steric complementarity. The virtual screening of the ChemDiv database using QSAR methods identified two lead candidates: D685-0061 and C878-1660. Molecular docking analysis demonstrated that C878-1660 interacts with uPAR through advantageous hydrophobic and hydrogen-bond interactions, while D685-0061 displayed relatively weaker binding affinity. The ligand, C878-1660 formed a stable complex (RMSD ~ 1.5 Å over 500 ns), whereas D685-0061 showed higher flexibility (RMSD ~ 2.7 Å). The cytotoxic effects of both ligands were quantified using in vitro MTT assays. D685-0061 demonstrated a higher potency in MDA-MB-231 cells, with an IC₅₀ of 21.34 µM, in contrast to C878-1660, which had an IC₅₀ of 81.82 µM. Conversely, C878-1660 showed greater effectiveness in A431 cells, exhibiting an IC₅₀ of 18.93 µM compared to D685-0061's IC₅₀ of 28.34 µM. The observed apoptotic morphology was consistent with a dose-dependent relationship, supporting these findings. This study presents a thorough computational-experimental pipeline that identifies two promising lead molecules targeting uPAR and provides mechanistic insights into their binding and cytotoxic profiles. The gathered evidence substantiates the need for further optimization and progression toward preclinical evaluation for uPAR-driven cancers.