Targeting TMPRSS2 for Prostate Cancer Therapy: A Multi-Step Computational Approach for Identifying Novel Inhibitors

Introduction: Prostate cancer is one of the most prevalent death-causing diseases among males, and metastatic progression leads to significant mortality. The transmembrane serine protease 2 (TMPRSS2) is often overexpressed or fused with the ETS-related gene (ERG) in prostate cancer patients. These molecular alterations play a pivotal role in tumour progression and metastasis. This integrated computational drug repurposing approach aimed to identify and characterize potential TMPRSS2 inhibitors among Food and Drug Administra-tion (FDA) approved compounds. Given the exclusively in silico nature of this work, the find-ings should be interpreted as preliminary. Methods: A high-throughput molecular docking screening was conducted using FDA-ap-proved compounds from DrugBank against the TMPRSS2 catalytic domain (active site). Top-scoring ligands were further analysed through pharmacokinetic profiling. Likewise, 1 μs mo-lecular dynamics simulations (MDS) were performed within a lipid bilayer environment. Sta-bility and interaction strength were evaluated through root mean square deviation (RMSD), hydrogen bonding, and radius of gyration (rGyr). Moreover, solvent-accessible surface area (SASA), polar surface area (PSA), cluster analysis, and principal component analysis (PCA) were also used for evaluation. Results: Through high binding affinities and stable interactions of drugs, Iotrolan, Iodixanol, and Hyaluronate emerged as top candidates. The Iotrolan-TMPRSS2 complex demonstrated the greatest stability, with an average protein RMSD of 2.44 Å. Likewise 6,988 hydrogen bonds, and 9,100 water-bridge formations contributed to the stability of Iotrolan-TMPRSS2 complex. Structural metrics (rGyr, SASA, PSA) indicated compact and stable ligand confor-mations throughout the simulation. Clustering analysis showed that over 60% of simulation frames for Iotrolan localized within five dominant conformational clusters. PCA revealed that the first three eigenvectors accounted for 63% of the total motion, suggesting ligand-induced modulation of TMPRSS2 dynamics. Discussion: Iotrolan stabilizes the protein structure and restricts its conformational flexibility exhibited strong and persistent interactions with TMPRSS2, which suggests a stronger inhib-itory potential compared to in silico. Iodixanol showed moderate stabilization with fewer per-sistent contacts. Hyaluronate displayed internal ligand stability but induced higher protein flexibility, potentially reducing inhibitory efficacy. Despite promising interactions, poor oral bioavailability and membrane permeability highlight the need for optimization and further ex-perimental evaluation. Conclusion: This multi-step combinational computational analysis identifies Iotrolan as a pre-liminary and promising candidate for further investigation as a TMPRSS2 inhibitor. These findings represent preliminary computational predictions only and should be interpreted with caution. They require rigorous in vitro and in vivo validation before any biological or thera-peutic relevance can be established. This work provides a structural framework that may guide future experimental studies in TMPRSS2-targeted drug discovery.