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

Area of Science:

• Computational drug discovery
• Molecular biology
• Oncology

Background:

• Prostate cancer metastasis is a major cause of mortality in males.
• Transmembrane serine protease 2 (TMPRSS2) alterations drive tumor progression and metastasis.
• Targeting TMPRSS2 offers a potential therapeutic strategy for prostate cancer.

Purpose of the Study:

• To identify potential TMPRSS2 inhibitors among FDA-approved drugs using computational methods.
• To characterize the binding interactions and stability of identified compounds with TMPRSS2.
• To provide a structural basis for developing novel TMPRSS2-targeted therapies.

Main Methods:

• High-throughput molecular docking screening of FDA-approved compounds against TMPRSS2.
• Pharmacokinetic profiling of top-scoring ligands.
• Molecular dynamics simulations (MDS) to assess complex stability and interactions.
• Analysis of structural metrics (RMSD, rGyr, SASA, PSA) and dynamic behavior (PCA, clustering).

Main Results:

• Iotrolan, Iodixanol, and Hyaluronate were identified as top drug candidates.
• Iotrolan demonstrated the highest stability and strongest interactions with TMPRSS2.
• Molecular dynamics simulations revealed Iotrolan's ability to stabilize TMPRSS2 structure and restrict flexibility.

Conclusions:

• Iotrolan shows significant potential as a TMPRSS2 inhibitor based on computational analysis.
• Further in vitro and in vivo studies are required for validation due to preliminary findings.
• This research provides a foundation for future drug discovery efforts targeting TMPRSS2.