Targeting programmed death ligand 1 for anticancer therapy using computational drug repurposing and molecular...

Area of Science:

• Computational drug discovery
• Pharmacology
• Bioinformatics

Background:

• Discovering novel cancer drugs is challenging.
• Drug repurposing offers an efficient strategy for identifying new therapeutic uses for existing drugs.
• Programmed death-ligand 1 (PD-L1) is a key target in cancer immunotherapy.

Purpose of the Study:

• To identify FDA-approved drugs that can inhibit programmed death-ligand 1 (PD-L1) using an in-silico drug repurposing approach.
• To evaluate the binding efficiency and stability of potential drug candidates targeting PD-L1.

Main Methods:

• Integrated in-silico approach combining molecular docking and molecular dynamics (MD) simulations.
• Screening of FDA-approved drugs for binding affinity to PD-L1.
• 500 ns MD simulations to assess complex stability and conformational changes.

Main Results:

• Several drug molecules were identified as potential PD-L1 binders.
• Lumacaftor and Vedaprofen demonstrated high binding affinity and suitable drug profiles.
• MD simulations confirmed stable binding and structural integrity of PD-L1-Lumacaftor and PD-L1-Vedaprofen complexes over 500 ns.

Conclusions:

• Lumacaftor and Vedaprofen show significant potential as repurposed drugs for inhibiting PD-L1.
• Computational bioinformatics approaches are valuable for identifying novel anticancer agents through drug repurposing.
• Experimental validation is required to confirm the efficacy and specificity of these identified drug candidates.