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

Gorde:
Argitaratua izan da:Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 28742-28754
Egile nagusia: Khan, Mohd Shahnawaz
Beste egile batzuk: Shamsi, Anas, Shahwan, Moyad, Dinislam, Khuzin, Yadav, Dharmendra Kumar
Argitaratua:
Nature Publishing Group
Gaiak:
Food & Drug Administration > FDA
Cancer
PD-L1 protein
Binders
Bioinformatics
Therapeutic targets
Binding sites
Biological products
Drug development
Computer applications
Molecular dynamics
Efficiency
Proteins
Simulation
Principal components analysis
FDA approval
Immune checkpoint
Antineoplastic drugs
Pharmacokinetics
Ligands
Immunosuppressive agents
Cancer therapies
Environmental
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Laburpena:Discovering new drug candidates for complex diseases like cancer is a significant challenge in modern drug discovery. Drug repurposing provides a cost-effective and time-efficient strategy to identify existing drugs for novel therapeutic targets. Here, we exploited an integrated in-silico approach to identify repurposed drugs that could inhibit programmed death-ligand 1 (PD-L1). PD-L1 is a crucial protein that plays a pivotal role in immune checkpoint regulation, making it a potential target for cancer treatment. Using a drug repurposing approach, we combined molecular docking and molecular dynamics (MD) simulations to study the binding efficiency of FDA-approved drug molecules targeting PD-L1. From the binding affinities and interaction analysis of the first screening, several molecules emerged as PD-L1 binders. Two of them, Lumacaftor and Vedaprofen, showed appropriate drug profiles and biological activities and stood out as highly potent binding partners of the PD-L1. MD simulation was performed for 500 ns to assess the conformational and stability changes of PD-L1-Lumacaftor and PD-L1-Vedaprofen complexes. The simulations revealed sustained structural integrity and stable binding of both complexes throughout the 500 ns trajectories, supporting their potential as PD-L1 inhibitors. While the findings are promising, they remain computational and require experimental validation to confirm biological efficacy and specificity. This study also emphasizes the role of bioinformatics approaches in drug repurposing that can help in the identification of novel anticancer agents.
ISSN:2045-2322
DOI:10.1038/s41598-025-14503-0
Baliabidea:Science Database