Computational screening of repurposed drugs targeting lactoferrin for neurodegenerative diseases

Background: Lactoferrin (LTF), a conserved glycoprotein, plays a pivotal role in iron homeostasis, oxidative stress management, and anti-inflammatory responses. Its high iron-binding affinity and protective actions make it a promising therapeutic target for neurodegenerative diseases, including Alzheimer's and Parkinson's, characterized by oxidative damage and disrupted metal homeostasis. Objective: This study aimed to identify FDA-approved drugs that can be repurposed to modulate LTFs activity, providing a cost-effective therapeutic strategy for neurodegenerative diseases. Methods: We employed molecular docking to screen a library of ∼3500 FDA-approved drugs for binding affinity to LTF. Drug profiling and PASS analysis were used to predict biological activities. Molecular dynamics (MD) simulations evaluated the stability of LTF-drug complexes, with key parameters such as RMSD, RMSF, radius of gyration, solvent-accessible surface area, and hydrogen bonding assessed. Principal component analysis and free energy landscape mapping further explored protein-ligand interactions and conformational stability. Results: Ten drugs with high binding affinities were identified, among which Mosapramine and Quinupramine emerged as promising candidates based on their drug profiling and PASS activities. These drugs demonstrated stable binding to LTFs iron-binding pocket, as confirmed by MD simulations. Drug profiling indicated neuroprotective properties, including anti-inflammatory and cognitive-enhancing activities. PCA and free energy analyses revealed conformational stability of the LTF-drug complexes. Conclusions: This study highlights Mosapramine and Quinupramine as potential modulators of LTF for neurodegenerative diseases. The findings underscore the efficacy of computational screening in repurposing drugs, paving the way for further experimental validation and therapeutic development.