Computational pharmacovigilance of Lifitegrast in dry eye disease using machine learning and network toxicology.

Lifitegrast, as a novel therapeutic agent for dry eye disease (DED), has garnered considerable clinical attention, yet the prediction and risk assessment of its adverse drug events (ADEs) remain methodologically challenging. This investigation seeks to establish a comprehensive predictive framework for Lifitegrast ADEs and evaluate therapeutic risks through advanced computational pharmacovigilance methodologies. Utilizing the FDA Adverse Event Reporting System (FAERS) database (2016Q1-2024Q4), we constructed a multi-tiered ADE prediction framework incorporating statistical learning algorithms and network toxicology approaches. Neural network architectures were employed to analyze drug-gene interaction networks, computational linguistics techniques were utilized to extract adverse reaction patterns, and ensemble learning methodologies were implemented to optimize risk prediction accuracy. An automated risk assessment platform was developed to facilitate personalized medication safety surveillance. Analysis encompassed 4511 reports, with the constructed computational prediction model demonstrating superior performance in ADE identification (AUC = 0.892, accuracy = 0.847). Advanced algorithms successfully identified 16 significant ADE signals, including instillation site pain and dysgeusia. Network toxicology analysis established ICAM1, MMP9, and SRC as critical regulatory genes. Neural network models effectively predicted drug-target interactions, with molecular docking validation confirming strong binding affinities (binding energy < -9 kcal/mol). The developed automated risk assessment platform enables real-time monitoring and personalized risk stratification. This study established a computationally-enhanced Lifitegrast safety assessment framework, providing innovative methodological solutions for pharmacovigilance and precision medicine, substantially advancing the sophistication of drug safety monitoring systems.