Digital Transformation of Antiviral Drug Stability Studies: Integration of Quality byDesign, Liquid Chromatography Coupledwith Tandem Mass Spectrometry, and Artificial Intelligence-Based Predictive Modelling

Abstract

Antiviral drugs remain essential in combating emerging and re-emerging viral infections; however, many possess chemically labile functional groups that predispose them to hydrolytic, oxidative, thermal, and photolytic degradation. Conventional stability programs, guided by International council for harmonisation of technical requirements for pharmaceuticals for human use (ICH) frameworks, rely on empirical forced degradation studies and long-term storage testing supported by stability-indicating analytical techniques, such as liquid chromatography coupled with tandem
mass spectrometry (LC–MS/MS). While LC–MS/MS provides superior sensitivity, structural elucidation capability, and impurity profiling performance, traditional approaches are time-intensive, experimentally demanding, and largely retrospective. This review highlights the integration of artificial intelligence (AI) and machine learning (ML) with
LC–MS/MS-based degradation profiling to enable predictive and digitally transformed stability programs in antiviral drug development. Classical ML algorithms, including Random Forest, Support Vector Machines, Gradient Boosting, and k-Nearest Neighbors, are discussed for degradation risk classification and kinetic modeling. Advanced deep learning approaches, such as artificial neural networks for degradation rate prediction, convolutional neural networks for MS/MS spectral interpretation, and graph neural networks for molecular instability mapping are presented as
transformative tools for identifying degradation hotspots and simulating impurity formation pathways. A closed-loop AI–LC–MS/MS framework is proposed in which molecular structure input drives predictive degradation modeling, simulated impurity structures are generated, LC–MS/MS provides experimental confirmation, and iterative model
retraining enhances predictive accuracy. Integration of Quality by Design strategies with LC–MS/MS platforms enables optimized method performance, improved impurity detection, and enhanced understanding of degradation mechanisms. Incorporation with predictive shelf-life modeling, real-time stability monitoring, and risk-based testing
aligns this approach with ICH Q1A, Q2, Q12, and Q14 principles. Collectively, AI-enabled digital stability systems represent a paradigm
shift toward proactive, lifecycle-based, and data- driven antiviral drug stability assessment.