Artificial Intelligence in RP-HPLC Method Development: Quality By Design, Regulatory Perspectives, and Future Directions

Abstract

The integration of artificial intelligence (AI) and machine learning (ML) technologies into reversed-phase high-performance liquid chromatography (RP-HPLC) method development represents a paradigm shift in pharmaceutical analytical chemistry. This comprehensive review examines the current state, challenges, and future trajectories of AI-driven HPLC method development within the Quality by Design (QbD) framework. We systematically analyze AI algorithms including artificial neural networks, deep reinforcement learning, Bayesian optimization, and quantitative structure-retention relationship models that have demonstrated significant improvements in method optimization, retention time prediction, and mobile phase selection. The integration of AI with Design of Experiments approaches has enabled the establishment of robust Method Operable ICH guidelines are critically evaluated, highlighting validation requirements, data integrity considerations, and the challenges of implementing black-box models in GxP-regulated environments. Emerging technologies including explainable AI, digital twins, federated learning, and autonomous analytical platforms are discussed as enablers of next-generation chromatographic systems. This review synthesizes evidence from 150 peer-reviewed publications, providing a comprehensive roadmap for researchers, analytical scientists, and regulatory professionals navigating the convergence of AI and chromatographic science in pharmaceutical development.