Modern Optimization Strategies in High-Performance Liquid Chromatography Analysis

Optimization is a critical step in chromatographic method development. High-performance liquid chromatography (HPLC) remains the method of choice for pharmaceutical and biological analysis; however, traditional method development has relied on trial-and-error strategies. These approaches are often laborious, time-consuming, and highly dependent on analyst experience. To address these limitations, structured and data-driven optimization strategies such as Design of Experiments and Analytical Quality by Design have been introduced to improve efficiency and reproducibility. Nevertheless, these approaches often assume ideal system behavior and may not fully capture real-world chromatographic complexity, including peak tailing, matrix interference, and atypical biomolecule retention, particularly in advanced HPLC modes. In this context, artificial intelligence (AI) has emerged as a powerful tool to address nonlinear, multidimensional chromatographic systems. This review provides a comprehensive overview of classical, statistical, and AI-driven optimization strategies in HPLC, highlighting their advantages, limitations, and practical applications. It also proposes a structured framework for selecting appropriate optimization approaches based on system complexity and data availability.