Modern Optimization Strategies in High-Performance Liquid Chromatography Analysis.

Area of Science:

• Analytical Chemistry
• Chromatography
• Method Development

Background:

• High-Performance Liquid Chromatography (HPLC) is crucial for pharmaceutical and biological analysis.
• Traditional HPLC method development relies on inefficient trial-and-error strategies.
• Existing structured approaches like Design of Experiments may not fully address complex chromatographic challenges.

Purpose of the Study:

• To provide a comprehensive overview of HPLC optimization strategies.
• To compare classical, statistical, and AI-driven approaches.
• To propose a framework for selecting optimization methods based on complexity and data.

Main Methods:

• Review of classical, statistical (e.g., Design of Experiments, Analytical Quality by Design), and AI-driven optimization techniques in HPLC.
• Analysis of advantages, limitations, and practical applications of each strategy.
• Development of a structured framework for method selection.

Main Results:

• Traditional methods are time-consuming and analyst-dependent.
• Statistical methods improve efficiency but may oversimplify complex systems.
• Artificial Intelligence (AI) offers a powerful solution for nonlinear, multidimensional chromatographic systems, addressing challenges like peak tailing and matrix interference.

Conclusions:

• AI-driven optimization represents a significant advancement for complex HPLC analyses.
• The proposed framework aids in selecting the most suitable optimization strategy.
• Future HPLC method development can benefit from integrating AI for enhanced efficiency and accuracy.