AI-ENHANCED HPLC: BRIDGING ANALYTICAL EFFICIENCY AND INTELLIGENT DECISION-MAKING IN CHROMATOGRAPHY

Katikala Geetha Pravallika, Saripalli Sri Lakshmi*, Yerruboina Supriya

ABSTRACT

One of the most crucial analytical instruments in pharmaceutical research is High-Performance Liquid Chromatography (HPLC), which guarantees precise complex chemical separation, identification, and quantification. However, because several variables, including temperature, flow rate, mobile phase composition, and column selection, must be optimized, traditional method development continues to be labour-intensive and time-consuming. Artificial Intelligence (AI) integration with HPLC has become a game-changing solution. By analysing massive chromatographic datasets, machine learning (ML), deep learning (DL), and artificial neural networks (ANNs) facilitate intelligent decision-making, automatic optimization, and predictive modeling. AI reduces experimental trials and predicts retention times accurately, improving efficiency, repeatability, and data quality. Real-world case studies demonstrate its influence: Merck effectively used AI to improve chromatographic parameters for biologics, increasing robustness and reducing experimental workload, while Pfizer's AI-driven platform dramatically shortened method development time by forecasting ideal separation circumstances. AI-powered solutions also ensure regulatory compliance by supporting quality control, anomaly detection, and real-time monitoring. AI-enhanced HPLC represents a significant advancement in intelligent, data-driven analytical research with wide-ranging applications in medicines, food safety, clinical diagnostics, and environmental monitoring.

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