High-Performance Liquid Chromatography (HPLC) is an essential analytical technique in pharmaceutical analysis for the identification, quantification, and quality assessment of drug substances and formulations. Conventional HPLC method development is primarily based on empirical trial-and-error optimization and analyst expertise. Although this approach is reliable and regulatorily accepted, it is often time-consuming, labor-intensive, and associated with increased solvent and resource consumption, particularly for complex pharmaceutical preparations. Recent advances in artificial intelligence (AI), including machine learning and predictive optimization techniques, have introduced efficient alternatives for chromatographic method development. This study presents a comparative evaluation of conventional and AI-assisted HPLC method development approaches for pharmaceutical preparations. Conventional methods were optimized through sequential experimental trials, whereas AI-assisted approaches utilized historical chromatographic data and predictive models to recommend optimal separation conditions with fewer experimental iterations. The approaches were evaluated based on method development efficiency, chromatographic performance, solvent consumption, and operational consistency. The results demonstrate that AI-assisted HPLC significantly reduces development time, experimental workload, and solvent usage while achieving chromatographic performance comparable to conventional methods. Overall, the findings support AI-assisted HPLC as a complementary and practical strategy for pharmaceutical analytical method development and quality control.
G. Poonguzhali*, S. Pravin Kumar, C. Santhoshini, M. Deepak Kumar, S. Tharunkumar, G. Kathirvel (Wed,) studied this question.