Abstract
Traditional HPLC method development relies on trial-and-error, one-factor-at-a-time approaches that struggle to capture complex, multivariate interactions among method parameters, often resulting in suboptimal separations and transfer failures. While Design of Experiments and Analytical Quality by Design frameworks address some of these shortcomings under ICH Q2(R2) and Q14 guidelines, they remain vulnerable to noise and nonlinear retention behavior. This review examines how artificial intelligence and machine learning tools—including artificial neural networks, support vector regression, and quantitative structure-retention relationship modeling—are reshaping chromatographic method development, using finerenone, a novel non-steroidal mineralocorticoid receptor antagonist for cardiorenal protection, as a representative case study. It surveys finerenone’s physicochemical and pharmacokinetic profile, forced-degradation behavior, and existing bioanalytical and stability-indicating RP-HPLC methods, and evaluates how AI-assisted approaches can accelerate method optimization in line with Green and White Analytical Chemistry principles. The review further discusses regulatory considerations for validating machine learning models in GxP environments, including the OECD (Q)SAR framework, FDA and EMA guidance, and Predetermined Change Control Plans, alongside persistent challenges such as data scarcity, model interpretability, and chiral separation limitations. Finally, it outlines emerging directions—transfer learning, hybrid mechanistic-ML models, explainable AI, and autonomous closed-loop chromatography—as pathways toward more sustainable, intelligent analytical workflows for emerging pharmaceuticals.