AI-assisted design and optimization of two smart green HPLC methods for simultaneous determination of some antiviral drugs

Abstract

Two novel high-performance liquid chromatography (HPLC) methods were developed and validated for the determination of dolutegravir (DTG), lamivudine (3TC), and abacavir (ABA) in pharmaceutical formulations. In this study, AI engines such as Copilot, ChatGPT 5.2, Gemini, and Perplexity were integrated into HPLC method development. The selected predicted conditions underwent experimental optimization, validation, and refinement. Their practical implementation significantly improved efficiency and accuracy in the analytical process. The first method, an isocratic HPLC, was designed for the simultaneous quantification of DTG and 3TC using an XBridge® C18 column and a mobile phase of acetonitrile:phosphate buffer (pH 3.5, 50:50 v/v ). Detection was performed at 258.0 nm for DTG and 275.0 nm for 3TC. The second method, a gradient HPLC, enabled the simultaneous quantification of DTG, 3TC, and ABA on a Spherisorb® ODS2 C18 column with methanol and TEA/TFA buffer (pH 3.15) as the mobile phase. Detection wavelengths were 258.0, 278.0, and 294.0 nm for DTG, 3TC, and ABA, respectively. The calibration ranges were wide and showed excellent linearity. Both techniques were validated in accordance with ICH Q2(R2) guidelines. The approaches align with smart analytical chemistry principles, combining green and white analytical chemistry (GAC and WAC) with AI-driven method development, resulting in accurate, fast, and sustainable techniques suitable for routine quality control applications.