Simultaneous optimization of mobile phase composition and pH using retention modeling and experimental design

Chromatographic methods are progressing continuously. Increasing sample complexity and safety expectations lead to higher regulatory demands, hence challenges in liquid chromatography analysis are rising, even today, when faster and faster chromatographic systems are extensively employed and become widely accessible for successful method development. The goal of this study was to investigate the impact of mobile phase influences as important factors of selectivity tuning in method development. This would mitigate mobile phase-related robustness issues throughout the method's lifecycle. To discover and understand these effects, a new module of chromatographic modeling software DryLab (ver. 4.3.4. beta) was introduced and a special experimental design (DoE) was tested, allowing the simultaneous optimization of solvent-dependent parameters, such as gradient time (t_G), ternary eluent composition (t_C) and pH, requiring 18 input experiments (2 × 3 × 3 = 18). Additionally, the model creation, using a UPLC system and a narrow bore column (50 × 2.1 mm), the entire experimental work could be finished in 2-3 hours. To demonstrate the applicability of this new design, amlodipine and its related pharmacopoeia impurities (A-H) were subjected to be used in a case study. Predicted vs. Experimental (or Verification) runs showed excellent agreement, average retention time deviations were typically less than 1 s. Modelled robustness testing was also performed, elucidating all important mobile phase and instrument parameters that could influence a method's lifetime performance. Furthermore, as the in silico robustness testing is the least time consuming part of the method development process, it can be used extensively to evaluate robustness even at the very early part in stage 1 of the Method Life Cycle (MLC).