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

Area of Science:

• Analytical Chemistry
• Chromatography
• Method Development

Background:

• Liquid chromatography (LC) analysis faces increasing challenges due to complex samples and stringent regulatory demands.
• Faster LC systems are prevalent, yet robust method development requires careful consideration of mobile phase parameters.
• Mobile phase influences are critical for selectivity tuning and mitigating robustness issues throughout a method's lifecycle.

Purpose of the Study:

• To investigate the impact of mobile phase composition on selectivity in liquid chromatography method development.
• To mitigate mobile phase-related robustness issues across the entire method lifecycle.
• To demonstrate a new modeling approach for efficient and robust LC method development.

Main Methods:

• Utilized a new module in chromatographic modeling software (DryLab) for simultaneous optimization of gradient time, ternary eluent composition, and pH.
• Employed a special design of experiments (DoE) requiring 18 input experiments for model creation.
• Used a UPLC system with a narrow bore column (50 × 2.1 mm) for rapid model generation (2-3 hours).

Main Results:

• Achieved excellent agreement between predicted and experimental results, with average retention time deviations under 1 second.
• Demonstrated the applicability of the new design using amlodipine and its related impurities in a case study.
• Performed in silico robustness testing to identify critical mobile phase and instrument parameters affecting method performance.

Conclusions:

• The new modeling approach effectively optimizes mobile phase parameters for robust LC method development.
• In silico robustness testing can be extensively used early in the Method Life Cycle (MLC) to evaluate method reliability.
• This strategy significantly reduces development time and improves the overall robustness of chromatographic methods.