In Silico Multifactorial Modeling for Streamlined Development and Optimization of Two-Dimensional Liquid...

Area of Science:

• Analytical Chemistry
• Chromatography

Background:

• Two-dimensional liquid chromatography (2D-LC) adoption in industry requires streamlined method development.
• Current methods often rely heavily on user expertise and can be time-consuming.

Purpose of the Study:

• To introduce multifactorial modeling software for systematic and simplified 2D-LC method development.
• To demonstrate the software's ability to optimize separation conditions for improved resolution and efficiency.

Main Methods:

• Utilized LC simulator software (ACD/Labs) to build retention models for 2D separations.
• Investigated variables including gradient time, temperature, organic modifier blending, and buffer concentration.
• Employed resolution maps (planes/cubes) to visualize and locate optimal 2D conditions.

Main Results:

• Multifactorial retention models accurately predicted experimental retention times (<0.5% difference) for both 1D and 2D separations.
• Successfully identified conditions for baseline separation of co-eluting compounds.
• Demonstrated pharmaceutical applications using IEC-IEC and RPLC-chiral RPLC configurations.

Conclusions:

• The described framework makes 2D-LC method development more systematic and efficient.
• This approach facilitates optimization for diverse 2D-LC applications in industry and academia.
• Software-driven modeling reduces reliance on user expertise, promoting wider adoption of 2D-LC.