Uncertainty management for In Silico screening of reversed-phase liquid chromatography methods for small compounds

Area of Science:

• Analytical Chemistry
• Chromatography

Background:

• Reversed-phase liquid chromatography (RPLC) method development is often time-consuming and complex.
• Statistics-based strategies offer efficient, cost-effective, and flexible solutions for RPLC method optimization.

Purpose of the Study:

• To develop and evaluate a novel statistics-based strategy for RPLC method development.
• To integrate Bayesian response surface methodology with multi-criteria decision analysis (MCDA) for improved efficiency.
• To leverage pKa values and model uncertainty for enhanced retention behavior modeling.

Main Methods:

• Bayesian response surface methodology was employed to model compound retention behavior using pKa values.
• Multi-criteria decision analysis (MCDA) was developed to analyze uncertainty in model distributions.
• The approach was integrated with quantitative structure retention relationship (QSRR) models for in-silico screening.
• Method development included an optimization phase to generate a design space.

Main Results:

• One of the two presented MCDA methods demonstrated promising results.
• The developed strategy successfully integrated with QSRR models for initial in-silico screening.
• The optimization phase generated a design space that validated the selection phase outcomes.

Conclusions:

• The combined Bayesian response surface methodology and MCDA provide an efficient and robust approach to RPLC method development.
• This strategy effectively models retention behavior and utilizes uncertainty information for informed decision-making.
• The integration with QSRR models facilitates a streamlined in-silico screening process, reducing experimental workload.