User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known...

Area of Science:

• Pharmaceutical Analysis
• Computational Chemistry
• Chromatography

Background:

• Developing robust liquid chromatographic methods for pharmaceutical quality control is time-consuming.
• Existing methods, even with design of experiments, can be inefficient for new compound mixtures.
• In silico screening offers a potential solution to accelerate method development.

Purpose of the Study:

• To demonstrate the utility of a combined in silico screening strategy for accelerating chromatographic method development.
• To integrate quantitative structure-retention relationship (QSRR) modeling with response surface methodology (RSM) and multicriteria decision analysis (MCDA).
• To provide a flexible and robust approach for selecting optimal chromatographic conditions.

Main Methods:

• Development of a quantitative structure-retention relationship (QSRR) model for predicting retention times.
• Application of response surface methodology (RSM) to the QSRR predictions.
• Implementation of multicriteria decision analysis (MCDA) for selecting optimal chromatographic conditions based on multiple criteria.
• Utilizing compound pKa to enhance the flexibility of retention time prediction models.

Main Results:

• The combined RSM-MCDA strategy effectively screens chromatographic conditions using QSRR predictions.
• Models incorporating compound pKa demonstrated improved flexibility in retention time prediction.
• The MCDA approach proved robust, providing reliable decisions regardless of criterion weighting.
• The strategy successfully identified conditions with high desirability for further optimization.

Conclusions:

• The proposed in silico strategy significantly accelerates the screening phase of chromatographic method development in pharmaceutical quality control.
• This approach offers a flexible and robust method for selecting optimal chromatographic conditions based on multiple criteria.
• The integration of QSRR, RSM, and MCDA provides a powerful tool for efficient pharmaceutical analysis.