Estimation of lipophilicity and design of new 17β-carboxamide glucocorticoids using RP-HPLC and quantitative structure-retention relationships analysis

Eight 17β-carboxamide glucocorticoids with local anti-inflammatory activity were selected and their retention behavior tested in six RP-HPLC systems (I–VI). logkw, a, and φ0 parameters were calculated and correlation with previously determined logPo/w values was examined. RP-HPLC system IV, which consisted of cyano column and methanol–water mobile phases (50:50, 60:40, 70:30, and 80:20, v/v), was selected as the most reliable for lipophilicity prediction and used for the analysis of chromatographic behavior of remaining fourteen 17β-carboxamide glucocorticoids. Quantitative structure-retention relationships analysis was performed and PLS(logkw) model was selected as the most statistically significant. On the basis of selected model and interpretation of corresponding descriptors, new derivatives with higher logkw values and higher expected lipophilicity were designed.

Prediction of Chromatographic Elution Order of Analytical Mixtures Based on Quantitative Structure-Retention Relationships and Multi-Objective Optimization

Prediction of the retention time from the molecular structure using quantitative structure-retention relationships is a powerful tool for the development of methods in reversed-phase HPLC. However, its fundamental limitation lies in the fact that low error in the prediction of the retention time does not necessarily guarantee a prediction of the elution order. Here, we propose a new method for the prediction of the elution order from quantitative structure-retention relationships using multi-objective optimization. Two case studies were evaluated: (i) separation of organic molecules in a Supelcosil LC-18 column, and (ii) separation of peptides in seven columns under varying conditions. Results have shown that, when compared to predictions based on the conventional model, the relative root mean square error of the elution order decreases by 48.84%, while the relative root mean square error of the retention time increases by 4.22% on average across both case studies. The predictive ability in terms of both retention time and elution order and the corresponding applicability domains were defined. The models were deemed stable and robust with few to no structural outliers.