Rapid Method Development in Hydrophilic Interaction Liquid Chromatography for Pharmaceutical Analysis Using a Combination of Quantitative Structure–Retention Relationships and Design of Experiments

Abstract

A design-of-experiment (DoE) model was developed, able to describe the retention times of a mixture of pharmaceutical compounds in hydrophilic interaction liquid chromatography (HILIC) under all possible combinations of acetonitrile content, salt concentration, and mobile-phase pH with R > 0.95. Further, a quantitative structure-retention relationship (QSRR) model was developed to predict retention times for new analytes, based only on their chemical structures, with a root-mean-square error of prediction (RMSEP) as low as 0.81%. A compound classification based on the concept of similarity was applied prior to QSRR modeling. Finally, we utilized a combined QSRR-DoE approach to propose an optimal design space in a quality-by-design (QbD) workflow to facilitate the HILIC method development. The mathematical QSRR-DoE model was shown to be highly predictive when applied to an independent test set of unseen compounds in unseen conditions with a RMSEP value of 5.83%. The QSRR-DoE computed retention time of pharmaceutical test analytes and subsequently calculated separation selectivity was used to optimize the chromatographic conditions for efficient separation of targets. A Monte Carlo simulation was performed to evaluate the risk of uncertainty in the model's prediction, and to define the design space where the desired quality criterion was met. Experimental realization of peak selectivity between targets under the selected optimal working conditions confirmed the theoretical predictions. These results demonstrate how discovery of optimal conditions for the separation of new analytes can be accelerated by the use of appropriate theoretical tools.

Analysis of recombinant monoclonal antibodies in hydrophilic interaction chromatography: A generic method development approach

“…It can be utilized in optimizing information regarding the constituents of a polar mixture, especially of small molecules [1][2][3][4][5].…”

“…HILIC offers several additional benefits for biopharmaceutical characterization, as inherent compatibility with MS (better sensitivity in many cases compared to RPLC [3,11]), the use of moderate mobile phase temperature for several proteins that are poorly recovered in RPLC, and the possibility to couple several columns in series to improve resolving power (peak capacity), thanks to comparatively low mobile phase viscosity [6].…”

“…Such models regularly require several initial experimental runs, which make this approach time-consuming. Recently some quantitative structure-retention relationship (QSRR) based approaches were also reported for HILIC method development and retention prediction [3,[17][18][19]. In these studies, retention modeling was restricted to small molecules.…”

Mechanistic Chromatographic Column Characterization for the Analysis of Flavonoids Using Quantitative Structure-Retention Relationships Based on Density Functional Theory

“…In this work, we presented a novel approach for predicting the retention of 30 structurally-different flavonoids on three chemically-bonded stationary phases (classical octadecyl chain, pentafluorophenyl, and diacylated phosphatidylcholine) using genetic algorithms-partial least squares QSRR modeling [10,16,22] based on DFT-derived QM molecular descriptors. Mechanistic interpretations were given for the first time based on a comprehensive analysis of underlying relationships between the DFT-derived QM descriptors and retention inferring valuable physicochemical meaning.…”

Rapid HPLC Analytical Method Development for Herbal Medicine Formulae Based on Retention Rules Acquired from the Constituting Herbs

“…[9][10][11][12][13][14][15][16][17] In the AQbD approach, the relationships between the analytical parameters and the quality of the analytical methods are systematically investigated using risk assessment and the design of experiments (DoE) methodologies, and are then quantitatively represented in the form of mathematical models using multivariate statistical modeling methods. The AQbD approach is introduced in this work to acquire retention rules, i.e., to construct models relating the analytical parameters to the chromatographic parameters.…”