Optimization of chromatographic separation of aripiprazole and impurities: Quantitative structure-retention relationship approach

Abstract

A new optimization strategy based on the mixed quantitative structure– retention relationship (QSRR) model is proposed for improving the RPHPLC separation of aripiprazole and its impurities (IMP A-E). Firstly, experimental parameters (EPs), namely mobile phase composition and flow rate, were varied according to Box–Behnken design and thereafter, an artificial neural network (ANN) as a QSRR model was built correlating EPs and selected molecular descriptors (ovality, torsion energy and non-1,4-van der Waals energy) with the log-transformed retention times of the analytes. Values of the root mean square error (RMSE) were used for an estimation of the quality of the ANNs (0.0227, 0.0191 and 0.0230 for the training, verification and test set, respectively). The separations of critical peak pairs on chromatogram (IMP AB and IMP D-C) were optimized using ANNs for which the EPs served as inputs and the log-transformed separation criteria s as the outputs. They were validated by application of leave-one-out cross-validation (RMSE values 0.065 and 0.056, respectively). The obtained ANNs were used for plotting response surfaces upon which the analyses chromatographic conditions resulting in optimal analytes retention behaviour and the optimal values of the separation criteria s were defined. The optimal conditions were 54 % of methanol at the beginning and 79 % of methanol at the end of gradient elution programme with a mobile phase flow rate of 460 μL min-1.

Keywords

• gradient elution
• high performance liquid chromatography
• artificial neural networks