Optimization of chromatographic separation of aripiprazole and impurities: Quantitative structure–retention relationship approach Scientific paper
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Abstract
A new optimization strategy based on the mixed quantitative structure–retention relationship (QSRR) model is proposed for improving the RP-HPLC 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 A-B 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.
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Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution license 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
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Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja
Grant numbers 451-03-9/2021-14/200161
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