UNASSIGNED: Mathematical modeling of Quantitative Structure - Property Relationships met great interest in fields of in silico drug design and more recently, pharmaceutical analysis. In our approach we proposed automated method of creation Quantitative Structure-Retention Relationship (QSRR) for analysis of triptans, selective @entity750 5-HT1 receptor agonists used for the treatment of acute @entity10 . The method was created using hybrid machine learning approach, namely Genetic algorithm (GA) coupled with artificial neutral networks (ANN). Performance of proposed hybrid GA-ANN model was evaluated with predicting relative retention times of @entity22747 impurities. Several ANN types were coupled with GA and tested: single-layer ANN (SL-ANN), double-layer ANN (D-ANN) and higher order architectures: pi-sigma ANN (PS-ANN) and sigma-pi-sigma ANN (SPS-ANN). Partial Least Squares (PLS) method was used as a reference. The separation of @entity22747 and its related products (impurities and degradation products) was obtained by developing a gradient high-performance liquid chromatography method with diode-array detector (HPLC-DAD). Degradation products during acid-basic hydrolysis were identified with an electrospray ionization tandem mass spectrometry (Q-TOF-MS/MS) detector. Independent data for outer validation of QSRR model was obtained from the determination of related products of @entity9251 via an HPLC-DAD method. Accuracy of QSRR was measured by inner-validation on naratriptan data and outer validation on @entity9251 samples. The best performing model were PS-ANN and SPS-ANN with mean errors of 8% (Q2=0.87) and 15% (Q2=0.77) on an inner-validation data set, respectively. Validation on similar samples from an outer validation data set of @entity9251 impurities gave mean errors of 18% (R(2)pred=0.64) and 17% (R(2)pred=0.63) for the PS-ANN and SPS-ANN models, respectively.

Quantitative structure-retention relationship model for the determination of XXXX and its impurities based on artificial neural networks coupled with genetic algorithm.

@entity22747 :: ('MESH:C106783', 'Chemical') :: ['naratriptan hydrochloride'] @entity10 :: ('MESH:D006261', 'Disease') :: ['headache'] @entity750 :: ('MESH:D012701', 'Chemical') :: ['serotonin'] @entity9251 :: ('MESH:D018170', 'Chemical') :: ['sumatriptan succinate']
@entity22747 :: (MESH:C106783,Chemical) :: ['naratriptan hydrochloride']
