title: Quantitative structure-retention relationship studies for taxanes including epimers and isomeric metabolites in ultra fast liquid chromatography.
abstract: Seven pairs of epimers and one pair of isomeric metabolites of taxanes, each pair of which have similar structures but different retention behaviors, together with additional 13 taxanes with different substitutions were chosen to investigate the quantitative structure-retention relationship (QSRR) of taxanes in ultra fast liquid chromatography (UFLC). Monte Carlo variable selection (MCVS) method was adopted to choose descriptors. The selected four descriptors were used to build QSRR model with multi-linear regression (MLR) and artificial neural network (ANN) modeling techniques. Both linear and nonlinear models show good predictive ability, of which ANN model was better with the determination coefficient R(2) for training, validation and test set being 0.9892, 0.9747 and 0.9840, respectively. The results of 100 times' leave-12-out cross validation showed the robustness of this model. All the isomers can be correctly differentiated by this model. According to the selected descriptors, the three dimensional structural information was critical for recognition of epimers. Hydrophobic interaction was the uppermost factor for retention in UFLC. Molecules' polarizability and polarity properties were also closely correlated with retention behaviors. This QSRR model will be useful for separation and identification of taxanes including epimers and metabolites from botanical or biological samples.
authors.affiliation: Laboratory of Pharmaceutical Resource Discovery, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
authors.forename: Pei-Pei
authors.initials: PP
authors.lastname: Dong
authors.forename: Guang-Bo
authors.initials: GB
authors.lastname: Ge
authors.forename: Yan-Yan
authors.initials: YY
authors.lastname: Zhang
authors.forename: Chun-Zhi
authors.initials: CZ
authors.lastname: Ai
authors.forename: Guo-Hui
authors.initials: GH
authors.lastname: Li
authors.forename: Liang-Liang
authors.initials: LL
authors.lastname: Zhu
authors.forename: Hong-Wei
authors.initials: HW
authors.lastname: Luan
authors.forename: Xing-Bao
authors.initials: XB
authors.lastname: Liu
authors.forename: Ling
authors.initials: L
authors.lastname: Yang
mesh_terms: D002853:Chromatography, Liquid / Q000379:methods*; D057927:Hydrophobic and Hydrophilic Interactions; D007536:Isomerism; D016014:Linear Models; D008956:Models, Chemical; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D013329:Structure-Activity Relationship; D043823:Taxoids / Q000737:chemistry* / Q000378:metabolism
chemical_list: D043823:Taxoids