Multivariate approaches for efficient detection of potential metabolites from liquid chromatography/mass spectrometry data.
This work describes a novel method for rapid screening of unknown metabolites in urine samples that narrows down the list of potential metabolites. Prior to analysis by liquid chromatography/electrospray ionization mass spectrometry (LC/ESI-MS), urine samples were prepared using solid-phase extraction (SPE). Automatic curve resolution was used for deconvolution of the LC/MS data, followed by peak alignment. Preprocessed data were then used for metabolite pattern recognition using principal component analysis (PCA), parallel factor analysis (PARAFAC), and multilinear partial least squares (N-PLS). This approach enabled the rapid detection of metabolites of citalopram in urine by maximizing the information extracted. The metabolites thus identified were compared with earlier studies on the metabolism of citalopram. In addition, new, unreported metabolites were found and characterized by LC/MS/MS and accurate mass measurements. A combination of data from positive and negative ionization enhanced the identification of metabolites.
Idborg
Helena
H
Department of Analytical Chemistry, Stockholm University, SE-106 91 Stockholm, Sweden.
Edlund
Per-Olof
PO
Jacobsson
Sven P
SP
D000818:Animals; D015415:Biomarkers / Q000652:urine; D002853:Chromatography, Liquid / Q000379:methods*; D015283:Citalopram / Q000493:pharmacokinetics / Q000652:urine; D003627:Data Interpretation, Statistical; D004353:Drug Evaluation, Preclinical; D008297:Male; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated; D051381:Rats; D017208:Rats, Wistar; D017367:Selective Serotonin Reuptake Inhibitors / Q000493:pharmacokinetics / Q000652:urine; D021241:Spectrometry, Mass, Electrospray Ionization / Q000379:methods*
D015415:Biomarkers; D015283:Citalopram; D017367:Selective Serotonin Reuptake Inhibitors