Statistical Total Correlation Spectroscopy Editing of 1H NMR Spectra of Biofluids: Application to Drug Metabolite Profile Identification and Enhanced Information Recovery

Abstract

Here we present a novel method for enhanced NMR spectral information recovery, utilizing a statistical total correlation spectroscopy editing (STOCSY-E) procedure for the identification of drug metabolite peaks in biofluids and for deconvolution of drug and endogenous metabolite signals. Structurally correlated peaks from drug metabolites and those from closely related drug metabolite pathways are first identified using STOCSY. Subsequently, this correlation information is utilized to scale the biofluid (1)H NMR spectra across these identified regions, producing a modified set of spectra in which drug metabolite contributions are reduced and, thus, facilitating analysis by pattern recognition methods without drug metabolite interferences. The application of STOCSY-E is illustrated with two exemplar (1)H NMR spectroscopic data sets, posing various drug metabolic, toxicological, and analytical challenges viz. 800 MHz (1)H spectra of human urine (n = 21) collected over 10 h following dosing with the antibiotic flucloxacillin and 600 MHz (1)H NMR spectra of rat urine (n = 27) collected over 48 h following exposure to the renal papillary toxin 2-bromoethanamine (BEA). STOCSY-E efficiently identified and removed the major xenobiotic metabolite peaks in both data sets, providing enhanced visualization of endogenous changes via orthogonal to projection filtered partial least-squares discriminant analysis (OPLS-DA). OPLS-DA of the STOCSY-E spectral data from the BEA-treated rats revealed the gut bacterial-mammalian co-metabolite phenylacetylglycine as a previously unidentified surrogate biomarker of toxicity. STOCSY-E has a wide range of potential applications in clinical, epidemiology, toxicology, and nutritional studies where multiple xenobiotic metabolic interferences may confound biological interpretation. Additionally, this tool could prove useful for applications outside of metabolic analysis, for example, in process chemistry for following chemical reactions and equilibria and detecting impurities.