NMR-Based Metabonomic Studies on the Biochemical Effects of Commonly Used Drug Carrier Vehicles in the Rat

Abstract

The biochemical effects of a series of commonly used drug carrier vehicles were investigated using (1)H NMR spectroscopic and pattern recognition based metabonomic analysis. Animals were treated by oral gavage with six dosage vehicles: 0.5% (w/v) sodium carboxymethylcellulose/0.2% (v/v)tween; microemulsion (consisting of propylene glycol, ethanol, cremophor, and corn oil glycerides); labrafil [consisting of poly(ethylene glycol) 300 esterified with oleic acid] (30%)/corn oil (70%); 0.1 M sodium phosphate buffered water; poly(ethylene glycol) 300 and 0.5% methocel. Urine samples (n = 7) collected over a 96 h period post administration were analyzed using 600 MHz (1)H NMR spectroscopy, and principal components analysis of the spectral data was used to analyze these data. Of the six vehicles studied, three (labrafil/corn oil, PEG 300 and microemulsion) gave rise to strong vehicle-related signals in the (1)H NMR spectra of urine and were, therefore, deemed to be less suitable for NMR-based toxicity studies. To investigate any biochemical consequences of vehicle dosing, PCA was used to analyze spectral regions that did not contain vehicle-related signals, i.e., the NMR-detectable endogenous metabolite profile. PEG 300 and labrafil/corn oil induced changes in the biochemical composition of urine including increased concentrations of dicarboxylic acids, creatinine, taurine, and sugars, indicating that these vehicles were bioactive in their own right and that this might confound interpretation of biochemical effects of weakly toxic drugs dosed in these carriers. This study shows the importance of selecting appropriate vehicles for NMR-based metabonomic studies with a view to minimizing the possibility of vehicle resonances obscuring endogenous compound peaks. Furthermore, we have shown that at least two of the commonly used drug carrier vehicles caused metabolic perturbations in the urine profile. These alterations in the biochemical profile reflect vehicle-induced changes in the physiological status of the organism that may obscure the pharmacologic or toxicologic effects of drugs.

Metabonomic and Microbiological Analysis of the Dynamic Effect of Vancomycin-Induced Gut Microbiota Modification in the Mouse

“…However, drug vehicle was detected in the fecal profiles on day 1 from both groups and was eliminated within 48 h. Certain drug vehicles have been shown to effect urinary biochemical profiles. However, in a study conducted by Beckwith-Hall et al 36 Tween exerted the smallest effect on the metabolic profiles after saline consistent with the present study. 36 The spectral regions affected by the vehicle were removed from the data set prior to mathematical modeling to minimize its effect on data analyses and interpretation of the metabolic data.…”

Characterization of Hepatic Mitochondrial Injury Induced by Fatty Acid Oxidation Inhibitors

“…Spectra were Fourier transformed, phase-and baseline-corrected using XWINNMR (Bruker, Karlsruhe, Germany), and reduced to 256 integrated regions of equal width (0.04 ppm) using the AMIX (Analysis of MIXtures) software package (version 2.5, Bruker BioSpin GmbH, Rheinstetten, Germany,). Metabolite assignments were made on the basis of previous literature data ( Beckwith-Hall et al 1998;Beckwith-Hall et al 2002) and in certain cases confirmed by spiking. Spectral regions corresponding to drug carrier vehicles, drug metabolites, or parent compound were removed from the analysis.…”

Gender‐related and age‐related urinalysis of healthy subjects by NMR‐based metabonomics

“…Of the analytical techniques used, 1 H-NMR spectroscopy has been shown to be the main metaboliteprofiling tool, as it enables many endogenous metabolites to be quantified rapidly and reproducibly without derivatization or separation (1)(2)(3)(4)(5). The NMR-generated metabolic datasets can be effectively interpreted and classified by the application of multivariate statistical analysis including pattern-recognition methods, such as principal components analysis (PCA) and partial least-squares (PLS) discriminate analysis (1,(5)(6)(7)(8)(9)(10).…”