Retention prediction in reversed-phase liquid chromatography systems with methanol/water mobile phases containing different alkanols as additives.

In an effort to gain enhancement of selectivity in reversed-phase liquid chromatography, retention was tuned in this study by introducing short and medium straight-chained-length alkanol additives (methanol (MeOH), ethanol, 1-propanol, 1-butanol, 1-pentanol, 1-hexanol or 1-heptanol) at low concentrations in mobile phases containing MeOH as the main organic solvent. A six-parameter retention model considering simultaneously the contents of the main organic modifier and of the alcohol additive as well as of the number of alkyl chain of additive was developed by a direct combination of equations expressing separately a linear dependence of the retention upon each of these factors. The effectiveness of the above model was tested in the retention prediction of a mixture of six alkylbenzenes under isocratic conditions with mobile phases containing as an additive any member of the homologues series of alkanols (with 1-7 carbon atoms) at different low concentrations in a wide range of MeOH-water mixtures. The prediction was excellent in all cases even when the alkanol additives used in experiments for the fitting procedure are different than those used in chromatographic runs done for testing the prediction ability of the proposed model.

Sample preparation optimization in fecal metabolic profiling.

Metabolomic analysis of feces can provide useful insight on the metabolic status, the health/disease state of the human/animal and the symbiosis with the gut microbiome. As a result, recently there is increased interest on the application of holistic analysis of feces for biomarker discovery. For metabolomics applications, the sample preparation process used prior to the analysis of fecal samples is of high importance, as it greatly affects the obtained metabolic profile, especially since feces, as matrix are diversifying in their physicochemical characteristics and molecular content. However there is still little information in the literature and lack of a universal approach on sample treatment for fecal metabolic profiling. The scope of the present work was to study the conditions for sample preparation of rat feces with the ultimate goal of the acquisition of comprehensive metabolic profiles either untargeted by NMR spectroscopy and GC-MS or targeted by HILIC-MS/MS. A fecal sample pooled from male and female Wistar rats was extracted under various conditions by modifying the pH value, the nature of the organic solvent and the sample weight to solvent volume ratio. It was found that the 1/2 (wf/vs) ratio provided the highest number of metabolites under neutral and basic conditions in both untargeted profiling techniques. Concerning LC-MS profiles, neutral acetonitrile and propanol provided higher signals and wide metabolite coverage, though extraction efficiency is metabolite dependent.

QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression.

Modified quantitative structure retention relationships (QSRRs) are proposed and applied to describe two retention data sets: A set of 94 metabolites studied by a hydrophilic interaction chromatography system under organic content gradient conditions and a set of tryptophan and its major metabolites analyzed by a reversed-phase chromatographic system under isocratic as well as pH and/or simultaneous pH and organic content gradient conditions. According to the proposed modification, an additional descriptor is added to a conventional QSRR expression, which is the analyte retention time, tR(R), measured under the same elution conditions, but in a second chromatographic column considered as a reference one. The 94 metabolites were studied on an Amide column using a Bare Silica column as a reference. For the second dataset, a Kinetex EVO C18 and a Gemini-NX column were used, where each of them was served as a reference column of the other. We found in all cases a significant improvement of the performance of the QSRR models when the descriptor tR(R) was considered.