The aim of this pilot study is to evaluate sarcosine, uracil, and kynurenic acid in urine as potential biomarkers in prostate cancer detection and progression monitoring. Sarcosine, uracil, and kynurenic acid were measured in urine samples of 32 prostate cancer patients prior to radical prostatectomy, 101 patients with increased prostate-specific antigen prior to ultrasonographically-guided prostatic biopsy collected before and after prostatic massage, and 15 healthy volunteers (controls). The results were related to histopathologic data, Gleason score, and PSA (Prostate Specific Antigen). Metabolites were measured after analysis of urine samples with Ultra-High Performance Liquid Chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) instrumentation. Multivariate, nonparametric statistical tests including receiver operating characteristics analyses, one-way analysis of variance (Kruskal–Wallis test), parametric statistical analysis, and Pearson correlation, were performed to evaluate diagnostic performance. Decreased median sarcosine and kynurenic acid and increased uracil concentrations were observed for patients with prostate cancer compared to participants without malignancy. Results showed that there was no correlation between the concentration of the studied metabolites and the cancer grade (Gleason score <7 vs. ≥7) and the age of the patients. Evaluation of biomarkers by ROC (Receiving Operating Characteristics) curve analysis showed that differentiation of prostate cancer patients from participants without malignancy was not enhanced by sarcosine or uracil levels in urine. In contrast to total PSA values, kynurenic acid was found a promising biomarker for the detection of prostate cancer particularly in cases where collection of urine samples was performed after prostatic massage. Sarcosine and uracil in urine samples of patients with prostate cancer were not found as significant biomarkers for the diagnosis of prostate cancer. None of the three metabolites can be used reliably for monitoring the progress of the disease.

The effect of endogenous interferences of serum in multi-targeted metabolite profiling HILIC-MS/MS analysis was investigated by studying different sample preparation procedures. A modified QuEChERS dispersive SPE protocol, a HybridSPE protocol, and a combination of liquid extraction with protein precipitation were compared to a simple protein precipitation. Evaluation of extraction efficiency and sample clean-up was performed for all methods. SPE sorbent materials tested were found to retain hydrophilic analytes together with endogenous interferences, thus additional elution steps were needed. Liquid extraction was not shown to minimise matrix effects. In general, it was observed that a balance should be reached in terms of recovery, efficient clean-up, and sample treatment time when a wide range of metabolites are analysed. A quick step for removing phospholipids prior to the determination of hydrophilic endogenous metabolites is required, however, based on the results from the applied methods, further studies are needed to achieve high recoveries for all metabolites.

A toxicology laboratory often receives a high number of samples from cases (autopsies or clinical) that may require the quick delivery of trustworthy, accurate results. Thus, there is a great need for a fast and reliable method that is capable of identifying and determining a large number of drugs and drugs of abuse in biological matrices, and especially in blood. In the present study, we describe the development of a fast and simple gas chromatography–mass spectrometry (GC-MS) method for the determination of 41 drugs and drugs of abuse (DOA) in blood. Sample pre-treatment by alkaline liquid–liquid extraction (LLE) was studied through the utilization of different solvents and solvent-to-sample ratios (v/v), which aimed to achieve a greater extraction efficiency and detection sensitivity with a decreased need for large sample volumes. Butyl acetate with a sample-to-solvent ratio of 4:1 (1 mL blood: 0.25 mL butyl acetate) was the most efficient. The method was validated for all analytes, and the evaluation parameters were within the acceptance criteria. The coefficient of determination (R2) was between 0.9934 and 1, the limits of detection (LODs) ranged between 1 ng/mL and 113 ng/mL, and the limits of quantification (LOQs) were between 4 ng/mL and 375 ng/mL for all analytes. The determinations were accurate (accuracy% from 84% to 114%) and precise (RSD% from 0.66% to 14.8% for low concentrations). Deconvolution Reporting Software (DRS) for GC-MS was optimized and applied for data analysis to enhance the identification potential, thereby avoiding false identifications (false positives) and increased productivity. The NIST Automated Mass Spectral Deconvolution and Identification Software (AMDIS) and the analytical utility Retention Time Lock (RTL) Database Library assisted in data evaluation. The method was applied to 89 postmortem cases (history of mental disorders and use of psychiatric pharmaceuticals) in which diazepam (0.13 to 4.34 μg/mL), citalopram (0.04 to 0.24 μg/mL), alprazolam (0.01 to 0.12 μg/mL), olanzapine (0.009 to 0.083 μg/mL), mirtazapine (0.01 to 0.33 μg/mL), venlafaxine (0.006 to 0.92 μg/mL), haloperidol (0.007 to 0.13 μg/mL), and zolpidem (0.01 to 0.16 μg/mL) were successfully quantitated.