A New Automated Method for the Analysis of Aromatic Amines in Human Urine by GC–MS/MS

摘要

Cigarette smoking significantly increases the risk of cancer and cardiovascular diseases as well as premature death. Aromatic amines (AAs) such as o-toluidine, 2-aminonaphthalene and 4-aminobiphenyl are found in cigarette smoke and are well-established human bladder carcinogens presumably acting via the formation of DNA adducts. These amines may be metabolized in the liver to acetylated or glucuronidated forms or oxidized to a hydroxylamine which may react with protein and DNA to form adducts. Free, acetylated and glucuronidated AAs are excreted in urine and can be measured as exposure biomarkers. Using isotope dilution GC–MS/MS, our laboratory quantifies six urinary AAs that are known or suspected carcinogens–o-toluidine, 2,6-dimethylaniline, o-anisidine, 1-aminonaphthalene, 2-aminonaphthalene and 4-aminobiphenyl–for large population studies such as the National Health and Nutrition Examination Survey (NHANES). We also monitor two additional corresponding structural isomers–2-aminobiphenyl and 3-aminobiphenyl–to verify isomer separation. A new and improved automated sample preparation method was developed to quantify these AAs, in which, sample cleanup was done via Supported Liquid Extraction (SLE+ ISOLUTE^®) on a Hamilton STAR™ workstation. This automated method increased sample throughput by reducing sample cleanup time from 8 to 4 h while maintaining precision (intra and inter-run coefficient of variation <7%) and accuracy (±17%). Recent improvements in our GC/MS method have enhanced our assay sensitivity and specificity, resulting in longer analytical column life and maintaining or reducing the limit of detection for all six analytes. Indigo ASCENT™ software (3.7.1, Indigo BioAutomation, Inc.) is used for peak integration, calibration and quantification. A streamlined sample data flow was created in parallel with the automated method, in which samples can be tracked from receiving to final laboratory information management system output with minimal human intervention, minimizing potential human error. This newly validated, automated method and sample data flow are currently applied in biomonitoring of AAs in the US noninstitutionalized population NHANES 2013–2014 cycle.