Variability in urinary concentrations of primary aromatic amines

Analysis of aromatic amines in human urine using comprehensive multi-dimensional gas chromatography-mass spectrometry (GCxGC-MS)

Several aromatic amines (AA) are classified as human carcinogens, and tobacco smoke is one of the main sources of exposure. Once in the human body, they undergo different metabolic pathways which lead to either their excretion or ultimately to the formation of DNA and protein adducts. The aim of this study was to investigate AA in 68 urine samples (aged 29-79, 47% female), including 10 smokers (S), 28 past-smokers (PS) and 30 never-smokers (NS), and to study if there was a relation between the smoking status and the amount of the AA present. GCxGC-MS was used to analyze AA in complex urine samples due to its high peak capacity and the fact that it provides two sets of retention times and structural information, which facilitates the separation and identification of the target analytes. First, a qualitative comparison of an example set of a NS, PS and S sample was carried out, in which 38, 45 and 46 AA, respectively, could be tentatively identified. Afterwards, seven AA were successfully quantified in the samples. Of these, 4-ethylaniline (4EA, p = 0.015), 2,4,6-trimethylaniline (2,4,6TMA, p = 0.030), 2-naphthylamine (2NA, p = 0.014) and the sum of 2,4- and 2,6-dimethylaniline (DMA, p = 0.017) were found in significantly different (α = 0.05) concentrations for the S, 29 ± 14, 87 ± 49, 41 ± 26, and 105 ± 57 ng/L respectively, compared to the NS, 15 ± 6, 42 ± 30, 16 ± 6, and 48 ± 28 ng/L. And 2,4,6TMA (39 ± 26, p = 0.022), 2NA (18 ± 9, p = 0.025) and DMA (53 ± 46, p = 0.030), were also found at significantly higher concentrations in samples from S when compared to PS. However, some samples had AA concentrations outside the calibration curve and could not be taken into account, especially for 2-methylaniline (2MA). Therefore, all the samples were evaluated using a quantitative screening approach, by which the intensities of 4EA (p = 0.019), 2,4,6TMA (p = 0.048), 2NA (p = 0.016), DMA (p = 0.019) and 2MA (p = 0.006) in S were found to be significantly (α = 0.05) higher than in the NS, and 2MA (p = 0.019) and 4EA (p = 0.023) in S were found to be significantly higher than in the PS. An association between the smoking status and the amount of certain AA present could therefore be found. This information could be used to study the relation between the smoking status, the amount of AA present, and smoking related diseases like bladder cancer.

Assessment of urinary aromatic amines in Brazilian pregnant women and association with DNA damage: Influence of genetic diversity, lifestyle, and environmental and socioeconomic factors

Aromatic amines (AAs) are polar organic chemicals with a wide environmental distribution originating from various sources, such as tobacco smoke, diesel exhaust, and dermal absorption from textile products with azo dyes. The toxicity profile of AAs is directly related to the amino group's metabolic activation and the generation of the reactive intermediate, forming DNA adducts and potential carcinogenicity. Urinary levels of 8-hydroxy-2'-deoxyguanosine (8OHdG) are an important biomarker of DNA damage. Since AAs have been shown to cross the placental barrier, being a risk factor for adverse birth outcomes, prenatal exposure is a great public health concern. The present study aimed to measure the urinary levels of 58 AAs in Brazilian pregnant women (n = 300) and investigated the impact of this exposure on DNA damage by quantifying 8OHdG levels. The influence of tobacco smoke exposure and dermal absorption of AAs by clothes on urinary levels was also assessed. The results showed a 100% detection rate for eight AAs, two of them regulated by the European Union (2,6-dimethylaniline and 2,4-diaminotolune). Hundreds of AAs may be derived from aniline, which here showed a median of 1.38 ng/mL. Aniline also correlated positively with 2,6-dimethylaniline, p-aminophenol, and other AAs, suggesting exposure to multiple sources. The present findings suggest that both tobacco smoke and dermal contact with clothes containing azo dyes are potential sources that might strongly influence urinary levels of AAs in Brazilian pregnant women. A multiple regression linear model (R2 = 0.772) suggested that some regulated AAs (i.e., 2-naphthylamine and 4-aminobiphenyl), nicotine, smoke habit, age, and Brazilian region could induce DNA damage occurrence, increasing the levels of 8OHdG. Given the limited available data on human exposure to carcinogenic AAs, as well as the lack of toxicological information on those non-regulated, further studies focused on measuring their levels in human fluids and the potential exposure sources are clearly essential.

Occurrence of primary aromatic amines and nicotine in sediments collected from the United States

Despite extensive use of primary aromatic amines (AAs) in consumer products, little is known about their occurrence in the environment. In this study, we investigated the occurrence of 14 AAs and nicotine in 75 sediment samples collected from seven estuarine and freshwater ecosystems in the Unites States. Additionally, risk quotients (RQs) were calculated to assess potential risks of these chemicals to aquatic organisms. Of the 14 AAs analyzed, seven of them were found in sediments. The sum concentrations of seven AAs in sediments were in the range of 10.2 to 1810 ng/g, dry wt (mean: 388 ng/g). Aniline was the most abundant compound, accounting for, on average, 53 % of the total concentrations. Nicotine was found in sediments at a concentration range of <LOQ to 1340 ng/g, dry wt (mean: 119 ng/g). Among the seven sampling locations studied, AAs and nicotine concentrations were the highest in sediment from Altavista wastewater lagoon in Virginia (AV, mean: 1700 ng/g) followed in descending order by Chicago Sanitary and Ship Canal (CSSC, mean: 807 ng/g), Indiana Harbor and Ship Canal (IHSC, mean: 698 ng/g) and New Bedford Harbor (NBH, mean: 482 ng/g). Sediments from the upper Mississippi River (MISS, mean: 63.4 ng/g) and Tittabawassee River (TBR, mean: 52.3 ng/g) contained the lowest concentrations. The RQ values for AAs in sediment ranged from 0 to 733 and that for nicotine ranged from 0 to 2060. Among AAs, the highest RQ value was found for 4-chloroaniline. Nicotine exhibited notable RQ values, which suggested risk from this chemical to aquatic organisms. This is the first study to report the occurrence of AAs in sediments and our results suggest the need for further investigations on the sources and ecological impacts of these chemicals in aquatic ecosystems.

Literature review and evaluation of biomarkers, matrices and analytical methods for chemicals selected in the research program Human Biomonitoring for the European Union (HBM4EU)

Humans are potentially exposed to a large amount of chemicals present in the environment and in the workplace. In the European Human Biomonitoring initiative (Human Biomonitoring for the European Union = HBM4EU), acrylamide, mycotoxins (aflatoxin B1, deoxynivalenol, fumonisin B1), diisocyanates (4,4'-methylenediphenyl diisocyanate, 2,4- and 2,6-toluene diisocyanate), and pyrethroids were included among the prioritized chemicals of concern for human health. For the present literature review, the analytical methods used in worldwide biomonitoring studies for these compounds were collected and presented in comprehensive tables, including the following parameter: determined biomarker, matrix, sample amount, work-up procedure, available laboratory quality assurance and quality assessment information, analytical techniques, and limit of detection. Based on the data presented in these tables, the most suitable methods were recommended. According to the paradigm of biomonitoring, the information about two different biomarkers of exposure was evaluated: a) internal dose = parent compounds and metabolites in urine and blood; and b) the biologically effective = dose measured as blood protein adducts. Urine was the preferred matrix used for deoxynivalenol, fumonisin B1, and pyrethroids (biomarkers of internal dose). Markers of the biological effective dose were determined as hemoglobin adducts for diisocyanates and acrylamide, and as serum-albumin-adducts of aflatoxin B1 and diisocyanates. The analyses and quantitation of the protein adducts in blood or the metabolites in urine were mostly performed with LC-MS/MS or GC-MS in the presence of isotope-labeled internal standards. This review also addresses the critical aspects of the application, use and selection of biomarkers. For future biomonitoring studies, a more comprehensive approach is discussed to broaden the selection of compounds.

Updated in Silico Prediction Methods for Fractions Absorbed and Key Input Parameters of 355 Disparate Chemicals for Physiologically Based Pharmacokinetic Models for Time-Dependent Plasma Concentrations after Virtual Oral Doses in Humans

Human metabolic profiles for substances such as toxic food-derived compounds are usually allometrically extrapolated from traditionally determined in vivo rat concentration profiles. To evaluate internal exposures in humans without any reference to experimental data, physiologically based pharmacokinetic (PBPK) modeling could be used if the model input parameters could be estimated in silico. This approach would simplify the use of PBPK models for forward dosimetry after oral doses. In this study, the in silico estimation of input parameters for PBPK models (i.e., fraction absorbed × intestinal availability, absorption rate constants, and volumes of the systemic circulation) was updated for an panel of 355 chemicals (212 previously analyzed and 143 additional substances) using a light gradient boosting machine learning algorithms (LightGBM) based on between 11 and 29 in silico-calculated chemical descriptors. Simplified human PBPK models were then used to calculate virtual maximum plasma concentrations (C_max) and areas under the concentration-time curve (AUC) based on two sets of input parameters, i.e., traditionally derived values from in vivo data and those calculated in silico using the current updated systems. Both sets of C_max and AUC data were well correlated (r = 0.87 and r = 0.73, respectively; p < 0.01, n = 355). Therefore, input parameters for human PBPK models for a diverse range of compounds could be successfully estimated using chemical descriptors and in silico tools. This approach to pharmacokinetic modeling has potential for application in computational toxicology and in the clinical setting for assessing the potential risk of general chemicals.