Blood and urine multi-omics analysis of the impact of e-vaping, smoking, and cessation: from exposome to molecular responses.

Cigarette smoking is a major preventable cause of morbidity and mortality. While quitting smoking is the best option, switching from cigarettes to non-combustible alternatives (NCAs) such as e-vapor products is a viable harm reduction approach for smokers who would otherwise continue to smoke. A key challenge for the clinical assessment of NCAs is that self-reported product use can be unreliable, compromising the proper evaluation of their risk reduction potential. In this cross-sectional study of 205 healthy volunteers, we combined comprehensive exposure characterization with in-depth multi-omics profiling to compare effects across four study groups: cigarette smokers (CS), e-vapor users (EV), former smokers (FS), and never smokers (NS). Multi-omics analyses included metabolomics, transcriptomics, DNA methylomics, proteomics, and lipidomics. Comparison of the molecular effects between CS and NS recapitulated several previous observations, such as increased inflammatory markers in CS. Generally, FS and EV demonstrated intermediate molecular effects between the NS and CS groups. Stratification of the FS and EV by combustion exposure markers suggested that this position on the spectrum between CS and NS was partially driven by non-compliance/dual use. Overall, this study highlights the importance of in-depth exposure characterization before biological effect characterization for any NCA assessment study.

Delivery efficiencies of constituents of combustion-derived aerosols across the air-liquid interface during in vitro exposures.

In vitro aerosol exposure of epithelial cells grown at the air-liquid interface is an experimental methodology widely used in respiratory toxicology. The exposure depends to a large part on the physicochemical properties of individual aerosol constituents, as they determine the transfer kinetics from the aerosol into the cells. We characterized the transfer of 70 cigarette smoke constituents from the smoke into aqueous samples exposed in the Vitrocell® 24/48 aerosol exposure system. The amounts of these compounds in the applied smoke were determined by trapping whole smoke in N,N-dimethylformamide and then compared with their amounts in smoke-exposed, phosphate-buffered saline, yielding compound specific delivery efficiencies. Delivery efficiencies of different smoke constituents differed by up to five orders of magnitude, which indicates that the composition of the applied smoke is not necessarily representative for the delivered smoke. Therefore, dose metrics for in vitro exposure experiments should, if possible, be based on delivered and not applied doses. A comparison to literature on in vivo smoke retention in the respiratory tract indicated that the same applies for smoke retention in the respiratory tract.

Prediction Models of Retention Indices for Increased Confidence in Structural Elucidation during Complex Matrix Analysis: Application to Gas Chromatography Coupled with High-Resolution Mass Spectrometry.

Monitoring of volatile and semivolatile compounds was performed using gas chromatography (GC) coupled to high-resolution electron ionization mass spectrometry, using both headspace and liquid injection modes. A total of 560 reference compounds, including 8 odd n-alkanes, were analyzed and experimental linear retention indices (LRI) were determined. These reference compounds were randomly split into training (n = 401) and test (n = 151) sets. LRI for all 552 reference compounds were also calculated based upon computational Quantitative Structure-Property Relationship (QSPR) models, using two independent approaches RapidMiner (coupled to Dragon) and ACD/ChromGenius software. Correlation coefficients for experimental versus predicted LRI values calculated for both training and test set compounds were calculated at 0.966 and 0.949 for RapidMiner and at 0.977 and 0.976 for ACD/ChromGenius, respectively. In addition, the cross-validation correlation was calculated at 0.96 from RapidMiner and the residual standard error value obtained from ACD/ChromGenius was 53.635. These models were then used to predict LRI values for several thousand compounds reported present in tobacco and tobacco-related fractions, plus a range of specific flavor compounds. It was demonstrated that using the mean of the LRI values predicted by RapidMiner and ACD/ChromGenius, in combination with accurate mass data, could enhance the confidence level for compound identification from the analysis of complex matrixes, particularly when the two predicted LRI values for a compound were in close agreement. Application of this LRI modeling approach to matrixes with unknown composition has already enabled the confirmation of 23 postulated compounds, demonstrating its ability to facilitate compound identification in an analytical workflow. The goal is to reduce the list of putative candidates to a reasonable relevant number that can be obtained and measured for confirmation.