500+ Toxic Chemicals in e-liquids Detected by AI Model Reflects Underlying Risks of Vaping


A group of authors used a graph-convolutional neural network (NN) to predict and analyze the thermal decomposition products of e-liquid flavours, correlating them with mass spectrometry (MS) data to assess potential health risks. Nicotine inhalation has long been detrimental to public health. Vaping e-liquids, seen as a safer alternative, have evolved from simple compositions to include numerous flavor additives, which now often exceed nicotine levels. This shift has particularly appealed to younger demographics, raising concerns about long-term health impacts and the re-normalization of nicotine use. The 2019 outbreak of vaping-related lung injuries, linked to additives like vitamin E acetate, underscores the potential risks of inhaling chemically complex e-liquids.

To completely comprehend the long-term health impacts of the intricate chemical interactions seen in heated and inhaled e-liquids, more research is required. Based on previously published research, 180 taste compounds that have been linked to the usage of e-liquids worldwide are examined in this study. Numerous functional groups, including 66 esters, 46 ketones/aldehydes, 26 aromatic compounds/heterocycles/carbocycles, 27 alcohols/acetals, and 15 carboxylic acids/amides, were identified by analyzing their chemical structures. This variety points to a wide range of possible pyrolysis reactions.

Additional structural study took into account characteristics like polarity and molecular weight; a 3D chemical space visualization showed that there was moderate variability, mostly due to surface area, rotational flexibility, and molecular weight. A usually volatile set of compounds was indicated by the average molecular weight of 146.2. A methodology that used experimental MS data with NN: predictions of pyrolysis reactions was used to estimate the risk for 180 e-liquid flavours.
Initially, the chemical structures were transformed into a format compatible with the Simplified Molecular-Input Line-Entry System (SMILES).

Following a correlation with MS data revealing chemical ions, fragmentation masses, and their abundances, a graph-convolutional neural network model was used to predict the pyrolysis transformations and products.

Using the Globally Harmonized System (GHS), matches between NN-predicted goods and MS fragments were further categorized for potential health hazards. In addition to estimating reaction activation energies for noteworthy health hazards, our automated approach arranged the information into an extensive list for every flavour.

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