Toxicant Production and Mitigation in the Electronic-Cigarette Reaction Vessel

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 DESCRIPTION (provided by applicant): Despite the fact that their health effects are unknown, electronic cigarettes have become so popular that some sources project their sales to surpass those of traditional cigarettes within the next decade. Currently, however, regulatory efforts are greatly hampered by a severe deficiency of relevant scientific data. The main objectives of this proposal are to clarify (i) the origins and levels of toxins produced during e-cigarette "vaping" and (ii) the properties of the particles that are inhaled by vapers and those exposed by "second-hand" means. The central hypothesis is that e-cigarettes are reaction vessels that promote free radical reactions that lead to a range of products formed at high temperatures. The rationale underlying the proposed research is that a comprehensive investigation of the reaction variables influencing toxicant formation and aerosol properties will provide regulatory agencies, manufacturers, and home chemists a clearer understanding of conditions to be avoided when using e-cigarettes. A multidisciplinary team has been assembled to address the chemical, engineering, analytical and biological aspects of e-cigarettes. Preliminary studies have led to the discovery of a previously-unknown reaction pathway that leads to total formaldehyde levels that approach those of delivered nicotine. The research plan builds upon our preliminary results and involves four Specific Aims. Aim 1 is to define the effect of vaping upon glycerol and propylene glycol, the primary aerosol-forming molecules. Aim 2 is to define the effect of vaping upon nicotine, as well as related alkaloids likely to be present in some e-cigarette liquids. Aim 3 is t define the effect of flavorants and environmental contaminants on vaping products. Aim 4 is to determine the ability of formaldehyde to be released from carrier product molecules, and to examine the toxicities of e- cigarette aerosols by various bioassays. We will generate predictive models that will enable probing design variables and processes controlling product formation and particle size distributions (PSDs).