Abstract

E-Vapor products generate aerosols typically containing nicotine, flavor ingredients, and aerosol formers (propylene glycol and vegetable glycerin). Whilst many flavor ingredients are “generally recognized as safe (GRAS)” for oral use in the food industry, there exist knowledge gaps on their effects when delivered by the inhalation route1,2. Due to the large number of available ingredients and potential combinations used to create e-liquids, toxicological and analytical evaluation of each flavor ingredient is impractical. Moreover, chemical characterization requires analytical methods to be developed and validated to measure key ingredients, as well as stability assessments to demonstrate that these test materials were stable during the testing period, which is equally challenging. In this study, we present a pragmatic approach of preparing “pre-blends” prior to making a test formulation, containing 38 flavor ingredients as an example case, ahead of pre-clinical toxicity testing. We used the pre-blends to simplify the preparation and the characterization of test formulations, establishing the stability criteria for the subsequent toxicity testing. We prepared pre-blends by dividing the 38 flavor ingredients into five (5) pre-blend groups based on structural moiety, solubility, and chemical reactivity. These pre-blends were mixed to make two different “final” test formulations (containing all 38 flavor ingredients with and without nicotine). We evaluated the stability of the pre-blends and the two test formulations prior to the subsequent in vivo inhalation studies. Based on the analytical assessment, all the pre-blends were stable up to 4 weeks at 0-4 ͦC. When all pre-blends were mixed, the test formulation was stable up to 3 days in the presence of nicotine and 10 days without nicotine when stored at 0-4 ͦC. These stability results were used to set the frequency of test formulations preparation for the in vivo inhalation studies, ensuring stability of test materials prior to the biological testing.

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Supplementary data