Predicting the Toxicity of Electronic Cigarette based on a Systematic Approach

Abstract

Electronic cigarette (ECIG) is marketed as a “safe” alternative to combustible cigarette. This battery-powered device vaporizes a liquid on a heating coil. The liquid is mainly a solution of propylene glycol (PG), vegetable glycerin (VG), with or without nicotine and/or flavors. The number of ECIG users expanded rapidly from 7 million in 2011 to 41 million in 2018. However, many studies analyzed the chemical profile of ECIG liquids and aerosols and reported the detection of some toxicants, carcinogens, and oxidants in ECIG emissions. In an attempt to complement the literature, this thesis focuses on the quantification of reactive oxygen species (ROS) as a generic assessment of ECIG toxicity. The influence of the different ECIG operating parameters, i.e., ECIG device design, liquid composition, battery power output, user puff topography, and inhalation flow rate, on ROS emissions was assessed. In a first study, we compared ROS emissions from a USB stick-like ECIG known as JUUL in two different markets: US and UK, to check any possible differences. In a second study, ROS was measured for the above conditions in a highly customizable device known as sub-ohm ECIGs (coil resistance < 1 Ohm). A fluorescent probe was used to trap and semi-quantify ROS on a microplate reader. In a second approach to the assessment of the toxicity of ECIG, we attempted to establish a direct correlation between flavoring chemicals and aerosol toxicants. The flavor is considered as a major factor in increasing ECIG acceptance and safety perceptions among youth. Nowadays, more than 15000 flavors are available in the market. Our work focused on the chemical profiling of flavored liquids using non-targeted analysis via gas chromatography-mass spectrometry (GC-MS), and then categorizing the chemical constituents according to their functional groups. This unique approach helps to identify the reactions that might take place under ECIG realistic conditions, thus allowing the prediction of toxicant emissions.