Are In Silico Approaches Applicable As a First Step for the Prediction of e‑Liquid Toxicity in e‑Cigarettes?

Recent studies have raised concerns about e-cigarette liquid inhalation toxicity by reporting the presence of chemicals with European Union CLP toxicity classification. In this scenario, the regulatory context is still developing and is not yet up to date with vaping current reality. Due to the paucity of toxicological studies, robust data regarding which components in e-liquids exhibit potential toxicities, are still inconsistent. In this study we applied computational methods for estimating the toxicity of poorly studied chemicals as a useful tool for predicting the acute toxicity of chemicals contained in e-liquids. The purpose of this study was 3-fold: (a) to provide a lower tier assessment of the potential health concerns associated with e-liquid ingredients, (b) to prioritize e-liquid ingredients by calculating the e-tox index, and (c) to estimate acute toxicity of e-liquid mixtures. QSAR models were generated using QSARINS software to fill the acute toxicity data gap of 264 e-liquid ingredients. As a second step, the potential acute toxicity of e-liquids mixtures was evaluated. Our preliminary data suggest that a computational approach may serve as a roadmap to enable regulatory bodies to better regulate e-liquid composition and to contribute to consumer health protection.