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.