Electronic cigarettes (e-cigs) are designed to heat and aerosolized mixtures of propylene glycol, glycerol, flavorings, humectants and, optionally, nicotine. Unlike cigarettes, the process involves no tobacco and no combustion; however, the inhalation and exhalation of vapour is reminiscent of smoking. In this context, the use of these devices, might play an important role in smoking cessation and reduction; however, there is still a lack of international consensus over the public health role of the e-cig. Despite the large use of e-cigs, still few toxicological studies are available on the potential long term effects of inhaled of many characterizing flavors used in e-cig products. For instance, the FDA GRAS (Generally Recognized As Safe) designation for some flavorings compounds and for propylene glycol, does not apply to inhalation, and currently, there are no controlled long-term studies of the effects of inhaling heated aerosolized mixture in humans. Thus, there is legitimate concern over the health effects of chronically inhaling these substances and the lack of toxicological studies. In this respect, the aim of this study was to determine potential Cancerogenic, Mutagenic and Reprotoxic (CMR) properties of several e-liquid ingredients by means of in silico methods. With reference to our e-liquid ingredients and CMR effects, we first conducted an in depth screening, through the literature reviews; and we found experimental data gap for all the three categories. Specifically, for the investigated e-liquid ingredients, we observed 35%, 85% and 70% of experimental data gap for Cancerogenicity, Mutagenicity and Reprotoxicity effects, respectively. By following a battery approach, almost all data gaps were successfully filled using Quantitative Structure-Activity Relationship (Q) SAR methods. The predictions were performed using several open source software (VEGA, Toxtree, ToxRead and T.E.S.T.) and the results were combined to obtain the highest possible prediction accuracy (consensus approach). This in silico study is a part of a broader integrated approach (literature research, in chemico, in vitro and computational analysis) specifically designed to assess the potential risk associated with characterizing lavors and e-liquid ingredients.
Use of (Q)SAR models to investigate potential CMR properties of e-liquid ingredients
Orro A;
2019
Abstract
Electronic cigarettes (e-cigs) are designed to heat and aerosolized mixtures of propylene glycol, glycerol, flavorings, humectants and, optionally, nicotine. Unlike cigarettes, the process involves no tobacco and no combustion; however, the inhalation and exhalation of vapour is reminiscent of smoking. In this context, the use of these devices, might play an important role in smoking cessation and reduction; however, there is still a lack of international consensus over the public health role of the e-cig. Despite the large use of e-cigs, still few toxicological studies are available on the potential long term effects of inhaled of many characterizing flavors used in e-cig products. For instance, the FDA GRAS (Generally Recognized As Safe) designation for some flavorings compounds and for propylene glycol, does not apply to inhalation, and currently, there are no controlled long-term studies of the effects of inhaling heated aerosolized mixture in humans. Thus, there is legitimate concern over the health effects of chronically inhaling these substances and the lack of toxicological studies. In this respect, the aim of this study was to determine potential Cancerogenic, Mutagenic and Reprotoxic (CMR) properties of several e-liquid ingredients by means of in silico methods. With reference to our e-liquid ingredients and CMR effects, we first conducted an in depth screening, through the literature reviews; and we found experimental data gap for all the three categories. Specifically, for the investigated e-liquid ingredients, we observed 35%, 85% and 70% of experimental data gap for Cancerogenicity, Mutagenicity and Reprotoxicity effects, respectively. By following a battery approach, almost all data gaps were successfully filled using Quantitative Structure-Activity Relationship (Q) SAR methods. The predictions were performed using several open source software (VEGA, Toxtree, ToxRead and T.E.S.T.) and the results were combined to obtain the highest possible prediction accuracy (consensus approach). This in silico study is a part of a broader integrated approach (literature research, in chemico, in vitro and computational analysis) specifically designed to assess the potential risk associated with characterizing lavors and e-liquid ingredients.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
