The e-liquids used in electronic cigarettes (E-cigs) consist of propylene glycol (PG), vegetable glycerin (VG), nicotine, and chemical additives for flavoring. There are currently over 7,700 e-liquid flavors available, and while some have been tested for toxicity in the laboratory, most have not. Here, we developed a 3-phase, 384-well, plate-based, high-throughput screening (HTS) assay to rapidly triage and validate the toxicity of multiple e-liquids. Our data demonstrated that the PG/VG vehicle adversely affected cell viability and that a large number of e-liquids were more toxic than PG/VG. We also performed gas chromatography–mass spectrometry (GC-MS) analysis on all tested e-liquids. Subsequent nonmetric multidimensional scaling (NMDS) analysis revealed that e-liquids are an extremely heterogeneous group. Furthermore, these data indicated that (i) the more chemicals contained in an e-liquid, the more toxic it was likely to be and (ii) the presence of vanillin was associated with higher toxicity values. Further analysis of common constituents by electron ionization revealed that the concentration of cinnamaldehyde and vanillin, but not triacetin, correlated with toxicity. We have also developed a publicly available searchable website ( www.eliquidinfo.org ). Given the large numbers of available e-liquids, this website will serve as a resource to facilitate dissemination of this information. Our data suggest that an HTS approach to evaluate the toxicity of multiple e-liquids is feasible. Such an approach may serve as a roadmap to enable bodies such as the Food and Drug Administration (FDA) to better regulate e-liquid composition.

The e-liquids used in electronic cigarettes (E-cigs) typically consist of a mixture of propylene glycol (PG), vegetable glycerin (VG), and nicotine, as well as numerous chemical additives that are used for flavoring. There are currently over 7,700 different flavored e-liquids that are commercially available, but there is very limited information regarding either their chemical composition or toxicity. In this work, we developed a high-throughput screening (HTS) assay to rapidly triage and validate the toxicity of multiple e-liquids in parallel. Our data indicated that e-liquids are extremely heterogeneous, so we also performed gas chromatography–mass spectrometry (GC-MS) of all e-liquids to evaluate their composition/toxicity relationship. We found that the presence of either vanillin or cinnamaldehyde in e-liquids was associated with higher toxicity values. In addition, our data demonstrated that the PG/VG vehicle by itself was toxic at higher doses. We have also developed a publicly available and searchable website ( www.eliquidinfo.org ) that contains these chemical composition and toxicity data. Given the large numbers of available e-liquids, this website will serve as a resource to disseminate this information. Our HTS approach may serve as a roadmap to enable bodies such as the United States Food and Drug Administration (FDA) to better regulate e-liquid safety.

The sheer diversity and variability have made it difficult to comprehensively study e-liquids, and to date, very little to no research has been conducted to assess the safety of most available e-liquids. Many of the chemical constituents in e-liquids, including PG and VG, are on the Food and Drug Administration (FDA)’s Generally Recognized As Safe (GRAS) list. However, most GRAS studies on flavors were performed following oral ingestion in rats [ 14 , 15 ], and many GRAS chemicals have not been tested for safety after inhalation [ 16 – 18 ]. Indeed, the toxicity profile for inhalation is markedly different from the oral route. As a case in point, diacetyl, which is used as butter-flavored chemical, is on the GRAS list but causes bronchiolitis obliterans when inhaled [ 19 , 20 ]. Emerging studies have shown that e-liquids have measurable biological effects on cells, including altering Ca 2+ signaling, cell growth, viability, and inflammation. However, the research that has been conducted thus far has looked at only a small proportion of the available e-liquids, leaving the effects of many e-liquid flavors unknown [ 21 – 24 ]. Given the growing number of untested, commercially available e-liquids, new paradigms need to be introduced to rapidly screen these e-liquids using in vitro assays to better inform both the policy makers (i.e., the legislature/FDA) as well as the public. Here, we introduce a high-throughput screening (HTS) assay designed to assess growth characteristics, viability, and chemical composition of e-liquids. The overall goal of this work was to screen neat e-liquids and identify potential flavors and/or chemical constituents that were more toxic than PG/VG and would warrant additional, more detailed attention. Therefore, as a proof of concept, we screened 148 e-liquid flavors to determine their relative toxicity and chemical composition. We then validated these results in multiple cell types and after exposure to E-cig aerosols.

The e-liquid vehicle used in E-cigs is composed of propylene glycol (PG) and vegetable glycerin (VG) at varying ratios. There are currently over 7,700 e-liquid flavors on the market from over 1,200 different vendors in the United States, and the number continues to increase [ 10 ]. E-liquids come in many different flavors, colors, nicotine concentrations (0–36 mg/mL) and PG/VG ratios (e.g., 80:20, 70:30, 55:45, and 40:60). Despite their ubiquity, manufacturing standards for e-liquids do not currently exist, and they can differ in composition from vendor to vendor [ 13 ].

Electronic cigarettes (E-cigs), also known as electronic nicotine delivery systems (ENDS), are devices that deliver nicotine to the lung without combustion in a process known as “vaping” [ 1 ]. They differ from traditional cigarettes in that they do not contain tobacco, and—instead—they produce an aerosol by drawing and heating a liquid vehicle (e-liquid) over a battery-powered coil. This aerosol is inhaled and deposited in the lungs so that nicotine can be absorbed into the bloodstream and translocate to the brain [ 2 ]. E-cigs were introduced as a potentially safer alternative to tobacco smoking because they do not contain the toxic byproducts of tobacco combustion, including tar-phase chemicals [ 3 , 4 ]. However, vaped e-liquids also undergo pyrolysis and generate oxidative species, which may lead to the formation of additional toxic components (i.e., formaldehyde and carbonyls) that are similar to those seen in cigarettes [ 5 , 6 ]. In addition, while e-liquids do not contain tobacco, they may contain nicotine derived from tobacco and therefore may contain certain tobacco-related components such as nitrosamines [ 7 ]. However, despite these observations, little is known about the toxicity potential of most e-liquids. Since their inception, E-cig design has progressed rapidly. The first-generation E-cigs, dubbed “cigalikes,” were prefilled disposable devices that were designed to look like traditional cigarettes. In contrast, second- and third-generation E-cigs have interchangeable parts including an aerosol generator, a heating element (coil), a refillable tank, and much more powerful rechargeable batteries [ 8 , 9 ]. These devices have broken from the traditional design in favor of handheld tanks that have an increased and even customizable ability to deliver aerosolized nicotine (along with other aerosolized constituents) [ 10 ]. Moreover, second- and/or third-generation E-cigs produce a higher concentration of plasma nicotine metabolites (cotinine and trans-3’-hydroxycotinine) than the first-generation cigalikes that is now comparable to plasma cotinine levels seen in regular smokers [ 11 , 12 ].

Because presence or absence of chemical constituents showed some correlation with cell toxicity, we next investigated whether the actual concentration of chemical constituents (not presence or absence) could predict toxicity. As a proof of concept, vanillin and cinnamaldehyde concentrations were measured in several e-liquids via electron ionization mass spectra because these are flavoring agents regularly used in the food industry. We also measured triacetin as a nontoxic control. We compared the concentration of each constituent and its LC 50 values. In the case of vanillin, toxicity (i.e., LC 50 ) was proportional (R 2 = 0.62) to the actual vanillin concentration in the e-liquids tested ( Fig 8A ; S7 Data ). Similarly, cinnamaldehyde toxicity was also proportional (R 2 = 0.75) to the measured concentrations of cinnamaldehyde ( Fig 8B ; S7 Data ). On the contrary, triacetin did not correlate to toxicity (R 2 = 0.048; Fig 8C ; S7 Data ).

(A) NMDS of e-liquids chemical analysis (presence/absence). NMDS was performed using binary Euclidean distances (stress 0.1367). E-liquids are shown as circles of varying size and color. Dashed circles encompass e-liquids within the same k-mode cluster, showing a significant separation of, in general, high and low LC 50 e-liquids. Chemicals within each cluster were compared using a t test, in which resultant p-values were adjusted using Bonferroni correction. Chemicals within e-liquids are represented as triangles; gold coloration denotes those to be significantly (p < 0.0004) present after multiple testing correction and may be associated with lower LC 50 values. (B) Graph showing toxicity (LC 50 ) versus the number of chemicals in each e-liquid. Pearson correlation = −0.48; R 2 = 0.23. Raw data are available in S6 Data . LC 50 , concentration at which a given agent is lethal to 50% of the cells; NMDS, nonmetric multidimensional scaling.

We compared LC 50 values and chemical composition to identify key chemical constituents that might drive toxicity. We first compared LC 50 values with the presence/absence of constituents to determine whether any particular chemicals and/or diversity drive the toxicity. We performed nonmetric multidimensional scaling (NMDS) on the presence/absence matrix of chemical constituents in each liquid under binary Euclidean distances, which resulted in a stress value of 0.1367, indicating a weak correlation. A qualitative separation of high and low LC 50 values was observed that was generally supported via a k-modes clustering (k = 2). Here, a Welch two-sample t test showed significant differences (p < 0.0005) between the cluster LC 50 means (1.55 and 2.66, respectively; Fig 7A ; S6 Data ). When comparing the presence/absence data of each chemical between the 2 clusters, vanillin and ethyl-vanillin—among other chemicals—were present in significantly (Bonferroni corrected; p < 0.0004) higher abundance (98% and 100%, respectively) in the lower LC 50 cluster than in the higher cluster (10% and 17%, respectively). In addition, we saw a trend of negative correlation between the number of chemicals in individual e-liquids versus their toxicity (Pearson correlation = −0.48, R 2 = 0.16) ( Fig 7B ; S6 Data ).

In order to better understand how chemical composition contributed to e-liquid toxicity, we used gas chromatography–mass spectrometry (GC-MS) to identify e-liquid constituents. Chromatograms obtained using this approach were compared to the National Institute of Standards and Technology (NIST) 2014 mass spectral database for compound identification. Fig 6A shows a representative chromatogram of the “Dulce de Leche” e-liquid. As expected, PG, VG, and nicotine are clearly present at high concentrations. More than 10 other constituents were identified in this e-liquid, including vanillin, ethyl vanillin, and piperonal. Fig 6B depicts 10 representative chromatograms of additional e-liquids. We also performed electron ionization to obtain mass spectra to quantify select constituents present in representative e-liquids, and an example spectrum for vanillin is shown in Fig 6C .

(A) Mean normalized viability of HEK293T cells following exposure of vaped e-liquids. N ≥ 5 per treatment. (B) Mean normalized viability of primary human alveolar macrophages following exposure of vaped e-liquids. N ≥ 5 per treatment. (C) Mean normalized viability of HBECs following exposure to vaped e-liquids. N ≥ 5 per treatment. (D) Graph showing HEK293T vaped viability versus HEK293T toxicity (LC 50 ) obtained using neat e-liquids. Linear regression R 2 = 0.66. (E) Graph showing primary human alveolar macrophage vaped viability versus HEK293T toxicity (LC 50 ). Linear regression R 2 = 0.06. (F) HBEC viability using vaped e-liquids versus HEK293T toxicity (LC 50 ). Linear regression R 2 = 0.74. * = p < 0.05 different from control. For A, B, and C we performed statistical analysis using one-way ANOVA followed by Dunnett’s Test. B. N. B. Smoothie, Chocolate B., and Coconut Water. Raw data are available in S5 Data . B. N. B. Smoothie, Banana Nut Bread Smoothie; Chocolate B., Chocolate Banana; HBEC, human bronchial epithelial cells; HEK293T, human embryonic kidney 293 cells; LC 50 , concentration at which a given agent is lethal to 50% of the cells.

We have previously shown that vaped e-liquids exert similar toxicity as neat e-liquids [ 30 ]. However, during the course of vaping, e-liquids are heated to approximately 300 °C before inhalation, which may induce chemical transformations that could alter their toxicity [ 5 ]. While it is not currently possible to vape HEK293T cells under HTS conditions, we performed additional validation steps—due to the importance of this issue—by comparing the relative toxicity of e-liquids after vaping versus direct liquid addition. We selected a range of e-liquids that had high, medium, and low toxicity, as well as air and PG/VG controls. HEK293T cells and primary human macrophages were vaped in 96-well plates using a 3D printed manifold as described [ 30 , 31 ]. According to our published work, 10 × 4 sec, 70 ml puffs (see Materials and methods ) of e-liquid elicit significant effects on cell viability under these conditions [ 30 ]. We vaped HEK293T cells ( Fig 5A ; S5 Data ) and primary alveolar macrophages ( Fig 5B ; S5 Data ) using this approach. We also vaped well-differentiated human bronchial epithelial cells (HBECs) cultured at the air–liquid interface using an automated vaping system that allowed for selective exposure of HBEC mucosal surfaces to the e-liquid aerosol. Due to the larger chamber size for this system, we exposed HBECs to 70 puffs using the same puff parameters described above to achieve comparable exposures as per the HEK293T cells and macrophages. For all cell types, our data demonstrated that e-liquid vaping caused a significant decrease in viability relative to the controls that varied according to the individual e-liquids ( Fig 5A–5C ; S5 Data ). In agreement with our previous study [ 30 ], we did not find that vaping e-liquids changed their relative toxicity.

After having tested all e-liquids using HEK293T cells, we retested a subset of e-liquids in cell lines that are less suitable to HTS but more germane to the respiratory tract, namely the human adenocarcinomic alveolar basal epithelial (hA549) cell line, an immortalized cell line derived from human alveolar epithelia and primary human airway smooth muscle cells (hASMC), isolated from human large airways (see Materials and methods for details) [ 28 , 29 ]. We triaged the testing by choosing every 14th e-liquid from Fig 3F ( S3 Data ) and generated full-dose responses for cell viability ( Fig 4 ; S4 Data ). The tested e-liquids showed a slight left curve shift for hA549 cells, indicating that these e-liquids were more toxic in these cells than in HEK293T and hASMC cells ( Fig 4A–4C ; S4 Data ). Importantly, these e-liquids maintained the same relative toxicity in all cell lines and the LC 50 for Banana Pudding < Key Lime Pie < Popcorn < Blueberry Tobacco ( Fig 4D ; S4 Data ), suggesting that the use of HEK293T cells is valid.

Because PG/VG is an integral component of all commercially available e-liquids and appeared to induce toxicity ( Fig 1C and 1D ; S1 Data ), we then studied its effects on cell toxicity alone by performing dose–response curves for 55:45 PG/VG. Because live-cell fluorescence was more sensitive than cell growth ( Fig 1F ; S1 Data ), we extended this assay and simultaneously measured calcein and propidium iodide as markers or live and dead cells, respectively, as described for tobacco exposure [ 26 ]. Here, we used dimethyl sulfoxide (DMSO) as a known toxic control [ 27 ] and PBS as a nontoxic control. Serial dilutions in DMSO resulted in a decrease in cell viability with an LC 50 (i.e., the concentration at which a given agent was lethal to 50% of the cells) of 6.0 ± 0.4%. In contrast, serial dilutions of the media with PBS did not affect cell viability and therefore could not be fitted with the equation parameters required to calculate LC 50 . PG/VG caused dose-dependent decreases in cell growth with an LC 50 of 2.2 ± 0.2% ( Fig 2A and 2B ; S2 Data ). We then measured cell viability using the calcein/propidium iodide assay ( Fig 2C ; S2 Data ). PG/VG exerted a similar toxicity as DMSO (LC 50 = 5.5 ± 0.4%; p = 0.68, Fig 2C ; S2 Data ). To test whether higher levels of PG/VG affected cell viability by reducing media O 2 levels, we measured the partial pressure of O 2 (P O2 ) in the media after overnight addition of 30% PG/VG using solid-state O 2 electrodes. pO 2 was 20 ± 1.1% (n = 3) in control media and 18 ± 0.4% (n = 4) after addition of 30% PG/VG, suggesting that the observed changes in cell growth and/or viability were not due to reduced O 2 levels.

Cells were incubated for 8 h in 384-well plates, e-liquids were added as indicated for 24 h, and bright-field images were automatically obtained every 2 h to determine growth rates. Cell surface area, as an indicator of confluency, was normalized to the media control. All N = 4. (A) Bright-field images of HEK293T cells incubated overnight with vehicle, 10% PG/VG + media, or 1% banana pudding-, candy corn-, chocolate fudge-, or popcorn-flavored e-liquids. (B) Mean representative growth curves obtained from the bright-field images over time. Curves were categorized as follows: normal growth, media control and popcorn ; reduced growth, candy corn ; no growth, banana pudding ; and toxic, 10% PG/VG and chocolate fudge . (C) Images of HEK293T cells stained with calcein-AM after overnight incubation with vehicle, 10% PG/VG + media, 1% banana pudding-, candy corn-, chocolate fudge-, or popcorn-flavored e-liquids. (D) Quantification of calcein-AM fluorescence (i.e., viability) expressed as mean ± SEM. All n = 3. The positive control (10% PG/VG + media) used the same PG/VG ratio as the e-liquids. (E) Heat map depicting Δ growth (%) and live-cell fluorescence (%). Growth control and PG/VG controls are shown for reference. E-liquids are grouped in 3 distinct categories from the clustering: 1 (red), e-liquids that showed low Δ growth and live-cell fluorescence % (0%–40%); 2 (yellow), e-liquids that showed moderate (40%–100%) Δ growth and low live-cell fluorescence % (0%–40%); and 3 (green), e-liquids that showed high Δ growth and live-cell fluorescence % (80%–100%). (F) E-liquids were grouped according to Δ growth and live-cell fluorescence. Numbers represent number of e-liquids in a category. Raw data are available in S1 Data . HEK293T, human embryonic kidney 293 cells; PG, propylene glycol; VG, vegetable glycerin.

We initially designed 2 screens to assess cellular toxicity. The first method consisted of quantifying cell surface area by thresholding automatically acquired bright-field images over time as an indicator of cell growth. Using this approach, we assessed the effects of 148 e-liquids and a PG/VG control (added at 1% and 10%, respectively) to human embryonic kidney 293 (HEK293T) cells cultured in 384-well plates. Cells were plated at a density of 5,000 per well and placed in an imaging plate reader for 8 h at 37 °C, 5% CO 2 . After addition of the vehicle control (100% media), cells exhibited normal, log-phase growth over 12 to 32 h and showed duplication of cell surface area, consistent with healthy cell growth ( Fig 1A and 1B ; S1 Data ). Addition of 10% 55:45 PG/VG in media significantly attenuated cell growth, which served as a negative control in subsequent studies. Fig 1A ( S1 Data ) depicts representative images from cells exposed to different e-liquids (Popcorn, 88% Δ growth; Candy Corn, 86% Δ growth; Banana Pudding, 18% Δ growth; Chocolate Fudge, 14% Δ growth), as well as phosphate-buffered saline (PBS) and PG/VG controls. We classified the complete growth curves for these e-liquids as normal, reduced, no growth, and toxic ( Fig 1B ; S1 Data ). The second approach used to assess toxicity of e-liquids was to fluorescently measure the number of live cells using calcein-AM ( Fig 1C ; S1 Data ). Using this approach, we detected significant attenuation of viability (i.e., decreases in calcein fluorescence) after 24 h (see Fig 1D for representative examples and Fig 1E and 1F for summary data; S1 Data ). We then performed hierarchical clustering on all e-liquids tested, taking into account both Δ growth and live-cell fluorescence ( Fig 1E ; S1 Data ). Using complete agglomerative hierarchical clustering, e-liquids could be separated into 3 relevant categories: (i) red, e-liquids that showed low Δ growth and low live-cell fluorescence; (ii) yellow, e-liquids that showed moderate Δ growth and low live-cell fluorescence; and (iii) green, e-liquids that showed higher Δ growth and high live-cell fluorescence. Because we could discern distinct trends based on the clustering methods, we then compared e-liquids according to their growth rates and viability, and we found that the fluorescence assay—which meets these criteria—was more sensitive than the cell growth density ( Fig 1F ; S1 Data ). The coefficient of variation for this method, which indicates the variation of a standard measurement throughout a 384-well plate, was below 15% (≤20% is considered satisfactory). In addition, the signal-to-background ratio was 3.47, indicating a significant degree of separation between them. Finally, we calculated the Z’ score to quantify the suitability of this assay for use in high-throughput screens and found it to be 0.84. An assay with a Z’ score between 0.5 and 1.0 is considered an excellent assay because the separation between the positive and negative controls, relative to the variability, is significant [ 25 ].

Discussion

Fluorescent-based HTS techniques have been used for decades for drug discovery screens, toxicity, and for genetic screens, and their adoption by the scientific community has increased following the development of better fluorescent dyes and better instrumentation both in the pharmaceutical industry and academia [32, 33]. Despite the different endpoints, most HTS have common phases, including the initial phase (sometimes called the primary screen), which sets to capture as much preliminary data as possible—including false positives, which are subsequently confirmed or refuted. This key step allows for the inclusion of all possible prospective “hits” and minimization of lead loss. This is followed by the main (or secondary) screen in which extensive data are collected. Finally, validating (i.e., tertiary) screens are performed to confirm the main dataset. In our study, we performed the initial screen on 148 e-liquids to determine the optimum duration of the screen, find appropriate positive and negative controls, and to determine the most sensitive endpoint (Figs 1 and 2; S1 and S2 Data). Our colleagues have found that physiologic endpoints tend to be more sensitive than gross toxicological ones [34]. However, we chose assays that could be applied to most cells types, were readily available, and relatively easy to measure. We found that the viability assay, which meets these criteria, was more sensitive than the cell growth density (Fig 1F; S1 Data). That is, more e-liquids showed some alteration using the viability assay than the cell growth assay (126 e-liquids showed a decrease in viability, while 91 e-liquids showed a decrease in growth).

We used HEK293T cells for this screen because they are a cell line that is amenable to HTS and one of the favorites in the screening field [35, 36]. We also validated our findings using pulmonary cell lines. Importantly, we demonstrated that the toxicity seen in HEK293T cells was reproduced in hASMC and hA549 cells and the e-liquids showed similar rank order of toxicity in all cell lines (Fig 4; S4 Data). Similarly, using a 96-well–plate approach, we previously demonstrated that cultured human airway epithelial 3 (CALU3) cells showed differences in viability to 13 e-liquids [30]. In this study, we used the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay that measures dye uptake by live cells. However, we found that the MTT assay, while sensitive, was less amenable to HTS because it required more wash steps, which can cause cells to detach and required more preparation time. However, this assay may be useful as a validation step. It is unlikely that PG/VG will ever reach 10% or 30% in the lung lumen during normal vaping. However, we wanted to perform a full-dose response in order to fully understand the upper toxic limit of PG/VG. Accordingly, we can now say that it does not induce cell death below 1%. Furthermore, we have previously measured the P O2 in physiologic solutions (i.e., airway surface liquid) containing mucus gels that have exceeded 20% solids [1]. Despite a large amount of mucus and an extremely high viscous component, P O2 was normal, suggesting that similar levels of PG/VG will not alter P O2 levels. Other groups have reported cytotoxicity of e-liquids, but their screening capacity was 40 e-liquids or less [24, 37, 38]. Separately, studies have reported chemical composition analysis of e-liquids, but again, the total number was low [6, 39]. Overall, we are the first group to have designed and implemented a robust, high-throughput technique that allows for the parallel screening of hundreds of e-liquids.

Several researchers have found evidence of chemical transformation (i.e., pyrolysis) after vaping [5, 6]. However, in agreement with our previous study [30], we did not find that vaping e-liquids changed their relative toxicity, with the exception of one flavor (Hot Cinnamon Candy; Fig 5A and 5D; S5 Data), suggesting that this phenomenon may be flavor dependent. In our experience, vaping is more variable and less amenable to HTS. However, we vaped HEK293T cells, primary HBECs, and primary human macrophages. We found that the toxicity that occurred after vaping HEK293T cells and HBECs compared very well to the neat e-liquid addition (Fig 5D and 5F; S5 Data). Because the HBECs were well differentiated and exposed only apically under air–liquid interface conditions, this is perhaps the most realistic of conditions (Fig 5B and 5F; S5 Data). Similarly, after neat e-liquid exposure, hASM relative toxicities also correlated well with the HEK293T data (Fig 4; S4 Data). Surprisingly, the LC 50 did not correlate at all with the vaping of macrophages (Fig 5E; S5 Data). Therefore, while we do not appear to observe a general increase in e-liquid toxicity after vaping as compared to liquid addition, the HTS data may not be representative of all pulmonary cell types. The actual deposition fraction of vaped e-liquids into the lungs remains to be determined. However, the predicted deposition of E-cig aerosol in the lungs is approximately 25% [40]. Therefore, if 1 ml of e-liquid is aerosolized and inhaled—and assuming a total airway surface liquid volume in the lung of approximately 3 ml—this would lead to 0.25 ml being deposited, suggesting a dilution factor of 1:12, or approximately 8%. Given that e-liquids have a LC 50 of approximately 6% or less, this would suggest that e-liquids may reach biologically relevant levels in the lung. Indeed, it has recently been reported that vaping significantly alters the secreted human airway proteome, suggesting that this may be the case [41].

We are still understanding the relative toxicity of e-liquid constituents and their implications for airway exposure. PG is a common chemical used to produce polyester and as deicer/antifreeze, as well as being a base constituent in e-liquids. Intravenous PG can cause acute renal and central nervous system (CNS) toxicity [42], and PG inhalation causes renal and liver toxicity [43]. PG has previously been shown to inhibit renal glucose transport and corneal Na+/K+ATPase activity [44, 45]. Beyond PG, VG, and nicotine, we previously found no chemical similarity in 13 e-liquid flavors [30]. Therefore, given their heterogeneous nature, the overall goal of this project was to screen a greater number of neat e-liquids to identify flavors and/or chemical constituents that are more toxic and would direct additional studies. We found a number of highly toxic e-liquids that should be prioritized for study (Fig 1; S1 Table; S1 Data). Furthermore, in addition to identifying vanillin as potentially highly toxic, we also identified—using this screen—3 e-liquids that contain diacetyl (2,3-butanedione), which causes bronchiolitis obliterans, and 5 e-liquids that contain 2,3-butanedione monooxime, which is a chemical diphosphatase that blocks ATP-sensitive K+ channels [46–51]. Interestingly, many flavors, e.g., benzaldehyde (almond) and cinnamaldehyde (cinnamon), are aldehydes that can form protein adducts [52]. Benzaldehyde was only detected in 4 e-liquids. Cinnamaldehyde was found in 8 e-liquids (including Cinnamon Roll, Hot Cinnamon Candies, and Root Beer) and has previously been shown to impair phagocytosis in macrophages [34]. Sherwood and Boitano [24] recently exposed airway epithelia to 7 chemical flavors and concluded that vanillin and a chocolate flavor (2,5-dimethypyrazine) had the biggest effect on their cells. We found that vanillin was present in 63 out of 148 e-liquids. However, 2,5-dimethypyrazine was not detected in any of our e-liquids. Vanillin activates transient receptor potential cation channel subfamily V member (TRPV) channels. TRPV channels are expressed in neurons and serve as nonselective cation channels that can increase cytoplasmic Ca2+ levels in epithelia [53]. Of note, prolonged increases in cytosolic Ca2+ can alter cell division rates and are indicative of apoptosis [54].

Using an NMDS approach, we found only a weak correlation between the presence of flavorings and toxicity (i.e., LC 50 ). Of the e-liquids shown in Fig 7A and 7B (S6 Data), all had a constant ratio of 55% PG to 45% VG, indicating that PG/VG did not influence the change in toxicity. Furthermore, most flavors analyzed in Fig 7A (S6 Data) had 12 mg/ml nicotine, suggesting that changes in nicotine levels also did not account for the variability. However, of the 148 e-liquids tested, we found approximately 123 different chemicals. Therefore, we may have to further expand our HTS dataset in order to obtain a better correlation between the presence/absence of e-liquid constituents and toxicity, assuming that if we expand the dataset from 148 to 500 or 1,000 e-liquids, the number of chemical constituents eventually levels off and does not increase proportionally. We also determined the concentrations of cinnamaldehyde, vanillin, and triacetin against known concentrations of these compounds (see Fig 6C). Using this approach, we found a positive correlation between vanillin (0.62) and cinnamaldehyde (0.75) but not triacetin (0.048), with concentration/toxicity (Fig 8; S7 Data), suggesting that other chemicals present in these e-liquids may also have influence. Therefore, while we now have a better appreciation of the range of toxicities of different e-liquids, further work will be required to fully understand which additional components influence toxicity. A limitation of the proposed studies is that the chemical of interest will need to be available for purchase or be synthesizable and purifiable in order to be quantified by GC-MS or similar techniques.

To further categorize and evaluate these data, we have developed a database (www.eliquidinfo.org). This website is publicly available, and in its current form, it is most likely to be useful to academic and government researchers. Through this portal, one can browse LC 50 values, search for different chemicals, and determine which e-liquids contain them. Given the diversity of e-liquid toxicity and composition, we have found this website extremely useful in choosing new e-liquids for future studies. For example, we now select some less toxic, intermediate, and more toxic e-liquids when starting new investigations rather than just study one e-liquid. As we complete additional rounds of HTS, we hope to grow this database to make it more applicable. However, this website may also serve to inform the general public as to the relative toxicity and heterogeneity of e-liquids.

Given that the rise in vaping popularity has vastly outstripped our knowledge of its potential health benefits versus potential adverse effects, such HTS approaches will allow us to rapidly screen the approximately 7,700 different e-liquids that are on the market [10]. Whether or not PG/VG and nicotine are less harmful than inhaled tobacco is highly contentious [55]. However, HTS approaches for both e-liquids and their chemical constituents still have an important role in helping to shape future legislation for e-liquids and vaping. This is becoming all the more important, especially as researchers from the tobacco industry are now making claims that vaping represents a reduced risk of exposure compared to tobacco smoking [55, 56]. Therefore, it is vital that academic and government laboratories independently test as many different classes of these e-liquids as possible using multiple approaches and use evidence-based research as the guide for regulation. For example, when low-tar cigarettes were introduced and producers claimed they were a safer alternative to traditional cigarettes, these claims were later refuted. Given the claims of some groups, including tobacco companies and the public perception that e-liquids are safer than tobacco products, such an approach to study e-liquid toxicity may serve as a roadmap to enable bodies such as the US FDA to properly regulate e-liquid manufacturing and sale.