Open Access

Electronic cigarettes: overview of chemical composition and exposure estimation

  • Jürgen Hahn1,
  • Yulia B Monakhova2, 3, 4,
  • Julia Hengen2,
  • Matthias Kohl-Himmelseher2,
  • Jörg Schüssler2,
  • Harald Hahn1,
  • Thomas Kuballa2 and
  • Dirk W Lachenmeier2Email author
Contributed equally
Tobacco Induced Diseases201412:23

DOI: 10.1186/s12971-014-0023-6

Received: 23 May 2014

Accepted: 13 November 2014

Published: 9 December 2014

Abstract

Background

Electronic cigarettes (e-cigarettes) are advertised to tobacco users as a tool to decrease cigarette consumption and to reduce toxic exposure associated with conventional tobacco smoking. Little is known about the compounds contained in such products, their exposure and long-term health effects.

Methods

NMR spectroscopy was used to ascertain the content of several constituents of e-cigarette liquids including nicotine, solvents and some bioactive flavour compounds. Risk assessment was based on probabilistic exposure estimation and comparison with toxicological thresholds using the margin of exposure (MOE) approach.

Results

In 54 samples of e-cigarette liquids, the average nicotine content was 11 mg/ml. Only 18 from 23 samples were confirmed as nicotine-free samples and in one e-cigarette liquid nicotine was not detected while being declared on the labelling. Major compounds of e-cigarette liquids include glycerol (average 37 g/100 g), propylene glycol (average 57 g/100 g) and ethylene glycol (average 10 g/100 g). Furthermore, 1,3-propanediol, thujone and ethyl vanillin were detected in some samples. The average exposure for daily users was estimated as 0.38 mg/kg bw/day for nicotine, 8.9 mg/kg bw/day for glycerol, 14.5 mg/kg bw/day for 1,2-propanediol, 2.1 mg/kg bw/day for ethylene glycol, and below 0.2 mg/kg bw/day for the other compounds. The MOE was below 0.1 for nicotine, but all other compounds did not reach MOE values below 100 except ethylene glycol and 1,2-propanediol.

Conclusions

NMR spectroscopy is a useful and rapid method to simultaneously detect several ingredients in e-cigarette liquids. From all compounds tested, only nicotine may reach exposures that fall into a high risk category with MOE <1. Therefore, e-cigarette liquid products should be subjected to regulatory control to ensure consistent nicotine delivery. Solvents with more favourable toxicological profiles should be used instead of ethylene glycol and 1,2-propanediol, which may fall into a risk category with MOE < 100.

Keywords

Electronic cigarettes Electronic nicotine delivery systems Nicotine Risk assessment

Background

An electronic cigarette (e-cigarette) is a part of an emerging class of electronic nicotine-delivery systems. These devices aerosolize nicotine (if contained) and produce a vapour that emulates that of tobacco cigarettes but purportedly has fewer traditional toxins than secondhand smoke [1]. The awareness about these products and availability of e-cigarettes on the Internet, including in Web searches, virtual user communities and online shops has increased dramatically in recent years [1],[2], however, only few studies have been dedicated so far to the qualitative composition and toxicological characterization of these products [3],[4].

The benefits and risks of electronic cigarette use are a subject of discussion among health organizations and researchers [5]. First, only limited studies have evaluated the effects of electronic cigarettes on human health. While e-cigarettes may have negligible influence on blood count indices, carbon monoxide exposure and heart rate [5]-[8], it was found that e-cigarettes may contain additional impurities in the liquids or vapour (e.g., polyaromatic hydrocarbons, aldehydes and acrolein), although at much lower concentrations than the ones found in normal cigarettes [9],[10]. Furthermore, recent research suggests that these products may contain unexpected toxins and/or may provide unreliable nicotine delivery [1]. Finally, few and controversial empirical studies exist to determine whether e-cigarettes have potential as smoking cessation products [5],[9],[11].

In general, electronic cigarettes often contain ingredients such as propylene glycol, glycerol, ethylene glycol and polyethylene glycol mixed with concentrated flavours, and optionally, a variable percentage of nicotine [10],[12]-[14]. Besides these major compounds, a number of other organic substances can be found in liquid formulated products and/or the vapour phase produced by an e-cigarette unit. These include tobacco specific nitrosoamines (TSNA) such as N-nitrosonicotine (NNN), N-nitrosoanabasine (NAB), N-nitrosoanabatine (NAT) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) [10],[12],[14] or tobacco-specific impurities such as cotinine, anabasine and myosmine [12],[13]. It was recently shown that e-cigarette liquids can be adulterated with pharmacologically active ingredients or their analogues such as rimonabant and amino-tadalafil [15].

Given the large increase in awareness and use of e-cigarettes and the unknown impact of their use on cigarette smoking behaviours and long-term health, development of analytical methodologies for these products is required. Previous research involved typically gas or liquid chromatography, combined with mass spectrometric detection for the analysis of such products, however, only a limited number of samples have been investigated in each particular study [13],[14],[16]-[20]. Considering high throughput including minimal sample preparation, fast spectra acquisition and processing, we hypothesized that direct nuclear magnetic resonance (NMR) spectroscopy might be applicable for a high throughput screening of e-cigarette liquids.

Some studies have found very inconsistent nicotine contents in some products regarding the labelling, i.e., e-cigarette liquids advertised as containing no nicotine may contain the substance and, in contrast, some products did not contain nicotine despite its presence being labelled [13],[15]. Therefore, our study was principally aimed to develop a reliable and simple analytical tool based on NMR spectroscopy to control the absence/presence of nicotine in e-cigarette liquids. However, as the scope of quantitative NMR lies beyond quantification of a single analyte in a given matrix [21],[22], we expanded our methodology also for the determination of other major compounds in e-cigarette liquids (solvents and flavour compounds). The procedure was then applied to analyse a large sample collection (n = 54) of e-cigarette liquids that have been bought over the Internet. Finally, the exposure of the consumer is estimated based on the analytical results and a toxicological assessment is provided.

Methods

Sampling and chemical analysis

A total of 54 e-cigarette liquid samples was analysed. This includes e-cigarette liquids (n = 20) submitted to the CVUA Karlsruhe for official medicines and tobacco control purposes in Baden-Württemberg, Germany. Furthermore, an internet-based market research was conducted to identify products offered to German consumers. To do this, we searched for the German terms “E-Zigaretten Liquids kaufen”, “E-Liquids Kaufen”, “E-Liquids Flash”, “E-Liquids Hanf”, “E-Liquids Marihuana” using the web platforms google.de, amazon.de and ebay.de. We also included search terms such as “E-Liquids Cialis”, “E-Liquids Vitamin“, because the German Federal Institute for Risk Assessment (BfR) recently found pharmacological active substances such as tadalafil, caffeine or vitamins in these products [23]. In total, 13 on-line shops were selected, that are selling such liquids. Most of the shops (n = 8) were located in Germany, one was located in Spain, three were located in the United Kingdom and one was located in Romania. Based on the invoices, it seems that several shops, though being located in countries outside Germany, had local partners, who handled the shipping and returning of the good, if necessary. The liquids were sent to us in envelopes or parcels. Most of the products were labelled as “Liquids for E-Cigarettes” or simply “E-Liquid”, but in some cases the products did not contain any labelling and in one case the products were labelled as “Air freshener”.

From the identified on-line shops, we selected samples using a risk-oriented approach. For example, we preferentially obtained samples where the presence of bioactive flavour compounds could be assumed. Additionally, several samples suspected of containing illegal or unusual substances were bought, such as products labelled as “Marihuana-Flavour”, “Mary Jane Flavour”, “Vitamin-Mix” or “Multi Vitamin”. Furthermore, several samples with tobacco and beverage flavour (such as cola, wine, energy drink or absinthe flavour) were included. We tried to obtain all varieties of e-cigarette liquids regarding declared nicotine content. We obtained several samples, claimed as nicotine-free and samples with medium or high nicotine content (the labelled nicotine content varied between 6 and 54 mg/ml).

NMR analysis of electronic cigarette liquids

The chemical analysis was conducted using NMR spectroscopy based on a procedure previously developed for analysis of alcoholic beverages [24].

All solvents and reagents used were in pro analysis quality: nicotine, menthol, safrole (Sigma Aldrich, Steinheim, Germany), propylene glycol, 1,3-butanediol, 1,3-propanediol, ethylene glycol, glycerol, ethyl vanillin, camphor, α-thujone, coumarin, diethyleneglycol (Fluka, Buchs, Switzerland).

To obtain full quantitative information about e-cigarette liquids composition, we decided to apply two separate sample preparations for each sample. To measure water-soluble compounds (including nicotine), 60 μL of a sample was mixed with 480 μL of distilled water and 60 μL of NMR buffer (pH 7.4; 1.5 M KH2PO4 in D2O, 0.1 % 3-(trimethylsilyl)-propionate acid-d4 (TSP), 3 mM NaN3). Next, to get an overview of lipophilic substances, the following sample preparation was used: 100 μL of a sample was mixed with 800 μL CDCl3 containing 0.1% tetramethylsilane (TMS). After filtration (when necessary), 600 μL of the solution was poured into an NMR tube for direct measurement in both cases.

Stock standard solutions were prepared at a final concentration of about 10,000 mg/L in distilled water (nicotine bitartrate, propylene glycol, 1,3-butanediol, 1,3-propanediol, ethylene glycol, diethylene glycol and glycerol) or in deuterated chloroform (menthol, ethyl vanillin, coumarin, camphor, safrole and α-thujone). Calibration solutions were prepared by diluting the standard solutions in water or in deuterated chloroform and were measured as the authentic samples. The calibration curves were evaluated by integrating specific resonances of the selected compounds against TSP (in water) or TMS (in CDCl3) as an intensity reference.

All NMR measurements were performed on a Bruker Avance 400 Ultrashield spectrometer (Bruker Biospin, Rheinstetten, Germany) equipped with a 5-mm SEI probe with Z-gradient coils, using a Bruker Automatic Sample Changer (B-ACS 120). All spectra were acquired at 300.0 K.

NMR spectra of the aqueous solutions were acquired using Bruker standard water suppression 1D noesygppr1d pulse sequence with 64 scans (NS) and 4 prior dummy scans (DS). The sweep width (SW) was 19.9914 ppm and the time domain of the FID was 65536 (65 k). For the spectra acquisition of chloroform-dissolved samples, the Bruker experiment zg30 was used. After application of 2 dummy scans (DS), 8 free induction decays (FIDs) (NS = 128) were collected into a time domain of 131072 (131 k) complex data points using a 20.5503 ppm spectral width (SW) and a receiver gain (RG) of 101.

The data were acquired automatically under the control of ICON-NMR (Bruker Biospin, Rheinstetten, Germany), requiring about 40 min per sample for two measurements. All NMR spectra were phased and baseline-corrected.

The spectra of samples were compared to the spectra of the standards. Separated peaks corresponding to each substance were identified and integrated against TSP/TMS as an intensity reference using TopSpin v. 3.1 (Bruker Biospin, Rheinstetten, Germany). NMR ranges used for identification/integration for all compounds are listed in Table 1. Furthermore, all samples were screened for a range of small molecular weight molecules such as formaldehyde, acetaldehyde and acrolein.
Table 1

Selected resonances, limits of detection (LOD) and limits of quantification (LOQ) for compounds in e-cigarette liquids

 

Substance

Solvent

NMR range used for integration [ppm]

LOD [mg/L]

LOQ [mg/L]

1

Nicotine

Water

8.68-8.60 (multiplet)

1.6

5.5

2

Propylene glycol (1,2-propanediol)

3.47-3.42 (multiplet)

2.1

6.9

3

1,3-Butanediol

1.75-1.70 (multiplet)

2.3

7.6

4

1,3-Propanediol

1.85-1.75 (multiplet)

0.96

3.2

5

Ethylene glycol

3.69-3.67 (singlet)

0.17

0.56

6

Diethylene glycol

3.78-3.73 (multiplet)

0.51

1.7

7

Glycerol

3.82-3.75 (multiplet)

2.6

8.7

8

Menthol

Chloroform

2.00-1.92 (multiplet)

12

40

9

Ethyl vanillin

9.81-9.83 (singlet)

1.0

3.4

10

Coumarin

7.80-7.65 (multiplet)

3.2

10

11

Camphor

2.40-2.30 (multiplet)

13

44

12

Safrole

6.80-6.60 (multiplet)

2.6

8.6

13

Thujone (sum of α- and β-diastereomers)

2.12-2.09 (singlet)

3.4

11

We conducted a detailed validation for the determination of nicotine in e-cigarette liquids. For this substance the limits of detection (LOD) and quantification (LOQ) were calculated from the residual standard deviation of the regression line [25]. To assess reproducibility, standard solutions as well as an e-cigarette liquid sample were analyzed several times daily (n = 5). The recovery rate was ascertained by adding nicotine standard solution at four different concentrations to a real sample.

For other substances, the LOD and LOQ values were determined as signals for which the signal-to-noise ratios (SNR) are 3 and 10, correspondingly. SNR were calculated using the Bruker sino routine implemented in the Topspin 3.1 software package (Bruker Biospin, Rheinstetten, Germany). The limits of the noise and signal regions were located near to each other and were set manually for each spectrum.

The linearity of the calibration curves was evaluated in the range covering the concentrations found in the investigated products.

Risk assessment

The methodology for comparative quantitative risk assessment was based on a previous study conducted for compounds in alcoholic beverages [26] with the exception that probabilistic exposure estimation was conducted [27]-[29].

The toxicological thresholds, preferably benchmark doses (BMD) or if unavailable no observed effect levels (NOEL), no observed adverse effect levels (NOAEL) or lowest observed adverse effect levels (LOAEL), for the selected substances were typically identified in monographs of national and international risk assessment bodies such as EFSA, OECD SIDS, JECFA, and ATSDR [30]-[35], and if unavailable – as in the case of thujone - taken from an own study [36].

The MOE approach was used for risk assessment [37],[38]. The MOE is defined as the ratio between the lower one-sided confidence limit of the BMD (BMDL) or NOEL/NOAEL/LOAEL and estimated human intake of the same compound.

The exposure was estimated for daily users of e-cigarettes based on the contents found in our chemical analysis. Similar to the approach of Medeiros Vinci et al. [39] for probabilistic human exposure assessment of food contaminants, best fit distributions were applied to the substance contents and the resulting risk functions were entered into the probabilistic analysis. Further assumptions were literature data about typical e-cigarette liquid use per day [40] and literature data about vaporization percentage [19]. The bodyweight was assessed as normal distribution with average of 73.9 kg and standard deviation of 12 kg for males and females [41]. All risk functions were truncated at zero because negative values are factually impossible. Monte Carlo simulations were performed with 10,000 iterations using Latin Hypercube sampling and Mersenne Twister random number generator. Calculations were performed using the software package @Risk for Excel Version 5.5.0 (Palisade Corporation, Ithaca, NY, USA).

Results

NMR method development and validation results

Figure 1 shows the complete 1H NMR spectrum of a sample measured in aqueous solution of an e-cigarette liquid sample as well as a magnification of the δ 10–6.0 ppm region, where the resonances of aromatic protons (for instance, from nicotine and ethyl vanillin) can be observed. The mid-field region of NMR spectra contains information about major solvents such as glycerol, propylene glycol and ethylene glycol. NMR spectra of chloroform-dissolved samples have the same principal signals, however, some additional non-polar compounds (e.g. thujone, camphor) can be seen.
Figure 1

1 H NMR spectrum of the aqueous sample of a typical e-cigarette liquid sample. The insert shows 1H NMR spectra in the 10–6 ppm region.

The NMR ranges (i.e. peaks not overlapped or interfered by matrix) selected for quantification are given in Table 1. For example, nicotine, the principal compound of interest in our research, has signals at δ 8.65 ppm (m), δ 8.07 ppm (m), δ 7.60 ppm (m), δ 4.39 ppm (m), δ 4.32 ppm (s), δ 3.80 ppm (m), δ 3.30 ppm (m), δ 2.73 ppm (s), δ 2.68 ppm (m) and δ 2.38 ppm (m). However, the resonances in the aliphatic and mid-field ranges were found unsuitable for quantification because they showed strong overlap with other matrix compounds (Figure 1). Considering the signals in the aromatic region, we decided to use the multiplet at δ 8.65 ppm for quantification because this leads to the best sensitivity and this signal was not interfered in any case. As an example, Figure 2A shows the signal of nicotine in standard solutions and in four e-cigarette liquid samples (from these, two products contained 19.0 mg/ml and 15.8 mg/ml nicotine and the other two contained not detectable nicotine concentrations). For the other substances, signals not interfered by matrix were also identified (Table 1) and as example, spectra for glycerol, propylene glycol and ethylene glycol in standard solutions as well as in samples are presented in Figure 2B-D.
Figure 2

NMR resonances of nicotine (A), glycerol (B), propylene glycol (C), ethylene glycol (D) in standard solutions and e-cigarette liquid samples

Table 2 summarizes the NMR method validation results for nicotine. The 1H NMR assay was linear in a working concentration range of 5–10,000 mg/L (R = 0.9992), which means 0.050-100 mg/ml e-cigarette liquid considering sample preparation. This range basically covers the levels of nicotine in e-cigarette products and no further dilution/extraction of the matrix is required. The LOD and LOQ were 1.6 and 5.5 mg/L, correspondingly, which is equal to 0.016 and 0.055 mg/ml e-cigarette liquid. We propose to consider our LOD value as a cut-off level, at which we can distinguish nicotine-free and nicotine-containing products.
Table 2

Results of method validation for nicotine

Parameter

Result

Linear range

5-10,000 mg/L (0.050- 100 mg/ml sample)

LODa

1.6 mg/L (0.0157 mg/ml sample)

LOQa

5.5 mg/L (0.0546 mg/ml sample)

Recovery

99% (at 1000 mg/L)

104% (at 1500 mg/L)

115% (at 1750 mg/L)

108% (at 2000 mg/L)

107% (average)

Variation coefficient (n = 5)

Standard solution

1.2 %

E-cigarette liquid sample

2.1 %

aLimit of detection (LOD) and quantitation (LOQ) were determined by establishing a separate calibration curve near LOD (5.0-25 mg/L). The limits were calculated from the residual standard deviation of the regression line [25].

Furthermore, Table 1 contains LOD and LOQ values for all other compounds (except nicotine). The LOD values varied in the 0.17 - 13 mg/L range with the lowest values for glycols (for ethylene glycol 0.17 mg/L and for diethylene glycol 0.51 mg/L) and highest for camphor (13 mg/L) and menthol (12 mg/L) (Table 1). The LOQ values were in the range between 1.7 mg/L (ethylene glycol) and 40 mg/L (menthol). The high correlation coefficients (R > 0.99) obtained for the calibration graphs indicate a good linear response within the concentration range studied. From fifty four samples investigated only in five we were not able to directly quantify glycerol, ethylene glycol or propylene glycol due to spectral overlap.

The method was further validated by repeated sample preparation of a standard solution (1000 mg/L) and an e-cigarette liquid sample (nicotine content 19.0 mg/ml). Recoveries between 99% and 115% (average 107%) were obtained when the standard addition method is applied for an e-cigarette liquid sample. The variation coefficients were found to be 1.2% for the standard solution and 2.1% for the product. In general, the results of the method validation show that the method is sufficiently precise and reproducible and is adequate for the purpose of regulatory control of e-cigarette liquids (Table 1).

Moreover, reference HPLC analysis was performed for a subgroup of 15 samples (Table 3). Statistical analysis between results from the two methods (HPLC and NMR) revealed that the linear correlation is significant (ANOVA p < 0.0001, R = 0.98). No systematic or proportional differences between both methodologies were found, as the standard deviations of both the y-axis and the slope were encompassing 0 or 1. The results confirmed the accuracy of our NMR method and its comparability with HPLC.
Table 3

Comparison of nicotine quantification results between NMR and HPLC methods

Sample number

Nicotine content [mg/ml]

NMR

HPLC

1

19.0

17.9

2

15.8

17.2

3

17.7

16.3

4

21.0

17.6

5

n.d.

n.d.

6

n.d.

n.d.

7

n.d.

n.d.

8

n.d.

n.d.

9

n.d.

n.d.

10

13.7

13.9

11

23.5

22.5

12

14.3

17.0

13

24.3

22.9

14

14.1

17.5

15

18.7

17.5

Finally, we have also compared our NMR results with the reference GC-MS values for a set of 10 samples. It was found that the data of both methods are in good agreement with each other: the relative difference varied between 1.7% and 13% (average 8.1%) for 1,2-propanediol, between 2.8% and 7.3% (average 5.3%) for ethylene glycol and between 0.45% and 12% (average 7.5%) for glycerol.

Composition of e-cigarette liquids

The NMR methodology was used for analysis of 54 authentic liquids for use in e-cigarettes. The results of our investigations are summarized in Table 4. From the analysed 54 samples, 34 (63%) contained nicotine above the detection limit. The average and median concentrations of nicotine in all investigated samples were 11 and 6.8 mg/ml, correspondingly (Table 4). In general, the values were in agreement with labelling. However, from 23 samples that were declared as nicotine-free, only 18 were confirmed to contain undetectable nicotine concentrations by NMR. Nicotine was definitely detected in 5 allegedly “nicotine-free” samples in a concentration range from 0.11 mg/ml to 6.9 mg/ml. In contrast, one e-cigarette liquid sample did not contain nicotine, while its presence being declared on the labelling (12 mg/ml).
Table 4

Overview about constituents in electronic cigarettes with descriptive statistics and best fit distributions

Agent

Positive samples

Mean

Median

Standard deviation

Best fitting risk function for concentration of agent in the beveragea

Nicotine (mg/ml)

65%

11

6.8

13

RiskNormal(11.023;13.134;RiskTruncate(0;))

Glycerol (g/100 g)

94%

37

35

23

RiskWeibull(1.8104;44.812;RiskShift(−2.8327);RiskTruncate(0;))

1,2-Propanediol (g/100 g)

94%

57

64

30

RiskTriang(−18.939;91.8;100.45;RiskTruncate(0;))

Ethylene glycol (g/100 g)

91%

10

5

18

RiskLoglogistic(−0.40204;5.15;1.8215;RiskTruncate(0;))

1,3-Propanediol (g/100 g)

13%

0.6

0

1.7

RiskResample(2;[all measurements])

Thujone (mg/L)

4%

6.7

0

34

RiskResample(2;[all measurements])

Ethyl vanillin (mg/L)

26%

30

0

68

RiskResample(2;[all measurements])

aThe best fit distributions were selected based on Kolmogorov-Smirnov statistics. For 1,3-propanediol, thujone and ethyl vanillin, distribution fitting was not possible due to the low incidence. Random resampling is used for these agents from a data table with all measurements where all samples with not detectable concentrations were treated as zero.

Glycerol and propylene glycol were detected in all samples at concentrations ranging from 0.3 to 95 g/100 g (average 37 g/100 g) for glycerol and from 0.4 to 98 g/100 g (average 57 g/100 g) for propylene glycol. Generally, lower levels of another solvent ethylene glycol (average 10 g/100 g) were detected. 1,3-Propanediol was detected only in 7 samples in the concentration range of 3.3-10 g/100 g. 1,3-Butanediol, diethylene glycol, formaldehyde, acrolein, coumarin, camphor, safrole and menthol were negative in all samples.

The presence of the major compounds glycerol and propylene glycol corresponded to the labelling in the majority of cases, except 3 products contained no labelling information at all. Glycerol was not labelled on 5 products despite being present. Propylene glycol was not labelled in 2 products despite being present. In one case, “vegetal glycol” was labelled without specifying the exact chemical compound. Ethylene glycol was not labelled on any of the samples, which did contain the compound.

As for flavour compounds, which can be monitored in NMR spectra of chloroform-dissolved samples, we detected thujone (the sum of α-/β-diastereomers) in two samples (183 and 178 mg/L) and ethyl vanillin (concentration range 7.7-335 mg/L) in thirteen samples. We observed coumarin, camphor, safrole and menthol in none of the samples (the presence of menthol was not labelled on any of the samples, however). Interestingly, among other volatile aldehydic compounds (acetaldehyde, formaldehyde and acrolein) we observed only acetaldehyde in one sample at a concentration of about 38 mg/L. Only 4 products contained a detailed ingredients list with specific flavour compounds pointed out. Most of the products labelled only “flavour” or “natural and artificial flavours” without pointing out specific substances, or completely lacked any labelling.

Risk assessment for daily users of e-cigarettes

Nicotine, glycerol, 1,2-propanediol, ethylene glycol, 1,3-propanediol, thujone and ethyl vanillin were selected for exposure assessment. An overview of the concentrations of these compounds in e-cigarette liquids as well as the best fitting risk functions that were selected as input for probabilistic modelling are provided in Table 4. The toxicological thresholds are shown in Table 5[30]-[36],[42]-[48]. To provide a conservative assessment, the most sensitive toxicological endpoint was chosen, when several endpoints were available. Only for nicotine, human data were available as basis for the assessments. For the rest of the compounds, the assessments have to be based on animal data. The thresholds of the compounds vary over a very wide range, from 0.008 mg/kg bodyweight(bw)/day for nicotine to 10,000 mg/kg bw/day for glycerol.
Table 5

Toxicological thresholds selected for calculating the margin of exposure

Agent

Toxicological Endpointa

Value [mg/kg bw/day]

Type of endpointb

Reference

Nicotine

Heart rate acceleration in humans

0.008

LOAELc

EFSA [30] based on Lindgren et al. [42]

0.0008

ADI

Glycerol

2-year study in rats, no effects observed

10,000

NOAEL

OECD SIDS [31] based on Hine et al. [43]

1,2-Propanediol

2-year studies in rats and dogs, no effects observed in rats, increased erythrocyte destruction in dogs

2,500

NOAEL

JECFA [32] based on Gaunt et al. [44] and Weil et al. [45]

25

ADI

Ethylene glycol

Developmental toxicity data in mice (total malformations and a skeletal variation)

76

BMDL10

ATSDR [33] based on Neeper-Bradley et al. [46] and Tyl et al. [47]

0.8

MRL

1,3-Propanediol

Developmental toxicity study in rats

1,000

NOAEL

EFSA Panel on Contaminants in the Food Chain [34] based on unpublished data

Thujone

Clonic seizures in rats

11

BMDL10

Lachenmeier and Uebelacker [36] based on NTP [48]

0.11

ADI

Ethyl vanillin

13-week study in rats, no effects observed

500

NOEL

JECFA [35] based on unpublished data

3

ADI

aHuman data was preferred over animal data, if available. The most sensitive endpoint was chosen if dose–response data for several organ sites were available.

bBMDL10: lower one-sided confidence limit of the benchmark dose (BMD) for a 10% incidence of health effect. The No Effect Level (NOEL) or No Observed Adverse Effect Level (NOAEL) are used in cases when no usable BMD-modelling for oral exposure was identified in the literature. ADI: acceptable daily intake. MRL: minimal risk level.

cThe lowest observed adverse effect level (LOAEL) is considered by EFSA [30] as close to the NOAEL. The values were derived from a study in humans who were injected nicotine intravenously assuming an oral bioavailability of 44%.

Table 6 presents the probabilistic exposure estimates. In all cases, the highest exposure was detected for 1,2-propanediol (average 14.5 mg/kg bw/day), while the lowest was found for thujone (average 1.6E-04 mg/kg bw/day).
Table 6

Estimated exposure (mg/kg bw/day) of electronic cigarette users using Monte Carlo analysis (10,000 iterations) a

Agent

Mean

SD

Median

P5

P95

Nicotine

0.38

0.39

0.25

0.02

1.15

Glycerol

9.0

8.9

6.2

0.6

27.2

1,2-Propanediol

14.5

12.4

11.0

1.3

39.3

Ethylene glycol

2.1

6.7

0.9

0.1

7.1

1,3-Propanediol

0.14

0.51

0

0

1.1

Thujone

1.6E-4

9.7E-4

0

0

Max 0.01

Ethyl vanillin

7.2E-4

2.0E-3

0

0

Max. 0.03

aCalculated for all agents using the following formula with the risk functions defined in Table 4:

Exposure = Risk function of concentration * Risk function of e-cigarette liquid amount per day * Risk function of vaporization percentage / Risk function of bodyweight (kg).

The risk function of e-cigarette liquid amount per day was RiskNormalAlt(25%;3;75%;5;RiskTruncate(0;)) based on values from Farsalinos et al. [40]

The risk function of vaporization percentage was RiskUniform(6;81) based on values from Goniewicz et al. [19].

The risk function of bodyweight was RiskNormal(73.9;12) according to average and standard deviation from EFSA Scientific Committee [41].

The analysis also includes a sensitivity analysis, which allows a ranking of the input distributions which impact the exposure. In all cases, the concentration of the compound in the liquid had the highest influence, followed by vaporization percentage, e-cigarette liquid amount per day and a minor influence of bodyweight (regression coefficients for concentration ranging between 0.58 and 0.87, for vaporization percentage between 0.08 and 0.50, for e-cigarette liquid amount per day between 0.04 and 0.43 and for bodyweight between −0.02 and −0.17).

Finally, the margins of exposure (MOE) for all compounds are compared in Figure 3. Nicotine is the only compound, for which the complete distribution is below a MOE of 10, and on average below 0.1. From all other compounds, only ethylene glycol may reach MOEs below 100 in about 50% of cases and 1,2-propanediol in worst cases (above the 75th percentile).
Figure 3

Margin of Exposure (MOE) for compounds occurring in electronic cigarettes based on probabilistic exposure estimation (simulation with 10,000 iterations). (The box is determined by the 25th and 75th percentiles. The whiskers are determined by the 5th and 95th percentiles. 1st and 99th percentiles are marked by x, while minimum and maximum are marked with dash. Values above 1E7 are not shown).

Discussion

Chemical analysis of e-cigarette liquids

Regarding nicotine quantification in e-cigarette liquids, several analytical methodologies are available, including HPLC-UV [12],[13],[17],[49], headspace GC-MS [12], GC-FID [50], and GC-TID (Thermionic specific detector) [19]. As expected, our NMR method with a limit of detection (LOD) of 1.6 mg/L is not as sensitive as these methods (for example, the LOD of HPLC was about 0.1 mg/L [13]), however it is sufficient to control the nicotine content in nicotine-containing e-cigarette liquids (Table 1).

For the identification of the major ingredients (glycols and glycerol) and their relative concentrations, gas chromatography with flame ionization detector (GC-FID) or with mass spectrometry (MS) is usually used [50],[51]. These methods provide adequate sensitivity, but have the disadvantage of being very laborious and time-consuming. Moreover, some authors noted a significant matrix effect, which results in peak suppression of analytes [10],[14]. This matrix effect effectively limits the amount of propylene glycol and glycerol in the measurement solution and necessitates dilution of the samples with subsequent rise in LOD. For NMR, we found that a dilution factor of 10 is sufficient to obtain good phase- and baseline corrected spectra, which still provides sufficient sensitivity for all compounds in e-cigarette liquids. Thus, our results have shown that NMR is a good alternative for the control of nicotine and a number of other compounds in e-cigarette liquids and it avoids the use of two separate analytical techniques (HPLC for nicotine and GC for volatiles).

1H-NMR spectroscopy can therefore provide quantitative information necessary to judge about the composition of e-cigarette products in a short analysis period (about 20 minutes including sample preparation). First and foremost, we can provide very fast binary categorization, if an e-cigarette liquid sample contains nicotine or not (LOQ is about 0.050 mg/ml e-cigarette liquid) and, therefore, control the labelling. This is not only required for market control of the products but also important for consumer health protection as an e-cigarette user could be exposed to the hazard of nicotine dependence by purchasing a product, which while advertised as containing no nicotine does contain it. Moreover, all major compounds could be identified and quantified using the same spectral data. In case of spectral overlap, multivariate deconvolution methods can be supplementarily used [52].

Similar to our findings, other authors have also observed that the nicotine content of some e-cigarette liquids is often inconsistent with the labelling [13],[15].

Acetaldehyde, which we found in one sample, has been also recently detected in one e-cigarette liquid sample by Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) [10]. However, we think that it is currently not possible to estimate acetaldehyde exposure by e-cigarette consumption based on these single observations.

In our study we detected none of the tobacco-specific impurities and tobacco specific nitrosamines by NMR (data not shown). However, as these compounds are usually found in trace concentrations, the sensitivity of NMR is not sufficient to control the typical levels of these compounds in e-cigarette liquids [10]. For the analysis of tobacco-specific impurities and tobacco specific nitrosamines, hyphenated methods such as HPLC-MS/MS, GC-MS, headspace GC-MS or GC-MS/MS methods have to be used [10],[12],[14],[16],[49]. Even applying these methods, concentrations were usually found to be below LOQ levels [10],[12]. Tobacco-specific toxicants and trace nicotine impurities were judged as being below levels likely to cause harm [17],[20].

For our risk assessment, we have therefore focused on the constituents with major occurrence in e-cigarette liquids (nicotine, glycerol, 1,2-propanediol, and ethylene glycol). 1,3-Propanediol, thujone and ethyl vanillin were included as compounds of minor occurrence, but which could be relevant for consumers that prefer single brands of products in light of a worst-case scenario.

The risk of compounds in e-cigarette liquids

Exposure estimates may have considerable uncertainty especially in the case of non-normal distributions, as in our case of constituents in e-cigarette liquid. For this reason, we decided to apply a probabilistic method, which takes account of every possible value that each variable can take and weight each possible scenario by the probability of its occurrence [53]. To facilitate this, we use the Monte-Carlo approach, which has been previously applied in food science to model dietary exposure to chemicals in food [39],[53],[54], but this study is the first to apply it to estimate the exposure to chemicals in tobacco related products. The advantage of the approach is that rather than single values for each scenario it generates distributions of the MOE, which allow a direct visualization and comparison of all scenarios (Figure 3). In line of a worst case scenario, we assume that the vaporized percentage of the liquid may have a 100% bioavailability either orally or inhalatory. As toxicological thresholds for inhalation exposure were unavailable, we use thresholds for oral exposure, which may be seen as a limitation of the approach.

According to the typical interpretation of MOEs derived from animal experiments (i.e. for all our compounds except nicotine), MOE < 10 is judged to pose “high risk”, while MOE < 100 are judged as “risk”. MOEs above 100 are often judged as acceptable because the value of 100 corresponds to the default 100-fold uncertainty factor, which has been historically used in regulatory toxicology. The factor of 100 is based on scientific judgement and represents the product of two separate 10-fold factors that allow for interspecies differences and human variability [38],[55]. When the toxicological endpoint is based on human data on not on animal experiments as for nicotine, MOEs above 10 would be judged acceptable and MOEs below 1 as “high risk”. Using this interpretation of MOE, our evaluation clearly shows that nicotine is by far the compound with the highest risk in e-cigarette liquids. The MOE values of nicotine are all below 10 and may reach down to below 0.1, which is the lowest level of all compounds under study (Figure 3). Nicotine exposure would also exceed the ADI as proposed by EFSA [30] for nicotine residues in food products. Both 1,2-propanediol and ethylene glycol may reach MOEs below 100 in some scenarios, which may be interpreted as falling into the “risk” category.

Daily users of conventional cigarettes may have acquired a very high tolerance to nicotine, so that our assessment may overestimate the risk of this user group. An overestimation of the risk of nicotine may also occur by the endpoint of heart rate acceleration, which was selected by EFSA in their risk assessment of nicotine in foods [30]. It may be questioned if heart rate acceleration is a suitable (adverse) endpoint for risk assessment. The acute risk of nicotine may have been generally overstated in the past [56]. On the other hand, the chronic risk of nicotine may not be adequately considered by the endpoint chosen by EFSA [30] as well. For example, some new in vitro evidence points to possible direct carcinogenic and genotoxic effects of nicotine [57]-[60]. Nevertheless, epidemiological evidence for such an effect of nicotine appears to be unavailable so far; and while being on the “high priority” list for evaluation by the WHO International Agency for Research on Cancer, nicotine has so far not been classified by the agency.

In consideration of the potential over- or underestimation of the effects of nicotine, we agree with EFSA [30] to apply the heart-rate acceleration as most sensitive human endpoint till better data become available. Our risk assessment provides evidence that non-tolerant users of e-cigarettes should be clearly warned against using liquids with higher nicotine contents. It should also be noted as limitation that the potential hazard of nicotine dependence, which may depend on the form and speed of nicotine delivery [61], currently cannot be considered due to lack of adequate dose–response data.

Average ethylene glycol exposure would also exceed the minimal risk level of ATSDR [33]. It should be also noted that ethylene glycol (in contrast to propylene glycol) is not included on the U.S. Food and Drug Administration list of compounds “generally recognized as safe” (GRAS) [62].

Nevertheless, we think that compared to nicotine, the risks of 1,2-propanediol and ethylene glycol appear to be minor. However, the difference is that nicotine is intentionally ingested while the consumers certainly believes that all other constituents of the liquids are without risk (especially considering the advertisement of e-cigarettes as risk-free alternatives to tobacco cigarettes). For this reason in a conservative assessment, prudent risk management could be to avoid or reduce these compounds in the liquids and switch the formulations to other solvents with more favourable toxicological profiles. The risk of the flavour compounds thujone and ethyl vanillin appears to be rather low (MOE >1000) and mitigation measures for toxicological reasons appear to be not required.

Conclusions

E-cigarettes are a class of products that has emerged to the mass market during the last decade and the regulatory status of which has been unclear in a borderline area between regulations for medicinal products, tobacco products or general product safety rules.

One possibility would have been to regulate e-cigarettes as medicinal product. Our research clearly shows that the exposure of nicotine may reach amounts that facilitate a pharmacological action in the user of the product in the sense of the European medicines directive [63]. However, there was a political decision against this option. Such products will be regulated in the context of a revised tobacco products directive in the European Union, which was formally approved by the European Parliament on 26 February 2014. The maximum nicotine threshold will be 20 mg/ml [64].

We fully agree with the intention of the regulation to ensure uniform within-device dosing (i.e. the same product should always be consistent in nicotine delivery, which may be achieved by quality control of the liquids as well as by ensuring consistent nicotine delivery from the e-cigarette device) [65]. Our study has shown that deviations from the labelling in certain products would demand quality control procedures by official authorities similar to other consumer products such as alcohol and tobacco cigarettes. Liquids with high nicotine levels may pose risks especially for non-tolerant users (i.e. users that have not smoked before). Furthermore, the vaporization percentage of the device has an important influence on the exposure, so that the dosing in the device itself must be controlled for consistency.

Notes

Declarations

Acknowledgments

The authors warmly thank M. Böhm, B. Siebler and J. Geisser for excellent technical assistance. YBM acknowledges funding in the framework of the state contract 4.1708.2014 K of the Russian Ministry of Education. The other authors did not receive external funding. The research and preparation of the manuscript was funded exclusively by own funds of the CVUA Sigmaringen and the CVUA Karlsruhe.

Authors’ Affiliations

(1)
Chemisches und Veterinäruntersuchungsamt (CVUA) Sigmaringen
(2)
Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe
(3)
Institute of Chemistry, Saratov State University
(4)
Bruker Biospin GmbH, Silberstreifen

References

  1. Yamin CK, Bitton A, Bates DW: E-cigarettes: a rapidly growing internet phenomenon. Ann Intern Med. 2010, 153: 607-609. 10.7326/0003-4819-153-9-201011020-00011.View ArticlePubMedGoogle Scholar
  2. Regan AK, Promoff G, Dube SR, Arrazola R: Electronic nicotine delivery systems: adult use and awareness of the ‘e-cigarette’ in the USA. Tob Control. 2011, 22: 19-23. 10.1136/tobaccocontrol-2011-050044.View ArticlePubMedGoogle Scholar
  3. Farsalinos KE, Romagna G, Allifranchini E, Ripamonti E, Bocchietto E, Todeschi S, Tsiapras D, Kyrzopoulos S, Voudris V: Comparison of the cytotoxic potential of cigarette smoke and electronic cigarette vapour extract on cultured myocardial cells. Int J Environ Res Public Health. 2013, 10: 5146-5162. 10.3390/ijerph10105146.PubMed CentralView ArticlePubMedGoogle Scholar
  4. Romagna G, Allifranchini E, Bocchietto E, Todeschi S, Esposito M, Farsalinos KE: Cytotoxicity evaluation of electronic cigarette vapor extract on cultured mammalian fibroblasts (ClearStream-LIFE): comparison with tobacco cigarette smoke extract. Inhal Toxicol. 2013, 25: 354-361. 10.3109/08958378.2013.793439.View ArticlePubMedGoogle Scholar
  5. Odum LE, O’Dell KA, Schepers JS: Electronic cigarettes: do they have a role in smoking cessation?. J Pharm Pract. 2012, 25: 611-614. 10.1177/0897190012451909.View ArticlePubMedGoogle Scholar
  6. Vansickel AR, Cobb CO, Weaver MF, Eissenberg TE: A clinical laboratory model for evaluating the acute effects of electronic “cigarettes”: nicotine delivery profile and cardiovascular and subjective effects. Cancer Epidemol Biomarkers Prev. 2010, 19: 1945-1953. 10.1158/1055-9965.EPI-10-0288.View ArticleGoogle Scholar
  7. Flouris AD, Poulianiti KP, Chorti MS, Jamurtas AZ, Kouretas D, Owolabi EO, Tzatzarakis MN, Tsatsakis AM, Koutedakis Y: Acute effects of electronic and tobacco cigarette smoking on complete blood count. Food Chem Toxicol. 2012, 50: 3600-3603. 10.1016/j.fct.2012.07.025.View ArticlePubMedGoogle Scholar
  8. Trtchounian A, Williams M, Talbot P: Conventional and electronic cigarettes (e-cigarettes) have different smoking characteristics. Nicotine Tob Res. 2013, 12: 905-912. 10.1093/ntr/ntq114.View ArticleGoogle Scholar
  9. Foulds J, Veldheer S, Berg A: Electronic cigarettes (e-cigs): views of aficionados and clinical/public health perspectives. Int J Clin Pract. 2011, 65: 1037-1042. 10.1111/j.1742-1241.2011.02751.x.View ArticlePubMedGoogle Scholar
  10. Laugesen M: Safety Report on the Ruyan E-cigarette Cartridge and Inhaled Aerosol. 2008, Health New Zealand Ltd, Christchurch, New ZealandGoogle Scholar
  11. Polosa R, Caponnetto P, Morjaria JB, Papale G, Campagna D, Russo C: Effect of an electronic nicotine delivery device (e-Cigarette) on smoking reduction and cessation: a prospective 6-month pilot study. BMC Public Health. 2011, 11: 786-10.1186/1471-2458-11-786.PubMed CentralView ArticlePubMedGoogle Scholar
  12. Lindsay JC: Technical Review and Analysis of FDA Report: “Evaluation of E-cigarettes”. 2009, Exponent Health Sciences, Houston, TX, USAGoogle Scholar
  13. Trehy ML, Ye W, Handwiger ME, Moore TW, Allgire JF, Woodruff JT, Ahadi SS, Black JC, Westenberger BJ: Analysis of electronic cigarette cartridges, refill solutions, and smoke for nicotine and nicotine related impurities. J Liq Chrom Rel Technol. 2011, 34: 1442-1458. 10.1080/10826076.2011.572213.View ArticleGoogle Scholar
  14. Thomas B: Njoy Study to Determine Presence of TSNAs in Njoy Vapor. 2009, Ben Thomas Group , LLC, Houston, TX, USAGoogle Scholar
  15. Hadwiger ME, Trehy ML, Ye W, Moore T, Allgire J, Westenberger BJ: Identification of amino-tadalafil and rimonabant in electronic cigarette products using high pressure liquid chromatography with diode array and tandem mass spectrometric detection. J Chromatogr A. 2010, 1217: 7547-7555. 10.1016/j.chroma.2010.10.018.View ArticlePubMedGoogle Scholar
  16. Alliance Technologies: Characterization of Regal Cartridges for Electronic Cigarettes - Phase II. 2009, Alliance Technologies, Monmouth Junction, NJ, USAGoogle Scholar
  17. Etter JF, Zather E, Svensson S: Analysis of refill liquids for electronic cigarettes. Addiction. 2013, 108: 1671-1679. 10.1111/add.12235.View ArticlePubMedGoogle Scholar
  18. Goniewicz ML, Kuma T, Gawron M, Knysak J, Kosmider L: Nicotine levels in electronic cigarettes. Nicotine Tob Res. 2013, 15: 158-166. 10.1093/ntr/nts103.View ArticlePubMedGoogle Scholar
  19. Goniewicz ML, Hajek P, McRobbie H: Nicotine content of electronic cigarettes, its release in vapour and its consistency across batches: regulatory implications. Addiction. 2014, 109: 500-507. 10.1111/add.12410.View ArticlePubMedGoogle Scholar
  20. Goniewicz ML, Knysak J, Gawron M, Kosmider L, Sobczak A, Kurek J, Prokopowicz A, Jablonska-Czapla M, Rosik-Dulewska C, Havel C, Peyton IJ, Benowitz N: Levels of selected carcinogens and toxicants in vapour from electronic cigarettes. Tob Control. 2014, 23: 133-139. 10.1136/tobaccocontrol-2012-050859.View ArticlePubMedGoogle Scholar
  21. Monakhova YB, Kuballa T, Löbell-Behrends S, Maixner S, Kohl-Himmelseher M, Ruge W, Lachenmeier DW: Standardless 1H NMR determination of pharmacology active substances in dietary supplements and medicines that have been illegaly traded over the Internet. Drug Test Anal. 2012, 5: 400-411. 10.1002/dta.1367.View ArticlePubMedGoogle Scholar
  22. Maes P, Monakhova YB, Kuballa T, Reusch H, Lachenmeier DW: Qualitative and quantitative control of carbonated cola beverages using 1H NMR spectroscopy. J Agric Food Chem. 2012, 60: 2778-2784. 10.1021/jf204777m.PubMed CentralView ArticlePubMedGoogle Scholar
  23. BfR: Liquids from E-cigarettes can be Detrimental to Health. BfR Opinion No. 016/2012. 2012, Federal Institute for Risk Assessment, Berlin, GermanyGoogle Scholar
  24. Lachenmeier DW, Breaux TA, Kuballa T, Schlee C, Monakhova YB: Composition of distilled Perique tobacco liqueur: a connoisseur’s spirit or a health risk due to nicotine?. Food Chem. 2014, 159: 230-235. 10.1016/j.foodchem.2014.02.152.View ArticlePubMedGoogle Scholar
  25. German Institute for Standardization: DIN 32 645. Chemical Analysis - Decision Limit, Detection Limit and Determination Limit Under Repeatability Conditions - Terms, Methods, Evaluation. 2008, Beuth Verlag, Berlin, GermanyGoogle Scholar
  26. Lachenmeier DW, Przybylski MC, Rehm J: Comparative risk assessment of carcinogens in alcoholic beverages using the margin of exposure approach. Int J Cancer. 2012, 131: E995-E1003. 10.1002/ijc.27553.View ArticlePubMedGoogle Scholar
  27. Lachenmeier DW, Rehm J: Unrecorded alcohol - no worries besides ethanol: a population-based probabilistic risk assessment. Alcohol Policy in Europe: Evidence from AMPHORA. Edited by: Anderson P, Braddick F, Reynolds J, Gual A. 2013, Alcohol Measures for Public Health Research Alliance (AMPHORA), Barcelona, Spain, 118-130. 2Google Scholar
  28. Lachenmeier DW, Godelmann R, Witt B, Riedel K, Rehm J: Can resveratrol in wine protect against the carcinogenicity of ethanol? A probabilistic dose–response assessment. Int J Cancer. 2014, 134: 144-153. 10.1002/ijc.28336.View ArticlePubMedGoogle Scholar
  29. Lachenmeier DW, Wegert K, Kuballa T, Schneider R, Ruge W, Reusch H, Alexy U, Kersting M, Winkler G: Caffeine intake from beverages in German children, adolescents, and adults. J Caffeine Res. 2013, 3: 47-53. 10.1089/jcr.2013.0008.View ArticleGoogle Scholar
  30. EFSA: Potential risks for public health due to the presence of nicotine in wild mushrooms. EFSA J. 2009, RN-286: 1-47.Google Scholar
  31. OECD SIDS: Glycerol CAS No: 56-81-5. 2002, UNEP Publications, Nairobi, KenyaGoogle Scholar
  32. JECFA: 1,2-Propylene glycol. WHO Food Additives Series 5. Toxicological Evaluation of Some Food Additives Including Anticaking Agents, Antimicrobials, Antioxidants, Emulsifiers, and Thickening Agents. 1974, World Health Organization, Geneva, Switzerland, 275-277.Google Scholar
  33. ATSDR: Toxicological Profile for Ethylene glycol. 2010, U.S. Department of Health and Human Services. Public Health Service. Agency for Toxic Substances and Disease Registry, Atlanta, GeorgiaGoogle Scholar
  34. EFSA Panel on Contaminants in the Food Chain: Scientific Opinion on the evaluation of the substances currently on the list in the annex to Commission Directive 96/3/EC as acceptable previous cargoes for edible fats and oils - Part III of III. EFSA J. 2012, 10: 2984.Google Scholar
  35. JECFA: Ethyl vanillin. WHO Food Additives Series 35. Toxicological Evaluation of Certain Food Additives and Contaminants. 1996, World Health Organization, Geneva, Switzerland, 141-154.Google Scholar
  36. Lachenmeier DW, Uebelacker M: Risk assessment of thujone in foods and medicines containing sage and wormwood - evidence for a need of regulatory changes?. Regul Toxicol Pharmacol. 2010, 58: 437-443. 10.1016/j.yrtph.2010.08.012.View ArticlePubMedGoogle Scholar
  37. U.S.EPA: The Use of the Benchmark Dose Approach in Health Risk Assessment. EPA/630/R-94/007. 1995, Office of Research and Development. US Environmental Protection Agency, Washington, DCGoogle Scholar
  38. EFSA: Opinion of the Scientific Committee on a request from EFSA related to a harmonised approach for risk assessment of substances which are both genotoxic and carcinogenic. EFSA J. 2005, 282: 1-31.Google Scholar
  39. Medeiros Vinci R, Jacxsens L, Van Loco J, Matsiko E, Lachat C, de Schaetzen T, Canfyn M, Van Overmeire I, Kolsteren P, De Meulenaer B: Assessment of human exposure to benzene through foods from the Belgian market. Chemosphere. 2012, 88: 1001-1007. 10.1016/j.chemosphere.2012.03.044.View ArticlePubMedGoogle Scholar
  40. Farsalinos KE, Romagna G, Tsiapras D, Kyrzopoulos S, Voudris V: Evaluating nicotine levels selection and patterns of electronic cigarette use in a group of “vapers” who had achieved complete substitution of smoking. Subst Abuse. 2013, 7: 139-146.PubMed CentralPubMedGoogle Scholar
  41. EFSA Scientific Committee: Guidance on selected default values to be used by the EFSA Scientific Committee, Scientific Panels and Units in the absence of actual measured data. EFSA J. 2012, 10: 2579.Google Scholar
  42. Lindgren M, Molander L, Verbaan C, Lunell E, Rosen I: Electroencephalographic effects of intravenous nicotine–a dose–response study. Psychopharmacol (Berl). 1999, 145: 342-350. 10.1007/s002130051067.View ArticleGoogle Scholar
  43. Hine CH, Anderson HH, Moon HD, Dunlap MK, Morse MS: Comparative toxicity of synthetic and natural glycerin. AMA Arch Ind Hyg Occup Med. 1953, 7: 282-291.PubMedGoogle Scholar
  44. Gaunt IF, Carpanini FM, Grasso P, Lansdown AB: Long-term toxicity of propylene glycol in rats. Food Cosmet Toxicol. 1972, 10: 151-162. 10.1016/S0015-6264(72)80193-7.View ArticlePubMedGoogle Scholar
  45. Weil CS, Woodside MD, Smyth HF, Carpenter CP: Results of feeding propylene glycol in the diet to dogs for two years. Food Cosmet Toxicol. 1971, 9: 479-490. 10.1016/0015-6264(71)90078-2.View ArticlePubMedGoogle Scholar
  46. Neeper-Bradley TL, Tyl RW, Fisher LC, Kubena MF, Vrbanic MA, Losco PE: Determination of a no-observed-effect level for developmental toxicity of ethylene glycol administered by gavage to CD rats and CD-1 mice. Fundam Appl Toxicol. 1995, 27: 121-130. 10.1006/faat.1995.1115.View ArticlePubMedGoogle Scholar
  47. Tyl RW, Ballantyne B, France KA, Fisher LC, Klonne DR, Pritts IM: Evaluation of the developmental toxicity of ethylene glycol monohexyl ether vapor in Fischer 344 rats and New Zealand white rabbits. Fundam Appl Toxicol. 1989, 12: 269-280. 10.1016/0272-0590(89)90044-4.View ArticlePubMedGoogle Scholar
  48. NTP: Toxicology and carcinogenesis studies of a,b-thujone (Cas No. 76231-76-0) in F344/N rats and B6C3F1 mice (gavage studies). Natl Toxicol Program Tech Rep Ser. 2011, 570: 1-260.Google Scholar
  49. Westenberger BJ: Evaluation of E-cigarettes. DPATR-FY-09-23. 2009, FDA Department of Health & Human Services, St. Lous, MOGoogle Scholar
  50. Alliance Technologies: Chemical Composition of “Instead” Electronic Cigarette Smoke Juice and Vapour. 2009, Alliance Technologies, Monmouth Junction, NJ, USAGoogle Scholar
  51. Alliance Technologies: Characterization of Liquid “Smoke Juice” for Electronic Cigarettes. 2009, Alliance Technologies, Monmouth Junction, NJ, USAGoogle Scholar
  52. Monakhova YB, Tsikin AM, Kuballa T, Lachenmeier DW, Mushtakova SP: Independent component analysis (ICA) algorithms for improved spectral deconvolution of overlapped signals in 1H NMR analysis: application to foods and related products. Magn Reson Chem. 2014, 52: 231-240. 10.1002/mrc.4059.View ArticlePubMedGoogle Scholar
  53. Lambe J: The use of food consumption data in assessments of exposure to food chemicals including the application of probabilistic modelling. Proc Nutr Soc. 2002, 61: 11-18. 10.1079/PNS2001125.View ArticlePubMedGoogle Scholar
  54. Gibney MJ, van der Voet H: Introduction to the Monte Carlo project and the approach to the validation of probabilistic models of dietary exposure to selected food chemicals. Food Addit Contam. 2003, 20 (Suppl 1): S1-S7. 10.1080/0265203031000134947.View ArticlePubMedGoogle Scholar
  55. IPCS: Environmental Health Criteria 240: Principles and Methods for the Risk Assessment of Chemicals in Food. 2009, World Health Organization, GenevaGoogle Scholar
  56. Mayer B: How much nicotine kills a human? Tracing back the generally accepted lethal dose to dubious self-experiments in the nineteenth century. Arch Toxicol. 2014, 88: 5-7. 10.1007/s00204-013-1127-0.PubMed CentralView ArticlePubMedGoogle Scholar
  57. Ginzkey C, Kampfinger K, Friehs G, Kohler C, Hagen R, Richter E, Kleinsasser NH: Nicotine induces DNA damage in human salivary glands. Toxicol Lett. 2009, 184: 1-4. 10.1016/j.toxlet.2008.09.009.View ArticlePubMedGoogle Scholar
  58. Ginzkey C, Steussloff G, Koehler C, Hackenberg S, Richter E, Hagen R, Kleinsasser NH: Nicotine causes genotoxic damage but is not metabolized during long-term exposure of human nasal miniorgan cultures. Toxicol Lett. 2014, 229: 303-310. 10.1016/j.toxlet.2014.06.842.View ArticlePubMedGoogle Scholar
  59. Ginzkey C, Steussloff G, Koehler C, Burghartz M, Scherzed A, Hackenberg S, Hagen R, Kleinsasser NH: Nicotine derived genotoxic effects in human primary parotid gland cells as assessed in vitro by comet assay, cytokinesis-block micronucleus test and chromosome aberrations test. Toxicol In Vitro. 2014, 28: 838-846. 10.1016/j.tiv.2014.03.012.View ArticlePubMedGoogle Scholar
  60. Catassi A, Servent D, Paleari L, Cesario A, Russo P: Multiple roles of nicotine on cell proliferation and inhibition of apoptosis: implications on lung carcinogenesis. Mutat Res. 2008, 659: 221-231. 10.1016/j.mrrev.2008.04.002.View ArticlePubMedGoogle Scholar
  61. Schroeder MJ, Hoffman AC: Electronic cigarettes and nicotine clinical pharmacology. Tob Control. 2014, 23 (Suppl 2): ii30-ii35. 10.1136/tobaccocontrol-2013-051469.PubMed CentralView ArticlePubMedGoogle Scholar
  62. FDA: CFR - Code of Federal Regulations Title 21. Foods and Drugs. Chapter I. Subchapter B. Part 184 - Direct Food Substances Affirmed as Generally Recognized as Safe. 2013, U.S. Food and Drug Administration, Silver Spring, MD, USAGoogle Scholar
  63. Lachenmeier DW, Steffen C, El-Atma O, Maixner S, Löbell-Behrends S, Kohl-Himmelseher M: What is a food and what is a medicinal product in the European Union? Use of the benchmark dose (BMD) methodology to define a threshold for “pharmacological action”. Regul Toxicol Pharmacol. 2012, 64: 286-295. 10.1016/j.yrtph.2012.08.017.View ArticlePubMedGoogle Scholar
  64. European Commission: E-cigarettes myth buster. 2014 [http://ec.europa.eu/health/tobacco/docs/tobacco_mythbuster_en.pdf]
  65. European Commission: E-cigarettes. 2014 [http://ec.europa.eu/health/tobacco/docs/fs_ecigarettes_en.pdf]

Copyright

© Hahn et al.; licensee BioMed Central. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Advertisement