Metabolic effects of inhaled salbutamol determined by exhaled breath analysis

Abstract

We explore whether real-time breath analysis by high resolution mass spectrometry is suitable to monitor changes at the metabolic level due to inhaling bronchodilator medication. We compared the breath levels of metabolites in a group of patients (n = 50) at baseline and 10 and 30 min after inhalation of 200 μg salbutamol. The same procedure was performed with a group of controls (n = 48) inhaling a placebo spray. A total of 131 mass spectral features were significantly altered as a result of inhaling medication, but not after inhaling placebo. We found that homologous series of chemical classes correlated strongly with each other, strengthening the notion that certain biochemical processes can be monitored. For example, a series of fatty acids was found to be increased after salbutamol intake, suggesting lipolysis stimulation. Peaks corresponding to salbutamol, its main metabolite salbutamol-4-O-sulfate and formoterol were found to be generally increased in patients inhaling the drugs on an as-needed basis, as compared to non-medicated volunteers. Overall, these results suggest such real-time breath analysis is a useful tool for non-invasive therapeutic drug monitoring.

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Journal of Breath Research
PAPER • OPEN ACCESS
Metabolic effects of inhaled salbutamol determined by exhaled breath
analysis
To cite this article: Martin T Gaugg et al 2017 J. Breath Res. 11 046004
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J. Breath Res. 11 (2017)046004 https://doi.org/10.1088/1752-7163/aa7caa
PAPER
Metabolic effects of inhaled salbutamol determined by exhaled
breath analysis
Martin T Gaugg
1,4
, Anna Engler
2,4
, Yvonne Nussbaumer-Ochsner
2
, Lukas Bregy
1
, Anna S Stöberl
2
,
Thomas Gaisl
2
, Tobias Bruderer
1
, Renato Zenobi
1
, Malcolm Kohler
2
and Pablo Martinez-Lozano Sinues
1,3
1
Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology, Zurich, Switzerland
2
Sleep Disorders Centre and Pulmonary Division, University Hospital of Zurich, Zurich, Switzerland
3
University Children’s Hospital Basel, University of Basel (Switzerland)
4
Equal contribution to this work.
E-mail: pablo.mlsinues@ukbb.ch
Keywords: bronchodilator, metabolism, breath analysis
Supplementary material for this article is available online
Abstract
We explore whether real-time breath analysis by high resolution mass spectrometry is suitable to
monitor changes at the metabolic level due to inhaling bronchodilator medication. We compared the
breath levels of metabolites in a group of patients (n=50)at baseline and 10 and 30 min after
inhalation of 200 μg salbutamol. The same procedure was performed with a group of controls
(n=48)inhaling a placebo spray. A total of 131 mass spectral features were significantly altered as a
result of inhaling medication, but not after inhaling placebo. We found that homologous series of
chemical classes correlated strongly with each other, strengthening the notion that certain biochemical
processes can be monitored. For example, a series of fatty acids was found to be increased after
salbutamol intake, suggesting lipolysis stimulation. Peaks corresponding to salbutamol, its main
metabolite salbutamol-4-O-sulfate and formoterol were found to be generally increased in patients
inhaling the drugs on an as-needed basis, as compared to non-medicated volunteers. Overall, these
results suggest such real-time breath analysis is a useful tool for non-invasive therapeutic drug
monitoring.
Introduction
Inhaling drugs is an attractive administration route
due to rapid absorption into the systemic circulation
and low concentrations of drug-metabolizing enzymes
in the lungs compared with the oral route [1].In
addition, it provides rapid local effects in the lung,
minimizing systemic drug effects. For these reasons,
inhaled medications have been used for many years for
the treatment of respiratory diseases (i.e. lungs are the
target organs), as well as more recently to treat
systemic diseases (e.g. diabetes)[1,2]. During drug
development programs, optimal aerosol particle size is
sought, to target the optimal site of deposition.
Typically, clinical response (e.g. forced expiratory
volume in 1 s (FEV
1
)) is assessed upon drug inhalation.
Parallel determination of drug response at a molecular
level is of course needed to fully understand mechan-
isms of action. Metabolic profiling of body fluids is a
possible approach to quantifying physiological
response to inhaled medication [3,4]. For example, a
recent nuclear magnetic resonance-based serum and
urine metabolomics study has mapped the overall
metabolic changes after inhalation of budesonide and
salbutamol in asthmatic children during acute exacer-
bation, concluding that seven metabolic pathways
were altered as a result [5]. However, inhaled drugs
tend to act quickly (e.g. hydrophobic molecules
absorbed within seconds)[1], posing some challenges
to the capture of highly dynamic responses to plasma-
or urine-based metabolomics. In addition, sample
collection [6]and manipulation [7,8], as well as
subjecting metabolites to harsh processes (e.g. heat
leading to thermal degradation [9])may lead to biased
results. To overcome these issues, we propose that
in vivo, real-time analysis of exhaled breath metabo-
lites provides an attractive means to track response to
inhaled medication rapidly and non-invasively.
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REVISED
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ACCEPTED FOR PUBLICATION
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Selected ion flow tube- and proton transfer reaction-
mass spectrometry (SIFT-MS and PTR-MS)are two
real-time techniques that have been widely used in a
clinical context [10–12], although few examples can be
found in the literature for monitoring therapeutic
intervention [13–16].
To prove this novel concept, we used secondary
electrospray ionization [17]-high resolution mass
spectrometry (SESI-HRMS)in this work to study
changes in endogenous metabolites in response to
inhaled medication, in contrast to a group inhaling
placebo. By using a placebo inhaler identical to the
medication inhaler except for the active ingredient, we
ruled out confounding factors such as contamination
of the oral cavity or changes due to the inhalation
maneuver. We chose to study the effect of salbutamol,
which is a short-acting bronchodilator widely used in
the treatment of obstructive lung diseases such as
asthma and chronic obstructive pulmonary disease
(COPD)[18].
Methods
Subjects
A total of 98 participants were included in this study:
48 inhaled placebo and 50 inhaled salbutamol. Of the
48 subjects inhaling placebo, eight suffered from
asthma and 40 did not have any lung disease. Of the 50
patients inhaling salbutamol, 13 suffered from asthma
and 37 suffered from moderate to severe COPD. The
anthropometric data is shown in table 1. Participants
were asked to refrain from eating, drinking (except
water), chewing gum, smoking and brushing their
teeth at least 1 h prior to measurement. The study was
approved by the local ethical committee (PB_2016-
00421, KEK-ZH-Nr. 2015-0148, KEK-ZH-Nr. 2014-
0088)and all subjects gave written, informed consent
to participate.
Real-time breath measurements
Exhaled breath analysis was performed at baseline, 10
and 30 min after the inhalation of 200 μg salbutamol
(n=50)or placebo (n=48). The placebo contained
only norflurane. Figure 1(a)shows the timing used in
the study. Figure 1(b)displays a picture of the modified
mass spectrometer (Sciex Triple TOF 5600+)to allow
breath analysis by SESI-HRMS. The breath analysis
approach has been described in greater detail
elsewhere [19,20]. In brief, the atmospheric pressure
ion source of a quadrupole-time-of-flight mass
spectrometer was replaced by a lab-built SESI chamber
[21,22]. A nano-electrospray of 0.1% formic acid in
water was infused at ∼200 nl min
−1
through a silica
capillary (PicoTip emitter—O.D. 360 μm–I.D.
20 μm). The electrospray was operated in the cone-jet
mode [23,24]and its current (∼100 nA)constantly
monitored. In addition, a lens was used sporadically to
visually inspect the Taylor cone [25]. The breath
sampling tube connected to the SESI chamber con-
sisted of stainless steel (6 mm I.D.), and was heated to
80 °C to prevent water condensation and adsorption
of low-volatility metabolites onto the walls. In order to
avoid confounding factors such as differences in
exhalation maneuvers [26], the sampling protocol was
standardized across all participants. The subjects
would typically provide five–six replicate exhalations.
Full exhalations were provided at a constant flow rate
by keeping the pressure in the sampling line at 12 mbar
(regulated by bio-feedback by the breathing subjects).
Measurements were recorded in positive and negative
ion mode, leading to mainly protonated and deproto-
nated species, respectively. The mass range in positive
ion mode was 40–700 Da and in negative ion mode
40–450 Da. Accumulation time was set to 1 s.
Compound identification
Identification of 10-hydroxydecanoic acid was per-
formed using on-line MS/MS experiments as well as
UHPLC-MS/MS experiments of exhaled breath con-
densate (EBC)and a standard. EBC was collected using
a sampling device constructed in-house following the
guidelines set by the ATS/ERS task force [27]. Two
healthy, non-smoking subjects were asked to exhale
into a glass cold trap. The collected EBC samples were
pooled (V
total
=32 ml), 0.5 ml were extracted and
stored at 4 °C and the remainder lyophilized to dryness
[28]. The residue was then reconstituted in a mixture
of 142.5 μl of the previously aliquoted EBC, 7.5 μl
methanol and acidified with 0.1% formic acid. This
up-concentrated sample was directly subjected to
UHPLC-HRMS/MS analysis. UHPLC separation was
done on an ACQUITY UPLC system (Waters, MA,
USA)with a C18 column (1.7 μm, 2.1×50 mm,
Waters)with pre-column filter. The flow rate was set
to 500 μl min
−1
using a binary mixture of solvent A
(water with 0.1% formic acid)and solvent B (methanol
Table 1. Anthropometric data of the participants in this study.
Placebo (n=48)Salbutamol (n=50)
Mean age±SD (years)41.8±17.8 55.3±14.2
Male/Female 22/26 28/22
COPD(GOLD1-2/GOLD3-4)/Asthma/Healthy 0/8/40 37(8/29)/13/0
Mean FEV1 pre±SD (% pred)96.2±12.1 50.8±25.1
Mean FVC pre±SD (% pred)101.5±11.9 80.0±20.6
Smokers/Non-smokers/Ex-smokers 9/33/68/13/29
2
J. Breath Res. 11 (2017)046004 M T Gaugg et al

with 0.1% formic acid). The 10-hydroxydecanoic acid
standard (Apollo Scientific Ltd)was prepared at a
concentration of 100 ng ml
−1
and analyzed with an
injection volume of 10 μl. A Triple TOF 5600+(AB
Sciex, Concord, ON, Canada)mass spectrometer was
used to measure a mass range of 40–500 Da in negative
ion mode, with 1–5 ppm mass accuracy. Collision
energy was set at 40±30 eV.
Data analysis
Raw
*
.wiff (Sciex’s proprietary format)data was
converted into
*
.mzXML format via MSConvert
(Proteowizard)[29]. Each sample file was interpolated
linearly and centroided, generating as a result a m/z
list of 1208 peaks present in at least 40% of the samples.
The mass spectra were normalized dividing by the sum
of a set of 904 signals found to be present in 90% of our
samples. We then computed the log
2
of the fold change
(i.e. ratio between measurements after 10 min over
baseline and after 30 min over baseline). Significant
differences in exhaled metabolite levels in response to
salbutamol, but not to placebo inhalation were sought.
A paired two-tailed t-test followed by estimation of
false discovery rate (FDR)in multiple comparisons
was used for this purpose [30]. Statistical significance
was set to FDR<0.05. We further considered only
the signals changing significantly after 10 and 30 min
of salbutamol inhalation, but not changing signifi-
cantly after placebo inhalation. Among the significant
ones, we report here only those signals changing by at
least log
2
(fold change)>0.15 after 10 or 30 min of
inhaling salbutamol. The signals satisfying these con-
ditions were subjected to an agglomerative hierarchi-
cal cluster tree (Ward method; Euclidean distance).In
order to identify groups of signals showing similar
behavior, we constructed agglomerative clusters using
a cutoff Euclidean distance of 5. Finally, for the set of
molecules that were chemically identified, we com-
puted the correlation coefficients between pairs of
variables for the group inhaling salbutamol.
Results and discussion
Monitoring of exhaled compounds to adjust inhaled
medication dose to maximize efficacy and minimize
toxicity is an attractive approach. For example, this
concept has been implemented by measuring exhaled
NO in asthmatic patients to titrate treatment with
inhaled corticosteroids [31]. In the present work we
have explored whether comprehensive real-time
breath analysis can assist in a similar approach towards
a more personalized therapeutic regime.
Metabolites levels are altered after salbutamol
inhalation, but not after placebo
Subjects’breath was analyzed, detecting as a result
1208 features (866 and 342 features in positive and
negative mode, respectively)present in at least 40% of
the samples. A total of 131 mass spectral features were
found to be significantly altered in response to
salbutamol inhalation, but not to placebo inhalation.
Table S1, available online at stacks.iop.org/JBR/11/
046004/mmedia lists all of these along with their mean
changes, 95% confidence intervals, p-values and FDR.
Figure 2shows the histograms of the log
2
(fold change)
for the 131 significantly altered ions after 10 and
30 min of inhalation of salbutamol. The distributions
for the placebo group are fairly symmetrical around 0
(i.e. mean 10 min after placebo=0.0254; mean
30 min after placebo=0.0220). In contrast, the
salbutamol distributions are flatter, and their means
are displaced to the right (mean 10 min after salbuta-
mol=0.1458; mean 30 min after placebo=0.1963),
suggesting a general increase of these exhaled
compounds.
Among 131 altered signals, 121 increased, while 10
signals were reduced after salbutamol inhalation, but
did not change significantly in the placebo group.
Figure 3shows the breath-to-breath time trace for one
selected compound (m/z 259.1903; C
14
H
26
O
4
)for one
subject before and after inhalation of placebo (a)and
for a different subject inhaling salbutamol (b). The
five–six replicate measurements within each time
point show a satisfactory repeatability (4.8% average
Figure 1. (a)Study timing; (b)Modified high resolution mass spectrometer featuring a SESI ion source to allow for real-time analysis
of exhaled metabolites.
3
J. Breath Res. 11 (2017)046004 M T Gaugg et al

coefficient of variation). Clearly, the breath levels of
this compound for this particular patient increased
significantly shortly after salbutamol inhalation, and
this change could be easily captured by on-line breath
analysis. In contrast, the levels for the subject inhaling
just placebo did not vary significantly. The overall pic-
ture of the trends for this compound for the placebo
and medication groups is shown in figures 2(b)and
(c), respectively. Mean log
2
(fold change)for the pla-
cebo group after 10 and 30 min was 0.01 (95%
CI=−0.06/0.07; FDR=0.93)and −0.02 (95%
CI=−0.12/0.07; FDR=0.7), respectively. In con-
trast, mean log
2
(fold change)for the salbutamol group
after 10 and 30 min was 0.19 (95% CI=0.10/0.28;
FDR=4.84×10
−4
)and 0.26 (95% CI=0.12/0.4;
FDR=1.44×10
−3
), respectively. However, despite
the significant changes for this particular molecule
(molecular formula C
14
H
26
O
4
)in the salbutamol
group, there is clear inter-individual variability, with
some subjects experiencing no changes at all. This
seems to be in line with the known lack of response to
medication for large parts of the population. For
example, it has been estimated that a daunting 75%–
96% do not respond to the top-ten grossing drugs in
the market. Our data suggests that breath analysis has
potential to contribute to the identification of sub-
populations of responders and non-responders, at
least for certain drugs such as bronchodilators.
Metabolite associations and compound
identification
We attempted the chemical identification of some of
the exhaled metabolites, to provide a biochemical
interpretation of our results. First, we computed an
agglomerative hierarchical cluster tree (Ward method;
Euclidean distance)and subsequently constructed
clusters from the hierarchical cluster tree using as
cutoff a distance of 5. This resulted in 26 clusters of
signals behaving similarly. The resulting clusters are
listed in table S1. As a result, it became apparent that
homologous series of closely related molecular for-
mulae (and most likely compounds)tend to cluster
together. This is a first indication that this real-time
technique is capable of capturing metabolic cascades
in response to medication. For example, cluster
number 16 consisted of acetic, propionic and butyric
acids, which were in all cases found to be increased
after salbutamol inhalation. Similarly, heptanoic,
octanoic and nonanoic acids [21]clustered together
(number 22)and were found to increase after salbuta-
mol inhalation. Another interesting series of com-
pounds was cluster number 10. They all have similar
molecular formulae and were detected in negative ion
polarity. Compounds detected by SESI-MS operated
in negative ion mode are dominated by deprotonated
acids [21,32]; thus, we hypothesize that these com-
pounds are likely fatty acids. This was confirmed by
deploying a comprehensive analytical strategy.
Figure 4shows experimental evidence indicating that
the molecular formula C
10
H
20
O
3
corresponded to 10-
Hydroxydecanoic acid. Figure 4(a)shows the extracted
ion chromatograms at m/z187.1339 in negative ion
mode for EBC and the standard of 10-hydroxydeca-
noic acid. Figure 4(b)shows a head-to-tail MS/MS for
EBC and the standard. The perfect match in retention
time and fragmentation pattern provides strong evi-
dence for 10-hydroxydecanoic acid in exhaled breath
condensate. Figure 4(c)shows the fragmentation
spectrum at m/z 187.1339 during a real-time exhala-
tion. The diagnostic fragments of this compound were
indeed present, indicating that 10-hydroxydecanoic
acid is present in exhaled in real-time and detected by
Figure 2. Salbutamol leads to different distribution of some compounds in breath as compared to placebo. Histograms of the log
2
(fold
change)after 10 and 30 min for 131 breath signals found to be significantly altered in the salbutamol group (n=50), but not in the
placebo group (n=48).
4
J. Breath Res. 11 (2017)046004 M T Gaugg et al

Figure 3. Detection of rapid response to medication by breath analysis; exemplary raw time traces for one compound (C
14
H
26
O
4
)for a
subject before and after inhaling placebo (a)and for another patient inhaling medication (b). Note that in 3 min several replicate
exhalations were performed. Note also the significant increase of this molecule after inhalation of 200 μg of salbutamol, but not after
inhaling placebo; the same trend was observed for the whole population investigated. Relative changes for the same exhaled molecule
for the (c)placebo (n=48)and (d)salbutamol (n=50)groups.
Figure 4. Identification of 10-hydroxydecanoic acid. (a)Extracted ion chromatograms of 187.1340±0.002 Da of EBC and standard
(normalized to target peak apex);(b)head-to-tail plot of the MS
2
spectra (precursor selection: 187.1 Da)of EBC and standard; (c)on-
line MS
2
(precursor selection: 187.1 Da)breath spectrum. Due to the preselection window of ∼1 Da, multiple precursor ions are
selected, leading to a complex fragmentation spectrum. Nonetheless, thanks to the high mass resolution of the TOF-analyzer, it is
possible to identify the fragments corresponding to 10-hydroxydecanoic acid (indicated with inverted triangles).
5
J. Breath Res. 11 (2017)046004 M T Gaugg et al

SESI-MS. However, it is obvious that other fragments
are also present, because the isolation window of the
quadrupole is 1 Da, leading to the fragmentation of all
the species present at nominal mass 187 Da. It is also
interesting to note that different isomers of 10-
hydroxydecanoic acid were found in breath conden-
sate (figure 4(a)). For example, the most abundant
peak in the chromatogram at retention time
∼3.55 min, corresponds to 3-hydroxydecanoic acid
(Gaugg et al in preparation), which is an intermediate
in fatty acid biosynthesis. It is therefore very likely that
all different isomers are exhaled simultaneously.
To better understand the interactions of 10-
hydroxydecanoic acid with the rest of the compounds
in the same cluster we computed their correlations (r)
for the salbutamol group and plotted the correlation
network. Figure 5(a)shows the results (only r>0.65
are shown). Overall, we found strong positive correla-
tions among the different nodes (i.e. molecules). The
strongest correlation (r=0.86)was between
C
10
H
18
O
4
and C
10
H
16
O
4
. The former of these is
Figure 5. Exhaled compounds with similar molecular formulae correlate with each other after exposure to salbutamol. (a)Fatty acids
correlation network after salbutamol inhalation. Each node represents one breath compound (molecular formula shown), and the
value on each bond is the correlation coefficient. The connection width is proportional to the correlation coefficient (i.e. the thicker
the line, the stronger the correlation). Only significant correlations are shown (p<0.001 and r>0.65). Note the association between
hydroxydecanoic acid and C
10
H
18
O
4
,C
9
H
14
O
4
and C
10
H
18
O
3
.(b)Correlation between hydroxydecanoic acid and C
10
H
18
O
4
,
C
9
H
14
O
4
and C
10
H
18
O
3
in the group of salbutamol inhalers (to ease visualization, two outliers are not shown).
6
J. Breath Res. 11 (2017)046004 M T Gaugg et al

sebacic acid (Gaugg et al in preparation), and the latter
an unsaturated form of it. As to the identified hydro-
xydecanoic acid, the network shows a correlation with
sebacic acid (r=0.7),C
9
H
14
O
4
(r=0.66)and
C
10
H
18
O
3
(r=0.69). The striking correlation
between these compounds is demonstrated in
figure 5(b), which shows the log
2
(fold change)of
hydroxydecanoic vs. log
2
(fold change)for C
10
H
18
O
4
,
C
9
H
14
O
4
and C
10
H
18
O
3
. While the complete network
remains to be fully characterized, overall, our breath
analysis data are consistent with previous studies that
describe significant increase of non-esterified fatty
acids [33], and in general with the lipolytic effects of
beta-adrenergic agonists [34]. Moreover, these chan-
ges in fatty acid concentrations are known to happen
very rapidly, in the order of minutes [35,36], in line
with our own observations here. Additional correla-
tion networks are shown in figure S1.
Drug detection
We have just discussed that SESI-HRMS seems to be
sensitive and selective enough to capture significant
changes of exhaled metabolites such as fatty acids
shortly after salbutamol inhalation, in contrast to a
placebo group. We argue that such information may
provide a more comprehensive understanding of the
mechanism of action of drugs allowing, for example,
responders and non-responders to medication to be
identified [37]. Obviously, monitoring of the adminis-
tered drug itself would complement such metabolic
information. In this regard, we have recently shown
the potential of SESI-HRMS as a rapid and non-
invasive platform to perform pharmacokinetic studies
of injected drugs by measuring them in vivo, in mouse
breath [38,39]. Initially, we did not observe obvious
changes in salbutamol levels above the background
levels. One explanation could be that the chemical
background in the m/zregion where the salbutamol is
expected (i.e. m/z240.1594; [C
13
H
21
NO
3
+H]
+
)was
Figure 6. Tentative detection of drugs in exhaled breath. Real-time traces for protonated salbutamol (a)and its main metabolite
salbutamol-4-O-sulfate (b)for 40 controls who had never inhaled salbutamol and 13 patients inhaling salbutamol on an as-needed
basis. The highest levels were found for four patients suffering from COPD, suggesting that they inhale salbutamol more frequently
than the asthma patients. The insets show the mass spectra in the region of interest. The theoretical mass for both ions is indicated with
a vertical bar. The spectra suggest that care should be taken with the interpretation, because the peaks are not fully resolved at this mass
resolution (∼20 000).
7
J. Breath Res. 11 (2017)046004 M T Gaugg et al

generally very crowded. Figure 6(a)shows representa-
tive mass spectral data at nominal mass 240 along with
the chemical structure of salbutamol. For reference,
the theoretical exact mass where protonated salbuta-
mol would be expected is indicated with a vertical line.
Clearly, even at mass resolution as high as ∼20 000, the
separation capability is insufficient to discriminate
salbutamol from other isobaric species [20,40].We
nevertheless inspected the time traces for protonated
salbutamol, as well as for its main metabolite, salbuta-
mol-4-O-sulfate (figure 6(b);m/z320.1162;
[C
13
H
21
NO
6
S+H]
+
)[41]. Figure 6shows the time
traces for salbutamol (a)and salbutamol-4-O-sulfate
(b)for the 40 healthy controls that had never inhaled
salbutamol and 13 patients that according to their
medical records inhale salbutamol on an as-needed
basis. The first four of the latter were COPD patients,
and nine suffered from asthma. Note that the traces
shown correspond to the baseline measurement, when
salbutamol was not yet inhaled. While we cannot give
definite proof that these two mass spectral peaks
indeed correspond to salbutamol and its main meta-
bolite, the high correlation between the two sets of
time traces suggests an association. Also, the intensity
of the time traces seems in general higher in the
patients, especially in the COPD group.
To further investigate whether common broncho-
dilators can be detected in breath by SESI-HRMS, we
expanded our data analysis. In particular, according to
their medical records, fourteen of the patients were
inhaling formoterol on an as-needed basis. For-
moterol is a long-acting beta2-agonist used in the
management of asthma and COPD. Its chemical
structure is shown in figure 7, along with a typical mass
spectrum in the region of m/z345. The exact mass at
which this molecule would be expected in protonated
form [C
19
H
24
N
2
O
4
+H]
+
is m/z345.1809, which is
indicated in the mass spectrum by a vertical bar. As in
the case of salbutamol, many isobaric species prevent
us from clearly resolving the expected peak. However,
similarly to salbutamol, the time traces tend to be
higher in the patients using the medication on an as-
needed basis as compared to the healthy controls who
did not inhale any medication. Further investigations
via UPLC-MS/MS analysis of EBC and higher resolu-
tion mass spectrometry are required to confirm that
salbutamol and formoterol can be detected in breath.
However, the evidence shown in previous studies
where drugs such as ketamine (MW 237 Da)were
unambiguously detected in breath and correlated with
plasma levels [38,39]and the data shown in figures 6
and 7strongly supports the idea that SESI-HRMS is
suitable to monitor drugs and their metabolites in
breath. If this hypothesis is confirmed, it additionally
opens attractive possibilities to screen for doping
agents in breath of athletes. Note in this regard that all
beta2-agonists are currently prohibited in and out of
competition by the World Anti-Doping Agency [42].
While this work suggests that breath analysis could
contribute to ongoing efforts to elucidate response
mechanisms to therapeutic intervention and possibly
drug monitoring, this study has—of course—limita-
tions. For example, the unambiguous identity of the
molecules significantly altered, precludes gaining fur-
ther insights on reprogrammed metabolic pathways
associated with salbutamol. In this regard, we cannot
entirely exclude the possibility that some of the chan-
ges observed are actually due to bronchial dilation,
resulting in improved gas exchange at the lung level.
Figure 7. Detection of drugs in exhaled breath. Real-time traces of protonated formoterol for 40 controls who had never inhaled
salbutamol and 24 patients inhaling formoterol on an as-needed basis. The insets show the mass spectrum in the region of interest.
The theoretical mass for protonated formoterol is indicated with a vertical bar. The spectrum suggests that care should be taken with
the interpretation, as the peaks are not fully resolved at this mass resolution (∼20 000).
8
J. Breath Res. 11 (2017)046004 M T Gaugg et al

Conclusions
We aimed at exploring the concept of inhalational
drug monitoring via real-time breath analysis. We
demonstrated that analysis of exhaled breath by real-
time mass spectrometry allows one to capture rapid
metabolic changes induced by inhaled medication. We
confirmed that the use of high resolution and high
mass accuracy mass analyzers with MS/MS capabil-
ities is crucial to determining the identity of some of
these compounds. We noted that homologous series
of compounds tended to correlate, suggesting that
cascades of metabolic changes were monitored. For
example, observed intensity changes of families of fatty
acids are consistent with previous blood-based studies.
Uncovering altered metabolic routes is likely to
provide insights into the mechanisms by which activa-
tion of beta-adrenergic receptors lead to relaxation of
smooth muscles in the airways. We also conclude that
a mass resolution of ∼20 000 is clearly insufficient to
unambiguously detect the inhaled drugs investigated
in this study. Our data suggests that higher resolving
power may allow for the unambiguous detection of
salbutamol and formoterol in breath. Overall, this
study supports the notion that therapeutic drug
monitoring is plausible via real-time breath analysis.
Acknowledgments
This work was supported by grants from the Swiss
National Science Foundation (CR23I2_149617 and
32003B_143365/1)and a Marie-Curie European
Reintegration Grant (to PMLS)within the 7th Eur-
opean Community Framework Programme (276860).
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