# Short-term modification of breathprint by Elexacaftor/Tezacaftor/Ivacaftor in a paediatric cohort

Emmanuelle Bardin, Charlotte Roy, Thao Nguyen-Khoa, Mairead Kelly, Isabelle Sermet-Gaudelus, Yasmine Tabache, Frédérique Chedevergne, Nicolas Hunzinger, Elodie Lamy, Bingqing Zhou, et al.

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Emmanuelle Bardin, Charlotte Roy, Thao Nguyen-Khoa, Mairead Kelly, Isabelle Sermet-Gaudelus, et al.. Short-term modification of breathprint by Elexacaftor/Tezacaftor/Ivacaftor in a paediatric cohort. Journal of Cystic Fibrosis, 2025, 10.1016/j.jcf.2025.01.004. hal-05029186

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Original Article

Short-term modification of breathprint by Elexacaftor/Tezacaftor/Ivacaftor
in a paediatric cohort

Emmanuelle Bardin<sup>a,b,c,d</sup>,<sub>ID</sub> Nicolas Hunzinger<sup>c</sup>, Elodie Lamy<sup>c</sup>, Camille Roquencourt<sup>d</sup>,
Bingqing Zhou<sup>c</sup>, Yasmine Tabache<sup>b</sup>, Laurence Le Clainche<sup>f ID</sup>, Natascha Remus<sup>g</sup>,
Charlotte Roy<sup>a,b,h ID</sup>, Philippe Devillier<sup>d,i</sup>, Thao Nguyen-Khoa<sup>a,b,h,j</sup>, Frédérique Chedevergne<sup>b</sup>,
Clément Pontoizeau<sup>h,k</sup>, Mairead Kelly<sup>a,h ID</sup>, Stanislas Grassin Delyle<sup>c,d ID</sup>,
Isabelle Sermet-Gaudelus<sup>a,b,h,l,* ID</sup>

ARTICLE INFO

Keywords:

CFTR modulators

breathomics

Volatile organic compounds

Lung function

biomarkers

A B S T R A C T

**Background:** The triple combination Elexacaftor/Tezacaftor/Ivacaftor (ETI) translates into major respiratory improvements in adults; yet current clinical endpoints may prove insufficiently sensitive in young children. We hypothesised that ETI rapidly modifies the lungs' metabolism, resulting in changes in breath composition.

**Methods:** Eleven children with CF were enrolled in a longitudinal pilot study at the paediatric Necker hospital. Breath was collected on sorbent tubes using a ReCIVA® device before, after one week and one month of ETI. Samples were analysed by 2D-gas chromatography-mass spectrometry (2D-GC-MS). A linear mixed-effect model, corrected for clinical confounding factors, identified exhaled metabolites differentially expressed throughout the visits. Correlations were calculated between these and clinical indicators.

**Results:** Breath collection was successful in all children from six years old. They presented a decreased sweat chloride and improved lung function as early as within one week of ETI. Breath composition gradually evolved over the visits. ETI induced significant modifications in the level of 12 breath metabolites. Amongst those, dimethyl sulphide and tetradecene changes correlated with improvements in forced expiratory volume in one second (FEV₁) and forced expiratory flow (FEF₂₅-₇₅), whilst 3-methyldecane and 3-(chloromethyl)-heptane were predictive of changes in lung clearance index (LCI₂.₅).

**Conclusions:** ETI impacts the breath profile from the first week of treatment. Not only could "breathomics" bring mechanistic insights into the metabolic impact of ETI, but it may also offer novel non-invasive options to monitor CF disease and predict therapeutic response.

**Abbreviations:** AB, antibiotic; CF, cystic fibrosis; CFTR, cystic fibrosis transmembrane conductance regulator; ETI, Elexacaftor/Tezacaftor/Ivacaftor; FDR, false discovery rate; (2D-)GC, (two-dimensional-)gas chromatography; IQR, interquartile range; LCI₂.₅, lung clearance index₂.₅; MS, mass spectrometry; PCA, principal component analysis; PFT, pulmonary function tests; PLS-DA, partial-least square discriminant analysis; ppFEF₂₅-₇₅, percent predicted forced expiratory flow; ppFEV₁, percent predicted forced expiratory volume in one second; ppFVC, percent predicted of forced vital capacity; SCC, sweat chloride concentration; VOC(s), volatile organic compound(s).

* Corresponding author: Inserm U1151 - CNRS UMR 8253 – Université Paris Cité – 160 rue Vaugirard, Paris 75015, France.

E-mail address: isabelle.sermet@aphp.fr (I. Sermet-Gaudelus).

https://doi.org/10.1016/j.jcf.2025.01.004

Received 24 October 2024; Received in revised form 20 December 2024; Accepted 5 January 2025

1569-1993/© 2025 The Author(s). Published by Elsevier B.V. on behalf of European Cystic Fibrosis Society. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

<footnote><sup>a</sup> INSERM U1151, Institut Necker Enfants Malades, Paris, France</footnote>

<footnote><sup>b</sup> Hôpital Necker Enfants Malades, Centre de Référence Maladies Rares Mucoviscidose et Maladies apparentées, Paris, France</footnote>

<footnote><sup>c</sup> Université Paris-Saclay, UVSQ, INSERM U1173, Infection et inflammation, Département de Biotechnologie de la Santé, Montigny le Bretonneux, France</footnote>

<footnote><sup>d</sup> Exhalomics®, Hôpital Foch, Suresnes, France</footnote>

<footnote><sup>f</sup> Hôpital Robert Debré, Centre de Ressources et de Compétences de la Mucoviscidose, Paris, France</footnote>

<footnote><sup>g</sup> Centre Hospitalier Intercommunal de Créteil, Centre de Ressources et de Compétences de la Mucoviscidose, Créteil, France</footnote>

<footnote><sup>h</sup> Université Paris-Cité, Paris, France</footnote>

<footnote><sup>i</sup> Université Paris-Saclay, UVSQ, UFR Simone Veil - Santé, VIM-Suresnes, UMR 0892, Suresnes, France</footnote>

<footnote><sup>j</sup> Laboratoires de Biochimie et du Centre Régional de Dépistage Néonatal de l'Ile de France, Hôpital Necker-Enfants Malades, Paris, France</footnote>

<footnote><sup>k</sup> Unité fonctionnelle de métabolomique, Laboratoire de Biochimie, Hôpital Necker-Enfants malades, Paris, France</footnote>

<footnote><sup>l</sup> European Reference Network-Lung. Frankfurt, Germany</footnote>
# 1. Introduction

The triple combination of cystic fibrosis transmembrane conductance regulator (CFTR) modulators Elexacaftor/Tezacaftor/Ivacaftor (ETI) induces unprecedented improvements in respiratory symptoms and quality of life in people with cystic fibrosis (CF) [1]. Restoration of CFTR activity in airways translates into rehydration of lung-lining fluid and improvement in mucociliary clearance, as early as in the first week of treatment [2]. Although exact mechanisms are still being investigated, studies point towards an anti-inflammatory effect in the lungs [3, 4], a decrease in bacterial load [4,5] and a reshape of the lung microbiome [4,6,7].

ETI is currently accessible to people with CF (pwCF) from two years old carrying the most common CF mutation, p.Phe508Del (F508del thereafter). In adults and adolescents, it translates into major respiratory function gain and a decrease in sputum production [1]. Yet, these endpoints prove insufficiently sensitive in patients with a mild disease and a normal basal respiratory function such as school-age children. Alternative evaluation tools such as the lung clearance index<sub>2.5</sub> (LCI<sub>2.5</sub>) may detect ventilation inhomogeneity even when classical function tests are in the normal range [8]. However, this time-consuming measurement requires cooperative children and only a few centres are equipped. Thus, there is an urgent need for novel clinical endpoints to evaluate the respiratory effectiveness of CF therapies.

Volatilomics, i.e. the study of volatile organic compounds (VOCs) exhaled in breath, may unravel biomarkers that could be useful in the personalised monitoring of respiratory diseases such as CF [9]. Biological VOCs are terminal or byproducts of respiratory cell metabolism of low molecular weight that are eliminated in volatile form. They commonly result from oxidation or degradation of other metabolites stemming from physiological processes such as digestion, ageing or inflammatory reactions. Breath VOCs have been explored in adults for the diagnosis and monitoring of acute and chronic respiratory diseases including asthma and chronic obstructive pulmonary disease [9,10]. Studies focusing on CF have highlighted differences in breath profiles compared to healthy subjects or people with asthma [10–13] and there is accumulating data suggesting that there are breath patterns associated with pulmonary exacerbations [13–15], severity of the pulmonary disease [13,14,16] and infection by *Staphylococcus aureus* [10,11,17] or *Pseudomonas aeruginosa* [10,11,18].

We hypothesised that ETI rapidly modifies the lungs' metabolism, leading to significant changes in breath composition. We studied the breath profile of children with a mild respiratory disease before ETI initiation and at one month post initiation thanks to an innovative breath analysis and data processing approach.

# 2. Methods

## 2.1. Study design

Following marketing authorisation in France in January 2022, children aged 6–12 years old with CF were enrolled in a prospective open-label study at Necker paediatric hospital (Paris, Clinical trial ID-RCB/EUDRACT n°2016-A00309-42) with parents' informed written consent. The study was reviewed and approved by the Ile-de-France2 Ethics Committee (CPP IDF2:2010-05-03-3). It included three visits: before ETI initiation (V0), during the first week (between day 3 and day 7) (V1) and after one month of ETI treatment (V2). Breath samples were collected at each visit together with classical clinical readouts, including sweat chloride concentration (SCC), weight, microbiology of induced sputum, and pulmonary function tests (PFT) such as forced expiratory volume in one second (FEV₁), forced vital capacity (FVC) and forced expiratory flow (FEF₂₅–₇₅) all expressed as percentage predicted (pp) [19]. LCI₂.₅ was measured at V0 and after six months of treatment.

## 2.2. Breath sample collection

Children breathed in medical air through a disposable mouthpiece (or a facemask) fitting a ReCIVA® device (Owlstone Medical Ltd, UK) and wore a nose clip to prevent them from breathing ambient air. Throughout the collection, they were encouraged to breathe normally and to keep calm with the help of a parent. They were advised not to talk and to communicate with hands if they wished to stop the procedure.

The ReCIVA® was set to collect 500 mL of total exhaled breath (defined as air with >5 % CO₂) onto four Tenax TA tubes at a flow rate of 200 mL/min (Markes Ltd, UK). To minimise confounding environmental factors, samples were always taken in the same room, in the early morning after overnight fasting in order to limit the impact of digestion; room blanks were collected. Within 24 h, tubes were flushed with nitrogen (99,4 % purity, 5 min, 30 mL/min) to eliminate humidity and stored in air-tight containers filled with nitrogen for up to three months until analysis.

## 2.3. Breath sample analysis

Breath samples were analysed as previously detailed by Lamy *et al.* [20] together with daily quality control tubes spiked with a VOC standards solution. VOCs were transferred onto a Tenax TA cold trap before injection into a two-dimensional-gas chromatography (2D-GC) instrument. The 8890 GC system (Agilent technologies, CA, US) was fitted with Rtx-5MS (30 m x 0.25 mm x 1 µm) and Rxi-17 (2 m x 0.25 mm x 0.25 µm) columns (Restek, Lisses, France) and followed a temperature programme described previously [20]. Modulation period between the two columns was set to 1.7 s (modulator oven +15 °C). Compounds were detected by time-of-flight mass spectrometry (MS) (BT4D, LECO Corporation, MI, USA) within the mass/charge (m/z) range 35 – 450 (extraction frequency 32 kHz, acquisition rate 200 Hz).

## 2.4. Data processing and statistics

Clinical data are presented as medians with interquartile range (IQR).

Spectral signals were detected, aligned and putatively annotated using spectral libraries and the ChromaTOF-StatCompare software (V4.74.2.0, Leco Corporation, MI, USA). Variables with >70 % of missing values were discarded; remaining missing values were imputed using a k-nearest neighbour algorithm [21]. Breath features whose mean intensity were at least three times higher than in room blanks were kept and averaged amongst sample replicates.

In order to compare breath profiles with a high number of variables, i.e. VOCs, multivariate analyses were carried out on the Metaboanalyst platform (V6.0) [22] after log transformation and autoscaling. Breath profiles were first compared using principal component analysis (PCA) to unveil separating trends without a priori knowledge. Then, to unmask a potential underlying effect of the treatment, partial-least square discriminant analysis (PLS-DA) was performed. This supervised method builds a mathematical model focusing on the dimensions – or components (i.e. combinations of variables) – that most discriminate the classes of interest. PLS-DA models are characterised by prediction accuracy (R2) and predictive power (Q2) metrics. PCA and PLS-DA results may be represented as scores plots, where each dot is one breath sample, positioned in the graph considering all of its VOCs.

A linear mixed-effect model [23], accounting for both treatment duration and patient identity, was applied to all breath samples to determine VOCs that were differentially expressed in breath throughout the visits; p-values were adjusted for multiple testing using a false discovery rate correction (FDR <5 %). This model also systematically integrated lung infection and concomitant antibiotic (AB) treatment as covariates in order to account for their possible confounding effects [24]. This approach resulted in the identification of VOCs that were independent of concomitant AB treatment or infection.
Correlations (Spearman) were calculated between changes in clinical indicators (when available) and changes in the level of VOCs of interest detected in samples collected on the same day as the PFT measurements; correlations were considered significant with $|r| > 0,75$ and an associated $p$-value < 0,05.

# 3. Results

## 3.1. Study cohort

Eleven children, median age 8.6 years old (min 6 – max 12) were enrolled in the study. Their characteristics at baseline and during the first month of ETI treatment are reported in Fig. 1 and Supplementary Table S1. Nine children were compound heterozygous for F508del and a minimal function mutation, two were F508del-homozygous. None were asthmatic. All but one F508del-homozygous patient, who was previously on dual combination Lumacaftor/Ivacaftor, were naive of CFTR modulators.

All children were chronically infected with *Staphylococcus aureus* at baseline; two were also positive for *Achromobacter xylosoxidans*, one for *Pseudomonas aeruginosa* and one for *Stenotrophomonas maltophilia*. Whilst *S. aureus* persisted in eight patients at V2, the other pathogens were no longer detected. Five patients had 14 days AB therapy after ETI initiation because of increased sputum production.

Eight children successfully performed PFT before and after treatment initiation and six children performed reliable LCI measurements. At V0, they presented with a mild respiratory disease and normal ppFEV₁ (94 % (16)) and most showed a slight distal obstruction as indicated by their moderately low ppFEF₂₅. 75 (65 % (35)) and elevated LCI₂.₅ (9.71 (3.93)). As early as day 7, SCC had decreased by 53 (43) mmol/L, ppFEV₁ increased by 7 % (7), ppFVC by 8 % (4) and ppFEF₂₅ by 12 % (9) (*p* < 0.05 for all). At six months, LCI₂.₅ had decreased by 1.93 (4.27) (*p* = 0.10).

## 3.2. Breath collection

All children willingly complied with the test at each visit. One child refused to put a mouthpiece in the mouth and used a facemask instead.

Breath collection was quick (maximum 5 min), effortless and successful from the first take. There was no interruption, all samples showed good analytical quality and were free of contamination from saliva or the environment. Two patients could not attend V1 and one V1 sample was lost due to technical issues during analysis totalling 30 breath samples.

## 3.3. Breath analysis

A total of 1 395 volatile features out of 12 859 were retained after processing the data obtained from the 30 breath samples. Unsupervised PCA revealed a homogeneous dataset without any outlier samples (data not shown). The first principal components did not reveal any *a priori* separation of the samples that could be attributed to patient's identity, treatment duration (V0, V1, V2), pathogen colonisation or AB treatment, suggesting that these effects were not the main contributors to the dataset variance.

Supervised PLS-DA showed a clear, longitudinal delineation of breath samples according to the visit, i.e. treatment duration, with no overlap between the confidence intervals (Fig. 2). This indicated that ETI modified the composition of breath over time in a systematic manner from the first week of treatment.

A linear mixed-effect model applied to the whole set of 30 breath samples identified 12 volatile features that were significantly modified over time throughout patients, independently of concomitant AB treatment or infection. Their complete list is provided in Table 1 and their longitudinal evolution is shown in Supplementary Figure S1. Eleven VOCs out of the 12 were identified and consisted primarily of aliphatic hydrocarbons in the C6–C14 range including alkenes, methylated and chlorinated alkanes (e.g. tetradecene, 3-methyldecane, 2,6-dimethy-lundecane, 3-(chloromethyl)-heptane). Other differentially expressed VOCs were one fatty acid (C9), one branched fatty alcohol (2-butyl-1-octanol), one ketone (butanone) and one sulphide compound (dimethyl sulphide). All VOCs increased after treatment initiation except for 2-butyl-1-octanol and the twelfth, unidentified compound.

A clustering analysis based on the intensity of these 12 significant VOCs distributed breath samples into three main branches (Fig. 3): one first group gathered samples mostly collected at V0 (top branch of the heatmap), one intermediary group was a mix of samples mainly from V0

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Clinical evolution of the study population. At baseline (V0), one week (V1), one month (V2) or six months (M6) treatment with ETI. \* *p* < 0.05; \*\* *p* < 0.005.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Evolution of breath profiles throughout the first month of ETI treatment. PLS-DA scores plot showing the distribution of breath samples based on their VOC profile, according to two main components (i.e. algorithmic combinations of variables (VOCs); dataset variance explained is given in brackets), collected from patients at baseline visit in red (V0), after one week in green (V1), and one month of treatment in blue (V2); shaded areas represent confidence intervals (95 %) of the prediction PLS-DA model (R2 = 0.977; Q2 = 0.129; using 3 components).</figcaption></figure>

and V1 (middle branch), and the last, most different group primarily consisted of samples taken at V2 (bottom branch).

### 3.4. Correlation of breath VOCs with patient clinical evolution

Correlations between these VOCs significantly impacted by the treatment and clinical outcomes were calculated for the eight children who performed PFT and the six children who performed LCI<sub>2.5</sub>. Results are shown in Fig. 4. SCC did not show significant correlations with any of the selected VOCs. In contrast, changes in ppFEV<sub>1</sub> and ppFEF<sub>25-75</sub> significantly correlated with an increase in dimethyl sulphide and tetradecene breath levels whilst 2,6-dimethylundecane was negatively correlated. Butanone correlated negatively with changes in ppFEV<sub>1</sub> and ppFVC. Increased breath levels of 3-methyldecane and 3-(chloromethyl)-heptane correlated with an improved LCI<sub>2.5</sub> at 6 months of treatment.

<table><caption>**Table 1**

Breath VOCs significantly modified upon ETI treatment.</caption><thead><tr><th>Tentative annotation</th><th>Longitudinal p-value (FDR)</th><th>Longitudinal slope%</th><th>Significant correlations (p < 0.05)</th><th>Chemical family</th></tr></thead><tbody><tr><td>3-(Chloromethyl)-heptane</td><td>0.017</td><td>+1.74E+06</td><td>LCI<sub>2.5</sub> (r = -0.83)</td><td>Halogenated alkane</td></tr><tr><td>3-Methyldecane</td><td>0.012</td><td>+9.83E+05</td><td>LCI<sub>2.5</sub> (r = -0.83)</td><td>Methylated alkane</td></tr><tr><td>2,4-Dimethylhexane</td><td>0.024</td><td>+4.39E+06</td><td></td><td>Methylated alkane</td></tr><tr><td>2,6-Dimethylundecane</td><td>0.025</td><td>+1.25E+06</td><td></td><td>Methylated alkane</td></tr><tr><td>2,6-Dimethyldecane</td><td>0.037</td><td>+6.88E+05</td><td>ppFEV<sub>1</sub> (r = -0.75); ppFEF<sub>25-75</sub> (r = -0.77)</td><td>Methylated alkane</td></tr><tr><td>(E)-3-Tetradecene</td><td>0.041</td><td>+8.64E+05</td><td>ppFEV<sub>1</sub> (r = +0.79); ppFEF<sub>25-75</sub> (r = +0.83)</td><td>Alkene</td></tr><tr><td>(E)-2-Methyl-3-undecene</td><td>0.024</td><td>+4.22E+05</td><td></td><td>Methylated alkene</td></tr><tr><td>2-Butyl-1-octanol</td><td>0.028</td><td>-1.58E+05</td><td></td><td>Branched alcohol</td></tr><tr><td>Nonanoic acid</td><td>0.017</td><td>+1.03E+06</td><td></td><td>Fatty acid</td></tr><tr><td>2-Butanone</td><td>0.049</td><td>+1.25E+07</td><td>ppFEV<sub>1</sub> (r = -0.89); ppFVC (r = -0.81)</td><td>Ketone</td></tr><tr><td>Dimethyl sulphide</td><td>0.028</td><td>+2.05E+07</td><td>ppFEV<sub>1</sub> (r = +0.82); ppFEF<sub>25-75</sub> (r = +0.77)</td><td>Sulphide</td></tr><tr><td>Unknown (1)</td><td>0.028</td><td>-4.57E+06</td><td></td><td>Unknown</td></tr></tbody></table>

### 4. Discussion

Our results show that ETI rapidly changes breath composition in young children with CF. The high-quality samples, coupled to tailored data processing and statistics, enabled the identification of 12 compounds differentially expressed in the first month after ETI initiation. This breathprint correlated with clinically meaningful changes in respiratory function metrics.

Few studies have investigated the impact of CFTR modulators on breath. Neerincx et al. first documented metabolic changes in F508del-homozygous adults after three months of treatment with Ivacaftor/Lumacaftor CFTR modulator dual therapy [6]. Three VOCs, annotated as butadiene, 4-ethylbenzoic acid 2-pentyl ester, and 1-methylpentyl hydroperoxide, were persistently modified after initiating the treatment. Only two studies examined the effect of ETI. Woollam et al. observed lower levels of 2,8-trimethyldecane, nonanal and octanal in adolescents on ETI compared to an untreated control group [13]. More recently, Mustafina et al. associated some VOCs with impaired respiratory function in adults with CF but did not identify any VOCs related to ETI [16]. However, drawing conclusions from these two studies is difficult as they lacked longitudinal assessment and included patients with varying levels of pulmonary severity. We hypothesised that the very short-term follow-up of a homogeneous population of children with a milder disease would limit inter- and intra-patient variability and unravel an ETI-specific breathprint.

As expected, SCC quickly normalised within one week of ETI. Despite the small number of measurements and the high basal lung function, FEV<sub>1</sub> and FVC significantly improved after only one week. FEF<sub>25-75</sub> and LCI<sub>2.5</sub> indicated a marked improvement of distal airway obstruction and ventilation distribution. To further keep interfering factors to a minimum, patients breathed in medical air and breath samples were always collected in the same conditions. Breath VOCs were directly loaded and concentrated onto sorbent tubes thanks to a ReCIVA® unit. This collection, easy to implement, proved reliable in children as young as 6 years old. Compared to intermediary Tedlar bags used by Neerincx et al. and Woollam et al., this limits the loss of VOCs and the risk of contaminations, and allows a quicker stabilisation of the VOCs on the sorbent material. Furthermore, 2D-GC-MS expands the detection range compared to 1D-GC-MS employed by Neerincx et al. and Woollam et al. [6,13]. Indeed, 2D-GC-MS is particularly suited for VOC analysis as it better separates low molecular weight compounds, resulting in increased sensitivity, enabling the detection of rich breath profiles. Chemical identification is also improved compared to direct breath analysis by proton-transfer reaction-mass spectrometry (PTR-MS) applied by Mustafina et al. [16]. Finally, sophisticated data processing and statistical approaches accounting for inter-subject variability and relevant confounding factors were applied on our resulting high-dimensional dataset. This unmasked modifications in breath composition occurring within the first days of treatment which evolved progressively throughout the first month. Remarkably, we were able to robustly identify 12 VOCs that were significantly modified by the
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Evolution of differentiating VOCs throughout the first month of treatment. Clustering analysis and associated heatmap showing VOC intensities in samples collected at baseline (V0), first-week (V1) and first-month (V2).</figcaption></figure>

treatment despite the small cohort size.

The volatilomics field is gaining interest due to the non-invasive nature of breath analysis, yet only a limited number of studies have been published in children and even less in children with CF [11]. In the present study, most of the VOCs of interest were previously reported in humans [25] and/or microorganisms [26]. Amongst them, the most concentrated were dimethyl sulphide and 2-butanone. Sulphides are associated with the metabolism of sulphur-containing amino acids and have been found to differentiate pwCF from the healthy population in several studies [10,14,16]. Butanone is produced by bacteria through carbohydrate fermentation or metabolism of branched amino acids [25]; in humans, it is known to arise from isoleucine and cyanoamino acid catabolism [27]. Methylated aliphatic hydrocarbons such as 3-methyldecane and 2,6-dimethylundecane were previously associated with respiratory inflammatory diseases [11,28]. Together with nonanoic acid and butyl-1-octanol, they are thought to stem from lipid peroxidation and inflammation processes [25,29,30]. These lipid-derived

compounds may also result from membrane remodelling, changes in lipid transport and broader lipid metabolism. Recently, Welsh *et al*. reported that loss of CFTR impaired fatty acid uptake by the lungs, pointing towards a central role of CFTR in lung metabolic homeostasis, and more generally in the metabolism of multiple organs [31]. Our observations in breath indicate that ETI impacts fatty acid and amino acid metabolism and may be involved in metabolic rewiring beyond the lungs.

Interestingly, six of the VOCs modified by ETI significantly correlated with changes in lung function measurements. In particular, dimethyl sulphide and tetradecene strongly, positively correlated with both ppFEV₁ and ppFEF₂₅⁻₇₅ whilst 3-methyldecane and 3-(chloromethyl)-heptane were predictive of changes in LCI₂,₅, making these VOCs potential predictive biomarkers of lung function evolution. Regarding the difference in VOCs correlating with ppFEV₁ or LCI₂,₅, this may be related to the fact that ppFEV₁ reflects the relatively large airway conduction whereas LCI₂,₅ reflects global inhomogeneity of ventilation
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Correlations between VOCs and clinical readouts. Heatmap shows correlations between changes in clinical readouts and the evolution of the 12 significant VOCs between baseline (V0) and one month of treatment (V2) (or six months of treatment for LCI<sub>2.5</sub>); colour scale indicates the level of the correlation coefficient *r* from -1 (negative correlation in dark blue) to +1 (positive correlation in dark red); * indicate statistically significant correlations (*p*-values < 0.05).</figcaption></figure>

distribution in the lungs, combining ventilation inhomogeneity in the very distal airways and in the acinus [32]. Several VOCs have been linked to lung function in CF before, such as pentane [14], 3,7-dimethyldecane [13], methyl acetate, indole, 1,3-pentadiene, or methanethiol [16]. Sola Martinez *et al*. also found correlations between acetone, tetradecane, and pentadecane with PFT in healthy infants (3–8 months) [33]. Together with our results, this highlights the feasibility of breath analysis in young children and underscores its potential for monitoring lung function.

The main limitation of this pilot study is the small number of participants and the presence of confounding factors, such as infection and concomitant treatments, which limit the generalisability of the findings. Despite this, the implementation of an appropriate workflow accounting for confounding factors revealed significant trends, emphasising the promising clinical potential of breath analysis in CF to capture internal processes such as those triggered by the restoration of CFTR-mediated ion transport. However, it remains difficult to distinguish the contribution of the VOCs originating from bacteria from that of the host. Moreover, it cannot be asserted that the observed evolution is solely due to metabolic alterations, as modifications in mucus properties and in the lung environment brought by ETI are likely to influence VOC release in breath. Most importantly, since hydrocarbons are widely and repeatedly detected in the breath of healthy people [34], they are not necessarily indicative of a pathological condition. Their significance may depend on their precise identity and distribution, and interpreting modifications at this stage remains challenging. Thus, any breath-based test would likely rely on a combination of VOCs – a “breathprint” – rather than individual VOCs, given the broad and nonspecific origins of these compounds. Therefore, more extensive studies are required to confirm the relevance

and origin of the VOCs identified here. In addition, future studies should include patients switching from Ivacaftor/Lumacaftor to ETI as well as healthy controls in order to determine whether prior CFTR modulator treatments affect breath evolution and whether breath composition normalises.

**5. Conclusions**

This study reports for the first time a very short-term impact of ETI on breath composition in children with a mild lung disease and lays the groundwork for identifying early VOC biomarkers modified by CFTR correction. Not only could “breathomics” bring mechanistic insights, it may also offer novel non-invasive options to predict therapeutic response. This is highly relevant in the post-ETI era, where patients will maintain a high lung function and no longer produce sputum. In this context, exhaled breath holds great promises for monitoring CF disease progression and pulmonary infections. These initial findings set the stage for more extensive studies on larger cohorts and over a longer period, which will be pivotal to confirm the clinical relevance and predictive value of these VOCs.

**CRediT authorship contribution statement**

**Emmanuelle Bardin:** Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Writing – original draft. **Nicolas Hunzinger:** Data curation, Software. **Elodie Lamy:** Investigation. **Camille Roquencourt:** Data curation. **Bingqing Zhou:** Investigation. **Yasmine Tabache:** Investigation. **Laurence Le Clainche:** Investigation. **Natascha Remus:** Investigation. **Charlotte Roy:** Investigation,
Software. **Philippe Devillier:** Writing – review & editing. **Thao Nguyen-Khoa:** Investigation. **Frédérique Chedevergne:** Investigation. **Clément Pontoizeau:** Formal analysis. **Mairead Kelly:** Formal analysis. **Stanislas Grassin Delyle:** Conceptualization, Methodology, Formal analysis, Writing – review & editing. **Isabelle Sermet-Gaudelus:** Conceptualization, Methodology, Investigation, Formal analysis, Writing – original draft.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Isabelle Sermet-Gaudelus reports a relationship with Vertex Pharmaceuticals Incorporated that includes: consulting or advisory, funding grants, and travel reimbursement. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors warmly thank the patients and their family for participating in the study, as well as the nursing staff of Necker paediatric hospital (Paris, France) for their unfailing support.

Funding sources

This work was supported by financial grants from the European Cystic Fibrosis Society and CF Europe (2020–2022), by national programmes and grants under the supervision of Agence Nationale de la Recherche (RHU4 RECORDS, Programme d’Investissements d’Avenir, ANR-18-RHUS-0004), Région Île-de-France (DIM 1HEALTH 2019, MeLoMane), Assistance Publique-Hôpitaux de Paris (CRC 2022), Association Mucoviscidose ABCF2 and Odyssey Reinsurance Company.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jcf.2025.01.004.

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