Relationship Between Bronchodilator Reversibility and Spirometry Response to Dupilumab in Type 2 High Uncontrolled Severe Asthma

Airway hyperresponsiveness is a key tenet of persistent asthma, along with the type 2 inflammatory phenotype in most patients, while bronchodilator reversibility (BDR) may be absent in patients with a preserved FEV₁. Biologics act on downstream type 2 cytokine pathways inhibiting signalling of interleukins (IL) 4, 5 and 13 [1]. A retrospective study in severe asthma patients receiving anti-IL-5 or anti-IL-5Rα showed no difference in clinical outcomes when stratified by baseline BDR. Dupilumab is a monoclonal antibody which targets the alpha subunit of IL4 receptor which in turn inhibits IL4 and IL13 signalling, resulting in reduced exacerbations, improved symptom control and better lung function [2]. We wanted to evaluate the putative relationship between baseline BDR to salbutamol and the lung function response to dupilumab, expressed as either force expiratory volume in 1 s (FEV₁) or forced mid-expiratory flow (FEF_25-75).

Here we assessed patients with uncontrolled severe asthma (EudraCT 2021-005593-25) who had BDR measured at their initial screening visit. Then after a 4 week run-in period patients also had lung function measured after receiving 12 weeks of dupilumab 300 mg given every 2 weeks. We assessed the spirometry response to either salbutamol or dupilumab calculated as % predicted changes from baseline, as well as relative % change from baseline (post–pre/pre), and absolute change (FEV₁ in L and FEF_25-75 as L/s). To ensure an equal comparison, the pre-salbutamol baseline at screening prior to run-in was used for both assessments.

Spirometry (Micromedical, Chatham, UK) measurements were performed in triplicate according to the ERS guidelines [3]. 400 μg of salbutamol via a pressurised metered dose inhaler with an Aerochamber spacer device (Trudell Medical, London, Canada) was administered to all patients and after 20 min, the spirometry was repeated to assess BDR. Following the manufacturer's instructions and ERS guidelines, FeNO was obtained using NIOX Vero (NIOX, Oxford, UK) [4]. SPSS version 29 was used for statistical analysis. Paired students t-tests were applied with an alpha error of 5% and Pearsons test was used to evaluate correlations. Nominal p values are quoted as either p < 0.05, < 0.01 or < 0.001 (two tailed).

The cohort consisted of 24 patients, 14 females, mean (SEM) age 52.3 (2.96); BMI 30.0 (1.23). The mean % predicted pre-bronchodilator pulmonary function values and type 2 inflammation markers were: FEV₁ 88.1% (3.5); FEF_25-75 47.5% (3.0); FVC 105.8% (4.0) ACQ 3.0 (0.2); FeNO 68.0 ppb (8.9); eosinophils 510/μL (48), total IgE 204.7 kU/l (42.8).

There were significant improvements in FEV₁ and FEF_25-75 in response to salbutamol. However, the response to dupilumab was significant for FEV₁ but not FEF_25-75 (Table 1). There were no significant differences when comparing FEV₁ or FEF_25-75 responses between salbutamol versus dupilumab (Table 1).

Correlations between salbutamol BDR and dupilumab responses were weak for FEV₁ (0.43, p < 0.05) and FEF_25-75 (0.52, p < 0.05).

There were no significant differences between proportions of salbutamol versus dupilumab responders who exceeded the minimal important differences for severe asthma [5] for ≥ 150 mL improvement in FEV₁ (50% vs. 46%) or ≥ 0.21 L/s in FEF_25-75 (42% vs. 50%). The mean post dupilumab % predicted values were 95.02 FEV₁ for 56.6 for FEF_25-75.

Our data shows that in patients with uncontrolled severe asthma both salbutamol and dupilumab resulted in a statistically significant improvements in FEV₁ with a similar magnitude of mean changes. However, wider confidence intervals were seen in response to dupilumab compared to salbutamol, particularly evident when expressed as % predicted change in FEV₁.

When calculated as % predicted change, the confidence intervals were wider comparing FEF_25-75 to FEV₁ responses to either dupilumab or salbutamol. In this regard, using % predicted change is likely to account for differences in baseline airway geometry as compared to using relative % change. The FEF_25-75 measures volume dependent small airways closure which is predictive of poor asthma control in terms of salbutamol use and requirement for oral corticosteroids [6]. However, FEF_25-75 is also more variable than FEV₁ when assessing BDR in severe asthma patients [7]. Hence the significant effects of dupilumab on FEV₁ but not FEF_25-75 may reflect the greater variability in the latter along with the constraints of a limited sample size.

The wider variance for the dupilumab response along with its weak correlation to salbutamol response, may reflect the specific effects of salbutamol on airway smooth muscle alone via β₂-adrenoceptors, while dupilumab acts on airway smooth muscle along with type 2 mucosal inflammation via IL-4 and IL-13 [8]. Dupilumab has also been shown to result in larger improvements in FEV₁ in patients with higher biomarkers of type 2 inflammation including eosinophils and FeNO². Ideally, we might have repeated salbutamol BDR before and after the run-in period. However, patients had a mannitol bronchial challenge test performed after the run-in prior to the first dose of dupilumab, it was not feasible to also repeat the salbutamol BDR. Notably in the phase 3 LIBERTY QUEST study, dupilumab peak improvements in FEV₁ were observed by 6 weeks [2] in turn suggesting that the 12 weeks duration was sufficient in our study. In QUEST the mean change in FEV₁ in response to dupilumab 300 mg in patients with eosinophils > 300/μL was 0.24 L (95% CI 0.16, 0.32) which was comparable to 0.26 L (95% CI 0.04, 0.49) in our type 2 high patients, despite patients in QUEST having a lower % predicted FEV₁ baseline value.

In conclusion, the wider variance for dupilumab response may reflect effects on both airway smooth muscle and on endobronchial type 2 inflammation. Thus, it is conceivable that patients with limited salbutamol reversibility may still obtain appreciable improvements in lung function in response to dupilumab.

Robert Greig: Statistical analysis and writing. Kirsten Stewart: Data collection and review. Chris RuiWen Kuo: Trial design and submission, review. Rory Chan: Trial design and submission, review. Brian Lipworth: Trial design, data interpretation and analysis, writing.

No. 07/02/2022, 21/WS/0151, West of Scotland REC 1, EudraCT 2021–005593-25.

Dr. Kuo reports personal fees from AstraZeneca, personal fees from Chiesi, and non- financial support from GSK outside the submitted work. Dr. Chan reports personal fees (talks) and support attending ERS from AstraZeneca, personal fees (consulting) from Vitalograph, and personal fees (talks) from Thorasys. Dr. Lipworth reports non-financial support (equipment) from GSK; grants, personal fees (consulting, talks and advisory board), other support (attending ATS and ERS) and from AstraZeneca; personal fees (talks and consulting) from Sanofi, personal fees (consulting, talks and advisory board) from Circassia in relation to the submitted work; grants, personal fees (consulting, talks, advisory board), other support (attending ERS) from Teva, personal fees (talks and consulting), grants and other support (attending ERS and BTS) from Chiesi, personal fees (consulting) from Lupin, personal fees (consulting) from Glenmark, personal fees (consulting) from Dr. Reddy, personal fees (consulting) from Sandoz; grants, personal fees (consulting, talks, advisory board), other support (attending BTS) from Boehringer Ingelheim, grants and personal fees (advisory board and talks) from Mylan outside of the submitted work; and the son of BJL is presently an employee of AstraZeneca. The other authors declare no conflicts of interest.