Increase in FeNO Levels Following IL5/IL5R-Targeting Therapies in Severe Asthma: A Case Series
Bibliographic record
Abstract
Purpose: Monoclonal antibodies targeting interleukin-5 (IL5) and its receptor (IL5R), used for severe asthma treatment, reduce eosinophils to almost complete depletion. Fractional exhaled nitric oxide (FeNO), a surrogate marker of eosinophilic airway inflammation, is expected to decrease after their initiation. Our center noticed increased FeNO levels in a few patients in whom anti-IL5/IL5R therapy was initiated. Limited data are available on the kinetics of T2 inflammation biomarkers after initiation of a biologic in that population. This study aims to identify if a subgroup of severe asthma patients experiences increased FeNO levels after initiation of anti-IL5/IL5R therapy and to describe their clinical characteristics. Patients and Methods: This is a retrospective case series of 5 patients on Benralizumab (4M:1F) and 8 on Mepolizumab (5M:3F) who showed a significant increase in FeNO (> 20% AND > 25 ppb) following initiation of an anti-IL5/IL5R treatment. Clinical data, expiratory flows, and inflammation were extracted from the patients’ chart at initiation of treatment (T 0 ), 3 months (T 1 ) and 12 months (T 2 ) post-treatment. Descriptive statistics were used. Results: In patients treated with Benralizumab, the increase in FeNO was observed between T 0 and T 1 (mean delta = 82 ± 72 ppb) with a subsequent decrease (N = 3). In most patients taking Mepolizumab (N = 6), the FeNO increase was observed between T 1 and T 2 (mean delta = 57 ± 35 ppb). Under treatment, no Benralizumab patient experienced asthma exacerbation while two on Mepolizumab did. All patients had a significant decrease in blood eosinophils. Conclusion: Although initiation of anti-IL5/IL5R may cause a transient rise in FeNO levels in a subgroup of patients, it does not appear to affect clinical outcomes. A compensatory mechanism involving other inflammatory pathways such as IL13 or IL4, both involved in FeNO production, could theoretically explain these findings. Further investigation is needed to elucidate the actual underlying mechanisms. Keywords: biologics, biomarkers, Benralizumab, Mepolizumab
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".