Predicting dupilumab effectiveness with Type-2 biomarkers: A real-world study of severe asthma - Kenji Mizumura; Yasuhiro Gon; Norihiro Harada; Shiho Yamada; Asami Fukuda; Ryosuke Ozoe; Shuichiro Maruoka; Sumiko Abe; Kazuhisa Takahashi; Akihiko Tanaka; Hironori Sagara; Taisuke Akamatsu; et al. (Allergology International)
Background: The therapeutic effectiveness of dupilumab for severe asthma in real-world settings is yet to be prospectively investigated across multiple institutions, and uncertainties persist regarding predictive factors for its effectiveness. We aimed to assess the effectiveness of dupilumab and identify predictors of its effectiveness in real-world settings using two type-2 biomarkers: FeNO concentration and blood eosinophil count. Methods: This prospective multicenter study included 103 patients with severe asthma. Exacerbations and respiratory functions were monitored for 24 weeks. Asthma control was evaluated using the Asthma Control Questionnaire-5. Clinical symptoms and their impact on cough and sputum were assessed using the Cough and Sputum Assessment Questionnaire (CASA-Q). Subgroup analyses of type-2 biomarkers were conducted based on FeNO levels and blood eosinophil counts at baseline. Results: Treatment with dupilumab led to a reduction in exacerbations and enhancement in asthma control, FEV1, and CASA-Q scores. FEV1 improvement was correlated with enhancement in the sputum domain of the CASA-Q. Patients exhibiting elevated FeNO levels and blood eosinophil counts demonstrated more significant enhancements in FEV1. CASA-Q sputum domain scores were significantly higher in the group with elevated eosinophil counts. Regression analysis revealed that FeNO levels and blood eosinophil counts are significant predictors of FEV1 improvement, with blood eosinophil counts also predicting sputum improvement in patients treated with dupilumab. Conclusions: Type-2 biomarkers may act as indicators of improvement in FEV1 and sputum outcomes among patients with severe asthma undergoing dupilumab treatment in real-world settings.