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Globally, a small proportion (5–12%) of asthma patients are estimated to have severe disease. However, severe asthma accounts for disproportionately high healthcare resource utilization. The Global Initiative for Asthma (GINA) management committee recommends treating patients with asthma with inhaled corticosteroids plus long-acting β2-agonists and, when needed, adding a long-acting muscarinic receptor antagonist or biologic agent. Five biologics, targeting different effectors in the type 2 inflammatory pathway, are approved for asthma treatment. However, biologics have not been compared against each other or add-on inhaled therapies in head-to-head clinical trials. As a result, their positioning versus that of current and anticipated small-molecule strategies is largely unknown. Furthermore, with the emergence of biomarkers for predicting response to biologics, a more personalized treatment approach—currently lacking with inhaled therapies—may be possible. To gain perspective, we reviewed recent advances in asthma pathophysiology, phenotypes, and biomarkers; the place of biologics in the management and personalized treatment of severe asthma; and the future of biologics and small-molecule drugs. We propose an algorithm for the stepwise treatment of severe asthma based on recommendations in the GINA strategy document that accounts for the broad range of phenotypes targeted by inhaled therapies and the specificity of biologics. In the future, both biologics and small molecules will continue to play key roles in the individualized treatment of severe asthma. However, as targeted therapies, their application will continue to be focused on patients with certain phenotypes who meet the specific criteria for use as identified in pivotal clinical trials. [ABSTRACT FROM AUTHOR]

This article, published in the journal Allergy, discusses a study that explores the existence of different subtypes of asthma based on immune signaling pathways. The study recruited 200 asthmatic patients and measured levels of certain cytokines in their sputum. The results revealed two distinct pathways of immune signaling, each associated with different patterns of cytokine expression. One pathway was linked to more severe disease and frequent hospitalizations. The study suggests that understanding these subtypes could lead to more targeted treatments for asthma. However, further research is needed to confirm these findings. The article also includes a table with various data points related to asthma patients, providing valuable information for research purposes. [Extracted from the article]

Background: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis. Results: We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance. Conclusions: iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects. [ABSTRACT FROM AUTHOR]

Background: Previous research has shown greater efficacy of dupilumab in patients with uncontrolled asthma and type 2 inflammation. We analyzed dupilumab's efficacy in patients from the TRAVERSE study with or without evidence of allergic asthma and type 2 inflammation per current GINA guidelines (≥150 eosinophils/μL or FeNO ≥20 ppb). Methods: All patients aged ≥12 years who rolled over from the placebo‐controlled QUEST study (NCT02414854) to TRAVERSE (NCT02134028) received add‐on dupilumab 300 mg every 2 weeks for up to 96 weeks. We assessed annualized severe asthma exacerbation rates (AERs) and changes from parent‐study baseline (PSBL) in pre‐bronchodilator FEV1 and 5‐item asthma control questionnaire (ACQ‐5) score in patients with moderate‐to‐severe type 2 asthma with and without evidence of allergic asthma at PSBL. Results: In TRAVERSE, dupilumab consistently reduced AER across all subgroups. By Week 96, dupilumab increased pre‐bronchodilator FEV1 from PSBL by 0.35–0.41 L in patients receiving placebo during QUEST (placebo/dupilumab) and 0.34–0.44 L in those receiving dupilumab during QUEST (dupilumab/dupilumab) with an allergic phenotype at baseline. In patients without evidence of allergic asthma, pre‐bronchodilator FEV1 improved by 0.38–0.41 L and 0.33–0.37 L, respectively. By Week 48, ACQ‐5 scores decreased from PSBL by 1.63–1.69 (placebo/dupilumab) and 1.74–1.81 (dupilumab/dupilumab) points across subgroups with allergic asthma, and 1.75–1.83 (placebo/dupilumab) and 1.78–1.86 (dupilumab/dupilumab) in those without. Conclusions: Long‐term treatment with dupilumab reduced exacerbation rates and improved lung function and asthma control in patients with asthma with type 2 inflammation as per current GINA guidance and irrespective of evidence of allergic asthma. [ABSTRACT FROM AUTHOR]

Reference 1 Liu Y. iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects. Correction: iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects. [Extracted from the article]