Allergic rhinitis (AR) is a common and increasingly prevalent chronic inflammatory disorder of the nasal mucosa that severely impacts patients’ quality of life, causing symptoms like nasal congestion, sneezing, and itching. AR is primarily mediated by immunoglobulin E (IgE) when allergens are present, making it challenging to manage despite available therapies like pharmacotherapy, immunotherapy, and surgery. Recently, research has focused on biologics as an emerging therapeutic option for AR. Biologics target specific immune pathways in type 2 inflammation, which underlies many allergic diseases including AR. Biologics offer a targeted and potentially more effective alternative to traditional therapies, addressing the underlying immune mechanisms rather than simply alleviating symptoms. Based on key clinical trial evidence, this paper tentatively proposes a multidimensional strategy for selecting biologics in AR, integrating serum IgE levels, disease phenotypes (seasonal/persistent), and comorbid characteristics to guide individualized treatment. However, the long-term cost-effectiveness, optimal dosing regimens, and patient adherence to biologics require further validation through real-world data. Despite these challenges, recent advancements in biologics represent a promising step forward in AR management. With ongoing research and clinical trials, biologics may soon provide more effective and lasting relief for patients suffering from allergic rhinitis.

Allergic rhinitis (AR) is a common heterogeneous chronic disease characterized by high prevalence, complex pathogenesis, and susceptibility to multiple contributing factors. Currently, its prevalence ranges from 20% to 30% in adults and reaches up to 40% in children. Extensive research has confirmed significant differences in nasal microbiota composition between AR patients and healthy individuals, most notably alterations in the abundance of four dominant phyla: Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria. Among these, the most striking abundance alterations occur in Staphylococcus aureus and Streptococcus salivarius within the nasal mucosa of AR patients, suggesting a critical role of nasal microbiota in AR initiation and progression. In response, researchers have proposed microbiome-targeted therapeutic strategies. For example, nasal local administration of probiotics (e.g., Lactobacillus and Bifidobacterium) aims to reshape the nasal microbiota. Additionally, protective bacteria such as Corynebacterium accolens and Dolosigranulum pigrum can inhibit pathogenic bacteria, thereby correcting microbial dysbiosis and alleviating AR symptoms. This review summarizes the composition of the nasal microbiota, the latest research progress on its association with AR, and the underlying potential mechanisms. It provides novel insights and potential therapeutic strategies for the prevention and treatment of AR.

Background: Stapokibart is a novel biologic for chronic rhinosinusitis with nasal polyps (CRSwNP). We aimed to identify baseline biomarkers predicting early (4-week) and mid-term (16-week) responses to stapokibart in CRSwNP. Methods: A total of 57 patients were prospectively enrolled. Baseline clinical data and complete blood count (CBC) parameters were collected, and derived inflammatory indices were calculated. Patients were classified as responders or non-responders at week 4 and 16 based on achieving either a ≥8.9-point reduction in SNOT-22 or a ≥1-point decrease in Nasal Polyp Score (NPS). Results: Stapokibart significantly improved SNOT-22, VAS, and NPS at both week 4 and week 16 (all p < 0.001). At week 4, 80.7% achieved an early response. Responders showed significantly higher baseline eosinophil count and eosinophil percentage and lower neutrophil-to-eosinophil ratio (N/E) (all p < 0.05). Univariate analysis identified N/E, comorbid asthma, eosinophil count, and aggregate index of systemic inflammation (AISI) as predictors of early response (all p < 0.05). Multivariate analysis identified N/E as an independent predictor (OR = 0.943, p = 0.011; AUC = 0.756). At week 16, 75.4% of patients achieved a mid-term response. Responders had significantly higher baseline SNOT-22 scores and NPS (p < 0.05). Multivariate analysis showed that baseline NPS and SNOT-22 scores were independently associated with mid-term response, and their combined model showed good predictive performance (AUC = 0.832, 95% CI: 0.716–0.948). Conclusions: Peripheral blood inflammatory biomarkers, particularly N/E, may predict early response to stapokibart in CRSwNP, whereas mid-term response appears more strongly associated with baseline disease severity. These findings support biomarker-driven stratification for individualized treatment strategies in CRSwNP.