Biologics in the treatment of atopic dermatitis: approved active substances and monoclonal antibodies in advanced clinical trials

Royeck, S. Allergo J Int 34, 159–166 (2025). https://doi.org/10.1007/s40629-025-00340-0

Abstract

Atopic dermatitis (AD) is the most common chronic inflammatory skin disease in industrialized countries and is characterized by heterogeneous (endo)phenotypes and a high disease burden. In Europe, four biologics and three oral Janus kinase (JAK) inhibitors are currently approved by the European Medicines Agency (EMA) for the treatment of moderate to severe AD in adolescents (≥ 12 years) and adults: dupilumab (anti-interleukin [IL]-4Rα; 2017), tralokinumab (anti-IL-13; 2021), lebrikizumab (anti-IL-13; 2023) and nemolizumab (anti-IL-31Rα; 2024) as well as the JAK inhibitors baricitinib (JAK 1/2; 2020), upadacitinib (JAK 1; 2021) and abrocitinib (JAK 1; 2022). 

Approved biologic therapies for adults in Europe

Artificial intelligence in clinical allergy practice: current status, challenges, and future directions

Lee Qiyu M, Horsaengchai W, Horsaengchai P, Malik Q. Explor Asthma Allergy. 2025; 3:100992.
https://doi.org/10.37349/eaa.2025.100992

Abstract

Summary of the applications of AI in clinical allergy practice

Artificial intelligence (AI) is poised to transform clinical allergy practice by enhancing diagnostic accuracy, personalising treatment, and streamlining healthcare delivery. This narrative review critically examines the current landscape of AI in allergy care, spanning clinical workflows, diagnostics, immunotherapy, and research applications. AI-powered tools such as clinical decision support systems (CDSS), natural language processing (NLP), and conversational agents are being integrated into allergy services, offering improvements in documentation, risk stratification, and remote patient engagement—particularly in paediatric and multilingual settings. Diagnostic innovations include machine learning models that predict oral food challenge outcomes and interpret multi-omics data for personalised allergy phenotyping. AI also supports adaptive immunotherapy dosing, remote monitoring via wearable biosensors, and digital coaching to promote adherence.

Characterizing drug allergy management among allergists in Canada: a national survey study

Lee, E., Lee, B., Krishnan, S. et al.  Allergy Asthma Clin Immunol 21, 41 (2025). https://doi.org/10.1186/s13223-025-00981-4

Abstract

Background

Unverified drug allergy labels are common and associated with significant patient harm, yet infrastructure and testing practices vary across clinical settings in Canada.

Objective

To characterize variability in drug allergy management among allergists in Canada and identify setting-specific barriers to drug allergy testing and desensitization.

Methods

We developed a peer-reviewed 40-item survey, distributed via the Canadian Society of Allergy and Clinical Immunology, to assess practice patterns, testing modalities, and perceived barriers among allergists. Descriptive statistics and Fisher’s exact test were used to evaluate responses by practice setting.

Results

Comparison of drug allergy testing modalities by practice setting

Sixty-six allergists responded (30% estimated response rate), with 48.4% solely practicing in community clinics and 21.9% solely in hospital-based clinics. While 87.9% performed some form of drug allergy testing, hospital-based allergists were significantly more likely to perform intradermal (81.1% vs. 48.7%, p = 0.004) and patch testing (38.2% vs. 8.8%, p = 0.009), as well as non-oral drug challenges (63.6% vs. 20.0%, p = 0.0005).

What has changed with subcutaneous immunotherapy against house dust mites? Eight-year, single-center real-world data

Bulbul, Lida; Toprak, Ali; Nursoy, Mustafa A. Asia Pacific Allergy 15(3):p 176-185, September 2025. | DOI: 10.5415/apallergy.0000000000000202

Abstract

Introduction:
We aimed to investigate clinical and laboratory parameters that can predict clinical response in patients who completed subcutaneous immunotherapy (SCIT) against house dust mites (HDM) and to determine parameters associated with systemic adverse effects (SAE).

Methods:
In total, 108 patients who had complete medical data were evaluated.

Results:

Laboratory values and symptom scores before and after SCIT

Around 54.6% of patients were male; mean age was 11.7 ± 3.1 years. All patients had allergic rhinitis, 86% had asthma, and 22% had atopic dermatitis. After SCIT, mean symptom score (SS) for allergic rhinitis had decreased in 86% of patients, asthma severity had decreased in 35.5% of asthma patients. After SCIT, median eosinophil count and HDM skin prick test (SPT) diameters decreased, there was not statistically significant change in total IgE (P = 0.001, 0.001, and 0.824, respectively). No statistically significant difference was detected between the SS change groups (decreased, same, or increased) in terms of age, gender, disease duration, presence and severity of asthma, presence of atopic dermatitis, duration of SCIT, time after SCIT, initial sensitization status, total IgE, eosinophil and basophil counts, and SPT diameter for HDM.

Machine learning in allergy research: A bibliometric review

Kong E, Cucco A, Custovic A, Fontanella S. Immunol Lett. 2025 Sep 8;277:107088. doi: 10.1016/j.imlet.2025.107088.

Summary

Word clouds for topics obtained by Latent Dirichlet allocation (LDA)

The emergence of big data and analytic approaches initiated research efforts to characterise different subtypes of allergic diseases, including tracking disease progression and identifying patterns that may offer insight into their development and progression. Triangulation from different data sources and study types may help to elucidate the directionality of relationships between variables at a very individual level by modelling the complex interdependencies between multiple dimensions (e.g., genome, transcriptome, epigenome, microbiome, and metabolome), thereby moving away from associative to a more causal analysis.

The evolution of food protein-induced enterocolitis syndrome (FPIES): Global trends, emerging triggers, and natural history.

Anvari S, Gupta M, Nicolaides R et al. Ann Allergy Asthma Immunol. 2025 Sep 19:S1081-1206(25)01160-3. doi: 10.1016/j.anai.2025.09.006.

Abstract

Graphical timeline of our understanding of FPIES.

Food protein-induced enterocolitis syndrome (FPIES) is a non-IgE-mediated food allergy characterized by delayed gastrointestinal symptoms. It is thought that immune and neuroimmune mechanisms drive FPIES pathophysiology, but this remains incompletely understood. There are no specific biomarkers to confirm the FPIES diagnosis, monitor for resolution, or assess reactivity to additional food triggers.

Social Determinants of Health in Pediatric Asthma and Allergic Diseases: A Systematic Review

Koumpagioti, D., Moriki, D., Boutopoulou, B.et al. (2025).  Epidemiologia, 6(3), 56. https://doi.org/10.3390/epidemiologia6030056

Abstract

PRISMA diagram for the study selection process.

Objectives: This study aimed to synthesize evidence of the influence of multilevel social determinants of health (SDOHs) on asthma and allergic disease outcomes and healthcare utilization in children and adolescents, with a specific focus on how these determinants generate or widen health inequities, through a systematic review of the current literature and evidence. 

Methods: A literature search was performed in the PubMed, EBSCO, and Scopus databases. The search period for all databases was from 1 January 2020 to 15 January 2025. Studies published in English that evaluated the association between at least one SDOH, as a primary exposure or effect modifier, and asthma and/or allergic disease outcomes and healthcare utilization in children and adolescents aged ≤ 18 years were included.

Artificial Intelligence Diagnostic Accuracy and Clinical Utility in Allergic Rhinitis: Systematic Review

Dana A. Alrezq, Abdulaziz M. Aldaghmani, Jana J. Alali, et al.  Authorea. September 12, 2025.

DOI: 10.22541/au.175766646.65353803/v1

Abstract

Allergic rhinitis (AR) is one of the most common outpatient conditions, diagnosed through operator-dependent and resource-intensive methodologies. Consequently, there has been a surge in the development of diagnostic strategies using artificial intelligence (AI). This review aims to explore how AI can enhance diagnostic accuracy, personalize treatment, and support clinical decisions in AR care. The review followed PRISMA guidelines and was registered in PROSPERO. PubMed, Cochrane Central, Embase, and Google Scholar were searched for studies published between 2002 and 2025. Out of 1,109 identified studies, eight studies met the inclusion criteria.