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Three responder trajectory clusters were identified, showing AZD5069 reduced neutrophil measures in all clusters, ACQ-5 reduction in Cluster 2, and relative FEV1 refractoriness in Cluster 3.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/trajectory-based-clustering-to-identify-asthma-subgroups-responsive-to-the-selective-cxcr2-antagonist-azd5069/446899/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/trajectory-based-clustering-to-identify-asthma-subgroups-responsive-to-the-selective-cxcr2-antagonist-azd5069/446899.png","ImageObject",300,407,{"name":42,"@type":43},"Fans","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-01","2026-09-29",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the main objective of this reanalysis?","Question",{"text":63,"@type":64},"To identify potential responder asthma subgroups to AZD5069 among patients with presumed neutrophilic asthma using a trajectory-based clustering approach.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How was the patient data analyzed?",{"text":68,"@type":64},"A consensus clustering framework using dynamic time warping was applied to longitudinal timepoints from 1 to 6 months, based on raw values including pre-bronchodilator FEV1, ACQ-5, and blood neutrophil count.",{"name":70,"@type":61,"acceptedAnswer":71},"What patterns of response did the trajectory clusters show?",{"text":72,"@type":64},"Across all three clusters, the AZD5069 arm significantly reduced neutrophil count measures (NEUT/NEUTLE). Cluster 2 also showed a significant ACQ-5 reduction, while Cluster 3 remained relatively refractory regarding FEV1 change.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},446899,1791100586,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,115,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":107,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":30,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":143,"read_time":111,"preview_page_count":4},5909892330395,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Allergy  \nLETTER  OPEN ACCESS   \nTrajectory-Based Clustering to Identify Asthma Subgroups Responsive to the Selective CXCR2 Antagonist, AZD5069  \nKhezia Asamoah1 | Freda Yang2 | Ian M. Adcock2,3  | Dragana Vuckovic1 | Mohib Uddin4  | Kian Fan Chung2,3,5  | Marc Chadeau-Hyam1   \n1MRC Centre for Environment and Health & Department of Epidemiology and Biostatistics, Faculty of Medicine, School of Public Health, Imperial College London, London, UK | 2National Heart and Lung Institute, Imperial College London, London, UK | 3Data Science Institute, Department of Computing, Imperial College London, London, UK | 4AstraZeneca, BioPharmaceuticals R&D, Gothenburg, Sweden | 5Royal Brompton and Harefield Hospital, London, UK  \nCorrespondence: Marc Chadeau-Hyam ([m.chadeau@ic.ac.uk](m.chadeau@ic.ac.uk))  \nReceived: 14 September 2025 | Revised: 11 October 2025 | Accepted: 22 October 2025  \nFunding: This work was supported by AstraZeneca (No. NCT01704495) .  \nKeywords: asthma | asthma treatment | biomarkers  \nTo the Editor,  \nNeutrophilic asthma is a subphenotype of asthma that affects 20%–30% of all adult asthma patients and is typically harder to treat [1] . Usually driven by type 1 inflammation and external triggers, it is associated with a complex clinicopathobiology [2, 3] . AZD5069, a selective CXCR2 antagonist developed to block neutrophil trafficking without compromising immunity [4, 5], showed biological effects by lowering neutrophil countsin blood, sputum and bronchial biopsies in severe asthma [6] . However, it failed to improve clinical outcomes [7] . In this study, we reanalysed the data using a trajectory-based clustering approach to identify potential responder subgroups to AZD5069 among patients with presumed neutrophilic asthma.  \nThe data were obtained from a multicentre, double-blind, placebo-controlled, dose-ranging Phase 2b trial involving 640 patients randomised to four arms: AZD5069 at 5, 15 or 45 mg, or matched placebo. The study was done in accordance with the principles of the Declaration of Helsinki, the International Conference on Harmonisation guidelines for good clinical practice, and applicable regulatory requirements. All patients provided written informed consent before any study-specific assessments were done. 422 participants had complete longitudinal measurements for key variables across required timepoints  \nand their characteristics are reported in Table S1. A broad panel of 22 blood biomarkers was collected. To identify distinct patterns in patient response trajectories, a consensus clustering framework (using dynamic time warping) was used on timepoints from 1 to 6 months. Clustering was based on raw values for: pre-bronchodilator FEV1 (L), Asthma Control Questionnaire (ACQ)-5 and blood neutrophil count (NEUT) .  \nThree distinct trajectory clusters were identified based on changes in asthma-related outcomes and inflammatory markers (Figure 1) . Cluster 3 was characterised by a higher proportion of female patients, a greater prevalence of high baseline BMI, a higher baseline severe exacerbation rate and a lower FEV1/FVC ratio, together with elevated blood neutrophil counts compared with the other clusters. Each cluster included participants from both placebo and treatment arms. The treatment arm across all three clusters saw a significant reduction in NEUT or neutrophil count as a percentage of leukocytes (NEUTLE) (Figure 1, Table S2) . Cluster 2 (green) also showed a significant reduction in ACQ-5 (p = 0.019), with 42% showing a clinically important reduction of ≥0.5 in ACQ score (Table S3) . In contrast, Cluster 3 (blue) remained relatively refractory regarding FEV1 change. Trajectory groups differed significantly in several baseline characteristics (Table S4) .  \n\n| [Trial Registration:](Trial Registration: ClinicalTrials.gov)[ ClinicalTrials.gov](Trial Registration: ClinicalTrials.gov) number: NCT01704495 |\n| --- |\n| This is an open access article under the terms of the Creative Commons Attribution ","cbCaikPwe1aFusJc","https://ap.wps.com/l/cbCaikPwe1aFusJc","pdf",379402,"English","# Overview\n## Background and rationale for AZD5069\n## Study design and analysis approach\n## Trajectory clusters and responder patterns\n## Figures and biomarker response summaries","[{\"question\":\"What is the main objective of this reanalysis?\",\"answer\":\"To identify potential responder asthma subgroups to AZD5069 among patients with presumed neutrophilic asthma using a trajectory-based clustering approach.\"},{\"question\":\"How was the patient data analyzed?\",\"answer\":\"A consensus clustering framework using dynamic time warping was applied to longitudinal timepoints from 1 to 6 months, based on raw values including pre-bronchodilator FEV1, ACQ-5, and blood neutrophil count.\"},{\"question\":\"What patterns of response did the trajectory clusters show?\",\"answer\":\"Across all three clusters, the AZD5069 arm significantly reduced neutrophil count measures (NEUT/NEUTLE). Cluster 2 also showed a significant ACQ-5 reduction, while Cluster 3 remained relatively refractory regarding FEV1 change.\"}]","Trajectory-Based Clustering to Identify Asthma Subgroups Responsive to the Selective CXCR2 Antagonist, AZD5069 | PDF",1790717641]