Eosinophil Lineage-Committed Progenitors as a Therapeutic Target for Asthma
Bibliographic record
Abstract
Eosinophilic asthma is the most prevalent phenotype of asthma. Although most asthmatics are adequately controlled by corticosteroid therapy, a subset (5-10%) remain uncontrolled with significant therapy-related side effects. This indicates the need for a consideration of alternative treatment strategies that target airway eosinophilia with corticosteroid-sparing benefits. A growing body of evidence shows that a balance between systemic differentiation and local tissue eosinophilopoietic processes driven by traffic and lung homing of bone marrow-derived hemopoietic progenitor cells (HPCs) are important components for the development of airway eosinophilia in asthma. Interleukin (IL)-5 is considered a critical and selective driver of terminal differentiation of eosinophils. Studies targeting IL-5 or IL-5R show that although mature and immature eosinophils are decreased within the airways, there is incomplete ablation, particularly within the bronchial tissue. Eotaxin is a chemoattractant for mature eosinophils and eosinophil-lineage committed progenitor cells (EoP), yet anti-CCR3 studies did not yield meaningful clinical outcomes. Recent studies highlight the role of epithelial cell-derived alarmin cytokines, IL-33 and TSLP, (Thymic stromal lymphopoietin) in progenitor cell traffic and local differentiative processes. This review provides an overview of the role of EoP in asthma and discusses findings from clinical trials with various therapeutic targets. We will show that targeting single mediators downstream of the inflammatory cascade may not fully attenuate tissue eosinophilia due to the multiplicity of factors that can promote tissue eosinophilia. Blocking lung homing and local eosinophilopoiesis through mediators upstream of this cascade may yield greater improvement in clinical outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".