Monoclonal antibodies for the treatment of refractory asthma
Bibliographic record
Abstract
PURPOSE OF REVIEW: A small proportion of patients with asthma have severe disease characterized by persistent airflow obstruction, airway hyperresponsiveness and eosinophilic airway inflammation. This review focuses on the clinical efficacy of inhibiting T helper 2-cytokine-mediated inflammatory responses using monoclonal antibodies directed against immunoglobulin E (IgE), interleukin (IL)-5, and IL-4/IL-13 in patients with severe refractory asthma. RECENT FINDINGS: The heterogeneity of airway inflammation in severe asthma has led to the recognition of multiple pathophysiologically distinct severe asthma endotypes. Biomarkers are being developed and evaluated to identify these endotypes and to guide the use of specific biologics in the appropriate patients who remain uncontrolled on high doses of inhaled corticosteroids and long-acting bronchodilators or oral corticosteroids. Examples include the efficacy of omalizumab in patients with severe refractory atopic asthma characterized by raised serum total IgE, mepolizumab, reslizumab, and benralizumab in patients with recurrent eosinophilic exacerbations characterized by blood and sputum eosinophilia despite high doses of corticosteroids, and lebrikizumab, pitrakinra, dupilumab, and tralokinumab that target the IL-4/IL-13 signalling pathways in patients with eosinophilic asthma or raised serum periostin. SUMMARY: In severe refractory asthma, both an understanding of the underlying pathophysiologic mechanisms driving airway inflammation and the identification of appropriate biomarkers in individual patients are critical in guiding the use of biologics and monoclonal antibodies that target the specific pathological processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".